CN107038499B - Global energy optimization configuration method based on minimum deviation method - Google Patents
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Abstract
The invention discloses a global energy optimization configuration method based on a minimum deviation method, which is characterized by comprising the following steps: the method comprises the steps of dividing a global region into a plurality of regions, collecting the quantity and cost data of trans-regional conveying of each energy source among the regions, constructing a multi-objective optimization function by taking the conveying economic cost and the conveying loss as the minimum, taking the conveying amount of each energy source conveying path as a variable, taking the limit conveying amount of each conveying path as a constraint condition, carrying out optimization solution based on a minimum deviation method, and determining the optimal configuration result of each energy source in the global region. The invention provides technical reference for global energy transmission configuration under the background of global energy Internet, and cross-regional electric energy transmission quantity and power grid construction between countries, regions and regions, and between continents and continents.
Description
Technical Field
The invention relates to the field of global energy Internet macroscopic operation characteristics, in particular to a global energy optimization configuration method based on a minimum deviation method.
Background
With the rapid development of the world economic society, the global energy consumption structure is constantly changed, and the energy consumption subject is gradually changed from the main energy based on fossil energy to the main energy based on renewable energy. With the large-scale development and use of clean energy, the proportion of electric energy in energy consumption structures is increasing continuously, and the world presents a continuous electrification trend, so that the construction of cross-intercontinental power grids has an extremely important meaning for the optimal configuration of global energy. The electric power network is used for replacing part of traditional marine transportation and land transportation modes to carry out energy transmission and trade among continents, and the economic cost brought by energy in the transmission process is favorably reduced.
Whether fossil energy or clean energy used for power generation is unevenly distributed among regions from a global perspective, and a large amount of energy trading and transportation are needed among continents, so that how to select an appropriate energy transportation path and energy transportation amount of each transportation path to realize optimal configuration of global energy has great significance in the context of establishing a global energy internet.
The realization of an optimized configuration of energy sources has attracted much attention from researchers. However, the existing research is only based on one or a few areas to perform the optimal configuration of energy sources, and is not considered from the perspective of the global scope. Partial existing researches compare two energy conveying modes of coal conveying and power grid conveying, and under the condition that the requirement of energy conveying amount is met, the power grid conveying is superior to the coal conveying, the economic cost is reduced, and the method is complex for comparison of various energy conveying modes. From the perspective of global energy delivery, there is no practical engineering application value.
Disclosure of Invention
The invention aims to solve the problems and provides a global energy optimal configuration method based on a minimum deviation method, which takes the economic cost and the transmission loss of transmission as objective functions, takes the transmission quantity of each energy transmission path as a variable and the limit transmission quantity of each transmission path as a constraint condition, realizes reasonable optimal configuration of various energy sources in the global scope, obtains the energy flow pattern in the global scope, reduces the cost brought by the energy sources in the transmission process and provides decision support for strategic planning of the global energy Internet. Meanwhile, when the method is used for optimization calculation, the relative importance of the objective function does not need to be considered, a plurality of objective functions can be unified to the same measurement standard for optimization processing, and the method has practical engineering application value.
In order to achieve the purpose, the invention adopts the following technical scheme:
a global energy optimal configuration method based on a minimum deviation method divides a global region into a plurality of regions, collects the quantity and cost data of trans-regional transmission of each energy source among the regions, takes the minimum transmission economic cost and transmission loss as a target, takes the transmission quantity of each energy source transmission path as a variable, takes the limit transmission quantity of each transmission path as a constraint condition, constructs a multi-target optimization function, carries out optimal solution based on the minimum deviation method, and determines the optimal configuration result of each energy source in the global region.
Furthermore, the global region is divided into a plurality of regions according to different regions. Preferably, the world is divided into six regions including the asia-pacific region, the middle east region, north america, central and south america, europe and the continental europe and africa.
