CN112232984B - Distributed data center computing power and energy flow fused comprehensive energy system optimization scheduling method - Google Patents
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Abstract
The invention provides a distributed data central computing power and energy flow fused comprehensive energy system optimization scheduling method, which comprises the following steps: s1, establishing a comprehensive energy system mathematical model integrating distributed data central computing power and energy flow; s2, establishing a comprehensive energy system operation evaluation index system comprising three primary indexes of economy, safety and cleanness and a plurality of secondary indexes; s3, determining the comprehensive weight of each index by adopting a comprehensive evaluation method; s4, constructing an optimized dispatching model of the comprehensive energy system by taking the lowest operation cost, the highest safety and the lowest pollution emission as three objective functions; and S5, accessing the optimized dispatching model of the comprehensive energy system into the mathematical model of the comprehensive energy system to obtain an optimal dispatching method and a result. The distributed data center is incorporated into the comprehensive energy system, and the comprehensive optimization scheduling is carried out on the other energy sources such as computing power, electric power, heating power and the like on the whole, so that the energy waste is reduced, the consumption of clean energy is increased, and the flexibility of the system is improved.
Description
Technical Field
The invention belongs to the field of comprehensive energy systems, and particularly relates to a distributed data central computing power and energy flow fused comprehensive energy system optimization scheduling method.
Background
The distributed data center is used as an upgrading scheme of a centralized data center and is an information infrastructure of a next generation ultra-high speed network. At present, the construction of a new generation of information infrastructure, namely 'new infrastructure', is enhanced in China, and the information infrastructure construction is a main edition block, such as communication network infrastructure represented by 5G, internet of things, industrial Internet and satellite Internet, new technical infrastructure represented by artificial intelligence, cloud computing, block chains and the like, computing infrastructure represented by a data center and an intelligent computing center and the like. Distributed data centers have become an integral part of future industrial park infrastructure construction. However, the introduction of the distributed data center will inevitably increase the power consumption of the area greatly, and will cause a great impact on the planning and operation optimization of the comprehensive energy system in the area. Meanwhile, the power calculating equipment generates a large amount of heat during operation, and how to utilize the heat is one of the focuses of people.
Although many technologies for providing heating or hot water supply service for local users by using waste heat of computing equipment as a heat source appear at the present stage, in the technologies, a distributed data center is often independent of a local regional comprehensive energy system in the aspects of design, planning, operation and control, and cannot be comprehensively considered, and situations of untimely regulation and control and unmatched supply and demand often appear.
The distributed data center is incorporated into the comprehensive energy system, so that unified optimization scheduling is performed on other clean energy such as computing power, electric power, heating power and the like on the whole, the problems of untimely regulation and control, mismatching of supply and demand and the like can be well solved, energy waste is reduced, and the flexibility of the system is improved.
Disclosure of Invention
In order to solve the problems in the prior art and optimally schedule the integrated energy system integrating distributed data center computing power and energy flow, the invention discloses an optimized scheduling method of the integrated energy system integrating distributed data center computing power and energy flow.
The invention specifically adopts the following technical scheme:
a distributed data center computing power and energy flow fused comprehensive energy system optimization scheduling method comprises the following specific steps:
s1, establishing a comprehensive energy system mathematical model integrating distributed data central computing power and energy flow;
s2, establishing a comprehensive energy system operation evaluation index system comprising three primary indexes of economy, safety and cleanliness and a plurality of secondary indexes;
s3, determining the comprehensive weight of each index by adopting a comprehensive evaluation method;
s4, constructing an optimized scheduling model of the comprehensive energy system by taking the lowest operation cost, the highest safety and the lowest pollution emission as three objective functions;
and S5, accessing the optimized dispatching model of the comprehensive energy system into the mathematical model of the comprehensive energy system to obtain an optimal dispatching method and a result.
Further, the step S1 of establishing a mathematical model of the integrated energy system with integrated distributed data central computing power and energy flow includes:
the comprehensive energy system is modeled based on graph theory, the joints of equipment such as a power plant, a solar photovoltaic panel, a distributed data center, a transformer, a heat source, a heating power station, a user terminal and the like are abstracted into connection nodes, lines between the two nodes are abstracted into edges, each piece of equipment in the system is simplified into a physical model according to actual system data, and a topological structure of the comprehensive energy system is established.
