CN114282826A - Multi-energy system energy management method considering transmission loss and communication noise - Google Patents

Multi-energy system energy management method considering transmission loss and communication noise Download PDF

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CN114282826A
CN114282826A CN202111617485.5A CN202111617485A CN114282826A CN 114282826 A CN114282826 A CN 114282826A CN 202111617485 A CN202111617485 A CN 202111617485A CN 114282826 A CN114282826 A CN 114282826A
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energy system
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energy
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郑超铭
司大军
孙鹏
游广增
何烨
陈姝敏
高杉雪
肖友强
李玲芳
陈义宣
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Yunnan Power Grid Co Ltd
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Abstract

The application discloses a multi-energy system energy management method considering transmission loss and communication noise, which comprises the following steps: step 1, establishing a multi-energy system energy management mathematical model considering transmission loss, wherein the multi-energy system energy management mathematical model comprises a target function, equality constraint conditions and inequality constraint conditions; step 2, designing a complete distributed solving algorithm based on double consistency considering communication noise according to a system communication topological graph, wherein the complete distributed solving algorithm comprises consistency variable selection and distributed algorithm design; and 3, obtaining an optimal output scheme of the supply side by adopting the completely distributed solving algorithm in the step 2. The method and the device solve the problem of distributed solving of energy management of the multi-energy system considering transmission loss and communication noise, and achieve the optimization purposes of guaranteeing safe operation of the system and improving social benefits of the system.

Description

Multi-energy system energy management method considering transmission loss and communication noise
Technical Field
The application relates to the technical field of multi-energy system energy management, in particular to a multi-energy system energy management method considering transmission loss and communication noise.
Background
The energy management problem is one of the key problems of the operation of the power system, and aims to optimize the load distribution requirement and reasonably arrange the output of equipment to maximize the social benefit of the system on the premise of meeting the operation constraint of a system side and an equipment side. The energy management solving method is currently divided into a centralized method and a distributed method on the whole, however, the centralized method requires a single control center to collect information of all participants, so as to calculate the equipment output combination reaching the maximum social benefit of the system. Compared with a centralized method, the distributed method only needs adjacent participants to perform information interaction and cooperation to realize energy management calculation, so that huge communication and calculation burden is uniformly distributed to each participant, and better robustness and expansibility are achieved.
At present, the energy management problem of a multi-energy system is mainly focused on research in aspects of system modeling, a solving method, new energy consumption and the like, transmission loss is not taken into account in an energy transmission process and communication noise is not taken into account in an information interaction process, namely, the important influence of the transmission loss on supply and demand balance and the communication noise on a solving algorithm in the actual operation of the system is ignored based on ideal transmission and communication conditions, so that the robustness verification of the solving algorithm in the actual operation process is lacked, and the optimization result cannot well meet the actual demand of a load side.
In summary, it is necessary to provide a new solution to the problem of energy management of a multi-energy system, i.e., a distributed solution based on dual consistency of multiple agents under non-ideal transmission and communication conditions, so as to solve the problem of distributed solution of energy management of a multi-energy system considering transmission loss and communication noise, and achieve the optimization purposes of ensuring safe operation of the system and improving social benefits of the system.
Disclosure of Invention
The application provides a multi-energy system energy management method considering transmission loss and communication noise, and aims to solve the problems that the important influence of the transmission loss on supply and demand balance and the communication noise on a solving algorithm in the actual operation of a system is neglected in the multi-energy system energy management method in the prior art, the solving algorithm lacks robustness verification in the actual operation process, and an optimization result cannot well meet the actual demand of a load side.
The technical scheme adopted by the application is as follows:
a multi-energy system energy management method considering transmission loss and communication noise, comprising the steps of:
step 1, establishing a multi-energy system energy management mathematical model considering transmission loss, wherein the multi-energy system energy management mathematical model comprises a target function, equality constraint conditions and inequality constraint conditions;
step 2, designing a complete distributed solving algorithm based on double consistency considering communication noise according to a system communication topological graph, wherein the complete distributed solving algorithm comprises consistency variable selection and distributed algorithm design;
and 3, obtaining an optimal output scheme of the supply side by adopting the completely distributed solving algorithm in the step 2.
Preferably, the step 1 of establishing a mathematical model for energy management of the multi-energy system considering transmission loss, where the mathematical model includes an objective function, equality constraints and inequality constraints, and includes:
step 1.1, establishing mathematical models of various equipment of the multi-energy system;
step 1.2, establishing benefit functions of various devices of the multi-energy system according to the mathematical models of the various devices;
step 1.3, establishing a maximum objective function of social benefits of the multi-energy system according to the mathematical models and the benefit functions of the various devices;
and 1.4, establishing a multi-energy system energy management mathematical model considering transmission loss according to the mathematical model, the benefit function and the social profit maximum objective function of each type of equipment, wherein the multi-energy system energy management mathematical model comprises an objective function, an equality constraint condition and an inequality constraint condition.