Further, the quantity and cost data of each energy source specifically comprise energy demand p (i) and supply q (i) required for cross-intercontinental transportation in each region, traffic capacity constraint value r (i, j) of each transportation path of each energy source, unit transportation cost a (i, j) generated in the transportation process of each energy source, and loss rate b (i, j) of each energy source in the transportation process.
Furthermore, the quantity and cost data of each collected energy source are preprocessed and converted into values in a unified unit.
Further, the multi-objective optimization function comprises an economic cost minimization objective function f1(x) And a transfer loss minimization objective function f2(x) And constructing regional energy supply constraint, energy demand constraint and energy delivery path traffic capacity constraint conditions.
Further, the method for constructing the regional energy supply constraint condition and the energy demand constraint condition specifically comprises the following steps:
the method for constructing the energy transmission path traffic capacity constraint expression specifically comprises the following steps:
xk(i,j)≤rk(i,j)。
further, linear programming is carried out on the constructed multi-objective optimization function to obtain the maximum value and the minimum value of each objective function after linear programming.
Further, a unified objective function is constructed based on the maximum function value and the minimum function value of the two objective functions obtained by the minimum deviation method, the constraint condition is kept unchanged, linear planning is carried out, the unified objective function is enabled to reach the minimum value, the optimized conveying path of each energy source and the energy source conveying amount of each conveying path are obtained, and the optimized configuration of global energy sources is achieved.
The method for constructing the unified objective function according to the maximum function value and the minimum function value after the linear programming of each objective function of the multi-objective optimization function specifically comprises the following steps:
wherein, F is a unified objective function constructed based on a minimum deviation method, and the economic cost minimization objective function F1(x) And a transfer loss minimization objective function f2(x) Maximum value f after linear programming1 max，f2 maxAnd a minimum value f1 min，f2 min。
Further, the method also comprises a verification step, wherein the actual conveying amount of each energy source in each conveying path is multiplied by unit conveying cost, the products are added and summed to obtain the actual economic cost for conveying the energy source, and the actual conveying amount of each energy source in each conveying path is multiplied by the loss rate, the products are added and summed to obtain the actual conveying loss amount; and comparing the actual conveying economic cost and the actual conveying loss of the energy with the optimized conveying economic cost and conveying loss to verify the effectiveness of the energy optimization configuration.
Compared with the prior art, the invention has the beneficial effects that:
the method takes the minimum economic cost and the minimum transmission loss as objective functions, realizes the global energy optimization configuration based on the minimum deviation method, and does not need to consider the relative importance of the two objective functions and the mathematical characteristics of the two objective functions. The method can also be applied to energy optimization configuration of partial regions from the perspective of the global scope, and the application range is wide. In addition, the method is simple and easy to operate, can be applied to actual engineering, can provide reference for a global energy transmission system under the background of global energy Internet, and provides decision support for trans-regional electric energy transmission quantity between countries, regions and regions, and across-regional electric energy transmission quantity between continents and power grid construction.
Meanwhile, the invention describes that the quality of a system is measured by a plurality of indexes which may be inconsistent or even contradictory, and the minimum deviation method converts the multi-objective optimization problem into the single-objective optimization problem without considering the conflict among the objective functions.
Compared with the existing research method, the method is simple, high in execution speed, wide in application range and high in engineering applicability.
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The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a flow chart of energy flow after global optimal configuration of primary energy sources in an example of the invention;
fig. 3 is a flow chart of energy flow after the primary energy and the electric energy are optimally configured on a global scale in the embodiment of the invention.
The specific implementation mode is as follows:
the invention is further described with reference to the following figures and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As described in the background art, the preparation method in the prior art is complex and has no practical engineering application value, and in order to solve the above technical problems, the present application proposes a global energy optimization configuration method based on a minimum deviation method.
