CN113904320A - Aluminum-electricity collaborative optimization scheduling method and device, computer equipment and storage medium - Google Patents
Aluminum-electricity collaborative optimization scheduling method and device, computer equipment and storage medium Download PDFInfo
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- 238000005457 optimization Methods 0.000 title claims abstract description 55
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000003860 storage Methods 0.000 title claims abstract description 10
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 claims abstract description 108
- 229910052782 aluminium Inorganic materials 0.000 claims abstract description 108
- 238000010248 power generation Methods 0.000 claims abstract description 64
- 238000004519 manufacturing process Methods 0.000 claims abstract description 31
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 26
- 230000005611 electricity Effects 0.000 claims description 35
- 239000000446 fuel Substances 0.000 claims description 27
- 239000003245 coal Substances 0.000 claims description 26
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 22
- PNEYBMLMFCGWSK-UHFFFAOYSA-N aluminium oxide Inorganic materials [O-2].[O-2].[O-2].[Al+3].[Al+3] PNEYBMLMFCGWSK-UHFFFAOYSA-N 0.000 claims description 22
- 229910052799 carbon Inorganic materials 0.000 claims description 22
- 238000006477 desulfuration reaction Methods 0.000 claims description 20
- 230000023556 desulfurization Effects 0.000 claims description 20
- 230000006870 function Effects 0.000 claims description 16
- 150000004673 fluoride salts Chemical class 0.000 claims description 13
- 239000000463 material Substances 0.000 claims description 8
- KRHYYFGTRYWZRS-UHFFFAOYSA-N Fluorane Chemical class F KRHYYFGTRYWZRS-UHFFFAOYSA-N 0.000 claims description 6
- AZDRQVAHHNSJOQ-UHFFFAOYSA-N alumane Chemical class [AlH3] AZDRQVAHHNSJOQ-UHFFFAOYSA-N 0.000 claims description 6
- 238000004590 computer program Methods 0.000 claims description 5
- 239000004411 aluminium Substances 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 230000003993 interaction Effects 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims description 2
- 239000002245 particle Substances 0.000 description 6
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- 230000009467 reduction Effects 0.000 description 2
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- 206010063385 Intellectualisation Diseases 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000005868 electrolysis reaction Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
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- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- TWNQGVIAIRXVLR-UHFFFAOYSA-N oxo(oxoalumanyloxy)alumane Chemical compound O=[Al]O[Al]=O TWNQGVIAIRXVLR-UHFFFAOYSA-N 0.000 description 1
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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Abstract
The application provides an aluminum-electricity collaborative optimization scheduling method, an aluminum-electricity collaborative optimization scheduling device, computer equipment and a storage medium, wherein the method can be applied to an aluminum-electricity collaborative production operation system and comprises the following steps: constructing a power generation side cost model according to the current power generation side variation cost and the power generation side fixed cost of the system; constructing an electrolytic aluminum side cost model according to the electrolytic aluminum side variation cost and the electrolytic aluminum side fixed cost; constructing a cost model of the power purchasing side on the basis of the basic power cost and the electric power cost; determining an optimized scheduling model according to the power generation side cost model, the electrolytic aluminum side cost model and the online power purchase side cost model; and solving decision variables in the optimized scheduling model by using an optimization algorithm, wherein the decision variables comprise various production series currents of electrolytic aluminum and the output of various thermal power generating units. According to the method, a power generation side cost model, an electrolytic aluminum side cost model and an online power purchasing side cost model are established, an optimal economic target of an aluminum-electricity cooperative system is based, and an optimization algorithm is utilized to achieve a scheduling target with optimal economic performance and maximum operation profit.
Description
Technical Field
The present application relates to the field of power technologies, and in particular, to a method and an apparatus for aluminum-electricity cooperative optimization scheduling, a computer device, and a storage medium.
Background
In practical application, the aluminum-electricity collaborative production operation system is influenced by wind-electricity random intermittence, unit maintenance and the like, a local power grid may have a power shortage condition, if external power is supplemented, the production cost is increased, and the limitation of the power load on the electrolytic aluminum side causes the yield reduction of the electrolytic aluminum.
Therefore, how to balance the load reduction production and the electricity purchasing production is an urgent problem to be solved.
Disclosure of Invention
The application provides an aluminum-electricity collaborative optimization scheduling method and device, computer equipment and a storage medium.