Preferably, step 1.1, establishing mathematical models of various devices of the multi-energy system, including:
suppose that the multi-energy system comprises only the generator sets with the total number spNumber i-1, 2,3, …, spTotal number of cogeneration units is scNumber i-1, 2,3, …, scTotal number of heat-generating units only is shNumber i-1, 2,3, …, shTotal number of electric boilers is seNumber i-1, 2,3, …, seTotal number of electric power flexible loads is sefNumber i-1, 2,3, …, sefTotal number of electric rigid loads is serNumber i-1, 2,3, …, serTotal number of thermal flexible loads is shfNumber i-1, 2,3, …, shfTotal number of thermal rigidity loads is shrNumber i-1, 2,3, …, shrThen there is
Figure BDA0003435267880000021
Figure BDA0003435267880000022
Wherein, ai、βi、γiTo representIth genset-only cost function Cp,iThe fitting parameters of (a) are determined,
Figure BDA0003435267880000023
and
Figure BDA0003435267880000024
respectively represents the electric output P of the ith generator set onlyp,iThe upper and lower limits of (d);
Figure BDA0003435267880000025
Figure BDA0003435267880000026
wherein, ai、βi、γi、δi、θi、εiRepresenting the ith cogeneration unit cost function Cc,iThe fitting parameters of (a) are determined,
Figure BDA0003435267880000027
Figure BDA0003435267880000028
respectively representing the power output P of the ith cogeneration unitc,iAnd thermal output Hc,iA runnable domain of;
Figure BDA0003435267880000029
Figure BDA00034352678800000210
wherein alpha isi、βi、γiRepresenting the ith heat-only unit cost function Ch,iThe fitting parameters of (a) are determined,
Figure BDA00034352678800000211
and
Figure BDA00034352678800000212
respectively showing the heat output H of the ith heat-only unith,iThe upper and lower limits of (d);
He,i＝ηiPe,i (4a)
Figure BDA00034352678800000213
wherein He,iAnd ηiRespectively representing the thermal output and the conversion efficiency of the ith electric boiler,
Figure BDA00034352678800000214
and
Figure BDA00034352678800000215
respectively representing the electricity demand P of the ith electric boilere,iThe upper and lower limits of (d);
Figure BDA0003435267880000031
Figure BDA0003435267880000032
wherein, ai、bi、ciRepresents the ith electric power flexible load benefit function Uef,iThe fitting parameters of (a) are determined,
Figure BDA0003435267880000033
and
Figure BDA0003435267880000034
respectively represent the ith electric power flexible load electric demand Pef,iThe upper and lower limits of (d);
Figure BDA0003435267880000035
Figure BDA0003435267880000036
wherein, ai、bi、ciExpressing the ith electric rigid load benefit function Uer,iThe fitting parameters of (a) are determined,
Figure BDA0003435267880000037
and
Figure BDA0003435267880000038
respectively represent the ith electric rigid load electric demand Per,iThe upper and lower limits of (d);
Figure BDA0003435267880000039
Figure BDA00034352678800000310
wherein, ai、bi、ciRepresents the ith thermal flexible load benefit function Uhf,iThe fitting parameters of (a) are determined,
Figure BDA00034352678800000311
and
Figure BDA00034352678800000312
respectively represent the ith thermal flexible load heat demand Hhf,iThe upper and lower limits of (d);
Figure BDA00034352678800000313
Figure BDA00034352678800000314
wherein the content of the first and second substances,ai、bi、ciexpressing the ith thermodynamic rigidity load benefit function Uhr,iThe fitting parameters of (a) are determined,
Figure BDA00034352678800000315
and
Figure BDA00034352678800000316
respectively representing the ith thermodynamic rigid load heat demand Hhr,iThe upper and lower limits of (d);
Figure BDA00034352678800000317
wherein the content of the first and second substances,
Figure BDA00034352678800000318
and
Figure BDA00034352678800000319
respectively representing the transmission power P of the ith power grid linel,iThe upper and lower limits of (d);
Figure BDA00034352678800000320
Figure BDA00034352678800000321
Hi＝csmd,j(ts,i-tr,i) (10c)
wherein, ts,i、tr,i、HiRespectively showing the water supply temperature, the water return temperature and the transmission heat of the ith heat supply network node,
Figure BDA00034352678800000322
and
Figure BDA00034352678800000323
respectively represent ts,iThe upper and lower limits of (a) and (b),
Figure BDA00034352678800000324
and
Figure BDA00034352678800000325
respectively represents the transmission flow m of the j-th heat supply network pipelined,jUpper and lower limits of csIndicating the specific heat capacity of the heating medium.
Preferably, the step 1.2 of establishing the benefit function of each type of equipment of the multi-energy system according to the mathematical model of each type of equipment includes:
suppose that the trade price of electricity and heat of the multi-energy system are p respectivelyeAnd phThen there is
Figure BDA0003435267880000041
Figure BDA0003435267880000042
Figure BDA0003435267880000043
Fe,i＝phHe,i-pePe,i (11d)
Fef,i＝Uef,i-pePef,i (11e)
Fer,i＝Uer,i-pePer,i (11f)
Fhf,i＝Uhf,i-phHhf,i (11g)
Fhr,i＝Uhr,i-phHhr,i (11h)
Wherein, Fp,i、Fc,i、Fh,i、Fe,i、Fef,i、Fer,i、Fhf,i、Fhr,iRespectively representing the ith generator set only, cogeneration set only and heat production onlyThe benefit functions of the unit, the electric boiler, the electric flexible load, the electric rigid load, the thermal flexible load and the thermal rigid load,
Figure BDA0003435267880000044
and
Figure BDA0003435267880000045
respectively represents the electricity loss generated by the ith generator set only and the cogeneration set,
Figure BDA0003435267880000046
and
Figure BDA0003435267880000047
the heat losses generated by the ith cogeneration unit and the heat-only generating unit are respectively expressed as follows:
Figure BDA0003435267880000048
Figure BDA0003435267880000049
wherein, BiIndicating electrical losses
Figure BDA00034352678800000410
Fitting parameters of ljDenotes the length of the jth heat supply network pipe, ta,jDenotes the average temperature, R, of the medium surrounding the jth heat supply network pipehRepresenting the total thermal resistance per kilometer of tubing from heating medium to surrounding medium.
Preferably, the step 1.3 of establishing a maximum objective function of social profit of the multi-energy system according to the mathematical models and the benefit functions of the various types of equipment includes:
assuming that the social profit of the multi-energy system is W, there are
Figure BDA00034352678800000411
Wherein, DeltaeAnd ΔhThe electric power deviation and the thermal power deviation of the multi-energy system are respectively expressed, and the specific description is as follows:
Figure BDA00034352678800000412
Figure BDA00034352678800000413
preferably, step 1.4 is to establish a mathematical model of energy management of the multi-energy system considering transmission loss according to the mathematical model, the benefit function and the maximum social profit objective function of each type of equipment, and includes:
min F＝-W (16a)
s.t.(15a)(15b) (16b)
s.t.(1b)(2b)(3b)(4b)(5b)(6b)(7b)(8b)(9)(10)(16c)
wherein, (16a), (16b), (16c) respectively represent objective function, equality constraint condition and inequality constraint condition of the energy management mathematical model of the multi-energy system.