According to the method, global primary energy is optimally configured by utilizing data of global energy annual cross-intercontinental energy transmission (including petroleum, coal and natural gas), so that the optimized energy transmission economic cost and energy transmission loss are obtained, and the effectiveness of the method for realizing energy optimal configuration can be verified by comparing the optimized energy transmission economic cost and the optimized energy transmission loss with the actual energy transmission economic cost and the actual energy transmission loss. Because the global energy Internet is not successfully built, the cross-intercontinental electric energy transmission amount is an undetermined value, for this reason, the total global energy transmission amount is kept unchanged, the global electric energy cross-continent transmission amount accounts for 20% of the total transmission amount, the transmission amounts of other energy sources are correspondingly reduced, the secondary energy and the primary energy are simultaneously subjected to global-wide optimization configuration, the energy transmission cost and the transmission loss at the moment are compared with the result of simply transmitting the primary energy after optimization, and technical reference is provided for the problem to be noticed in the global energy Internet construction process.
The method specifically comprises the following steps:
(1) dividing the world into six regions (i, j is 1, …,6, i, j all represent six regions, and different values represent different regions), including Asia-Pacific region, middle east region, North America, Central and south America, Europe, European and Asia continental, Africa;
(2) the method comprises the steps of collecting data of oil, coal and natural gas needing to be transported across the intercontinental areas in each area, wherein the data comprises energy demand p (i) and supply q (i), traffic capacity constraint values r (i, j) of each transportation path of each energy source, unit transportation cost a (i, j) generated in the transportation process of each energy source, and loss rate b (i, j) of each energy source in the transportation process. Collecting unit power transmission cost and grid loss rate generated in the electric energy transmission process;
(3) preprocessing acquired data (demand, supply, traffic capacity constraint value and unit transportation cost) of each energy source, and converting data values of coal, natural gas and electric energy into data values taking million tons of oil equivalent as a unit;
(4) constructing a multi-objective optimization function comprising an economic cost minimization objective function f1(x) And a transfer loss minimization objective function f2(x) In that respect The specific expression is as follows:
wherein, ak(i, j) (k is 1,2,3, and represents coal, oil, and natural gas) is the transportation cost per unit energy between the areas i, j, and bk(i, j) is the loss rate in the process of energy transmission between the areas i, j, xk(i, j) represents the energy transmission amount of each energy transmission path between the areas i, j, and in this case, n is 3.
(5) And constructing a regional energy supply constraint expression, an energy demand constraint expression and an energy delivery path traffic capacity constraint expression. The specific expression is as follows:
1) energy supply constraint expression
Wherein p is1(i)，p2(i)，p3(i) Each represents the supply of coal, oil, and natural gas in the area i.
2) Energy demand constraint expression
Wherein q is1(i)，q2(i)，q3(i) Respectively representing the demand of coal, oil and natural gas in the region i.
3) Energy demand constraint expression
Wherein r is1(i,j)，r2(i,j)，r3And (i, j) respectively represent traffic capacity constraint values of energy transmission paths between the areas i and j.
(6) Inputting the objective function constructed in the step (4), the constraint condition constructed in the step (5), the energy source data in the step (3) and the loss rate of each energy source in the step (2) in the conveying process into GAMS software, executing a linear programming command, and respectively obtaining the objective function f1(x)，f2(x) Maximum after linear planning
And minimum value
(7) And (3) constructing a unified target function F by using the maximum function value and the minimum function value of the two target functions obtained in the step (6) based on a minimum deviation method, converting the target function into the F in GAMS software, keeping the energy data and the constraint condition unchanged, executing a linear programming instruction, enabling the target function F to reach the minimum value, obtaining the optimized conveying path of each energy source and the energy conveying amount of each conveying path, and realizing the optimized configuration of the energy sources. The specific expression of the objective function:
(8) multiplying the actual conveying capacity of each energy source in each conveying path by the unit conveying cost, adding and summing the products to obtain the actual economic cost for conveying the energy source, multiplying the actual conveying capacity of each energy source in each conveying path by the loss rate, and adding and summing the products to obtain the actual conveying loss;
(9) comparing the actual conveying economic cost and the actual conveying loss of each energy source with the optimized conveying economic cost and conveying loss to verify the effectiveness of the method for realizing energy source optimized configuration;
(10) the global energy source transcontinent total conveying quantity is kept unchanged, the global electric energy conveying quantity is assumed to account for 20% of the total conveying quantity, and the conveying quantity of other energy sources is correspondingly reduced. And (4) repeating the steps (4) to (7) to simultaneously carry out global optimal configuration on the electric energy and the primary energy. The economic cost and the transmission loss of the transmission obtained after the global optimal configuration of the electric energy and the primary energy are compared with the optimal result when only the primary energy is transmitted.