An embodiment of one aspect of the application provides an aluminum-electricity collaborative optimization scheduling method, which is applied to an aluminum-electricity collaborative production operation system, and the method includes:
constructing a power generation side cost model according to the current power generation side variation cost and the current power generation side fixed cost of the system;
constructing an electrolytic aluminum side cost model according to the electrolytic aluminum side variation cost and the electrolytic aluminum side fixed cost;
constructing a cost model of the power purchasing side on the basis of the basic power cost and the electric power cost;
determining an optimized scheduling model according to a power generation side cost model, an electrolytic aluminum side cost model and an online power purchase side cost model, wherein the optimized scheduling model comprises an objective function, constraint conditions and decision variables, the constraint conditions comprise electric balance constraint, tie line power flow constraint and electrolytic aluminum series load limit value constraint, the electric balance constraint is that the sum of the output sum of each thermal power unit, the sum of wind power output sum and photovoltaic output sum is equal to the electrolytic aluminum side load sum, and the decision variables comprise electrolytic aluminum production series currents and the output of each thermal power unit;
and solving the decision variables in the optimized scheduling model by using an optimization algorithm.
An embodiment of another aspect of the present application provides an aluminum-electricity collaborative optimization scheduling device, which is applied to an aluminum-electricity collaborative production operation system, and the device includes:
the building module is used for building a power generation side cost model according to the current power generation side variation cost and the current power generation side fixed cost of the system;
the construction module is also used for constructing an electrolytic aluminum side cost model according to the electrolytic aluminum side variation cost and the electrolytic aluminum side fixed cost;
the building module is also used for building an online power purchase side cost model according to the basic electric charge cost and the electric charge cost;
the system comprises a determining module, a scheduling optimizing module and a scheduling optimizing module, wherein the determining module is used for determining an optimizing scheduling model according to a power generation side cost model, an electrolytic aluminum side cost model and an online power purchasing side cost model, the optimizing scheduling model comprises an objective function, a constraint condition and a decision variable, the constraint condition comprises an electric balance constraint, a tie line power flow constraint and an electrolytic aluminum series load limit value constraint, the electric balance constraint is that the sum of the output sum of each thermal power unit, the sum of the wind power output sum and the photovoltaic output sum is equal to the electrolytic aluminum side load sum, and the decision variable comprises each production series current of electrolytic aluminum and the output sum of each thermal power unit;
and the calculation module is used for solving the decision variables in the optimized scheduling model by using an optimization algorithm.
Another embodiment of the present application provides a computer device, including a processor and a memory;
the processor executes a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the aluminum electric co-optimization scheduling method according to an embodiment of the above aspect.
Another embodiment of the present application provides a non-transitory computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the aluminum-electricity cooperative optimization scheduling method according to the foregoing one embodiment.
According to the aluminum-electricity collaborative optimization scheduling method, the device, the computer equipment and the storage medium, the optimal scheduling function is determined by constructing the power generation side cost model, the electrolytic aluminum side cost model and the online shopping power side cost model and according to the power generation side cost model, the electrolytic aluminum side cost model and the online shopping power side cost model, based on the optimal economic target of the aluminum-electricity collaborative system, the optimal allocation of new energy (such as wind power and photovoltaic) full consumption, thermal power output and energy utilization load are optimally controlled by using the optimization algorithm, and the scheduling management target with optimal economic performance and maximum operation profit is achieved.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic flowchart of an aluminum-electricity cooperative optimization scheduling method according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of an aluminum electric cogeneration operation system according to an embodiment of the present application;
fig. 3 is a schematic flow chart of a particle swarm algorithm provided in an embodiment of the present application;
fig. 4 is a schematic structural diagram of an aluminum-electric cooperative optimization scheduling apparatus according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
The following describes an aluminum-electricity cooperative optimization scheduling method, apparatus, computer device, and storage medium according to an embodiment of the present application with reference to the drawings.
Fig. 1 is a schematic flowchart of an aluminum-electricity cooperative optimization scheduling method according to an embodiment of the present disclosure.
The aluminum-electricity collaborative optimization scheduling method can be applied to an aluminum-electricity collaborative production operation system, and by constructing cost models of a power generation side, an electrolytic aluminum side and an online shopping power side and based on the optimal economic target of the aluminum-electricity collaborative system, the particle swarm optimization algorithm is utilized to realize full consumption of new energy (such as wind power and photovoltaic), optimal distribution of thermal power output and optimal control of energy utilization load, so that the scheduling management target with optimal economic performance and maximum operation profit is achieved.
