CN110826809A - Energy consumption weight automatic distribution system and method based on prediction and rolling optimization - Google Patents

Energy consumption weight automatic distribution system and method based on prediction and rolling optimization Download PDF

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CN110826809A
CN110826809A CN201911096099.9A CN201911096099A CN110826809A CN 110826809 A CN110826809 A CN 110826809A CN 201911096099 A CN201911096099 A CN 201911096099A CN 110826809 A CN110826809 A CN 110826809A
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顾一峰
胡炳谦
周浩
韩俊
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Shanghai Jicheng Energy Technology Co Ltd
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Abstract

At present, energy consumption rights of each energy consumption enterprise are mainly distributed by manual work in double control management of energy consumption total amount and intensity by government energy-saving governing departments, difficulty and workload are huge, and a set of information system tool and a method for scientifically, reasonably and effectively distributing the energy rights by assistance of a data intelligent analysis technology are lacked. The invention aims to distribute corresponding energy use rights to enterprises with different energy use ratings and enterprises with different energy consumption efficiencies by a prediction and rolling linear method, and reasonably and efficiently manage energy use, improve energy utilization efficiency and reduce unit energy consumption by using a system and a method for distributing the set of energy rights under the condition that the total energy consumption of a region is constant.

Description

Energy consumption weight automatic distribution system and method based on prediction and rolling optimization
Technical Field
The invention relates to the field of intelligent energy management, in particular to an energy consumption right automatic distribution system and method based on prediction and rolling linear optimization.
Background
At present, energy consumption rights of each energy consumption enterprise are mainly distributed by manual work in double control management of energy consumption total amount and intensity by government energy-saving governing departments, difficulty and workload are huge, and a set of information system tool and a method for scientifically, reasonably and effectively distributing the energy rights by assistance of a data intelligent analysis technology are lacked.
The invention aims to distribute corresponding energy use rights to enterprises with different energy use ratings and enterprises with different energy consumption efficiencies through a prediction and rolling linear method, and reasonably and efficiently manage energy use, improve energy utilization efficiency, reduce unit energy consumption and realize maximization of regional economic output value by using a system and a method for distributing the set of energy use rights under the condition that the total energy consumption of a region is constant.
Disclosure of Invention
The invention provides a system and a method for energy use right distribution based on prediction and rolling linear optimization, which are mainly applied to the application of an optimization model, scientific evaluation of an energy use right distribution scheme, matching with government and organization related requirements, reasonable distribution of enterprise energy use right and guarantee of optimal energy utilization. The whole process comprises the steps of enterprise per-mu yield government rating, enterprise energy utilization permission amount formulation, a linear optimization model and an energy utilization permission adjudication module, which are shown in figure 1.
Drawings
FIG. 1 is a flow chart of a system for optimizing energy allocation in the practice of the present invention.
FIG. 2 is a schematic diagram of scroll optimization in the practice of the present invention.
Detailed Description
Step one, carrying out government rating on yield per mu of an enterprise: the energy consumption enterprise per mu yield is graded by a government according to multiple dimensions (including 'per mu yield' efficiency, unit GDP, employment data and the like), the enterprise is generally divided into four grades of A, B, C and D, energy consumption weight parameter indexes are set aiming at enterprises of different grades, distribution weights of the energy consumption unit energy consumption weight linear optimization model are influenced, and specific parameters can be adjusted according to control indexes.
Step two, enterprise energy utilization permission quota establishment: the total limit of the energy consumption rights of the energy consumption enterprises is established according to the energy consumption situation of the last year, control indexes (reduction percentage and the like) and the like in a multi-dimension mode and is used for the energy consumption weight linear optimization model.
