CN112348343A - Uncertainty-considered multi-energy flow distribution network operation cost evaluation method - Google Patents

Uncertainty-considered multi-energy flow distribution network operation cost evaluation method Download PDF

Info

Publication number
CN112348343A
CN112348343A CN202011193581.7A CN202011193581A CN112348343A CN 112348343 A CN112348343 A CN 112348343A CN 202011193581 A CN202011193581 A CN 202011193581A CN 112348343 A CN112348343 A CN 112348343A
Authority
CN
China
Prior art keywords
gas
node
power
heat
uncertainty
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202011193581.7A
Other languages
Chinese (zh)
Other versions
CN112348343B (en
Inventor
孙可
楼伯良
黄晓明
樊印龙
肖修林
张宝
黄弘扬
马骏超
周丹
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zhejiang University of Technology ZJUT
Electric Power Research Institute of State Grid Zhejiang Electric Power Co Ltd
Hangzhou Yineng Energy Retrenchment Technology Co
Original Assignee
Zhejiang University of Technology ZJUT
Electric Power Research Institute of State Grid Zhejiang Electric Power Co Ltd
Hangzhou Yineng Energy Retrenchment Technology Co
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Zhejiang University of Technology ZJUT, Electric Power Research Institute of State Grid Zhejiang Electric Power Co Ltd, Hangzhou Yineng Energy Retrenchment Technology Co filed Critical Zhejiang University of Technology ZJUT
Priority to CN202011193581.7A priority Critical patent/CN112348343B/en
Publication of CN112348343A publication Critical patent/CN112348343A/en
Application granted granted Critical
Publication of CN112348343B publication Critical patent/CN112348343B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/067—Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/80—Management or planning
    • Y02P90/82—Energy audits or management systems therefor

Abstract

The invention discloses a method for evaluating the running cost of a multi-energy flow distribution power grid by considering uncertainty, belongs to the field of power distribution networks, and aims to solve the problem that the running cost of the power distribution network is not accurately evaluated by adopting the conventional model, and adopts the following technical scheme: a multi-energy flow distribution network operation cost assessment method considering uncertainty comprises the following steps: step 1, modeling a multi-energy flow distribution grid: establishing a natural gas network model; establishing a thermodynamic network model; establishing a power network model; and 2, evaluating the expected cost of the multi-energy flow distribution network by using a 2m +1 point estimation method. The method aims to evaluate the operation cost of the multi-energy flow distribution power grid, models the user behavior uncertainty, the renewable energy power generation uncertainty and the load uncertainty by adopting a 2m +1 point estimation method, decouples a multi-energy flow system, and solves the operation cost of each subsystem respectively, so that the operation cost of the multi-energy flow distribution power grid is obtained.

Description

Uncertainty-considered multi-energy flow distribution network operation cost evaluation method
Technical Field
The invention belongs to the field of power distribution networks, and particularly relates to a method for evaluating the operation cost of a multi-energy flow distribution network by considering uncertainty.
Background
With the increase of various energy consumption, the traditional independent network energy supply mode gradually changes into a multi-energy flow system with multiple energy sources supplying energy simultaneously. The system couples multiple energy sources together through the energy conversion equipment, and the energy utilization rate of the system is improved. Uncertainties in the system, such as load uncertainties, renewable power generation uncertainties, and energy price uncertainties, will all have an impact on system operation. In past studies, the uncertainty in the system was usually treated as a deterministic problem, but this approach did not take into account the effect of uncertainty on the system.
Some researches also consider uncertainty, but mainly model uncertainty of renewable energy power generation in the system, and lack of consideration of uncertainty of user behaviors existing in the system, so that cost estimation is not accurate.
Disclosure of Invention
Aiming at the problem that the operation cost of the power distribution network is not accurately evaluated by adopting the existing model, the invention provides the operation cost evaluation method of the multi-energy distribution network considering uncertainty, aiming at evaluating the operation cost of the multi-energy distribution network, modeling the user behavior uncertainty, the renewable energy power generation uncertainty and the load uncertainty by adopting a 2m +1 point estimation method, decoupling the multi-energy distribution network, and respectively solving the operation cost of each subsystem, thereby obtaining the operation cost of the multi-energy distribution network.
The technical scheme adopted by the invention is as follows: a multi-energy flow distribution network operation cost assessment method considering uncertainty comprises the following steps:
step 1, modeling a multi-energy flow distribution grid:
step 1.1, storing and conveying natural gas to users through pipelines and pressure stations, and accordingly building a natural gas network model;
step 1.2, the heat supply network is composed of insulated pipelines arranged between a heat generator (such as a gas boiler and the like) and a user, and heat is transmitted to the user through hot water or steam, so that a thermodynamic network model is established;
step 1.3, considering the coupling and uncertainty between preparations, establishing a power network model;
and 2, considering the user behaviors, various loads and uncertainty of renewable energy sources in the model established in the step 1, and evaluating the expected cost of the multi-energy distribution network by using a 2m +1 point estimation method.
The method aims to evaluate the operation cost of the multi-energy flow distribution power grid, models the user behavior uncertainty, the renewable energy power generation uncertainty and the load uncertainty by adopting a 2m +1 point estimation method, decouples a multi-energy flow system, and solves the operation cost of each subsystem respectively, so that the operation cost of the multi-energy flow distribution power grid is obtained.