As shown in fig. 1, the aluminum-electricity cooperative optimization scheduling method includes:
and 101, constructing a power generation side cost model according to the current power generation side variation cost and power generation side fixed cost of the system.
The aluminum-electricity collaborative optimization scheduling method can be applied to an aluminum-electricity collaborative production operation system, the structure of the system can be as shown in fig. 2, and the system can comprise an electrolytic aluminum production series, a thermal power generating unit, a power grid tide line, photovoltaic power, wind power and the like.
In the method and the device, the current power generation side variation cost of the system and the fixed cost of the power generation side can be obtained, and the sum of the power generation side variation cost and the fixed cost of the power generation side can be used as the power generation side cost, so that a power generation side cost model is obtained.
Wherein, the power generation side variation cost can be understood as that the cost is not fixed and is related to the system operation, such as the fuel cost and the like; the fixed cost on the power generation side can be understood as a fixed and relatively stable cost, such as fuel cost, water cost, material cost, depreciation cost, repair cost, salary cost, commission operation cost and the like.
And 102, constructing an electrolytic aluminum side cost model according to the electrolytic aluminum side variable cost and the electrolytic aluminum side fixed cost.
In the application, the current variation cost of the electrolytic aluminum side of the system and the fixed cost of the electrolytic aluminum side can be obtained, and the sum of the variation cost of the electrolytic aluminum side and the fixed cost of the electrolytic aluminum side can be used as the cost of the electrolytic aluminum side, so that the cost model of the electrolytic aluminum side can be obtained.
Wherein the electrolytic aluminum side shift cost is understood to be that the cost is not fixed and is related to the system operation, such as alumina cost, anode carbon cost, etc.; the fixed cost on the electrolytic aluminum side can be understood as a fixed and relatively stable cost, such as a raw material cost for carbon, a repair cost, other material cost, a financial cost, a sales cost, a management cost, a compensation cost, a deep purification operation cost and the like.
And 103, constructing a cost model of the power purchasing side according to the basic power cost and the electric power cost.
In this application, can acquire basic charges of electricity cost and electric degree charges of electricity cost, can regard basic charges of electricity cost and electric degree charges of electricity cost sum as net purchase electricity side cost to obtain net purchase electricity side cost model.
And step 104, determining an optimized scheduling model according to the power generation side cost model, the electrolytic aluminum side cost model and the online power purchase side cost model.
After the power generation side cost model, the electrolytic aluminum side cost model and the online shopping power side cost model are obtained, the optimized scheduling model can be determined according to the power generation side cost model, the electrolytic aluminum side cost model and the online shopping power side cost model. The optimized scheduling model may include an objective function, constraints, and decision variables, among others.
Wherein, the objective function can be the sum of the cost of the power generation side, the cost of the electrolytic aluminum side and the cost of the online power purchase side; the constraint conditions can comprise electric balance constraint, tie line current constraint, electrolytic aluminum series load limit constraint and the like; the decision variables can comprise the current of each production series of electrolytic aluminum and the output of each thermal power generating unit.
In the embodiment of the application, the electric balance constraint can be that the sum of the output of each thermal power generating unit, the sum of the output of wind power and the sum of the output of photovoltaic power are equal to the sum of loads on the electrolytic aluminum side. Therefore, the sum of thermal power output, wind power output and photovoltaic output equal to the electrolytic aluminum side load is taken as a constraint condition, so that full-allowance consumption of new energy such as wind power, photovoltaic output and the like, optimal distribution of thermal power output and optimal control of energy utilization load can be realized.
And 105, solving decision variables in the optimized scheduling model by using an optimization algorithm, wherein the decision variables comprise various production series currents of electrolytic aluminum and various thermal power generating units.
After the optimized scheduling model is determined, the decision variables in the optimized scheduling model can be solved by using an optimization algorithm, such as a particle swarm algorithm, an ant colony algorithm, and the like. For example, in an aluminum-electricity collaborative production operation system, if there are a production series and B thermal power generating units, the current of each production series in the a series and the output of each thermal power generating unit in the B thermal power generating units can be solved through an optimization algorithm.
According to the aluminum-electricity collaborative optimization scheduling method, the power generation side cost model, the electrolytic aluminum side cost model and the online shopping power side cost model are built, the optimized scheduling function is determined according to the power generation side cost model, the electrolytic aluminum side cost model and the online shopping power side cost model, based on the optimal economic target of the aluminum-electricity collaborative system, the optimization algorithm is utilized, the full consumption of new energy (such as wind power and photovoltaic), the optimal distribution of thermal power output and the optimal control of energy utilization load are achieved, and the scheduling management target with optimal economic performance and the maximum operation profit is achieved.