Preferably, in step 2, according to the system communication topological graph, a fully distributed solution algorithm based on dual consistency is designed, taking communication noise into consideration, where the fully distributed solution algorithm includes selecting consistency variables and designing a distributed algorithm, and includes:
step 2.1, selecting consistency variables:
selecting electricity price and heat price as double consistency variables according to KKT optimal conditions of equality constraint optimization problem composed of equations (16a) and (16b), and obtaining the optimal conditions
Figure BDA0003435267880000051
Wherein the content of the first and second substances,
Figure BDA0003435267880000052
and
Figure BDA0003435267880000053
respectively representing the electricity transmission loss factors of the ith generator set only and the cogeneration generator set;
Figure BDA0003435267880000054
wherein the content of the first and second substances,
Figure BDA0003435267880000055
and
Figure BDA0003435267880000056
respectively representing heat transfer loss factors of the ith heat-only unit and the cogeneration unit;
step 2.2, designing a distributed algorithm:
according to the double consistency variables selected in the step 2.1, a fully distributed algorithm based on double consistency considering communication noise is designed, and the algorithm comprises the following steps:
initialization: suppose that
Figure BDA0003435267880000057
And xi ═ Pp,Pc,Pef,Hc,Hh,Hhf]A column stack vector representing a coherency variable and a power output, respectively, then phi (0) may be further initialized from an initial value ξ (0) according to (17a) and (17 b);
iteration: suppose that
Figure BDA0003435267880000061
The column stack vector representing the local power offset is then
φ(k+1)＝Sφ(k)+χ+μ(k)ψ(k) (18)
Wherein, S ═ I-g (k) L, χ ═ g (k) DRW (k), I and L respectively represent an n-order identity matrix and a Laplace matrix, DR＝diag[R(1,:),R(2,:),…,R(n,:)]Denotes a diagonal matrix, R (i,: denotes the i-th row element of the dual random matrix R, W (k) [ w ]1(k),w2(k),…,wn(k)]Representing a column of stacked vectors, wi(k)＝[w1i(k),w2i(k),…,wni(k)]，wji(k) Representing the noise input of information interaction between a node j and a node i in the kth iteration, g (k) representing an attenuation gain function, mu (k) representing a time-varying correction factor, and L being specifically described as follows:
Figure BDA0003435267880000062
r is a dual random matrix obtained from the system communication topology, and is described in detail as follows:
Figure BDA0003435267880000063
wherein r isijI row and j column elements, N, representing the matrix RiSet of neighbor nodes representing the ith participant node, di、djRespectively representing the degrees of the ith participant node and the jth participant node;
ξ(k+1)＝Tφ(k+1)-ζ (19)
wherein the content of the first and second substances,
Figure BDA0003435267880000064
a diagonal matrix is shown and is represented,
Figure BDA0003435267880000065
a list of the stack vectors is represented,
ψ(k+1)＝Sψ(k)+x-(ξ(k+1)-ξ(k)) (20)。
preferably, the step 3 of obtaining an optimal supply-side output scheme by using the fully distributed solution algorithm in the step 2 includes:
suppose that
Figure BDA0003435267880000066
Indicates a convergence decision coefficient, then
Figure BDA0003435267880000071
Wherein V represents a set of all participant nodes;
when the absolute value of the maximum local power deviation is less than or equal to the convergence judgment coefficient, judging that the set convergence condition is met and outputting the equipment output, the electricity price and the heat price at the current k moment to obtain an optimal supply side output scheme; otherwise, continuing iterative computation by adopting the fully distributed solving algorithm in the step 2.
The technical scheme of the application has the following beneficial effects:
1. in the application, an electric heat transmission loss model is established by a multi-energy system energy management mathematical model, a communication noise model is established by a multi-energy system energy management solving method, and the important influence of transmission loss and communication noise on an optimization result and algorithm robustness is considered.
2. In the application, the calculation and communication burden is uniformly distributed by the multi-agent double-consistency-based solving algorithm, the fully distributed solving of the multi-energy system energy management under the transmission loss and the communication noise is realized, the communication dependence degree is low, the important privacy of participants is effectively protected, and the robustness and the expansibility are better.
3. The solving algorithm based on the double consistency of the multi-agent effectively solves the problem of output coupling of energy management of the multi-energy system, further weakens the complexity of model solving, and has quick convergence; on the premise of meeting the actual load requirement of the demand side, the supply side is guided to make an optimal output plan, so that the social benefit of the system is improved.
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In order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart illustrating a method for managing energy of a multi-energy system in consideration of transmission loss and communication noise according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a multi-energy system according to an embodiment of the present application;
fig. 3 is a communication topology diagram of a multi-energy system device according to an embodiment of the present application;
FIG. 4 is a waveform diagram of a power price consistency variable without considering transmission loss and communication noise in the embodiment of the present application;
FIG. 5 is a waveform diagram of a heat rate consistency variable without considering transmission loss and communication noise in an embodiment of the present application;
FIG. 6 is a waveform of an electrical output without considering transmission loss and communication noise according to an embodiment of the present application;
FIG. 7 is a waveform diagram of thermal output without considering transmission loss and communication noise according to an embodiment of the present application;
FIG. 8 is a waveform diagram of local deviation of electric power without considering transmission loss and communication noise according to an embodiment of the present application;
FIG. 9 is a graph of a thermal power local bias waveform without considering transmission loss and communication noise in accordance with an embodiment of the present application;
fig. 10 is a waveform diagram of a power rate consistency variable in consideration of transmission loss and communication noise according to an embodiment of the present application;
FIG. 11 is a waveform diagram of a heat rate consistency variable in consideration of transmission loss and communication noise according to an embodiment of the present application;
FIG. 12 is a waveform of an electrical output in consideration of transmission loss and communication noise according to an embodiment of the present application;
FIG. 13 is a waveform diagram of a thermal output considering transmission loss and communication noise according to an embodiment of the present application;
FIG. 14 is a waveform diagram of local deviation of electric power in consideration of transmission loss and communication noise according to an embodiment of the present application;
FIG. 15 is a graph of a thermal power local bias waveform with transmission loss and communication noise taken into account according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following examples do not represent all embodiments consistent with the present application. But merely as exemplifications of systems and methods consistent with certain aspects of the application, as recited in the claims.
Referring to fig. 1, a flow chart of a method for managing energy of a multi-energy system considering transmission loss and communication noise is shown.