Further, for a single energy conversion device, the relationship between the input IN and the output OUT is:
OUT＝μ·IN (1)
where μ represents a coupling coefficient between the input and the output;
for an integrated energy system comprising a plurality of energy transforming devices and a plurality of forms of energy, the relationship between input and output may be described by a matrix:
in the formula, subscripts P, G, H, C represent electric power, clean energy, thermal power, and computational power, respectively; mu.s PG The coupling coefficient between the input and the output when the electric power is used as the input and the clean energy is used as the output; mu.s GP The coupling coefficient between the input and the output is determined by taking clean energy as input and taking electric power as output.
Further, the operation evaluation system of the comprehensive energy system in the step S2 mainly comprises three primary indexes of economy, safety and cleanness;
wherein the economic indicator I comprises energy material cost I 1 Equipment operation maintenance cost I 2 Energy loss cost I 3 Three secondary indexes;
safety index II includes equipment failure rate II 1 And probability of fatigue cracking of heat supply network pipeline II 2 Corrosion failure rate II 3 Three secondary indexes;
cleanliness indices III include solid waste discharge III 1 Clean energy utilization rate III 2 Greenhouse gas emission III 3 Three secondary indexes.
Further, in the step S3, the comprehensive weight of each index is determined by using a comprehensive evaluation method, wherein an entropy weight method is used as an objective evaluation method, and an analytic hierarchy process is used as a subjective evaluation method. The entropy weight method obtains the objective weight of each index as
The analytic hierarchy process obtains the subjective weight of each index as
Then its composite weight is
i＝I,I 1 ,I 2 ,I 3 ,II,II 1 ,II 2 ,II 3 ,III,III 1 ,III 2 ,III 3 。
Further, the economic indicator I represents a function x (I) as:
the safety index II represents a function x (II) of:
the cleanliness index III represents a function x (III) of:
in the formulae (3), (4) and (5), w i The weight of each index is represented, and x (i) represents each influence index.
Further, the influence indexes are as follows:
x(I 1 )、x(I 2 ) And x (I) 3 ) Respectively as follows:
in the formula (I), the compound is shown in the specification,
respectively represent the prices for purchasing electric power, clean energy (natural gas, wind energy, solar energy, etc.); z P ,Z G Representing the amount of purchased electricity, clean energy (natural gas, wind, solar, etc.); gamma ray P ,γ G ,γ H ,γ C Respectively representing the unit cost of operation and maintenance of electric equipment, clean energy (natural gas, wind energy, solar energy and the like), thermal equipment and power calculation equipment, and converting the unit cost into unit power; p P ,P G ,P H ,P C Respectively representing electric power equipment and clean energy (day)Natural gas, wind energy, solar energy, etc.) equipment, thermal equipment, computational power equipment; delta phi P ,ΔΦ G ,ΔΦ H ,ΔΦ C Respectively represents the power loss, the loss of clean energy (natural gas, wind energy, solar energy and the like), the thermal loss and the computational power loss in the operation process.
Due to influence on equipment failure rate II 1 And probability of fatigue cracking of heat supply network pipeline II 2 Corrosion failure rate II 3 The three indexes have many factors, mainly including running time, medium temperature, performance coefficients of equipment, external environment factors and artificial destruction probability, so x (II) 1 )、x(II 2 ) And x (II) 3 ) The complex functions of (a) are:
in the formula, f () can be determined according to practical conditions based on experience, T represents the running time, p represents the bearing pressure of the equipment, T represents the temperature of a medium, alpha represents each performance coefficient of the equipment, beta represents an external environment factor, and lambda represents the probability of artificial damage.
x(III 1 )、x(III 2 ) And x (III) 3 ) Respectively as follows:
wherein epsilon represents the degree of contamination of the solid waste; s Solid body ,S Cleaning of ,S General (1) Respectively representing the solid waste discharge amount, the clean energy utilization amount and the total energy utilization amount; p Total output of The total generated heat and calculated force of the system are represented and converted into electric power; l is Greenhouse gases Indicating greenhouse gas emissions.
Further, in the step S4, the lowest operation cost, the highest safety, and the lowest pollution emission are used as three objective functions to construct an optimized scheduling model of the integrated energy system, which specifically includes:
an objective function f (x, w) of the comprehensive energy system optimization scheduling model:
constraint conditions of the model:
H＝H demand for ,C＝C Demand for (11)
In the formula, P, T, I and Y respectively represent the power, temperature, medium flow (current, heat flow, clean energy (natural gas, wind energy, solar energy and the like) flow and calculation flow) of each device in the comprehensive energy system and the bearable pressure of the devices; the subscripts min and max represent the minimum and maximum values of the index, respectively; h and C represent total heat and total computing power generated by the system; h Demand for ,C Demand for Is a constant representing the user's thermal and computational demands of the dispatch.