In one embodiment of the present application, the power generation side shift costs may include fuel costs and desulfurization and denitration costs, and the electrolytic aluminum side shift costs may include alumina costs, anode carbon block costs, anode scrap costs, fluoride salt costs, and the like.
The fuel cost model in the power generation side variation cost can be represented by the following formula:
wherein, CfuelFuel cost per unit time; r iscoalThe unit of the coal consumption rate for power generation can be g/kWh; hstdTaking 7000kcal/kg as standard coal calorific value; hrealThe unit of the actual coal calorific value can be kcal/kg; ccoalThe unit can be yuan/ton for the price of raw coal; ctrpThe unit can be Yuan/ton for freight; etataxThe tax rate is; ptThe unit is the real-time generating power of the unit, and the unit is kW.
The model of the desulfurization and denitration cost in the power generation side variation cost is shown as the following formula:
Cdesf＝rcoal*Pt*αdesf*cdesf
wherein, CdesfThe cost of desulfurization and denitration in unit time; r iscoalThe coal consumption rate for power generation; ptGenerating power for a unit in real time, wherein the unit is kW; alpha is alphadesfThe consumption of the desulfurization and denitration materials per kilowatt hour can be in units of kg/kWh; c. CdesfThe unit is the unit price of the desulfurization and denitration material, and can be Yuan/kg.
The alumina cost model in the electrolytic aluminum side variation cost is shown as follows:
CAlO＝γAlO*PAl*γAlO
wherein, CAlOThe alumina cost per unit time; gamma rayAlOIs alumina unit consumption in tons per ton of aluminium, which represents how many tons of alumina are consumed per ton of aluminium consumed; pAlIs aluminum per unit timeYield; gamma rayAlOIs monovalent for alumina.
Wherein, the mathematical model of the aluminum yield is shown as the following formula:
PAl＝0.3355*IAl*η1*nAl*t
wherein 0.3355 is the electrolytic stoichiometry of aluminum; i isAlProducing a series of electrical currents in amperes (a) for electrolysis of aluminum; eta1To be current efficiency; n isAlThe number of electrolytic tanks for producing series; t is time.
The current efficiency can be obtained by fitting actual production data, wherein a current efficiency fitting curve can be basically consistent with an actual production curve under the condition of three-time nonlinear fitting. According to the fitting result, the current efficiency model is shown as follows:
η1＝a1ΔI3+a2ΔI2+a3ΔI+a0
wherein, a0、a1、a2And a3Is a fitting coefficient; Δ I ═ IAl-INWherein, INTo produce a range of rated currents, the unit is amperes (a).
The model of the cost of the anode carbon block in the variation cost of the electrolytic aluminum side is shown as the following formula:
Cpos＝ppos*cpos
wherein, CposThe cost of the anode carbon block per unit time; p is a radical ofposThe consumption of the anode carbon block in unit time is ton; c. CposThe unit of the unit is yuan per ton for the anode carbon block.
The anode scrap cost model in the electrolytic aluminum side variation cost is shown as the following formula:
Cscrap＝rscrap*PAl*cscrap
wherein, CscrapThe anode scrap cost per unit time; r isscrapThe unit is the unit consumption of anode scrap, and the unit is ton/ton aluminum; c. CscrapIs the unit price of the anode scrap.
The fluoride cost model in the electrolytic aluminum side variation cost is shown as follows:
Cfs＝rfs*PAl*cfs
wherein, CfsFluoride salt cost per unit time; r isfsThe unit consumption of fluoride salt is ton/ton aluminum; c. CfsIs monovalent for fluoride salts.
The basic electricity rate model is shown as follows:
wherein, CgdfdBasic electricity charge is purchased for the monthly online; pgdmaxPurchasing the maximum demand of electricity for the network per month; pconDemand for contracts; c. C1The price is the basic standard price of the electricity charge agreed with the power grid.
The electricity charge model is shown as the following formula:
Cgdpro＝Pgdpro*cgdpro
wherein, CgdproThe electricity charge is the electricity charge of unit time; pgdproThe electricity is the electric power; c. CgdproThe unit price of the electric power fee.