Further, in the natural gas network model established in step 1.1,
gas flow f per pipegkAs follows:
Figure BDA0002753378060000021
Figure BDA0002753378060000022
Figure BDA0002753378060000023
Figure BDA0002753378060000024
in the formula: f. ofgkIs the flow rate of gas in the pipeline, m3/h；sign(fgk) Is a tracheal pressure sign function;
Figure BDA0002753378060000025
for pipeline grade correction, kPa2；Hg、HkRespectively the height of the pipeline, m; pg、PkGas pressure, kPa, at node g and node k, respectively;
Figure BDA0002753378060000026
is the average temperature of the gas in the pipeline, K; g is the acceleration of gravity, m/s2；
Figure BDA0002753378060000027
Is the average pressure of the pipeline, kPa; cgkIs the pipe constant between node g and node k; t is0Is the reference temperature, K;
Figure BDA0002753378060000028
is the diameter of the pipeline between the nodes g and K, mm; zaIs the gas compression factor at average temperature; l isgkThe length of the pipeline between the node g and the node k is mm; epIs qiAbsolute roughness of the pipe, mm; gamma rayGIs the specific gravity of gas and has no dimension;
Figure BDA0002753378060000029
the friction coefficient of the air pipe is dimensionless and can be calculated according to a Kerbulke equation:
Figure BDA00027533780600000210
in the formula:
Figure BDA00027533780600000211
is Reynolds number, dimensionless;
if the flow moves from node g to node k, fgkIs positive and vice versa. Therefore, if fgkIf > 0, sign (f)gk) Is +1, otherwise-1.
Since the natural gas/electricity consuming compressor is used to maintain the pressure of the gas stream, various parameters of the compressor are defined:
horsepower of compressor
Figure BDA0002753378060000031
Can be calculated by the following formula:
Figure BDA0002753378060000032
Figure BDA0002753378060000033
in the formula: horsepower of compressor
Figure BDA0002753378060000034
Matching;
Figure BDA0002753378060000035
is compressor efficiency, dimensionless;
Figure BDA0002753378060000036
the pressure of the input gas and the pressure of the output gas of the compressor are respectively kPa; lambda [ alpha ]GThe specific heat rate of natural gas is dimensionless;
Figure BDA0002753378060000037
is the ratio of the pressures of the compressor output and input gases, dimensionless; p0Base pressure, kPa;
power consumption of compressor
Figure BDA0002753378060000038
Can be calculated from the following formula:
Figure BDA0002753378060000039
gas consumption of gas compressor
Figure BDA00027533780600000310
The calculation is as follows:
Figure BDA00027533780600000311
in the formula (I), the compound is shown in the specification,
Figure BDA00027533780600000312
is the consumption coefficient of the compressor, and has no dimension;
Figure BDA00027533780600000313
is the gas consumption of the gas compressor, m3/h；
Gas consumption f of gas generator in electric power sub-networkGGThe calculation is as follows:
Figure BDA00027533780600000314
in the formula: f. ofGGFor gas consumption of gas-fired generator, m3/h；agen、bgen、cgen、dgen、egenThe consumption coefficient of the gas generator is dimensionless;
Figure BDA00027533780600000315
generating capacity of a gas generator, kWH;
Figure BDA00027533780600000316
the minimum generated energy of the gas generator, kWH; GHV is natural gas heat value, kW.h/m3；
The gas consumption of the combined heat and power plant (CHP) is:
Figure BDA0002753378060000041
in the formula:
Figure BDA0002753378060000042
for CHP gas consumption, m3/h；
Figure BDA0002753378060000043
Is the efficiency of CHP, dimensionless; the coefficient 3.412 is used to convert W units to BTU/h units;
Figure BDA0002753378060000044
active power of CHP, kW;
Figure BDA0002753378060000045
is the heat production power of CHP, kW.
The gas flow analysis of the natural gas network must satisfy the natural gas balance of each node, and the formula is as follows:
Figure BDA0002753378060000046
in the formula:
Figure BDA0002753378060000047
is the flow rate of gas injected at node g, m3/h；
Figure BDA0002753378060000048
Is the gas demand at node g, m3H, associated with gas load uncertainty; f. ofGGFor gas consumption of gas-fired generator, m3/h；
Figure BDA0002753378060000049
Is the gas consumption of the gas compressor, m3/h；
Figure BDA00027533780600000410
Is the gas consumption of the gas boiler, m3/h；fgkIs the gas flow of the pipe between node g and node k, m3H; where gas boiler and CHP are considered grid loads, there is a demand side response uncertainty.
Further, in the thermodynamic network model of step 1.2, the flow rate Φ of each pipe ishbAnd the temperature at the end of each node is calculated by the following equation:
Figure BDA00027533780600000411
Figure BDA00027533780600000412
in the formula: flow rate phi of heat pipehb，m3/h；cpIs the specific heat capacity of water, J/(Kg ℃ C.);
Figure BDA00027533780600000413
the mass flow of the pipeline between the node h and the node b is kg/h;
Figure BDA00027533780600000414
is the length of the heat pipeline, mm; u is the heat conduction coefficient, W/m.K; t isgIs ambient temperature, K; t isstart，hThe hot water inflow temperature at node h, K; t isend，bThe hot water outflow temperature at node b, K.
The pressure loss in each thermal pipe due to friction was calculated by the Darcy-Weisbach equation
Figure BDA00027533780600000415
Figure BDA00027533780600000416
Figure BDA00027533780600000417
In the formula: pressure loss
Figure BDA00027533780600000418
Pa；
Figure BDA00027533780600000419
The resistance coefficient of the heat pipeline is dimensionless; g is the acceleration of gravity, m/s2；ρWKg/m3 for water density;
Figure BDA00027533780600000420
is the diameter of the heat pipeline, mm;
Figure BDA00027533780600000421
is a symbolic function of the flow of the heat pipeline, and is dimensionless;
Figure BDA00027533780600000422
the coefficient of friction of the heat pipeline is dimensionless.
In a heat supply network, four flow balance equations, namely a thermal power balance equation, a loop pressure drop equation, a water supply and return water temperature balance equation, must be satisfied;
the node thermal power balance in the hydraulic analysis is described as:
Figure BDA0002753378060000051
in the formula:
Figure BDA0002753378060000052
the mass flow of hot water generated by the CHP at the node h and the gas boiler is kg/h after uncertainty is considered; phih，loadIs the thermal load power at node h, kW; t isstart，hThe hot water inflow temperature, K, is taken out of the node h; t isend，loadThe hot water outflow temperature at the thermal load, K; nh is the number of heat network nodes;
the loop pressure drop equation is:
Figure BDA0002753378060000053
in the formula, delta is the set of all pipelines in the loop; Δ hjThe pressure drop in the first and last sections of the pipeline j in the loop.