The application provides a multi-energy system energy management method considering transmission loss and communication noise, which comprises the following steps:
step 1, establishing a multi-energy system energy management mathematical model considering transmission loss, wherein the multi-energy system energy management mathematical model comprises a target function, equality constraint conditions and inequality constraint conditions;
step 2, designing a complete distributed solving algorithm based on double consistency considering communication noise according to a system communication topological graph, wherein the complete distributed solving algorithm comprises consistency variable selection and distributed algorithm design;
and 3, obtaining an optimal output scheme of the supply side by adopting the completely distributed solving algorithm in the step 2.
Preferably, the step 1 of establishing a mathematical model for energy management of the multi-energy system considering transmission loss, where the mathematical model includes an objective function, equality constraints and inequality constraints, and includes:
step 1.1, establishing mathematical models of various equipment of the multi-energy system;
step 1.2, establishing benefit functions of various devices of the multi-energy system according to the mathematical models of the various devices;
step 1.3, establishing a maximum objective function of social benefits of the multi-energy system according to the mathematical models and the benefit functions of the various devices;
and 1.4, establishing a multi-energy system energy management mathematical model considering transmission loss according to the mathematical model, the benefit function and the social profit maximum objective function of each type of equipment, wherein the multi-energy system energy management mathematical model comprises an objective function, an equality constraint condition and an inequality constraint condition.
In this embodiment, step 1.1, establishing mathematical models of various devices of the multi-energy system includes:
as shown in fig. 2, it is assumed that the multi-energy system includes only the total number of generator units 4, i is 1,2,3, 4, the total number of cogeneration units 2, i is 1,2, only the total number of heat generating units 2, i is 1,2, the total number of electric boilers 2, i is 1,2, the total number of flexible electric loads 2, i is 1,2, the total number of rigid electric loads 1, i is 1,2, the total number of flexible thermal loads 2, i is 1,2, and the total number of rigid thermal loads 1, i is 1; generator set d onlyp,1-dp,4Corresponding to the node 1-4, cogeneration unit dc,1-dc,2Corresponding to nodes 5-6, only producing heat unit dh,1-dh,2Corresponding node 7-8, electric boiler de,1-de,2Corresponding to nodes 9-10, electric power flexible load def,1-def,2Corresponding to nodes 11-12, electrical rigid load der,1Corresponding node 13, thermal flexible load dhf,1-dhf,2Corresponding to the nodes 14-15, the thermal rigid load dhr,1Corresponding to node 16, the power bus is 17, the thermal bus is 18, the solid line represents the power line, the dotted line represents the thermal conduit, there is
Figure BDA0003435267880000081
Figure BDA0003435267880000091
Wherein, ai、βi、γiRepresenting the ith genset-only cost function Cp,iThe fitting parameters of (a) are determined,
Figure BDA0003435267880000092
and
Figure BDA0003435267880000093
individual watchShows the electric output P of the ith generator set onlyp,iThe upper and lower limits of (d);
Figure BDA0003435267880000094
Figure BDA0003435267880000095
wherein, ai、βi、γi、di、θi、εiRepresenting the ith cogeneration unit cost function Cc,iThe fitting parameters of (a) are determined,
Figure BDA0003435267880000096
Figure BDA0003435267880000097
respectively representing the power output P of the ith cogeneration unitc,iAnd thermal output Hc,iA runnable domain of;
in this embodiment, the value of the parameter of the operable domain of the cogeneration unit is shown in table 1:
table 1 unit of operational domain parameters of cogeneration units: MW
Device Thermo-electric operable domain (H)c,Pc)
dc，1 A1(0,187),B1(153,132),C1(121,42),D1(0,63)
dc，2 A2(0,94),B2(122,68),C2(106,22),D2(0,36)
Figure BDA0003435267880000098
Figure BDA0003435267880000099
Wherein, ai、βi、γiRepresenting the ith heat-only unit cost function Ch,iThe fitting parameters of (a) are determined,
Figure BDA00034352678800000910
and
Figure BDA00034352678800000911
respectively showing the heat output H of the ith heat-only unith,iThe upper and lower limits of (d);
He,i＝ηiPe,i (4a)
Figure BDA00034352678800000912
wherein He,iAnd ηiRespectively representing the thermal output and the conversion efficiency of the ith electric boiler,
Figure BDA00034352678800000913
and
Figure BDA00034352678800000914
respectively representing the electricity demand P of the ith electric boilere,iThe upper and lower limits of (d);
in the embodiment, the electric requirements of the electric boiler are all 20MW, and the conversion efficiency is all 90%;
Figure BDA00034352678800000915
Figure BDA00034352678800000916
wherein, ai、bi、ciRepresents the ith electric power flexible load benefit function Uef,iThe fitting parameters of (a) are determined,
Figure BDA00034352678800000917
and
Figure BDA00034352678800000918
respectively represent the ith electric power flexible load electric demand PefiThe upper and lower limits of (d);
Figure BDA00034352678800000919
Figure BDA00034352678800000920
wherein, ai、bi、ciExpressing the ith electric rigid load benefit function Uer,iThe fitting parameters of (a) are determined,
Figure BDA0003435267880000101
and
Figure BDA0003435267880000102
respectively represent the ith electric rigid load electric demand Per,iThe upper and lower limits of (d);
Figure BDA0003435267880000103
Figure BDA0003435267880000104
wherein, ai、bi、ciRepresents the ith thermal flexible load benefit function Uhf,iThe fitting parameters of (a) are determined,
Figure BDA0003435267880000105
and
Figure BDA0003435267880000106