Further, in step S5, the comprehensive energy system optimized scheduling model is accessed to the mathematical model thereof to obtain an optimal scheduling method and result, and the specific method is as follows:
solving the optimal solution set of the model by adopting a non-dominated sorting genetic algorithm (NSGA-II) with an elite strategy to obtain a Pareto optimal solution set: f (x, w) = { f 1 (x,w),f 2 (x,w),…,f n (x, w) }, when y = { y = 1 ,y 2 ,…,y n Y is an operation parameter set of each system device in the scheduling process;
and selecting a solution closest to the optimal scheme from the Pareto optimal solution set by adopting a TOPSIS method to obtain an optimal scheduling result, wherein the weights of the three target functions are w respectively I ,w II ,w III 。
The beneficial effects of the invention are:
according to the comprehensive energy system mathematical model integrating the calculation capacity and the energy flow of the distributed data center, the calculation capacity of the distributed data center is contained in the comprehensive energy system to form one of the generalized energy flow carriers, so that on one hand, the waste heat emitted by the distributed data center can be better recovered, the energy waste is reduced, on the other hand, the consumption of clean energy in the comprehensive energy system can be increased by introducing the distributed data center, and the flexibility of the system is improved. Meanwhile, a comprehensive evaluation method is adopted, a comprehensive evaluation system of the comprehensive energy system is established, reasonable combination of the subjective and objective weight method is achieved, and the obtained result is more accurate.
Drawings
FIG. 1 is the main steps of the process of the present invention;
FIG. 2 is a diagram of a comprehensive evaluation system of the comprehensive energy system;
FIG. 3 is a schematic diagram of an integrated energy system energy flow with distributed data centric power and energy flow fusion;
FIG. 4 is a schematic diagram of a primary energy conversion process;
FIG. 5 is a flow chart of an optimization algorithm.
Detailed Description
The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings and embodiments. The technical scope of the present invention is not limited to the contents of the specification, and the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments, and various changes and modifications can be made by workers within the scope of the technical spirit of the present invention without departing from the technical scope of the present invention, and thus the technical scope thereof must be determined by the scope of the claims.
The following further illustrates the optimized scheduling method of the present invention by embodiments:
as shown in fig. 1, a distributed data center computing power and energy flow integrated comprehensive energy system optimization scheduling method specifically includes the following steps:
the step S1 of establishing a comprehensive energy system mathematical model integrating distributed data central computing power and energy flow comprises the following specific steps:
as shown in fig. 3, the integrated energy system is modeled based on graph theory, the joints of devices such as a power plant, a solar photovoltaic panel, a distributed data center, a transformer, a heat source, a heating power station, a user terminal and the like are abstracted into connection nodes, lines between the two nodes are abstracted into edges, each device in the system is simplified into a physical model according to actual system data, and a topological structure of the integrated energy system is established.
For a single energy conversion device, the relationship between the input IN and the output OUT is:
OUT＝μ·IN (1)
where μ represents a coupling coefficient between the input and the output;
as shown in fig. 4, the integrated energy system including a plurality of energy transforming devices and a plurality of energy forms can describe the relationship between the input and the output by a matrix:
wherein the subscript P, G, H, C represents electric power, clean energy, thermal power, computational power, μ PG The coupling coefficient between the input and the output when the electric power is used as the input and the clean energy is used as the output; mu.s GP The coupling coefficient between the input and output is determined by taking clean energy as input and taking electric power as output.
As shown in fig. 2, the operation evaluation system of the integrated energy system in step S2 mainly includes three primary indexes of economy, safety and cleanliness;
wherein the economic indicator I comprises energy material cost I 1 Equipment operating and maintenance cost I 2 Energy loss cost I 3 Three secondary indexes;
safety index II includes the failure rate of the equipment II 1 And the fatigue cracking probability II of the heat supply network pipeline 2 Corrosion failure rate II 3 Three secondary indexes;
cleanliness indices III include solid waste discharge III 1 Clean energy utilization rate III 2 Greenhouse gas emission III 3 Three secondary indexes.