In a typical embodiment of the present application, as shown in fig. 1, (1) the world is divided into six regions (i, j ═ 1, …,6, i, j each represent six regions, with different values representing different regions), including asia-pacific region, middle east region, north america, central and south america, europe and the continental europe, africa;
(2) data of petroleum, coal and natural gas needing to be transported across the interstates in each region in 2015 are collected, and the data comprise energy demand p (i), supply amount q (i), traffic capacity constraint values r (i, j) of each transportation path of each energy source, unit transportation cost a (i, j) generated in the transportation process of each energy source and loss rate b (i, j) of each energy source in the transportation process. And collecting unit power transmission cost and grid loss rate generated in the electric energy transmission process. The example of the traffic capacity constraint data of the petroleum transportation path is shown in table 1.
TABLE 1 oil transportation path traffic capacity constraint values (million tons oil equivalent)
(3) Preprocessing acquired data of each energy source, converting data values of coal, natural gas and electric energy into data values taking million tons of oil equivalent as a unit, taking supply/demand data of petroleum, coal and natural gas which need to be transported across continents in each region in 2015 as an example, as shown in table 2, the unit of data of units not shown in the table is million tons of oil equivalent;
TABLE 2 regional Primary energy supply/demand (million ton oil equivalent)
(4) Constructing a multi-objective optimization function comprising an economic cost minimization objective function f1(x) And a transfer loss minimization objective function f2(x) In that respect The specific expression is as follows:
min f1(x)＝a1(i,j)·x1(i,j)+a2(i,j)·x2(i,j)+a3(i,j)·x3(i,j)
min f2(x)＝b1(i,j)·x1(i,j)+b2(i,j)·x2(i,j)+b3(i,j)·x3(i,j)
wherein, ak(i, j) (k is 1,2,3, and represents coal, oil, and natural gas) is the transportation cost per unit energy between the areas i, j, and bk(i, j) is the loss rate in the process of energy transmission between the areas i, j, xk(i, j) represents the energy transfer amount of each energy transfer path between the areas i, j.
(5) And constructing a regional energy supply constraint expression, an energy demand constraint expression and an energy delivery path traffic capacity constraint expression. The specific expression is as follows:
1) energy supply constraint expression
Wherein p is1(i)，p2(i)，p3(i) Each represents the supply of coal, oil, and natural gas in the area i. Specific data are shown in Table 2The supply of the region is shown.
2) Energy demand constraint expression
Wherein q is1(i)，q2(i)，q3(i) Respectively representing the demand of coal, oil and natural gas in the region i. Specific data are shown in the demand amounts of the respective regions in table 2.
3) Energy demand constraint expression
Wherein r is1(i,j)，r2(i,j)，r3And (i, j) respectively represent traffic capacity constraint values of energy transmission paths between the areas i and j. Specific data are shown in table 1, using petroleum as an example.
(6) Inputting a target function, constraint conditions, energy data and the loss rate of each energy in the conveying process in GAMS software, executing a linear programming instruction, and respectively calculating a target function f1(x)，f2(x) Maximum after linear planning
And minimum value
The calculation results in this example are shown in table 3.