From the cost model described above, an optimal scheduling model can be determined as shown in the following equation:
wherein f (x) is an objective function, geq(x) 0 is the electric balance constraint, g (x) is less than or equal to 0 is the junctor power flow constraint, LbAnd UbX is an n-dimensional decision vector as the upper and lower bounds of the decision variable. Wherein the objective function f (x) can be represented by the following formula:
f(x)＝Cfuel+Cdesf+Ceefix+CAlO+Cpos+Cscrap+Cfs+CAlfix+C′gdfd+C′gdpro
wherein, CfuelFuel cost per unit time; cdesfThe cost of desulfurization and denitration in unit time; ceefixIs a unit of timeThe cost is fixed on the indirect power generation side; cAlOThe alumina cost per unit time; cposThe cost of the anode carbon block per unit time; cscrapThe anode scrap cost per unit time; cscrapFluoride salt cost per unit time; cAlfixThe aluminum side fixed cost per unit time; c'gdfdThe basic electricity charge is purchased for the unit time network; c'gdproThe unit time is the electric power charge of the online electric power purchase.
Wherein the electrical balance constraint may be represented by:
∑Pt+Pwind+Psolar-∑PAl＝0
wherein, Sigma PtSumming the output of each thermal power generating unit; pwindSumming the wind power output; psolarSumming the photovoltaic outputs; sigma PAlThe total load is the electrolytic aluminum side load.
After the optimal scheduling model is determined, the optimal scheduling strategy of the system scheduling target can be calculated in real time by solving the decision variables in the optimal scheduling model by using an optimization algorithm. The decision variables comprise the current of each production series of electrolytic aluminum and the output of each thermal power generating unit. The output of each thermal power generating unit solved here, namely the real-time generated power P of the thermal power generating unitt。
In the embodiment of the application, the optimization algorithm can adopt a particle swarm algorithm, and the algorithm can better solve the problem of nonlinear multi-decision variable comprehensive energy optimization scheduling. Fig. 3 is a schematic flow chart of a particle swarm algorithm according to an embodiment of the present disclosure.
As shown in fig. 3, parameters such as the upper and lower bounds of each variable, the population size, the number of iterations, the convergence rate, the attenuation factor, and the like may be input, the population may be initialized according to the parameters, the individual fitness value may be calculated, then the optimal value of the entire population may be obtained, and then all the individuals may move to the optimal individual, the particle cross-border processing may be performed, and the population may be updated. And after the population is updated, judging whether the iteration times are exceeded or not, if not, continuously calculating the individual fitness value to update the population, otherwise, ending the process.
The aluminum-electricity cooperative optimization scheduling method can also be applied to the aspect of integrated application. When the method is realized, the task Management and service integration platform can integrate an aluminum-electricity collaborative production operation System model and an optimization algorithm, real-time production data is read from a database through a Dispatch Management Information System (DMIS) network, the real-time production data is solved by the optimization algorithm, a calculation result is fed back to the database to be stored in the database, and each series of electrolytic aluminum currents and each thermal power generating unit output force can be displayed through a human-computer interaction interface.
The aluminum-electricity collaborative optimization scheduling method is suitable for aluminum-electricity project production operation decision and power scheduling, and intelligent energy management technical measures such as informatization, automation and intellectualization can be organically integrated with industrial production processes such as first-line electrolytic aluminum and aluminum oxide, so that the bottleneck problem of low energy efficiency of the traditional power utilization and power collaborative industry can be solved.
In order to implement the foregoing embodiments, an aluminum-electricity cooperative optimization scheduling apparatus is further provided in the embodiments of the present application. The aluminum-electricity collaborative optimization scheduling device can be applied to an aluminum-electricity collaborative production operation system.
Fig. 4 is a schematic structural diagram of an aluminum-electric cooperative optimization scheduling apparatus according to an embodiment of the present application.
As shown in fig. 4, the aluminum electric collaborative optimization scheduling apparatus 400 may include:
a building module 410, configured to build a power generation side cost model according to the current power generation side variation cost and power generation side fixed cost of the system;
the building module 410 is further used for building an electrolytic aluminum side cost model according to the electrolytic aluminum side variation cost and the electrolytic aluminum side fixed cost;
the building module 410 is further configured to build an online shopping power side cost model according to the basic power cost and the electric power cost;
the determining module 420 is configured to determine an optimized scheduling model according to a power generation side cost model, an electrolytic aluminum side cost model and an online power purchasing side cost model, where the optimized scheduling model includes an objective function, a constraint condition and a decision variable, the constraint condition includes an electric balance constraint, a tie line power flow constraint and an electrolytic aluminum series load limit constraint, the electric balance constraint is that the sum of the output sum of each thermal power unit, the sum of the wind power output sum and the sum of the photovoltaic output sum is equal to the electrolytic aluminum side load sum, and the decision variable includes each production series current of electrolytic aluminum and the output sum of each thermal power unit;
and the calculating module 430 is configured to solve the decision variables in the optimized scheduling model by using an optimization algorithm.