respectively represent the ith thermal flexible load heat demand Hhf,iThe upper and lower limits of (d);
Figure BDA0003435267880000107
Figure BDA0003435267880000108
wherein, ai、bi、ciExpressing the ith thermodynamic rigidity load benefit function Uhr,iThe fitting parameters of (a) are determined,
Figure BDA0003435267880000109
and
Figure BDA00034352678800001010
respectively representing the ith thermodynamic rigid load heat demand Hhr,iThe upper and lower limits of (d);
in this embodiment, the fitting parameter values of the cost function and the benefit function of each device are shown in table 2:
table 2 fitting parameters and output limit parameter units of various equipment cost functions and benefit functions: MW
Machine set α/a β/b γ/c δ θ ε Pm/Hm PM/HM
dp，1 10 3.0 0.01 -- -- -- 10 90
d p，2 20 2.8 0.01 -- -- -- 20 150
d p，3 40 2.7 0.00 -- -- -- 30 200
dp，4 65 2.6 0.00 -- -- -- 40 260
dc，1 1250 2.2 0.01 1.2 0.016 0.008 -- --
dc，2 680 1.2 0.02 0.4 0.022 0.021 -- --
dh，1 650 1.6 0.01 -- -- -- 0 1695
dh，2 520 1.4 0.01 -- -- -- 0 1250
d ef，1 0 3.4 0.01 -- -- -- 0 60
d ef，2 0 3.4 0.01 -- -- -- 0 60
d er，1 0 3.5 0.01 -- -- -- 470 630
d hf，1 0 2.2 0.02 -- -- -- 0 80
d hf，2 0 2.2 0.02 -- -- -- 0 80
d hr，1 0 2.0 0.02 -- -- -- 320 420
In the embodiment, the electric rigid load is 550MW, and the thermal rigid load is 370 MW;
Figure BDA00034352678800001011
wherein the content of the first and second substances,
Figure BDA00034352678800001012
and
Figure BDA00034352678800001013
respectively representing the transmission power P of the ith power grid linel,iThe upper and lower limits of (d);
in this embodiment, values of upper and lower limit parameters of transmission power of the power grid line are shown in table 3:
table 3 unit of upper and lower limit parameters of transmission power of power grid line: MW
Line Pl m Pl M Line Pl m Pl M
1-17 0 120 2-17 0 160
3-17 0 210 4-17 0 240
5-17 0 160 6-17 0 130
Figure BDA0003435267880000111
Figure BDA0003435267880000112
Hi＝csmd,j(ts,i-tr,i) (10c)
Wherein, ts,i、tr,i、HiRespectively showing the water supply temperature, the water return temperature and the transmission heat of the ith heat supply network node,
Figure BDA0003435267880000113
and
Figure BDA0003435267880000114
respectively represent ts,iThe upper and lower limits of (a) and (b),
Figure BDA0003435267880000115
and
Figure BDA0003435267880000116
respectively represents the transmission flow m of the j-th heat supply network pipelined,jUpper and lower limits of csIndicating the specific heat capacity of the heating medium.
The step 1.2 of establishing the benefit function of each type of equipment of the multi-energy system according to the mathematical model of each type of equipment comprises the following steps:
suppose that the trade price of electricity and heat of the multi-energy system are p respectivelyeAnd phThen there is
Figure BDA0003435267880000117
Figure BDA0003435267880000118
Figure BDA0003435267880000119
Fe,i＝phHe,i-pePe,i (11d)
Fef,i＝Uef,i-pePef,i (11e)
Fer,i＝Uer,i-pePer,i (11f)
Fhf,i＝Uhf,i-phHhf,i (11g)
Fhr,i＝Uhr,i-phHhr,i (11h)
Wherein, Fp,i、Fc,i、Fh,i、Fe,i、Fef,i、Fer,i、Fhf,i、Fhr,iRespectively representing the benefit functions of the ith generator set only, the cogeneration unit, the heat generator set only, the electric boiler, the electric flexible load, the electric rigid load, the thermal flexible load and the thermal rigid load,
Figure BDA00034352678800001110
and
Figure BDA00034352678800001111
respectively represents the electricity loss generated by the ith generator set only and the cogeneration set,
Figure BDA00034352678800001112
and
Figure BDA00034352678800001113
the heat losses generated by the ith cogeneration unit and the heat-only generating unit are respectively expressed as follows:
Figure BDA00034352678800001114
Figure BDA0003435267880000121
wherein, BiIndicating electrical losses
Figure BDA0003435267880000122
Fitting parameters of ljDenotes the length of the jth heat supply network pipe, ta,jDenotes the average temperature, R, of the medium surrounding the jth heat supply network pipehIndicating the total thermal resistance per kilometer of pipe from heating medium to surrounding medium。
In this embodiment, values of fitting parameters of electrical transmission loss are shown in table 4:
table 4 electrical transmission loss fit parameter units: x 10-6
Figure BDA0003435267880000126
In this embodiment, the values of the heat supply network pipeline parameters are shown in table 5:
table 5 heat supply network pipe parameter units: km, m3/h、m×℃/W、℃
Pipeline lj md m md M Rh Node point ts m ts M
5-18 3.0 0 2500 15 5 90 100
6-18 3.2 0 2500 15 6 90 100
7-18 2.6 0 2500 15 7 90 100
8-18 2.8 0 2500 15 8 90 100
In the embodiment, the specific heat capacity of the heating medium is 4.2J/(kg x DEG C), the node backwater temperature is 40 ℃, and the average temperature of the medium around the heat supply network pipeline is 0 ℃.
The step 1.3 of establishing a maximum objective function of social profit of the multi-energy system according to the mathematical models and the benefit functions of the various devices comprises the following steps:
assuming that the social profit of the multi-energy system is W, there are
Figure BDA0003435267880000123
Wherein, DeltaeAnd ΔhThe electric power deviation and the thermal power deviation of the multi-energy system are respectively expressed, and the specific description is as follows:
Figure BDA0003435267880000124
Figure BDA0003435267880000125
step 1.4, establishing a multi-energy system energy management mathematical model considering transmission loss according to the mathematical model, the benefit function and the social profit maximum objective function of each type of equipment, wherein the mathematical model comprises the following steps:
min F＝-W (16a)
s.t.(15a)(15b) (16b)
s.t.(1b)(2b)(3b)(4b)(5b)(6b)(7b)(8b)(9)(10)(16c)
wherein, (16a), (16b), (16c) respectively represent objective function, equality constraint condition and inequality constraint condition of the energy management mathematical model of the multi-energy system.