TABLE 3 maximum and minimum values of the objective function
(7) A unified target function F is constructed based on a minimum deviation method, the target function is converted into F in GAMS software, energy data and constraint conditions are unchanged, a linear programming instruction is executed, the target function F reaches the minimum value, the optimized conveying paths of all energy sources and the energy conveying amount of all conveying paths can be obtained, and the energy flow diagram of the inventor is obtained after the primary energy sources are optimized and configured in the global range and is shown in the attached figure 2. The specific expression of the objective function:
(8) the actual delivery amount of each primary energy source in each delivery path is multiplied by the unit delivery cost, the products are added and summed to obtain the actual economic cost of energy delivery of 243.22043 million dollars, and the actual delivery amount of each primary energy source in each delivery path is multiplied by the loss rate, the products are added and summed to obtain the actual delivery loss of 0.12350 million tons of petroleum.
(9) And comparing the actual conveying economic cost and the actual conveying loss of each energy source with the optimized conveying economic cost and conveying loss. As shown in table 4, both the transportation economic cost and the transportation loss amount are reduced, the energy transportation system is optimized, and the effectiveness of the method for realizing the energy optimization configuration is verified.
TABLE 4 Global optimal configuration results and actual values of Primary energy
(10) Keeping the total energy transfer across continents unchanged, assuming that the electric energy transfer across continents accounts for 20% of the total transfer,
TABLE 5 regional Primary energy supply/demand (million tons oil equivalent)
The other energy sources were delivered in reduced amounts, and the energy supply/demand in each region is shown in table 5. And (5) repeating the steps (4) to (7), and simultaneously carrying out global optimal configuration on the electric energy and the primary energy to obtain a global energy source flow diagram as shown in fig. 3. The energy optimization configuration results for different energy delivery ratios are compared as shown in table 6.
TABLE 6 Global energy optimal configuration result comparison at different energy delivery ratios
As can be seen from the results in table 6, the energy delivery economic cost is reduced, but the energy delivery loss amount is increased because the grid loss rate of the cross-regional power grid is larger than that of other energy sources. Therefore, in the construction process of the global energy internet, the loss rate of the power grid should be considered as an important factor to realize the double optimization of the economy and efficiency of the global energy transmission system.
As can be seen from fig. 2 and fig. 3, the energy optimization configuration is different under different energy consumption ratios. Taking the asia-pacific region, which is the most important energy consumption region in the world, as an example, the amount of oil imported from the middle east is reduced by about 2 million tons of oil equivalent compared with the amount of oil imported without electric energy transmission; the oil quantity imported from the former Soviet Union is reduced by 1887 ten thousand tons of oil equivalent; the amount of coal imported from the asian continental region was reduced by about 871 million tons oil equivalent; the import of lng from the middle east reduces the oil equivalent by 1932 million tons. Instead, the amount of electric energy delivered is increased. The increase of the electric energy transmission amount means that clean energy is effectively developed and utilized, the previous exploitation and use of fossil energy are replaced, and an energy configuration structure is optimized.