For the thermodynamic analysis of the heat network, a water supply and return temperature balance must be established for all nodes except the slack nodes:
Figure BDA0002753378060000054
Figure BDA0002753378060000056
in the formula: t isstart，CHP、Tstart，BoilerCHP and gas boiler outflow temperature, K, respectively;
Figure BDA0002753378060000057
the heat load mass flow of the node h is kg/h; t isstart、TendThe temperature of the water flow at the head end and the tail end of the pipeline is K;
the temperature at the mixed heat node is defined as:
Figure BDA0002753378060000058
in the formula (I), the compound is shown in the specification,
Figure BDA0002753378060000061
the flow rates of the water flows flowing in and out of the mixing heat node are kg/s respectively; t isin、ToutThe temperature of the incoming and outgoing water streams at the mixing hot node, K, respectively. It can be seen that the water flow temperature of each pipeline is different before mixing, and the water flow temperature flowing into other pipelines from the node is the same after mixing.
In a heating network, a heat pump is used to circulate hot water from a heat source to a user (heat load), and the power consumption of the heat pump can be calculated as follows:
Figure BDA0002753378060000062
in the formula:
Figure BDA0002753378060000063
the power consumption of the heat pump is kW;
Figure BDA0002753378060000064
the mass flow of water is kg/h; hPIs the heat pump lift, m; etaHPIs pump efficiency, dimensionless; g is the acceleration of gravity, m/s2；
The relationship between the gas consumption and the heat production of the gas boiler is as follows:
Figure BDA0002753378060000065
in the formula (I), the compound is shown in the specification,
Figure BDA0002753378060000066
is the gas consumption of the gas boiler, m3/h；
Figure BDA0002753378060000067
kW is the heat production of the gas boiler;
Figure BDA0002753378060000068
is the largest of gas-fired boilersHeat production, kW; a isboil、bboilAre respectively the polynomial coefficients of the gas boiler and have no dimension.
Further, in the power network model of step 1.3, an active power and reactive power balance equation is established for each node:
Figure BDA0002753378060000069
Figure BDA00027533780600000610
in the formula:
Figure BDA00027533780600000611
represents the power, kW, transmitted on the line between node i and node j; vi、VjThe amplitudes of the node i and the node j are respectively kV; y isijIs the line admittance, S; n is a radical ofEThe number of the power grid nodes is one;
Figure BDA00027533780600000612
active power and reactive power, kW, of the CHP at the node i are respectively obtained; pi gen、
Figure BDA00027533780600000613
Active power and reactive power, kW, of the generator at the node i are respectively obtained;
Figure BDA00027533780600000614
active power and reactive power requirements at node i, kW, respectively; pi Pump、Pi compThe electric energy consumed by the heat pump and the compressor at the node i is kW;
Figure BDA00027533780600000615
reactive power, kW, injected by a parallel capacitor at a node i;
Figure BDA00027533780600000616
respectively for wind-powered electricity generation and photovoltaic power generation, kW, that have the uncertainty of generating electricity.
Further, the 2m +1 point estimation method adopted in the step 2 is carried out according to the following steps:
step 2.1, reading the multi-energy flow distribution power grid data in the multi-energy flow distribution power grid modeling, wherein the read data comprises user behaviors, renewable resources and various loads;
2.2, inputting uncertain input random variables into the multi-energy flow distribution network model, as shown in a formula (26);
step 2.3, calculating the skewness of the input random variable according to the formula (29);
step 2.4, calculating a standard position according to the result of the step 2.3 of the formula (28);
step 2.5, calculating a concentration position and a weight factor according to the formula (27) and the formula (30);
step 2.6, placing the load demand and the power generation power of the renewable energy source at corresponding centralized positions, and meanwhile, fixing other input random variables at the average value or the prediction point to calculate the operation cost, as shown in a formula (31) and a formula (32);
step 2.7, decomposing the power flow by adopting a pure embedding method in the power network model according to the formulas (24) to (25);
step 2.8, updating the gas consumption of the gas generator according to the formula (10);
step 2.9, decomposing thermodynamic flow in the established thermodynamic network model by adopting a graph theory method according to the formula (13) -formula (23);
step 2.10, respectively updating the gas consumption of the gas boiler, the gas consumption of the CHP and the power consumption of the heat pump according to the formula (23), the formula (11) and the formula (22);
step 2.11, decomposing the natural gas flow in the established natural gas network model according to the formula (1) to the formula (12);
step 2.12, updating the power consumption of the electric compressor according to the formula (8); if the calculation is converged, executing the step 2.13, and if the calculation is not converged, returning to the step 2.7;
and 2.13, judging whether random variables exist, if no random variable exists, ending, and if the random variables exist, executing the step 2.2, namely inputting the uncertain input random variables.
In step 2.2, the PES input random variable S is a variable Z containing m uncertain input variablesdI.e.:
S＝F(z1，z2，...，zd，...，zm) (26)
in the invention, a PES (polyether sulfone) calculates statistical information of the operation cost of a multi-energy flow distribution network by converting an uncertainty problem with m input random variables into a 2m +1 equivalent certainty problem, wherein the m random variables comprise uncertainty of electricity, gas and heat loads, uncertainty of renewable energy power generation and uncertainty of user behavior;
each random variable consists of three centralized positions Zd，pP ═ 1, 2, 3 and the weighting factor ωd，pComposition, weight factor omegad，pDetermining the influence of the corresponding position on the total expected operation cost of the multi-energy flow distribution network;
the input random variable skewness in step 2.3 is
Figure BDA0002753378060000081
And
Figure BDA0002753378060000082
which satisfies the following conditions:
Figure BDA0002753378060000083
in the formula: e is an expected operator;
Figure BDA0002753378060000084
is an average value;
Figure BDA0002753378060000085
is the standard deviation;
standard position in step 2.4 is
Figure BDA0002753378060000086
Random variable skewness input from step 2.3
Figure BDA0002753378060000087
And
Figure BDA0002753378060000088
and calculating to obtain:
Figure BDA0002753378060000089
concentration position Z in step 2.5d，pThe following requirements are met:
Figure BDA00027533780600000810
in the formula:
Figure BDA00027533780600000811
is a standard position;
respective weighting factors for each concentration location
Figure BDA00027533780600000812
The calculation is as follows:
Figure BDA00027533780600000813
step 2.6, for each centralized position, calculating the operation cost of the multi-energy distribution network based on each sub-network as follows:
Figure BDA00027533780600000814
the expected operation cost of the multi-energy flow distribution grid considering the uncertainty of user behaviors, loads and renewable energy sources is as follows:
Figure BDA00027533780600000815
the invention has the following beneficial effects: the method aims to evaluate the operation cost of the multi-energy flow distribution power grid, models the user behavior uncertainty, the renewable energy power generation uncertainty and the load uncertainty by adopting a 2m +1 point estimation method, decouples a multi-energy flow system, and solves the operation cost of each subsystem respectively, so that the operation cost of the multi-energy flow distribution power grid is obtained.