Step three, linear optimization model:
for example, a five year plan:
wherein in the first year:
Max
Figure DEST_PATH_IMAGE001
wherein i is the number i of the energy-using enterprises, n is the total number of the energy-using enterprises,
Figure 166106DEST_PATH_IMAGE002
representing a businessThe specific energy consumption and output value is obtained,
Figure 718704DEST_PATH_IMAGE004
representing a business
Figure 648527DEST_PATH_IMAGE003
The specific correction coefficient is set when the per mu yield of the enterprise belongs to the government rating of the per mu yield of the enterprise.Representing a businessThe objective function of this step of optimizing the model is to maximize the total value of all enterprises in the first year under the following constraints, in the prediction of total energy consumption in the first year in the future.
Figure 186529DEST_PATH_IMAGE006
The constraint is to target all enterprisesOf total energy consumption, wherein
Figure DEST_PATH_IMAGE007
Representing a business
Figure 972345DEST_PATH_IMAGE008
The total energy consumption in the first year in the future,
Figure DEST_PATH_IMAGE009
representing a business
Figure 886337DEST_PATH_IMAGE008
The constraint indicates that the total energy consumption of the n enterprises in the first year of the future must be less than the total energy consumption of the past year multiplied by the total energy consumption of the past year,
Figure 609102DEST_PATH_IMAGE010
Representing the total energy consumption reduction ratio set according to macro planning requirements, here
Figure 549507DEST_PATH_IMAGE010
May be negative.
Figure 200062DEST_PATH_IMAGE012
The constraint is a constraint of total energy consumption efficiency, wherein
Figure DEST_PATH_IMAGE013
Representing a business
Figure 2059DEST_PATH_IMAGE008
The total value in the first year in the future,
Figure 54459DEST_PATH_IMAGE014
representing the total energy consumption efficiency of n enterprises in the target area in the next year,
Figure DEST_PATH_IMAGE015
representing a businessTotal yield in the past first year, so
Figure 42631DEST_PATH_IMAGE016
Representing the total energy consumption efficiency of n enterprises in the target area in the past year. The constraint indicates that the total energy consumption efficiency of the first year of the future of the n enterprises must be greater than the total energy consumption efficiency of the past year, and further
Figure DEST_PATH_IMAGE017
Representing a projected rate of improvement in overall energy consumption efficiency.
Figure DEST_PATH_IMAGE019
The constraint is a constraint on energy consumption of a single enterprise, wherein
Figure 664630DEST_PATH_IMAGE007
Representing a business
Figure 571537DEST_PATH_IMAGE008
The total energy consumption in the first year in the future,
Figure 853745DEST_PATH_IMAGE009
representing a business
Figure 213313DEST_PATH_IMAGE008
The constraint indicates that the total energy consumption of the enterprise i in the first year in the future cannot be more than 115 percent of the total energy consumption in the past year and the total energy consumption is not less than 115 percent of the total energy consumption in the past year
Figure 680415DEST_PATH_IMAGE020
Wherein
Figure 910670DEST_PATH_IMAGE020
According to the target enterprise
Figure 894938DEST_PATH_IMAGE008
The energy consumption management limit value is set by the government in a unified way when the yield per mu of a certain grade is graded.
In the second year:
Max
Figure DEST_PATH_IMAGE021
wherein i is the number i of the energy-using enterprises, n is the total number of the energy-using enterprises,representing a business
Figure 220451DEST_PATH_IMAGE003
The specific energy consumption and output value is obtained,
Figure 836371DEST_PATH_IMAGE004
representing a business
Figure 725961DEST_PATH_IMAGE003
The specific correction coefficient is set when the per mu yield rating belongs to a certain government.
Figure 48402DEST_PATH_IMAGE022
Representing a business
Figure 783271DEST_PATH_IMAGE003
The objective function of this step of optimizing the model is to maximize the total production value of all enterprises in the second year under the following constraints, in the prediction of total energy consumption in the next year.
Sub:
Figure DEST_PATH_IMAGE023
The constraint is a constraint on the total energy consumption of all enterprises in the target, wherein
Figure 535589DEST_PATH_IMAGE024
Representing a business
Figure 737025DEST_PATH_IMAGE026
The total energy consumption in the next year in the future,
Figure DEST_PATH_IMAGE027
representing a business
Figure 902690DEST_PATH_IMAGE026
The total energy consumption in the first year in the future is obtained from the solution of the optimization model in the first year, the constraint condition indicates that the total energy consumption in the second year in the future of the n enterprises must be less than the total energy consumption in the next year, the creation percentage indicates the total energy consumption reduction ratio set according to the macroscopic planning requirement, and the creation percentage can take a negative number.