Wherein, the step S3 adopts a comprehensive evaluation method to determine the comprehensive weight of each index, and the comprehensive evaluation method comprises an objective evaluation method and a subjective evaluation methodAnd a valence method, wherein an entropy weight method is adopted as an objective evaluation method, and an analytic hierarchy process is adopted as a subjective evaluation method. Obtaining objective weight of each index by entropy weight method
Obtaining subjective weight of each index by using analytic hierarchy process
Then its composite weight is
i＝I,I 1 ,I 2 ,I 3 ,II,II 1 ,II 2 ,II 3 ,III,III 1 ,III 2 ,III 3 。
The specific method comprises the following steps:
an entropy weight method is used to determine the objective (quantitative) weight of the selected indicator.
Firstly, normalization processing is carried out on each secondary index data, and the method comprises the following steps:
for example, the jth secondary index of the ith scheme is x ij Normalized to x i ' j The following two equations may be used for normalization:
If the index is a forward index, selecting a first formula; if the indicator is a negative indicator, then the second formula is selected. Where max (x) j ) Is the maximum value of the j index, min (x) j ) Is the minimum value of the j-th index. Obtaining a comprehensive evaluation index matrix X ' = (X ' consisting of m scheme n indexes after the treatment is finished ' ij ) m×n ，i＝1,2,…,m；j＝1,2,…,n。
When the second-level indexes are normalized, the qualitative indexes in the second-level indexes are converted into quantitative indexes by selecting 4 grades of index sets {20%,40%,60% and 80% } which respectively represent poor, good and good. Wherein the grade of the qualitative index can be determined according to expert opinions and empirical formulas.
Then, the entropy and the weight of each index are calculated, and the method comprises the following steps:
1) Calculating the specific gravity of the jth index of the ith user:
2) Calculating the information entropy of the j index:
wherein K is a constant, and K is a constant,
3) Calculating an objective (quantitative) weight of the jth index
Wherein
And determining the subjective (qualitative) weight of the evaluation index by combining an analytic hierarchy process with a Delphi method.
Firstly, a hierarchical structure model is established, and the structural layer comprises a target layer, a criterion layer and a scheme layer.
And (3) constructing a pair comparison matrix, starting from the second layer, using the pair comparison matrix and the scale from 1 to 9, wherein the construction method of each layer is as follows:
wherein, a i,j Represents the ithThe relative importance of the indicator to the factor of the previous layer relative to the jth indicator.
Then, calculating a single-rank-order weight vector and performing consistency check, namely:
and calculating the maximum eigenvalue and the corresponding eigenvector of each pair of comparison matrixes, and performing consistency check by using the consistency index, the random consistency index and the consistency ratio. If the test is passed, the feature vector is normalized to be a weight vector; if not, the comparison matrix needs to be reconstructed. Wherein:
the consistency index is defined as:
The random consistency index is RI, and the random consistency index is obtained by table look-up.
The consistency ratio is:
the identity test was considered passed when CR < 0.1.
And finally, calculating a total sorting vector and carrying out consistency check, wherein the total sorting consistency check method is consistent with the single-layer sorting consistency check method. After passing the test, the subjective (qualitative) weight can be obtained:
wherein the content of the first and second substances,
the objective (quantitative) weight and the subjective (qualitative) weight of each index are combined to obtain the comprehensive weight of each index.
w＝(w 1 ,w 2 ,...,w j ,…,w n ) (8)
Wherein w isComprehensive weight vector matrix, w j Is the integrated weight vector of the jth index,
the economic indicator I represents a function x (I) as follows:
the safety index II represents a function x (II) of:
the cleanliness index III represents a function x (III) of:
in the formulae (10), (11) and (12), w i The weight of each index is represented, and x (i) represents each influence index.
The above-mentioned various influence indexes are:
x(I 1 )、x(I 2 ) And x (I) 3 ) Respectively as follows:
in the formula (I), the compound is shown in the specification,
respectively represent the prices for purchasing electricity, clean energy (natural gas, wind energy, solar energy, etc.); z P ,Z G Representing the amount of purchased electricity, clean energy (natural gas, wind, solar, etc.); gamma ray P ,γ G ,γ H ,γ C Respectively representing power equipment and clean energy (natural gas and wind energy)Solar energy, etc.) equipment, thermodynamic equipment, computational power equipment, converted to unit power; p P ,P G ,P H ,P C Respectively representing the working power of electric equipment, clean energy (natural gas, wind energy, solar energy and the like) equipment, thermal equipment and computing power equipment; delta phi P ,ΔΦ G ,ΔΦ H ,ΔΦ C Respectively represents the power loss, the loss of clean energy (natural gas, wind energy, solar energy and the like), the thermal loss and the computing power loss in the operation process.