Drawings
FIG. 1 is a flow chart of step 2 of the present invention.
Detailed Description
The technical solutions of the embodiments of the present invention are explained and explained below with reference to the drawings of the present invention, but the following embodiments are only preferred embodiments of the present invention, and not all embodiments. Based on the embodiments in the implementation, other embodiments obtained by those skilled in the art without any creative effort belong to the protection scope of the present invention.
The uncertainty-considered operation cost evaluation method for the multi-energy distribution network comprises the following steps:
step 1, modeling a multi-energy flow distribution grid:
in the natural gas network model established in step 1.1,
gas flow f per pipegkAs follows:
Figure BDA0002753378060000091
Figure BDA0002753378060000092
Figure BDA0002753378060000093
Figure BDA0002753378060000094
in the formula: f. ofgkIs the pipeline gas flow, cfm; sign (f)gk) Is a pipeline pressure sign function;
Figure BDA0002753378060000095
for pipeline grade correction, kPa2；Hg、HkRespectively the height of the pipeline, m; pg、PkGas pressure, kPa, at node g and node k, respectively;
Figure BDA0002753378060000096
is the average temperature of the gas in the pipeline, K;
Figure BDA0002753378060000097
is the average pressure of the pipeline, kPa; cgkIs the pipe constant between node g and node k; t is0Is the reference temperature, K;
Figure BDA0002753378060000098
is the diameter of the pipeline between the nodes g and K, mm; zaIs the gas compression factor at average temperature; l isgkThe length of the pipeline between the node g and the node k is mm; epIs the absolute roughness of the gas pipeline, mm; gamma rayGIs the specific gravity of gas and has no dimension;
Figure BDA0002753378060000099
the friction coefficient of the gas pipeline can be calculated according to a Kerbuke equation:
Figure BDA0002753378060000101
in the formula:
Figure BDA0002753378060000102
is Reynolds number;
if the flow moves from node g to node k, fgkIs positive and vice versa. Therefore, if fgkIf > 0, sign (f)gk) Is +1, otherwise-1.
Since the natural gas/electricity consuming compressor is used to maintain the pressure of the gas stream, various parameters of the compressor are defined:
compressor power
Figure BDA0002753378060000103
Can be calculated by the following formula:
Figure BDA0002753378060000104
Figure BDA0002753378060000105
in the formula: horsepower of compressor
Figure BDA0002753378060000106
Matching;
Figure BDA0002753378060000107
is compressor efficiency, dimensionless;
Figure BDA0002753378060000108
the pressure of the input gas and the pressure of the output gas of the compressor are respectively kPa; lambda [ alpha ]GThe specific heat rate of natural gas is dimensionless;
Figure BDA0002753378060000109
is the ratio of the pressures of the compressor output and input gases, dimensionless; p0Base pressure, kPa.
Power consumption of compressor
Figure BDA00027533780600001010
Can be calculated from the following formula:
Figure BDA00027533780600001011
power consumption of compressor
Figure BDA00027533780600001012
A unit of (d);
gas consumption of gas compressor
Figure BDA00027533780600001013
The calculation is as follows:
Figure BDA00027533780600001014
in the formula (I), the compound is shown in the specification,
Figure BDA00027533780600001015
is the consumption coefficient of the compressor, and has no dimension;
gas consumption f of gas generator in electric power sub-networkGGThe calculation is as follows:
Figure BDA00027533780600001016
in the formula: f. ofGGFor gas consumption of gas-fired generator, m3/h；agen、bgen、cgen、dgen、egenThe consumption coefficient of the gas generator is dimensionless;
Figure BDA0002753378060000111
generating capacity of a gas generator, kWH;
Figure BDA0002753378060000112
the minimum generated energy of the gas generator, kWH; GHV is natural gas heat value, kW.h/m3；
The gas consumption of the combined heat and power plant (CHP) is:
Figure BDA0002753378060000113
in the formula:
Figure BDA0002753378060000114
consumption of gas for CHP，m3/h；
Figure BDA0002753378060000115
Is the efficiency of CHP, dimensionless; the coefficient 3.412 is used to convert W units to BTU/h units;
Figure BDA0002753378060000116
active power of CHP, kW;
Figure BDA0002753378060000117
is the heat production power of CHP, kW.
The gas flow analysis of the natural gas network must satisfy the natural gas balance of each node, and the formula is as follows:
Figure BDA0002753378060000118
in the formula:
Figure BDA0002753378060000119
is the flow rate of gas injected at node g, m3/h；
Figure BDA00027533780600001110
Is the gas demand at node g, m3H, associated with gas load uncertainty; f. ofGGFor gas consumption of gas-fired generator, m3/h；
Figure BDA00027533780600001111
Is the gas consumption of the gas compressor, m3/h；
Figure BDA00027533780600001112
Is the gas consumption of the gas boiler, m3/h；fgkIs the gas flow of the pipe between node g and node k, m3H; where gas boiler and CHP are considered grid loads, there is a demand side response uncertainty.