Figure DEST_PATH_IMAGE029
The constraint is a constraint of total energy consumption efficiency, wherein
Figure 734859DEST_PATH_IMAGE030
Representing a business
Figure DEST_PATH_IMAGE031
Total yield in the next year, so
Figure 217050DEST_PATH_IMAGE032
Representing the total energy consumption efficiency of the n enterprises in the next year in the future,representing a business
Figure 730333DEST_PATH_IMAGE026
Total yield in the first year in the future, so
Figure 39085DEST_PATH_IMAGE034
Representing the total energy consumption efficiency of the n enterprises in the first year in the future for the region. The constraint indicates that the total energy consumption efficiency of the n enterprises in the next year must be greater than the total energy consumption efficiency of the n enterprises in the first year, and
Figure 646915DEST_PATH_IMAGE017
representing a projected rate of improvement in overall energy consumption efficiency.
Figure 91934DEST_PATH_IMAGE036
The constraint is a constraint on energy consumption of the enterprise, wherein
Figure DEST_PATH_IMAGE037
Representing a businessThe total energy consumption in the next year in the future,representing a business
Figure 175232DEST_PATH_IMAGE008
The constraint indicates that the total energy consumption of the enterprise i in the next year can not be more than 115 percent of the total energy consumption of the first year and can not be less than 115 percent of the total energy consumption of the past year
Figure 881282DEST_PATH_IMAGE020
Wherein
Figure 720056DEST_PATH_IMAGE020
According to the target enterprise
Figure 737822DEST_PATH_IMAGE008
The method belongs to energy consumption management limit values set uniformly by government when the yield per mu is graded.
Step four, judging the energy right: and distributing the result according to the operation result of the linear optimization model and the relevant energy utilization weight of the ith enterprise report.
Step five, rolling optimization: rolling prediction and optimization of energy use weight distribution suggestion in the next year according to actual energy use total amount result after energy use weight distribution in the past year, andbased on the prediction and optimization of the first year in step 3, the system automatically feeds back and corrects the original model in the second year
Figure DEST_PATH_IMAGE039
Input values of actual total energy consumption, and re-roll predicting the model formula
Figure 981064DEST_PATH_IMAGE022
I.e. the next year total energy consumption value. By analogy year by year, the whole process is based on the principles of prediction control and rolling optimization, the actual energy consumption amount of each year is regarded as each sampling moment, the optimization indexes only cover the limited time domain of the moment in the coming years, the prediction and optimization indexes are solved and are not implemented one by one, the optimal value action and practice of the coming year are carried out, the optimization time domain of the next year automatically rolls forward along with the advance of the moment, and the actual result is fed back to the next optimization time domain for rolling prediction and optimization, as shown in fig. 2.
The method considers conditions such as the per-mu yield benefit and the production condition of an enterprise by applying a prediction and rolling linear optimization model, and is characterized in that under the conditions of total output value in a maximized area and total energy consumption limitation, a limit value is allowed to be assigned to a single enterprise according to economic indexes, the energy use right of related energy use enterprises can be more reasonably and effectively distributed, the energy utilization efficiency is improved, the fine and intelligent management level of the government on the energy use right is improved to a great extent, the energy use right related transaction system is influenced by strong basic application in the future, and the government effect on energy consumption double control management is effectively improved.

Claims (5)

1. Step one, carrying out government rating on yield per mu of an enterprise: the energy consumption enterprise per mu yield is graded by a government according to multiple dimensions (including 'per mu yield' efficiency, unit GDP, employment data and the like), the enterprise is generally divided into four grades of A, B, C and D, energy consumption weight parameter indexes are set aiming at enterprises of different grades, distribution weights of the energy consumption unit energy consumption weight linear optimization model are influenced, and specific parameters can be adjusted according to control indexes.