In one possible implementation manner of the embodiment of the application, the power generation side variation cost includes a fuel cost and a desulfurization and denitration cost, and the electrolytic aluminum side variation cost includes an alumina cost, an anode carbon block cost, a residual anode cost and a fluoride salt cost;
the objective function is shown as follows:
f(x)＝Cfuel+Cdesf+Ceefix+CAlO+Cpos+Cscrap+Cfs+CAlfix+Cg′dfd+Cg′dpro
wherein, CfuelFuel cost per unit time; cdesfThe cost of desulfurization and denitration in unit time; ceefixThe fixed cost of the power generation side per unit time; cAlOThe alumina cost per unit time; cposThe cost of the anode carbon block per unit time; cscrapThe anode scrap cost per unit time; cscrapFluoride salt cost per unit time; cAlfixThe aluminum side fixed cost per unit time; cg′dfdThe basic electricity charge is purchased for the unit time network; cg′dproThe unit time is the electric power charge of the online electric power purchase.
In one possible implementation manner of the embodiment of the present application, the fuel cost model is represented by the following formula:
wherein, CfuelFuel cost per unit time; r iscoalThe unit is g/kWh for the coal consumption rate of power generation; hstdThe standard coal heat value is used as the standard coal heat value,taking 7000 kcal/kg; hrealThe unit is kcal/kg; ccoalThe unit is yuan/ton which is the price of raw coal; ctrpThe unit is Yuan/ton for freight charge; etataxThe tax rate is; ptGenerating power for a unit in real time, wherein the unit is kW;
the desulfurization and denitration cost model is shown as the following formula:
Cdesf＝rcoal*Pt*αdesf*cdesf
wherein, CdesfThe cost of desulfurization and denitration in unit time; r iscoalThe coal consumption rate for power generation; ptGenerating power for a unit in real time, wherein the unit is kW; alpha is alphadesfThe consumption of the desulfurization and denitration materials per kilowatt hour is expressed in kg/kWh; c. CdesfThe unit price of the desulfurization and denitration material is Yuan/kg.
In one possible implementation manner of the embodiment of the present application, the alumina cost model is represented by the following formula:
CAlO＝γAlO*PAl*γAlO
wherein, CAlOThe alumina cost per unit time; gamma rayAlOThe unit consumption of alumina is ton/ton of aluminum; pAlThe aluminum yield per unit time; gamma rayAlOIs monovalent for alumina;
the anode carbon block cost model is shown as the following formula:
Cpos＝ppos*cpos
wherein, CposThe cost of the anode carbon block per unit time; p is a radical ofposThe consumption of the anode carbon block in unit time is ton; c. CposThe unit is the unit price of the anode carbon block, and the unit is yuan/ton;
the anode scrap cost model is shown as follows:
Cscrap＝rscrap*PAl*cscrap
wherein, CscrapThe anode scrap cost per unit time; r isscrapThe unit is the unit consumption of anode scrap per ton of aluminum; c. CscrapIs the unit price of the anode scrap;
the fluoride salt cost model is shown below:
Cfs＝rfs*PAl*cfs
wherein, CfsFluoride salt cost per unit time; r isfsThe unit consumption of fluoride salt is ton/ton aluminum; c. CfsIs monovalent for fluoride salts.
In a possible implementation manner of the embodiment of the present application, the basic electricity rate model is represented by the following formula:
wherein, CgdfdBasic electricity charge is purchased for the monthly online; pgdmaxPurchasing the maximum demand of electricity for the network per month; pconDemand for contracts; c. C1A basic standard price of electricity charge agreed with the power grid;
the kilowatt-hour electric charge model is shown as the following formula:
Cgdpro＝Pgdpro*cgdpro
wherein, CgdproThe electricity charge is the electricity charge of unit time; pgdproThe electricity is the electric power; c. CgdproThe unit price of the electric power fee.
In a possible implementation manner of the embodiment of the present application, after solving a decision variable of an optimized scheduling model by using an optimization algorithm, the method further includes:
and displaying the series of currents of the electrolytic aluminum and the output of each thermal power generating unit through a human-computer interaction interface.