Step 1.2, the heat supply network is composed of insulated pipelines arranged between a heat generator (such as a gas boiler and the like) and a user, and heat is conveyed to the user through hot water or steam, so that a thermodynamic network model is established:
flow rate phi per pipehbAnd the temperature at the end of each node is calculated by the following equation:
Figure BDA00027533780600001113
Figure BDA00027533780600001114
in the formula: flow rate phi of heat pipehb，m3/h；cpIs the specific heat capacity of water, J/(Kg ℃ C.);
Figure BDA00027533780600001115
the mass flow of the pipeline between the node h and the node b is kg/h;
Figure BDA00027533780600001116
is the length of the heat pipeline, mm; u is the heat conduction coefficient, W/m.K; t isgIs ambient temperature, K; t isstart，hThe hot water inflow temperature at node h, K; t isend，bThe hot water outflow temperature at node b, K.
The pressure loss in each thermal pipe due to friction was calculated by the Darcy-Weisbach equation
Figure BDA00027533780600001117
Figure BDA0002753378060000121
Figure BDA0002753378060000122
In the formula: pressure loss
Figure BDA0002753378060000123
Pa；
Figure BDA0002753378060000124
The resistance coefficient of the heat pipeline is dimensionless; g is the acceleration of gravity, m/s2；ρWKg/m3 for water density;
Figure BDA0002753378060000125
is the diameter of the heat pipeline, mm;
Figure BDA0002753378060000126
is a symbolic function of the flow of the heat pipeline, and is dimensionless;
Figure BDA0002753378060000127
the coefficient of friction of the heat pipeline is dimensionless.
In a heat supply network, four flow balance equations, namely a thermal power balance equation, a loop pressure drop equation, a water supply and return water temperature balance equation, must be satisfied;
the node thermal power balance in the hydraulic analysis is described as:
Figure BDA0002753378060000128
in the formula:
Figure BDA0002753378060000129
the mass flow of hot water generated by the CHP at the node h and the gas boiler is kg/h after uncertainty is considered; phih，loadIs the thermal load power at node h, kW; t isstart，hThe hot water inflow temperature, K, is taken out of the node h; t isend，loadThe hot water outflow temperature at the thermal load, K; nh is the number of heat network nodes;
the loop pressure drop equation is:
Figure BDA00027533780600001210
in the formula, delta is the set of all pipelines in the loop; Δ hjThe pressure drop in the first and last sections of the pipeline j in the loop.
For the thermodynamic analysis of the heat network, a water supply and return temperature balance must be established for all nodes except the slack nodes:
Figure BDA00027533780600001211
Figure BDA0002753378060000131
in the formula: t isstart，CHP、Tstart，BoilerCHP and gas boiler outflow temperature, K, respectively;
Figure BDA0002753378060000132
the heat load mass flow of the node h is kg/h; t isstart、TendRespectively the water flow temperature at the head end and the tail end of the pipeline, K;
the temperature at the mixed heat node is defined as:
Figure BDA0002753378060000133
in the formula (I), the compound is shown in the specification,
Figure BDA0002753378060000134
the flow rates of the water flows flowing in and out of the mixing heat node are kg/s respectively; t isin、ToutThe temperature of the incoming and outgoing water streams at the mixing hot node, K, respectively. It can be seen that the water flow temperature of each pipeline is different before mixing, and the water flow temperature flowing into other pipelines from the node is the same after mixing.
In a heating network, a heat pump is used to circulate hot water from a heat source to a user (heat load), and the power consumption of the heat pump can be calculated as follows:
Figure BDA0002753378060000135
in the formula:
Figure BDA0002753378060000136
the power consumption of the heat pump is kW;
Figure BDA0002753378060000137
the mass flow of water is kg/h; hPIs the heat pump lift, m; etaHPIs pump efficiency, dimensionless; g is the acceleration of gravity, m/s2；
The relationship between the gas consumption and the heat production of the gas boiler is as follows:
Figure BDA0002753378060000138
in the formula (I), the compound is shown in the specification,
Figure BDA0002753378060000139
is the gas consumption of the gas boiler, m3/h；
Figure BDA00027533780600001310
kW is the heat production of the gas boiler;
Figure BDA00027533780600001311
the maximum heat production capacity of the gas boiler is kW; a isboil、bboilAre respectively the polynomial coefficients of the gas boiler and have no dimension.
Step 1.3, considering the coupling and uncertainty between preparations, establishing a power network model:
establishing an active power and reactive power balance equation for each node:
Figure BDA0002753378060000141
Figure BDA0002753378060000142
in the formula:
Figure BDA0002753378060000143
represents the power, kW, transmitted on the line between node i and node j; vi、VjThe amplitudes of the node i and the node j are respectively kV; y isijIs the line admittance, S; n is a radical ofEThe number of the power grid nodes is one;
Figure BDA0002753378060000144
active power and reactive power, kW, of the CHP at the node i are respectively obtained; pi gen、
Figure BDA0002753378060000145
Active power and reactive power, kW, of the generator at the node i are respectively obtained;
Figure BDA0002753378060000146
active power and reactive power requirements at node i, kW, respectively; pi Pump、Pi compThe electric energy consumed by the heat pump and the compressor at the node i is kW;
Figure BDA0002753378060000147
reactive power, kW, injected by a parallel capacitor at a node i;
Figure BDA0002753378060000148
respectively for wind-powered electricity generation and photovoltaic power generation, kW, that have the uncertainty of generating electricity.