2. Step two, enterprise energy utilization permission quota establishment: the total limit of the energy consumption rights of the energy consumption enterprises is established according to the energy consumption situation of the last year, control indexes (reduction percentage and the like) and the like in a multi-dimension mode and is used for the energy consumption weight linear optimization model.
3. Step three, linear optimization model:
for example, a five year plan:
wherein in the first year:
Max
Figure 85432DEST_PATH_IMAGE001
wherein i is the number i of the energy-using enterprises, n is the total number of the energy-using enterprises,
Figure 854936DEST_PATH_IMAGE002
representing a business
Figure 549354DEST_PATH_IMAGE003
The specific energy consumption and output value is obtained,representing a business
Figure 791559DEST_PATH_IMAGE003
The specific correction coefficient is set when the per mu yield of the enterprise belongs to the government rating of the per mu yield of the enterprise.
Figure 466385DEST_PATH_IMAGE005
Representing a businessThe objective function of this step of optimizing the model is to maximize the total value of all enterprises in the first year under the following constraints, in the prediction of total energy consumption in the first year in the future.
The constraint is a constraint on the total energy consumption of all enterprises in the target, wherein
Figure 21935DEST_PATH_IMAGE007
Representing a business
Figure 398821DEST_PATH_IMAGE008
The total energy consumption in the first year in the future,representing a business
Figure 844157DEST_PATH_IMAGE008
The constraint indicates that the total energy consumption of the n enterprises in the first year of the future must be less than the total energy consumption of the past year multiplied by the total energy consumption of the past year
Figure 829693DEST_PATH_IMAGE010
,
Figure 643059DEST_PATH_IMAGE010
Representing the total energy consumption reduction ratio set according to macro planning requirements, here
Figure 2627DEST_PATH_IMAGE010
It may be a negative number or may be,
Figure 876254DEST_PATH_IMAGE011
the constraint is a constraint of total energy consumption efficiency, whereinRepresenting a businessThe total value in the first year in the future,
Figure 999958DEST_PATH_IMAGE013
representing the total energy consumption efficiency of n enterprises in the target area in the next year,
Figure 665557DEST_PATH_IMAGE014
representing a business
Figure 812636DEST_PATH_IMAGE008
Total yield in the past first year, so
Figure 702226DEST_PATH_IMAGE015
Representing the total energy consumption efficiency of n enterprises in the target area in the past year;
the constraint indicates that the total energy consumption efficiency of the first year of the future of the n enterprises must be greater than the total energy consumption efficiency of the past year, and further
Figure 770807DEST_PATH_IMAGE016
Represents a projected rate of increase in total energy consumption efficiency;
the constraint is a constraint on energy consumption of a single enterprise, wherein
Figure 761121DEST_PATH_IMAGE007
Representing a business
Figure 821612DEST_PATH_IMAGE008
The total energy consumption in the first year in the future,
Figure 377490DEST_PATH_IMAGE009
representing a business
Figure 181629DEST_PATH_IMAGE008
In the past yearThe constraint indicates that the total energy consumption of the enterprise i in the first year in the future can not be more than 115 percent of the total energy consumption in the past year, and the total energy consumption can not be less than 115 percent of the total energy consumption in the past year
Figure 100038DEST_PATH_IMAGE018
Wherein
Figure 597009DEST_PATH_IMAGE018
According to the target enterpriseThe energy consumption management limit value is set by the government in a unified way when the per mu yield rating belongs to a certain range;
in the second year:
Max
Figure 259731DEST_PATH_IMAGE019
wherein i is the number i of the energy-using enterprises, n is the total number of the energy-using enterprises,representing a business
Figure 372623DEST_PATH_IMAGE003
The specific energy consumption and output value is obtained,
Figure 903093DEST_PATH_IMAGE004
representing a business
Figure 252297DEST_PATH_IMAGE003
A specific correction coefficient is set when the per mu yield rating of a certain government belongs to a certain grade;
Figure 817401DEST_PATH_IMAGE020
representing a business
Figure 187334DEST_PATH_IMAGE003
The objective function of this step of optimizing the model is to maximize the total production value of all enterprises in the second year under the following constraints, in the prediction of total energy consumption in the next year.