Based on the method flow shown in the attached drawing 1, the effectiveness of the method is verified by designing the above calculation examples, the optimal configuration of various energy sources in the global range is realized, the optimal configuration results of the primary energy source and the secondary energy source under different transmission ratios are compared, and the technical reference is provided for the problem which needs to be noticed in the construction process of the global energy Internet.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (4)
1. A global energy optimization configuration method based on a minimum deviation method is characterized by comprising the following steps: dividing a global region into a plurality of regions, collecting the quantity and cost data of trans-regional conveying of each energy source among the regions, constructing a multi-objective optimization function by taking the conveying economic cost and the conveying loss as the minimum target, the conveying quantity of each energy source conveying path as a variable and the limit conveying quantity of each conveying path as a constraint condition, carrying out optimization solution based on a minimum deviation method, and determining the optimal configuration result of each energy source in the global region;
the multi-objective optimization function is constructed by an economic cost minimization objective function f1(x) And a transfer loss minimization objective function f2(x) The specific expression is as follows:
wherein, ak(i, j) is the transportation cost per unit energy between regions i, j, k represents the energy type, bk(i, j) is the loss rate in the process of energy transmission between the areas i, j, xk(i, j) is the energy transmission amount of each energy transmission path between the areas i, j;
the quantity and cost data of each energy source specifically comprise energy demand p (i) and supply q (i) required to be transported across the intercontinental areas in each area, traffic capacity constraint values r (i, j) of each transportation path of each energy source, unit transportation cost a (i, j) generated in the transportation process of each energy source, and loss rate b (i, j) of each energy source in the transportation process;
the method for constructing the unified objective function according to the maximum function value and the minimum function value after the linear programming of each objective function of the multi-objective optimization function specifically comprises the following steps:
wherein, F is a unified objective function constructed based on a minimum deviation method, and the economic cost minimization objective function F1(x) And a transfer loss minimization objective function f2(x) Maximum value f after linear programming1 max，f2 maxAnd a minimum value f1 min，f2 min；
Constructing regional energy supply constraint, energy demand constraint and energy delivery path traffic capacity constraint conditions;
the method for constructing the energy supply constraint conditions and the energy demand constraint conditions of the regions specifically comprises the following steps:
the method for constructing the energy transmission path traffic capacity constraint expression specifically comprises the following steps:
xk(i,j)≤rk(i,j)；
the method also comprises a verification step, wherein the actual conveying capacity of each energy source on each conveying path is multiplied by unit conveying cost, the products are added and summed to obtain the actual economic cost for conveying the energy source, and the actual conveying capacity of each energy source on each conveying path is multiplied by the loss rate, the products are added and summed to obtain the actual conveying loss; comparing the actual energy transmission economic cost and the actual energy transmission loss with the optimized energy transmission economic cost and the optimized energy transmission loss to verify the effectiveness of the energy optimization configuration;
and constructing a unified objective function according to the maximum function value and the minimum function value of the two objective functions obtained based on a minimum deviation method, keeping constraint conditions unchanged, and performing linear programming to enable the unified objective function to reach the minimum value, so that the optimized conveying path of each energy source and the energy conveying amount of each conveying path are obtained, and the optimized configuration of global energy sources is realized.
2. The global energy optimization configuration method based on the minimum deviation method as claimed in claim 1, wherein: the global region is divided into a plurality of regions according to the region, and the global region is divided into six regions including Asia-Pacific region, middle east region, North America, Central and south America, Europe, European continental Asia and Africa.
3. The global energy optimization configuration method based on the minimum deviation method as claimed in claim 1, wherein: and preprocessing the quantity and cost data of each acquired energy source, and converting the data into values in a unified unit.
4. The global energy optimization configuration method based on the minimum deviation method as claimed in claim 1, wherein: and performing linear programming on the constructed multi-objective optimization function to obtain the maximum value and the minimum value of each objective function after linear programming.
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| CN105071433B (en) * | 2015-07-31 | 2018-04-20 | 贵州大学 | A kind of configuration scheme of distributed generation resource |
| CN106651636A (en) * | 2016-10-11 | 2017-05-10 | 四川大学 | Multi-energy resource optimum allocation method for global energy internet |
-
2017
- 2017-04-07 CN CN201710224240.3A patent/CN107038499B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105337316A (en) * | 2015-10-27 | 2016-02-17 | 夏百战 | Distributed power generation optimal configuration method based on intelligent distribution |
| CN105701609A (en) * | 2016-01-11 | 2016-06-22 | 华北电力大学 | Peaking power source layout method based on comprehensive evaluation indexes |
Non-Patent Citations (2)
| Title |
|---|
| 多目标优化设计的最小偏差法;周美英 等;《机械设计》;20021231(第12期);参见第1-2页 * |
| 电力布局优化与能源综合运输体系;林伯强 等;《经济研究》;20090630(第6期);参见第105页，第107-111页 * |
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