x(II 1 )、x(II 2 ) And x (II) 3 ) Respectively as follows:
in the formula, f () can be determined according to practical conditions according to experience (in the case of the scheme, coefficients of influencing factors are obtained through multiple experiments by adopting a least square method), T represents running time, p represents bearing pressure of equipment, T represents medium temperature, alpha represents each performance coefficient of the equipment, beta represents an external environment factor, and lambda represents artificial destruction probability.
x(III 1 )、x(III 2 ) And x (III) 3 ) Respectively as follows:
wherein epsilon represents the degree of contamination of the solid waste; s Solid body ,S Cleaning of ,S General assembly Respectively representing the solid waste discharge amount, the clean energy utilization amount and the total energy utilization amount; p Total yield The total generated heat and calculated force of the system are expressed and converted into electric power; l is Greenhouse gases Indicating greenhouse gas emissions.
S4, constructing an optimized dispatching model of the comprehensive energy system by taking the lowest operation cost, the highest safety and the lowest pollution emission as three objective functions, wherein the specific method comprises the following steps:
an objective function f (x, w) of the comprehensive energy system optimization scheduling model:
constraint conditions of the model:
H＝H demand for ,C＝C Demand for (18)
In the formula, P, T, I and Y respectively represent power, temperature, medium flow (current, heat flow, clean energy (natural gas, wind energy, solar energy and the like) flow and computational power flow) of each device in the comprehensive energy system and other important performance coefficients; the subscripts min and max represent the minimum and maximum values of the index, respectively; h and C represent total heat and total computing power generated by the system; h Demand for ,C Demand for Is a constant representing the user's thermal and computational demands of the dispatch.
As shown in fig. 5, in step S5, the comprehensive energy system optimized scheduling model is accessed to the mathematical model thereof to obtain an optimal scheduling method and result, and the specific method includes:
solving the optimal solution set of the model by adopting a non-dominated sorting genetic algorithm (NSGA-II) with an elite strategy to obtain a Pareto optimal solution set: f (x, w) = { f 1 (x,w),f 2 (x,w),...,f n (x, w) }, when y = { y = 1 ,y 2 ,...,y n Y is an operation parameter set of each system device in the scheduling process;
selecting a solution closest to the optimal scheme from the Pareto optimal solution set by adopting a TOPSIS method to obtain an optimal scheduling result, wherein the weights of the three target functions are w respectively I ,w II ,w III 。
Claims (7)
1. A distributed data center computing power and energy flow fused comprehensive energy system optimization scheduling method is characterized by comprising the following steps:
s1, establishing a comprehensive energy system mathematical model integrating distributed data central computing power and energy flow;
s2, establishing a comprehensive energy system operation evaluation index system comprising three primary indexes of economy, safety and cleanness and a plurality of secondary indexes;
s3, determining the comprehensive weight of each index by adopting a comprehensive evaluation method;
s4, constructing an optimized dispatching model of the comprehensive energy system by taking the lowest operation cost, the highest safety and the lowest pollution emission as three objective functions;
s5, accessing the optimized scheduling model of the comprehensive energy system into the mathematical model of the comprehensive energy system to obtain an optimal scheduling method and a result;
the step S1 specifically includes: for a single energy conversion device, the relationship between the input IN and the output OUT is:
OUT＝μ·IN (1)
where μ represents a coupling coefficient between the input and the output;
for an integrated energy system comprising a plurality of energy transforming devices and a plurality of forms of energy, the relationship between input and output may be described by a matrix:
in the formula, subscripts P, G, H, C represent electric power, clean energy, thermal power, and computational power, respectively; mu.s PG The coupling coefficient between the input and the output when the electric power is used as the input and the clean energy is used as the output; mu.s GP When the clean energy is used as input and the electric power is used as output, the coupling coefficient between the input and the output is the coupling coefficient;
the step S4 specifically comprises the following steps:
an objective function f (x, w) of the comprehensive energy system optimization scheduling model:
constraint conditions of the model:
H＝H demand for ,C＝C Demand for (5)
In the formula, P, T, I and Y respectively represent the power, temperature, medium flow and bearable pressure of each device in the comprehensive energy system; the subscripts min and max represent the minimum and maximum values of the index, respectively; h and C represent total heat and total computing power generated by the system; h Demand for ,C Demand for Is a constant representing the user's thermal and computational demands of the dispatch.