Sub:
Figure 408362DEST_PATH_IMAGE021
The constraint is a constraint on the total energy consumption of all enterprises in the target, whereinRepresenting a business
Figure 562625DEST_PATH_IMAGE023
The total energy consumption in the next year in the future,
Figure 509983DEST_PATH_IMAGE024
representing a businessThe total energy consumption in the first year in the future is obtained from the solution of the optimization model in the first year, the constraint condition indicates that the total energy consumption of the next year of the n enterprises must be less than the total energy consumption in the next year, the creation percentage indicates the total energy consumption reduction ratio set according to the macroscopic planning requirement, and the creation percentage can take a negative number;
Figure 768368DEST_PATH_IMAGE025
the constraint is a constraint of total energy consumption efficiency, wherein
Figure 308065DEST_PATH_IMAGE026
Representing a businessTotal yield in the next year, so
Figure 215421DEST_PATH_IMAGE028
Representing the total energy consumption efficiency of the n enterprises in the next year in the future,
Figure 84020DEST_PATH_IMAGE029
representing a business
Figure 224363DEST_PATH_IMAGE023
Total yield in the first year in the future, so
Figure 841420DEST_PATH_IMAGE030
Representing the total energy consumption efficiency of the n enterprises in the first year in the future in the region;
the constraint indicates that the total energy consumption efficiency of the n enterprises in the next year must be greater than the total energy consumption efficiency of the n enterprises in the first year, and
Figure 258757DEST_PATH_IMAGE016
represents a projected rate of increase in total energy consumption efficiency;
Figure 619462DEST_PATH_IMAGE031
the constraint is a constraint on energy consumption of the enterprise, whereinRepresenting a business
Figure 718448DEST_PATH_IMAGE008
The total energy consumption in the next year in the future,
Figure 154239DEST_PATH_IMAGE033
representing a business
Figure 53056DEST_PATH_IMAGE008
The constraint indicates that the total energy consumption of the enterprise i in the next year cannot be greater than the total energy consumption of the enterprise i in the next year115 percent of the total energy consumption in the first year in the future, and the total energy consumption is not less than the total energy consumption in the last year
Figure 144554DEST_PATH_IMAGE018
WhereinAccording to the target enterpriseThe method belongs to energy consumption management limit values set uniformly by government when the yield per mu is graded.
4. Step four, judging the energy right: and distributing the result according to the operation result of the linear optimization model and the relevant energy utilization weight of the ith enterprise report.
5. Step five, rolling optimization: the method is characterized in that the energy use weight distribution proposal of the next year is predicted and optimized according to the actual energy use total amount result after the energy use weight distribution is carried out in the past year, namely, on the basis of the prediction and optimization of the first year in the step 3, the system automatic feedback correction of the second year is carried out in the original model
Figure 10900DEST_PATH_IMAGE034
Input values of actual total energy consumption, and re-roll predicting the model formula
Figure 234202DEST_PATH_IMAGE020
Namely the total energy consumption value in the second year,
by analogy year by year, the whole process is based on the principles of prediction control and rolling optimization, the actual energy consumption amount of each year is regarded as each sampling moment, the optimization indexes only cover the limited time domain of the moment in the coming years, the prediction and optimization indexes are solved and are not implemented one by one, the optimal value action and practice of the coming year are carried out, the optimization time domain of the next year automatically rolls forward along with the advance of the moment, and the actual result is fed back to the next optimization time domain for rolling prediction and optimization, as shown in fig. 2.
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CN111459461A (en) * 2020-04-24 2020-07-28 上海积成能源科技有限公司 Energy use right verification and transaction system and method based on Internet of things and block chain technology

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