Step 2, considering the uncertainty of the user behavior, various loads and renewable energy in the model established in the step 1, estimating the expected cost of the multi-energy distribution grid by using a 2m +1 point estimation method, and performing the following steps:
step 2.1, reading the multi-energy flow distribution power grid data in the multi-energy flow distribution power grid modeling, wherein the read data comprises user behaviors, renewable resources and various loads;
step 2.2, inputting uncertain input random variables into the multi-energy flow distribution network model; PES input random variable S is a variable Z containing m uncertain input variablesdI.e.:
S＝F(z1，z2，...，zd，...，zm) (26)
in the invention, a PES (polyether sulfone) calculates statistical information of the operation cost of a multi-energy flow distribution network by converting an uncertainty problem with m input random variables into a 2m +1 equivalent certainty problem, wherein the m random variables comprise uncertainty of electricity, gas and heat loads, uncertainty of renewable energy power generation and uncertainty of user behavior;
each random variable consists of three centralized positions Zd，pP ═ 1, 2, 3 and the weighting factor ωd，pComposition, weight factor omegad，pDetermining the influence of the corresponding position on the total expected operation cost of the multi-energy flow distribution network;
and 2.3, calculating the skewness of the input random variable according to the formula (29):
skewness of input random variable
Figure BDA0002753378060000149
And
Figure BDA00027533780600001410
satisfies the following conditions:
Figure BDA0002753378060000151
in the formula: e is an expected operator;
Figure BDA0002753378060000152
is an average value;
Figure BDA0002753378060000153
is the standard deviation;
step 2.4, Standard position is
Figure BDA0002753378060000154
Random variable skewness input from step 2.3
Figure BDA0002753378060000155
And
Figure BDA0002753378060000156
and calculating to obtain:
Figure BDA0002753378060000157
step 2.5, the concentration location and the weight factor ω are calculated according to the equations (27) and (30)d，p(ii) a The following requirements are met:
Figure BDA0002753378060000158
respective weight factor omega for each concentration locationd，pThe calculation is as follows:
Figure BDA0002753378060000159
step 2.6, the load demand and the power generation power of the renewable energy source are placed at corresponding centralized positions, meanwhile, other input random variables are fixed at the average value or the prediction point of the load demand and the power generation power of the renewable energy source to calculate the operation cost, and for each centralized position, the operation cost of the multi-energy flow distribution network based on each sub-network is calculated as follows:
Figure BDA00027533780600001510
the expected operation cost of the multi-energy flow distribution grid considering the uncertainty of user behaviors, loads and renewable energy sources is as follows:
(32)
step 2.7, decomposing the power flow by adopting a pure embedding method in the power network model according to the formulas (24) to (25);
step 2.8, updating the gas consumption of the gas generator according to the formula (10);
step 2.9, decomposing thermodynamic flow in the established thermodynamic network model by adopting a graph theory method according to the formula (13) -formula (23);
step 2.10, respectively updating the gas consumption of the gas boiler, the gas consumption of the CHP and the power consumption of the heat pump according to the formula (23), the formula (11) and the formula (22);
step 2.11, decomposing the natural gas flow in the established natural gas network model according to the formula (1) to the formula (12);
step 2.12, updating the power consumption of the electric compressor according to the formula (8); if the calculation is converged, executing the step 2.13, and if the calculation is not converged, returning to the step 2.7;
and 2.13, judging whether random variables exist, if no random variable exists, ending, and if the random variables exist, executing the step 2.2, namely inputting the uncertain input random variables.
The method aims to evaluate the operation cost of the multi-energy flow distribution power grid, models the user behavior uncertainty, the renewable energy power generation uncertainty and the load uncertainty by adopting a 2m +1 point estimation method, decouples a multi-energy flow system, and solves the operation cost of each subsystem respectively, so that the operation cost of the multi-energy flow distribution power grid is obtained.
While the invention has been described with reference to specific embodiments, the scope of the invention is not limited thereto, and those skilled in the art will appreciate that the invention includes, but is not limited to, the accompanying drawings and the description of the embodiments above. Any modification which does not depart from the functional and structural principles of the present invention is intended to be included within the scope of the claims.

Claims (10)

1. A multi-energy flow distribution network operation cost assessment method considering uncertainty is characterized by comprising the following steps:
step 1, modeling a multi-energy flow distribution grid:
step 1.1, storing and conveying natural gas to users through pipelines and pressure stations, and accordingly building a natural gas network model;
step 1.2, the heat supply network consists of insulated pipelines arranged between the heat generator and the user, and heat is transmitted to the user through hot water or steam, so that a thermodynamic network model is established;
step 1.3, considering the coupling and uncertainty between preparations, establishing a power network model;
and 2, considering the user behaviors, various loads and uncertainty of renewable energy sources in the model established in the step 1, and evaluating the expected cost of the multi-energy distribution network by using a 2m +1 point estimation method.
2. The uncertainty-considered multi-energy distribution grid operation cost evaluation method according to claim 1, characterized in that, in the natural gas network model established in step 1.1,
gas flow f per pipegkAs follows:
Figure FDA0002753378050000011
Figure FDA0002753378050000012
Figure FDA0002753378050000013
Figure FDA0002753378050000014
in the formula: f. ofgkIs the flow rate of gas in the pipeline, m3/h；sign(fgk) Is a tracheal pressure sign function;
Figure FDA0002753378050000015
for pipeline grade correction, kPa2；Hg、HkRespectively the height of the pipeline, m; pg、PkGas pressure, kPa, at node g and node k, respectively;
Figure FDA0002753378050000016
is the average temperature of the gas in the pipeline, K; g is the acceleration of gravity, m/s2；
Figure FDA0002753378050000017
Is the average pressure of the pipeline, kPa; cgkIs the pipe constant between node g and node k; t is0Is the reference temperature, K;
Figure FDA0002753378050000018
is the diameter of the pipeline between the nodes g and K, mm; zaIs the gas compression factor at average temperature; l isgkThe length of the pipeline between the node g and the node k is mm; epIs the absolute roughness of the gas pipeline, mm; gamma rayGIs the specific gravity of gas and has no dimension;
Figure FDA0002753378050000019
the friction coefficient of the air pipe is dimensionless and can be calculated according to a Kerbulke equation: :
Figure FDA0002753378050000021
in the formula:
Figure FDA0002753378050000022
is Reynolds number;
if the flow moves from node g to node k, fgkIs positive and vice versa; therefore, if fgkIf > 0, sign (f)gk) Is +1, otherwise-1;