It should be noted that the explanation of the embodiment of the aluminum-electricity cooperative optimization scheduling method is also applicable to the aluminum-electricity cooperative optimization scheduling apparatus of the embodiment, and therefore is not described herein again.
The aluminum-electricity collaborative optimization scheduling device comprises a power generation side cost model, an electrolytic aluminum side cost model and an online shopping power side cost model, wherein the power generation side cost model, the electrolytic aluminum side cost model and the online shopping power side cost model are constructed, an optimized scheduling function is determined according to the power generation side cost model, the electrolytic aluminum side cost model and the online shopping power side cost model, based on the optimal economic target of an aluminum-electricity collaborative system, an optimization algorithm is utilized, full consumption of new energy (such as wind power and photovoltaic), optimal distribution of thermal power output and optimal control of energy utilization load are achieved, and the scheduling management target of optimal economy and maximum operation profit is achieved.
In order to implement the foregoing embodiments, an embodiment of the present application further provides a computer device, including a processor and a memory;
the processor reads the executable program codes stored in the memory to run programs corresponding to the executable program codes, so as to implement the aluminum electric collaborative optimization scheduling method according to the embodiment.
In order to implement the foregoing embodiments, the present application further proposes a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the aluminum-electricity cooperative optimization scheduling method according to the foregoing embodiments.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (10)
1. An aluminum-electricity collaborative optimization scheduling method is applied to an aluminum-electricity collaborative production operation system, and comprises the following steps:
constructing a power generation side cost model according to the current power generation side variation cost and the current power generation side fixed cost of the system;
constructing an electrolytic aluminum side cost model according to the electrolytic aluminum side variation cost and the electrolytic aluminum side fixed cost;
constructing a cost model of the power purchasing side on the basis of the basic power cost and the electric power cost;
determining an optimized scheduling model according to a power generation side cost model, an electrolytic aluminum side cost model and an online power purchase side cost model, wherein the optimized scheduling model comprises an objective function, constraint conditions and decision variables, the constraint conditions comprise electric balance constraint, tie line power flow constraint and electrolytic aluminum series load limit value constraint, the electric balance constraint is that the sum of the output sum of each thermal power unit, the sum of wind power output sum and photovoltaic output sum is equal to the electrolytic aluminum side load sum, and the decision variables comprise electrolytic aluminum production series currents and the output of each thermal power unit;
and solving the decision variables in the optimized scheduling model by using an optimization algorithm.
2. The method of claim 1, wherein the power generation side shift costs comprise fuel costs and desulfurization strip costs, and the electrolytic aluminum side shift costs comprise alumina costs, anode carbon block costs, anode scrap costs, and fluoride salt costs;
the objective function is shown as follows:
f(x)＝Cfuel+Cdesf+Ceefix+CAlO+Cpos+Cscrap+Cfs+CAlfix+C′gdfd+C′gdpro
wherein, CfuelFuel cost per unit time; cdesfThe cost of desulfurization and denitration in unit time; ceefixThe fixed cost of the power generation side per unit time; cAlOThe alumina cost per unit time; cposThe cost of the anode carbon block per unit time; cscrapThe anode scrap cost per unit time; cscrapFluoride salt cost per unit time; cAlfixThe aluminum side fixed cost per unit time; c'gdfdThe basic electricity charge is purchased for the unit time network; c'gdproThe unit time is the electric power charge of the online electric power purchase.
3. The method of claim 2, wherein the fuel cost model is represented by the following equation:
wherein, CfuelFuel cost per unit time; r iscoalThe unit is g/kWh for the coal consumption rate of power generation; hstdTaking 7000kcal/kg as standard coal calorific value; hrealThe unit is kcal/kg; ccoalThe unit is yuan/ton which is the price of raw coal; ctrpThe unit is Yuan/ton for freight charge; etataxThe tax rate is; ptGenerating power for a unit in real time, wherein the unit is kW;
the desulfurization and denitration cost model is shown as the following formula:
Cdesf＝rcoal*Pt*αdesf*cdesf
wherein, CdesfThe cost of desulfurization and denitration in unit time; r iscoalThe coal consumption rate for power generation; ptGenerating power for a unit in real time, wherein the unit is kW; alpha is alphadesfThe consumption of the desulfurization and denitration materials per kilowatt hour is expressed in kg/kWh; c. CdesfThe unit price of the desulfurization and denitration material is Yuan/kg.