2. The distributed data center computing power and energy flow fused comprehensive energy system optimization scheduling method according to claim 1, wherein the step S1 specifically comprises:
the method comprises the steps of modeling the comprehensive energy system based on graph theory, abstracting the connection position of equipment into connection nodes, abstracting lines between the two nodes into edges, simplifying each equipment in the system into a physical model according to actual system data, and establishing a topological structure of the comprehensive energy system.
3. The distributed data center computing power and energy flow fused comprehensive energy system optimization scheduling method according to claim 1, wherein in step S2, of the three primary indexes:
the economic indicator I comprises energy material cost I 1 Equipment operation maintenance cost I 2 Energy loss cost I 3 Three secondary indexes;
safety index II includes equipment failure rate II 1 And the fatigue cracking probability II of the heat supply network pipeline 2 Corrosion failure rate II 3 Three secondary indexes;
cleanliness indices III include solid waste discharge III 1 Clean energy utilization rate III 2 Greenhouse gas emission III 3 Three secondary indexes.
4. The distributed data center computing power and energy flow fused comprehensive energy system optimization scheduling method according to claim 3, wherein the step S3 specifically comprises: obtaining objective weight of each index by entropy weight method
Obtaining subjective weight of each index by using analytic hierarchy process
Then its composite weight is
i＝I,I 1 ,I 2 ,I 3 ,II,II 1 ,II 2 ,II 3 ,III,III 1 ,III 2 ,III 3 。
5. The distributed data center computing power and energy flow fused comprehensive energy system optimization scheduling method according to claim 4,
the economic indicator I represents a function x (I) as follows:
the safety index II represents a function x (II) of:
the cleanliness index III represents a function x (III) of:
in the formulae (6), (7) and (8), w i The weight of each index is represented, and x (i) represents each influence index.
6. The method for optimizing and scheduling a distributed data center computing power and energy flow integrated comprehensive energy system according to claim 5, wherein the influence indexes are as follows:
x(I 1 )、x(I 2 ) And x (I) 3 ) Respectively as follows:
in the formula (I), the compound is shown in the specification,
respectively representing the prices of electric power and clean energy; z P ,Z G Representing the amount of purchased electric power and clean energy; gamma ray P ,γ G ,γ H ,γ C Respectively representing the unit cost of operation and maintenance of electric power equipment, clean energy equipment, thermal power equipment and power calculation equipment, and converting the unit cost into unit power; p P ,P G ,P H ,P C Respectively representing the working power of electric equipment, clean energy equipment, thermal equipment and force calculating equipment; delta phi P ,ΔΦ G ,ΔΦ H ,ΔΦ C Respectively representing power loss, clean energy loss, heat loss and computational power loss in the operation process;
x(II 1 )、x(II 2 ) And x (II) 3 ) Respectively as follows:
in the formula, T represents the running time, p represents the bearing pressure of the equipment, T represents the medium temperature, alpha represents each performance coefficient of the equipment, beta represents the external environment factor, and lambda represents the artificial destruction probability;
x(III 1 )、x(III 2 ) And x (III) 3 ) Respectively as follows:
wherein epsilon represents the degree of contamination of the solid waste; s Solid body ,S Cleaning of ,S General assembly Respectively representing the solid waste discharge amount, the clean energy utilization amount and the total energy utilization amount; p Total yield The total generated heat and calculated force of the system are expressed and converted into electric power; l is Greenhouse gases Indicating greenhouse gas emissions.
7. The distributed data center computing power and energy flow fused comprehensive energy system optimization scheduling method according to claim 1, wherein the step S5 specifically comprises:
adopting a non-dominated sorting genetic algorithm NSGA-II with an elite strategy to solve the optimal solution set of the model to obtain a Pareto optimal solution set: f (x, w) = { f 1 (x,w),f 2 (x,w),...,f n (x, w) }, when y = { y = 1 ,y 2 ,...,y n Y is an operation parameter set of each system device in the scheduling process;
selecting a solution closest to the optimal scheme from the Pareto optimal solution set by adopting a TOPSIS method to obtain an optimal scheduling result, wherein the weights of the three target functions are w respectively I ,w II ,w III 。
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