since the natural gas/electricity consuming compressor is used to maintain the pressure of the gas stream, various parameters of the compressor are defined:
compressor power
Figure FDA0002753378050000023
Can be calculated by the following formula:
Figure FDA0002753378050000024
Figure FDA0002753378050000025
in the formula: horsepower of compressor
Figure FDA0002753378050000026
Matching;
Figure FDA0002753378050000027
is compressor efficiency, dimensionless;
Figure FDA0002753378050000028
the pressure of the input gas and the pressure of the output gas of the compressor are respectively kPa; lambda [ alpha ]GIs the specific heat rate of natural gas;
Figure FDA0002753378050000029
is the ratio of the pressures of the compressor output and input gases; p0Base pressure, kPa;
power consumption of compressor
Figure FDA00027533780500000210
Can be calculated from the following formula:
Figure FDA00027533780500000211
in the formula (I);
Figure FDA00027533780500000212
power consumption of the compressor, kW;
gas consumption of gas compressor
Figure FDA00027533780500000213
The calculation is as follows:
Figure FDA00027533780500000214
in the formula (I), the compound is shown in the specification,
Figure FDA00027533780500000215
is the consumption coefficient of the compressor;
Figure FDA00027533780500000216
is the gas consumption of the gas compressor, m3/h；
Gas consumption f of gas generator in electric power sub-networkGGThe calculation is as follows:
Figure FDA00027533780500000217
in the formula: f. ofGGFor gas consumption of gas-fired generator, m3/h；agen、bgen、cgen、dgen、egenThe consumption coefficient of the gas generator is dimensionless;
Figure FDA0002753378050000031
the power generation capacity of the gas generator is MWH;
Figure FDA0002753378050000032
the minimum generated energy of the gas generator, MWH; GHV is natural gas heat value, kcal/m3；
The gas consumption of the combined heat and power plant (CHP) is:
Figure FDA0002753378050000033
in the formula:
Figure FDA0002753378050000034
for CHP gas consumption, m3/h；
Figure FDA0002753378050000035
Is the efficiency of CHP, dimensionless; the coefficient 3.412 is used to convert W units to BTU/h units;
Figure FDA0002753378050000036
active power for CHP, MW;
Figure FDA0002753378050000037
heat generation power for CHP, MW;
the gas flow analysis of the natural gas network must satisfy the natural gas balance of each node, and the formula is as follows:
Figure FDA0002753378050000038
in the formula:
Figure FDA0002753378050000039
is the flow rate of gas injected at node g, m3/h；
Figure FDA00027533780500000310
Is the gas demand at node g, m3H, associated with gas load uncertainty; f. ofGGFor gas consumption of gas-fired generator, m3/h；
Figure FDA00027533780500000311
Is the gas consumption of the gas compressor, m3/h；
Figure FDA00027533780500000312
Is the gas consumption of the gas boiler, m3/h；fgkIs the gas flow of the pipe between node g and node k, m3H; where gas boiler and CHP are considered grid loads, there is a demand side response uncertainty.
3. The uncertainty-aware multi-energy distribution grid operating cost assessment method according to claim 2, wherein in the thermodynamic network model of step 1.2, the flow rate Φ per pipe ishbAnd the temperature at the end of each node is calculated by the following equation:
Figure FDA00027533780500000313
Figure FDA00027533780500000314
in the formula: flow rate phi of heat pipehb，m3/h；cpIs the specific heat capacity of water, J/(Kg ℃ C.);
Figure FDA00027533780500000315
the mass flow of the pipeline between the node h and the node b is kg/h;
Figure FDA00027533780500000316
is the length of the heat pipeline, mm; u is the heat conduction coefficient, W/m.K; t isgIs ambient temperature, K; t isstart，hThe hot water inflow temperature at node h, K; t isend，bIs the hot water outflow temperature at node b, K;
the pressure loss in each thermal pipe due to friction was calculated by the Darcy-Weisbach equation
Figure FDA00027533780500000317
Figure FDA00027533780500000318
Figure FDA0002753378050000041
In the formula: pressure loss
Figure FDA0002753378050000042
Pa；
Figure FDA0002753378050000043
The resistance coefficient of the heat pipeline is dimensionless; g is the acceleration of gravity, m/s2；ρWIn terms of water density, kg/m3；
Figure FDA0002753378050000044
Is the diameter of the heat pipeline, mm;
Figure FDA0002753378050000045
is a symbolic function of the flow of the heat pipeline, and is dimensionless;
Figure FDA0002753378050000046
the coefficient of friction of the heat pipeline is dimensionless;
in a heat supply network, four flow balance equations, namely a thermal power balance equation, a loop pressure drop equation, a water supply and return water temperature balance equation, must be satisfied;
the node thermal power balance in the hydraulic analysis is described as:
Figure FDA0002753378050000047
in the formula:
Figure FDA0002753378050000048
the mass flow of hot water generated by the CHP at the node h and the gas boiler is kg/h after uncertainty is considered; phih，loadIs the thermal load power at node h, MW; t isstart，hThe hot water inflow temperature, K, is taken out of the node h; t isend，loadThe hot water outflow temperature at the thermal load, K; nh is the number of heat network nodes;
the loop pressure drop equation is:
Figure FDA0002753378050000049
in the formula, delta is the set of all pipelines in the loop; Δ hjThe pressure drop of the first section and the last section of the pipeline j in the loop;
for the thermodynamic analysis of the heat network, a water supply and return temperature balance must be established for all nodes except the slack nodes:
Figure FDA00027533780500000410
Figure FDA00027533780500000411
in the formula: t isstart，CHP、Tstart，BoilerCHP and gas boiler outflow temperature, K, respectively;
Figure FDA00027533780500000412
the heat load mass flow of the node h is kg/h; t isstart、TendThe temperature of the water flow at the head end and the tail end of the pipeline is K;
the temperature at the mixed heat node is defined as:
Figure FDA0002753378050000051
in the formula (I), the compound is shown in the specification,
Figure FDA0002753378050000052
the flow rates of the water flows flowing in and out of the mixing heat node are kg/s respectively; t isin、ToutThe temperature of the water flow flowing in and out of the mixing heat node, K; it can be seen that the water flow temperature of each pipeline is different before mixing, and the mixed water flows into other pipelines from the nodeThe water flow temperature is the same;
in a heating network, a heat pump is used to circulate hot water from a heat source to a user (heat load), and the power consumption of the heat pump can be calculated as follows:
Figure FDA0002753378050000053
in the formula:
Figure FDA0002753378050000054
the power consumption of the heat pump is kW;
Figure FDA0002753378050000055
the mass flow of water is kg/h; hPIs the heat pump lift, m; etaHPIs pump efficiency, dimensionless; g is the acceleration of gravity, m/s2；
The relationship between the gas consumption and the heat production of the gas boiler is as follows:
Figure FDA0002753378050000056
in the formula (I), the compound is shown in the specification,
Figure FDA0002753378050000057
is the gas consumption of the gas boiler, m3H (in formula (12): is
Figure FDA0002753378050000058
Need to change to be consistent);
Figure FDA0002753378050000059
kW is the heat production of the gas boiler;
Figure FDA00027533780500000510
the maximum heat production capacity of the gas boiler is kW; a isboil、bboilAre respectively the polynomial coefficients of the gas boiler and have no dimension.