4. The method of claim 2, wherein the alumina cost model is represented by the formula:
CAlO＝γAlO*PAl*γAlO
wherein, CAlOThe alumina cost per unit time; gamma rayAlOThe unit consumption of alumina is ton/ton of aluminum; pAlThe aluminum yield per unit time; gamma rayAlOIs monovalent for alumina;
the anode carbon block cost model is shown as the following formula:
Cpos＝ppos*cpos
wherein, CposThe cost of the anode carbon block per unit time; p is a radical ofposThe consumption of the anode carbon block in unit time is ton; c. CposThe unit is the unit price of the anode carbon block, and the unit is yuan/ton;
the anode scrap cost model is shown as follows:
Cscrap＝rscrap*PAl*cscrap
wherein, CscrapThe anode scrap cost per unit time; r isscrapIs a residual anodeUnit consumption, unit is ton/ton aluminum; c. CscrapIs the unit price of the anode scrap;
the fluoride salt cost model is shown below:
Cfs＝rfs*PAl*cfs
wherein, CfsFluoride salt cost per unit time; r isfsThe unit consumption of fluoride salt is ton/ton aluminum; c. CfsIs monovalent for fluoride salts.
5. The method of claim 2, wherein the basic electricity rate model is represented by the following equation:
wherein, CgdfdBasic electricity charge is purchased for the monthly online; pgdmaxPurchasing the maximum demand of electricity for the network per month; pconDemand for contracts; c. C1A basic standard price of electricity charge agreed with the power grid;
the kilowatt-hour electric charge model is shown as the following formula:
Cgdpro＝Pgdpro*cgdpro
wherein, CgdproThe electricity charge is the electricity charge of unit time; pgdproThe electricity is the electric power; c. CgdproThe unit price of the electric power fee.
6. The method of any of claims 1-5, after solving the decision variables of the optimized scheduling model using an optimization algorithm, further comprising:
and displaying the series of currents of the electrolytic aluminum and the output of each thermal power generating unit through a human-computer interaction interface.
7. The utility model provides an aluminium electricity collaborative optimization scheduling device which characterized in that, is applied to aluminium electricity collaborative production operating system, the device includes:
the building module is used for building a power generation side cost model according to the current power generation side variation cost and the current power generation side fixed cost of the system;
the construction module is also used for constructing an electrolytic aluminum side cost model according to the electrolytic aluminum side variation cost and the electrolytic aluminum side fixed cost;
the building module is also used for building an online power purchase side cost model according to the basic electric charge cost and the electric charge cost;
the system comprises a determining module, a scheduling optimizing module and a scheduling optimizing module, wherein the determining module is used for determining an optimizing scheduling model according to a power generation side cost model, an electrolytic aluminum side cost model and an online power purchasing side cost model, the optimizing scheduling model comprises an objective function, a constraint condition and a decision variable, the constraint condition comprises an electric balance constraint, a tie line power flow constraint and an electrolytic aluminum series load limit value constraint, the electric balance constraint is that the sum of the output sum of each thermal power unit, the sum of the wind power output sum and the photovoltaic output sum is equal to the electrolytic aluminum side load sum, and the decision variable comprises each production series current of electrolytic aluminum and the output sum of each thermal power unit;
and the calculation module is used for solving the decision variables in the optimized scheduling model by using an optimization algorithm.
8. The apparatus of claim 7, wherein the power generation side fluctuating costs include fuel costs and desulfurization denitration costs, and the electrolytic aluminum side fluctuating costs include alumina costs, anode carbon block costs, anode scrap costs, and fluoride salt costs;
the objective function is shown as follows:
f(x)＝Cfuel+Cdesf+Ceefix+CAlO+Cpos+Cscrap+Cfs+CAlfix+C′gdfd+C′gdpro
wherein, CfuelFuel cost per unit time; cdesfThe cost of desulfurization and denitration in unit time; ceefixThe fixed cost of the power generation side per unit time; cAlOThe alumina cost per unit time; cposThe cost of the anode carbon block per unit time; cscrapThe anode scrap cost per unit time; cscrapCost of fluoride salt per unit time；CAlfixThe aluminum side fixed cost per unit time; c'gdfdThe basic electricity charge is purchased for the unit time network; c'gdproThe unit time is the electric power charge of the online electric power purchase.
9. A computer device comprising a processor and a memory;
wherein the processor executes a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the aluminum electric collaborative optimization scheduling method according to any one of claims 1 to 6.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the program, when executed by a processor, implements the aluminum electric collaborative optimization scheduling method according to any one of claims 1 to 6.
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