Step 2, designing a complete distributed solving algorithm based on double consistency considering communication noise according to a system communication topological graph, wherein the complete distributed solving algorithm comprises consistency variable selection and distributed algorithm design, and comprises the following steps:
step 2.1, selecting consistency variables:
selecting electricity price and heat price as double consistency variables according to KKT optimal conditions of equality constraint optimization problem composed of equations (16a) and (16b), and obtaining the optimal conditions
Figure BDA0003435267880000131
Wherein the content of the first and second substances,
Figure BDA0003435267880000132
and
Figure BDA0003435267880000133
respectively representing the electricity transmission loss factors of the ith generator set only and the cogeneration generator set;
Figure BDA0003435267880000134
wherein the content of the first and second substances,
Figure BDA0003435267880000135
and
Figure BDA0003435267880000136
respectively representing heat transfer loss factors of the ith heat-only unit and the cogeneration unit;
step 2.2, designing a distributed algorithm:
according to the double consistency variables selected in the step 2.1, a fully distributed algorithm based on double consistency considering communication noise is designed, and the algorithm comprises the following steps:
initialization: suppose that
Figure BDA0003435267880000137
And xi ═ Pp,Pc,Pef,Hc,Hh,Hhf]A column stack vector representing a coherency variable and a power output, respectively, then phi (0) may be further initialized from an initial value ξ (0) according to (17a) and (17 b);
in this embodiment, the initial value of the output is ξ (0) ═ 30,100,150,200,100,70,30, 102,82,95,115,30,30 ].
Iteration: suppose that
Figure BDA0003435267880000138
A column stack vector representing a local power offset, thenIs provided with
φ(k+1)＝Sφ(k)+χ+μ(k)ψ(k) (18)
Wherein, S ═ I-g (k) L, x ═ g (k) DRW (k), I and L respectively represent an n-order identity matrix and a Laplace matrix,R＝diag[R(1,:),R(2,:),…,R(n,:)]denotes a diagonal matrix, R (i,: denotes the i-th row element of the dual random matrix R, W (k) [ w ]1(k),w2(k),…,wn(k)]Representing a column of stacked vectors, wi(k)＝[w1i(k),w2i(k),…,wni(k)]，wji(k) Representing the noise input of information interaction between a node j and a node i in the kth iteration, g (k) representing an attenuation gain function, mu (k) representing a time-varying correction factor, and L being specifically described as follows:
Figure BDA0003435267880000141
in this embodiment, the local power deviation is derived by ψ (0) ([ 0,0,0,0,0,0,0,0,0,0,0,0,0,0], the attenuation gain function is derived by g (k) ("1/(0.005 k + 1)"), the time-varying correction factor μ (k) ("1/(200 + k)"), and the communication noise follows a standard normal distribution N [0,0.01 ];
further, R is a dual random matrix obtained from the system communication topology, and is described in detail as follows:
Figure BDA0003435267880000142
wherein r isijI row and j column elements, N, representing the matrix RiSet of neighbor nodes representing the ith participant node, di、djRespectively representing the degrees of the ith participant node and the jth participant node;
in this embodiment, the matrix R determined according to the communication topology of the device shown in FIG. 3 may be defined by the power communication sub-matrix ReWith thermodynamic communication sub-matrix RhThe description is as follows:
Figure BDA0003435267880000143
Figure BDA0003435267880000151
ξ(k+1)＝Tφ(k+1)-ζ (19)
wherein the content of the first and second substances,
Figure BDA0003435267880000152
a diagonal matrix is shown and is represented,
Figure BDA0003435267880000153
a list of the stack vectors is represented,
ψ(k+1)＝Sψ(k)+x-(ξ(k+1)-ξ(k)) (20)。
the step 3 of obtaining an optimal output scheme of the supply side by using the fully distributed solving algorithm in the step 2 includes:
convergence: suppose that
Figure BDA0003435267880000157
Indicates a convergence decision coefficient, then
Figure BDA0003435267880000154
Wherein V represents a set of all participant nodes;
when the absolute value of the maximum local power deviation is less than or equal to the convergence judgment coefficient, judging that the set convergence condition is met and outputting the equipment output, the electricity price and the heat price at the current k moment to obtain an optimal supply side output scheme; otherwise, continuing iterative computation by adopting the fully distributed solving algorithm in the step 2.
In the present embodiment, the convergence determination coefficient is obtained
Figure BDA0003435267880000158
To illustrate the effectiveness of the proposed fully distributed solution algorithm, this embodiment is verified by the following two examples, the simulation platform is implemented by Matlab operation, and the example simulation results are shown in tables 6 to 8:
table 6 optimum electrical output units for the equipment under two calculation examples: MW
Figure BDA0003435267880000155
Table 7 optimal thermal output units for the device under two examples: MW
Figure BDA0003435267880000156
Table 8 system electricity and heat rate units under two examples: (ii) w/MW
Figure BDA0003435267880000161
The first calculation example: the effectiveness of the fully distributed energy management method under transmission loss and communication noise is not considered. In the case of neglecting transmission loss and communication noise, the power price and the heat price respectively reach consistency convergence through 200 times of iterative calculation, the power output and the heat output respectively tend to be stable, the power deviation meets the convergence judgment condition, and the simulation waveform is shown in fig. 4-9.
Example two: the effectiveness of a fully distributed energy management approach under transmission loss and communication noise is considered. In the case of considering transmission loss and communication noise, the electricity price and the heat price respectively reach consistency convergence after 600 times of iterative computation, the electricity output and the heat output respectively tend to be stable, the power deviation meets the convergence judgment condition, and the simulation waveform is shown in fig. 10-15.
The following conclusions can be drawn from the above specific embodiments:
(1) the introduction of transmission loss slightly increases the arrangement output of equipment, the introduction of communication noise obviously increases the iteration times of the algorithm, the proposed fully distributed algorithm can effectively deal with the problem of energy management of the multi-energy system considering loss and noise, and information interaction between adjacent participants protects the privacy of the participants to a great extent and has rapid convergence.
(2) Under the optimal scheduling of the energy management of the multi-energy system, the arrangement output of the equipment is in negative correlation with the unit energy consumption cost of the equipment, namely the smaller the unit energy consumption cost is, the more the equipment is preferentially arranged to output, the system cost is reduced as much as possible on the premise of meeting the operation constraint, and therefore the social benefit of the multi-energy system under the distributed energy management is maximum.
The method solves the problems that the important influence of transmission loss on supply and demand balance and communication noise on the solving algorithm in the actual operation of the system is neglected by the multi-energy system energy management method in the prior art, so that the solving algorithm lacks robustness verification in the actual operation process, and the optimization result cannot well meet the actual demand of a load side.
The embodiments provided in the present application are only a few examples of the general concept of the present application, and do not limit the scope of the present application. Any other embodiments extended according to the scheme of the present application without inventive efforts will be within the scope of protection of the present application for a person skilled in the art.

Claims (8)

1. A method for managing energy of a multi-energy system considering transmission loss and communication noise, comprising the steps of:
step 1, establishing a multi-energy system energy management mathematical model considering transmission loss, wherein the multi-energy system energy management mathematical model comprises a target function, equality constraint conditions and inequality constraint conditions;
step 2, designing a complete distributed solving algorithm based on double consistency considering communication noise according to a system communication topological graph, wherein the complete distributed solving algorithm comprises consistency variable selection and distributed algorithm design;
and 3, obtaining an optimal output scheme of the supply side by adopting the completely distributed solving algorithm in the step 2.
2. The method for energy management of a multi-energy system considering transmission loss and communication noise according to claim 1, wherein the step 1 of establishing a mathematical model for energy management of a multi-energy system considering transmission loss, the mathematical model comprising an objective function, an equality constraint and an inequality constraint, comprises:
step 1.1, establishing mathematical models of various equipment of the multi-energy system;
step 1.2, establishing benefit functions of various devices of the multi-energy system according to the mathematical models of the various devices;
step 1.3, establishing a maximum objective function of social benefits of the multi-energy system according to the mathematical models and the benefit functions of the various devices;
and 1.4, establishing a multi-energy system energy management mathematical model considering transmission loss according to the mathematical model, the benefit function and the social profit maximum objective function of each type of equipment, wherein the multi-energy system energy management mathematical model comprises an objective function, an equality constraint condition and an inequality constraint condition.
3. The method for managing energy of multi-energy system according to claim 2, wherein the step 1.1 of establishing mathematical models of various devices of multi-energy system includes:
suppose that the multi-energy system comprises only the generator sets with the total number spNumber i-1, 2,3, …, spTotal number of cogeneration units is scNumber i-1, 2,3, …, scTotal number of heat-generating units only is shNumber i-1, 2,3, …, shTotal number of electric boilers is seNumber i-1, 2,3, …, seTotal number of electric power flexible loads is sefNumber i-1, 2,3, …, sefTotal number of electric rigid loads is serNumber i-1, 2,3, …, serTotal number of thermal flexible loads is shfNumber i-1, 2,3, …, shfTotal number of thermal rigidity loads is shrNumber i-1, 2,3, …, shrThen there is
Figure FDA0003435267870000011
Figure FDA0003435267870000012
Wherein alpha isi、βi、γiRepresenting the ith genset-only cost function Cp,iThe fitting parameters of (a) are determined,
Figure FDA0003435267870000013
and
Figure FDA0003435267870000014
respectively represents the electric output P of the ith generator set onlyp,iThe upper and lower limits of (d);
Figure FDA0003435267870000015
Figure FDA0003435267870000016
wherein alpha isi、βi、γi、di、θi、eiRepresenting the ith cogeneration unit cost function Cc,iThe fitting parameters of (a) are determined,
Figure FDA0003435267870000017
Figure FDA0003435267870000018
respectively representing the power output P of the ith cogeneration unitc,iAnd thermal output Hc,iA runnable domain of;
Figure FDA0003435267870000019
Figure FDA0003435267870000021
wherein alpha isi、βi、γiRepresenting the ith heat-only unit cost function Ch,iThe fitting parameters of (a) are determined,
Figure FDA0003435267870000022
and
Figure FDA0003435267870000023
respectively showing the heat output H of the ith heat-only unith,iThe upper and lower limits of (d);
He,i＝ηiPe,i (4a)
Figure FDA0003435267870000024
wherein He,iAnd ηiRespectively representing the thermal output and the conversion efficiency of the ith electric boiler,
Figure FDA0003435267870000025
and
Figure FDA0003435267870000026
respectively representing the electricity demand P of the ith electric boilere,iThe upper and lower limits of (d);
Figure FDA0003435267870000027
Figure FDA0003435267870000028
wherein, ai、bi、ciRepresents the ith electric power flexible load benefit function Uef,iThe fitting parameters of (a) are determined,
Figure FDA0003435267870000029
and
Figure FDA00034352678700000210
respectively represent the ith electric power flexible load electric demand Pef,iThe upper and lower limits of (d);
Figure FDA00034352678700000211
Figure FDA00034352678700000212
wherein, ai、bi、ciExpressing the ith electric rigid load benefit function Uer,iThe fitting parameters of (a) are determined,
Figure FDA00034352678700000213
and
Figure FDA00034352678700000214
respectively represent the ith electric rigid load electric demand Per,iThe upper and lower limits of (d);
Figure FDA00034352678700000215
Figure FDA00034352678700000216
wherein, ai、bi、ciRepresents the ith thermal flexible load benefit function Uhf,iFitting parameter ofThe number of the first and second groups is,
Figure FDA00034352678700000217
and
Figure FDA00034352678700000218
respectively represent the ith thermal flexible load heat demand Hhf,iThe upper and lower limits of (d);
Figure FDA00034352678700000219
Figure FDA00034352678700000220
wherein, ai、bi、ciExpressing the ith thermodynamic rigidity load benefit function Uhr,iThe fitting parameters of (a) are determined,
Figure FDA00034352678700000221
and
Figure FDA00034352678700000222
respectively representing the ith thermodynamic rigid load heat demand Hhr,iThe upper and lower limits of (d);
Figure FDA00034352678700000223
wherein the content of the first and second substances,
Figure FDA00034352678700000224
and
Figure FDA00034352678700000225
respectively representing the transmission power P of the ith power grid linel,iThe upper and lower limits of (d);
Figure FDA00034352678700000226
Figure FDA00034352678700000227
Hi＝csmd,j(ts,i-tr,i) (10c)
wherein, ts,i、tr,i、HiRespectively showing the water supply temperature, the water return temperature and the transmission heat of the ith heat supply network node,
Figure FDA0003435267870000031
and
Figure FDA0003435267870000032
respectively represent ts,iThe upper and lower limits of (a) and (b),
Figure FDA0003435267870000033
and
Figure FDA0003435267870000034
respectively represents the transmission flow m of the j-th heat supply network pipelined,jUpper and lower limits of csIndicating the specific heat capacity of the heating medium.
4. The method for managing energy of multi-energy system according to claim 3, wherein said step 1.2 of establishing benefit function of each type of device of multi-energy system according to mathematical model of each type of device comprises:
suppose that the trade price of electricity and heat of the multi-energy system are p respectivelyeAnd phThen there is
Figure FDA0003435267870000035
Figure FDA0003435267870000036
Figure FDA0003435267870000037
Fe,i＝phHe,i-pePe,i (11d)
Fef,i＝Uef,i-pePef,i (11e)
Fer,i＝Uer,i-pePer,i (11f)
Fhf,i＝Uhf,i-phHhf,i (11g)
Fhr,i＝Uhr,i-phHhr,i (11h)
Wherein, Fp,i、Fc,i、Fh,i、Fe,i、Fef,i、Fer,i、Fhf,i、Fhr,iRespectively representing the benefit functions of the ith generator set only, the cogeneration unit, the heat generator set only, the electric boiler, the electric flexible load, the electric rigid load, the thermal flexible load and the thermal rigid load,
Figure FDA0003435267870000038
and
Figure FDA0003435267870000039
respectively represents the electricity loss generated by the ith generator set only and the cogeneration set,
Figure FDA00034352678700000310
and
Figure FDA00034352678700000311
specifically, the heat loss from the ith cogeneration unit and the heat-only unit is represented byThe description is as follows:
Figure FDA00034352678700000312
Figure FDA00034352678700000313
wherein, BiIndicating electrical losses
Figure FDA00034352678700000314
Fitting parameters of ljDenotes the length of the jth heat supply network pipe, ta,jDenotes the average temperature, R, of the medium surrounding the jth heat supply network pipehRepresenting the total thermal resistance per kilometer of tubing from heating medium to surrounding medium.
5. The method for energy management of multi-energy system according to claim 4, wherein the step 1.3 of establishing a social profit maximum objective function of multi-energy system according to the mathematical model and benefit function of each type of equipment comprises:
assuming that the social profit of the multi-energy system is W, there are
Figure FDA0003435267870000041
Wherein, DeltaeAnd ΔhThe electric power deviation and the thermal power deviation of the multi-energy system are respectively expressed, and the specific description is as follows:
Figure FDA0003435267870000042
Figure FDA0003435267870000043
6. the method for energy management of multi-energy system considering transmission loss and communication noise according to claim 5, wherein step 1.4 is to establish a mathematical model for energy management of multi-energy system considering transmission loss according to the mathematical model, benefit function and the social profit maximum objective function of each type of equipment, and comprises:
min F＝-W (16a)
s.t.(15a)(15b) (16b)
s.t.(1b)(2b)(3b)(4b)(5b)(6b)(7b)(8b)(9)(10) (16c)
wherein, (16a), (16b), (16c) respectively represent objective function, equality constraint condition and inequality constraint condition of the energy management mathematical model of the multi-energy system.
7. The method for energy management of multi-energy system considering transmission loss and communication noise according to claim 6, wherein the step 2 of designing a fully distributed solution algorithm based on dual consistency considering communication noise according to a system communication topological graph, the fully distributed solution algorithm comprises selecting consistency variables and designing a distributed algorithm, and comprises:
step 2.1, selecting consistency variables:
selecting electricity price and heat price as double consistency variables according to KKT optimal conditions of equality constraint optimization problem composed of equations (16a) and (16b), and obtaining the optimal conditions
Figure FDA0003435267870000044
Wherein the content of the first and second substances,
Figure FDA0003435267870000045
and
Figure FDA0003435267870000046
respectively representing the ith generator set only and the cogeneration setElectrical transmission loss factor of;
Figure FDA0003435267870000051
wherein the content of the first and second substances,
Figure FDA0003435267870000052
and
Figure FDA0003435267870000053
respectively representing heat transfer loss factors of the ith heat-only unit and the cogeneration unit;
step 2.2, designing a distributed algorithm:
according to the double consistency variables selected in the step 2.1, a fully distributed algorithm based on double consistency considering communication noise is designed, and the algorithm comprises the following steps:
initialization: suppose that
Figure FDA0003435267870000054
And xi ═ Rp，Rc，Ref，Hc，Hh，Hhf]A column stack vector representing a coherency variable and a power output, respectively, then phi (0) may be further initialized from an initial value ξ (0) according to (17a) and (17 b);
iteration: suppose that
Figure FDA0003435267870000055
The column stack vector representing the local power offset is then
φ(k+1)＝Sφ(k)+x+μ(k)ψ(k) (18)
Wherein, S ═ I-g (k) L, x ═ g (k) DRW (k), I and L respectively represent an n-order identity matrix and a Laplace matrix, DR＝diag[R(1,:),R(2,:),…,R(n,:)]Denotes a diagonal matrix, R (i,: denotes the i-th row element of the dual random matrix R, W (k) [ w ]1(k),w2(k),…,wn(k)]Representing a column of stacked vectors, wi(k)＝[w1i(k),w2i(k),…,wni(k)]，wji(k) Representing the noise input of information interaction between a node j and a node i in the kth iteration, g (k) representing an attenuation gain function, mu (k) representing a time-varying correction factor, and L being specifically described as follows:
Figure FDA0003435267870000056
r is a dual random matrix obtained from the system communication topology, and is described in detail as follows:
Figure FDA0003435267870000057
wherein r isijI row and j column elements, N, representing the matrix RiSet of neighbor nodes representing the ith participant node, di、djRespectively representing the degrees of the ith participant node and the jth participant node;
ξ(k+1)＝Tφ(k+1)-ζ (19)
wherein the content of the first and second substances,
Figure FDA0003435267870000061
a diagonal matrix is shown and is represented,
Figure FDA0003435267870000062
a list of the stack vectors is represented,
ψ(k+1)＝Sψ(k)+x-(ξ(k+1)-ξ(k)) (20)。
8. the method for energy management of a multi-energy system considering transmission loss and communication noise according to claim 7, wherein the step 3 of obtaining the optimal output scheme at the supply side by using the fully distributed solving algorithm in the step 2 comprises:
suppose that
Figure FDA0003435267870000064
Indicates a convergence decision coefficient, then
Figure FDA0003435267870000063
Wherein V represents a set of all participant nodes;
when the absolute value of the maximum local power deviation is less than or equal to the convergence judgment coefficient, judging that the set convergence condition is met and outputting the equipment output, the electricity price and the heat price at the current k moment to obtain an optimal supply side output scheme; otherwise, continuing iterative computation by adopting the fully distributed solving algorithm in the step 2.
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