4. The uncertainty-considered multi-energy distribution network operation cost evaluation method according to claim 3, characterized in that in the power network model of step 1.3, an active power and reactive power balance equation is established for each node:
Figure FDA00027533780500000511
Figure FDA00027533780500000512
in the formula:
Figure FDA00027533780500000513
represents the power, kW, transmitted on the line between node i and node j; vi、VjThe amplitudes of the node i and the node j are respectively kV; y isijIs the line admittance, S; n is a radical ofEThe number of the power grid nodes is one;
Figure FDA0002753378050000061
active power and reactive power, kW, of the CHP at the node i are respectively obtained; pi gen、
Figure FDA0002753378050000062
Active power and reactive power, kW, of the generator at the node i are respectively obtained;
Figure FDA0002753378050000063
active power and reactive power requirements at node i, kW, respectively; pi Pump、Pi compThe electric energy consumed by the heat pump and the compressor at the node i is kW;
Figure FDA0002753378050000064
reactive power, kW, injected by a parallel capacitor at a node i;
Figure FDA0002753378050000065
respectively for wind-powered electricity generation and photovoltaic power generation, kW, that have the uncertainty of generating electricity.
5. The uncertainty-considered multi-energy distribution network operation cost evaluation method according to claim 4, wherein the 2m +1 point estimation method adopted in the step 2 is performed according to the following steps:
step 2.1, reading the multi-energy flow distribution power grid data in the multi-energy flow distribution power grid modeling, wherein the read data comprises user behaviors, renewable resources and various loads;
step 2.2, inputting uncertain input random variables into the multi-energy flow distribution network model;
step 2.3, calculating the skewness of the input random variable;
step 2.4, calculating a standard position according to the result of the step 2.3;
step 2.5, calculating a centralized position and a weight factor;
step 2.6, placing the load demand and the power generation power of the renewable energy source at corresponding centralized positions, and simultaneously fixing other input random variables at the average value or a prediction point to calculate the operation cost;
step 2.7, decomposing the power flow by adopting a pure embedding method in the power network model;
step 2.8, updating the gas consumption of the gas generator;
step 2.9, decomposing the thermal flow in the established thermal network model by adopting a graph theory method;
step 2.10, respectively updating the gas consumption of the gas boiler, the gas consumption of the CHP and the power consumption of the heat pump;
2.11, decomposing the natural gas flow in the established natural gas network model;
step 2.12, updating the power consumption of the electric compressor; if the calculation is converged, executing the step 2.13, and if the calculation is not converged, returning to the step 2.7;
and 2.13, judging whether random variables exist, if no random variable exists, ending, and if the random variables exist, executing the step 2.2, namely inputting the uncertain input random variables.
6. The uncertainty-considered multi-energy distribution grid operation cost evaluation method according to claim 5, characterized in that in step 2.2, the PES input random variable S is a variable z containing m uncertain input variablesdI.e.:
S＝F(z1，z2，...，zd，...，zm) (26)
wherein the m random variables comprise uncertainty of electricity, gas and heat load, uncertainty of renewable energy power generation and uncertainty of user behavior; each random variable consists of three centralized positions Zd，pP ═ 1, 2, 3 and the weighting factor ωd，pComposition, weight factor omegad，pThe influence of the corresponding position on the total expected operation cost of the multi-energy distribution network is determined.
7. The uncertainty-aware multi-energy distribution grid operating cost assessment method according to claim 6,
and 2.3, calculating the skewness of the input random variable according to the formula (29):
skewness of input random variable
Figure FDA0002753378050000071
And
Figure FDA0002753378050000072
satisfies the following conditions:
Figure FDA0002753378050000073
in the formula: e is an expected operator;
Figure FDA0002753378050000074
is an average value;
Figure FDA0002753378050000075
is the standard deviation.
8. The uncertainty-aware multi-energy distribution grid operating cost assessment method according to claim 7, wherein in step 2.4, the standard position is
Figure FDA0002753378050000076
Random variable skewness input from step 2.3
Figure FDA0002753378050000077
And
Figure FDA0002753378050000078
and calculating to obtain:
Figure FDA0002753378050000079
9. the uncertainty-aware multi-energy distribution grid operating cost assessment method according to claim 8, characterized by step 2.5, centralizing the location Zd，pThe following requirements are met:
Figure FDA00027533780500000710
in the formula:
Figure FDA00027533780500000711
is a standard position;
respective weight factor omega for each concentration locationd，pThe calculation is as follows:
Figure FDA0002753378050000081
10. the uncertainty-aware multi-flow distribution grid operating cost estimation method according to claim 9, wherein step 2.6 calculates the multi-flow distribution grid operating cost based on each sub-network for each centralized location as follows:
Figure FDA0002753378050000082
the expected operation cost of the multi-energy flow distribution grid considering the uncertainty of user behaviors, loads and renewable energy sources is as follows:
Figure FDA0002753378050000083
CN202011193581.7A 2020-10-30 2020-10-30 Uncertainty-considered multi-energy flow distribution network operation cost evaluation method Active CN112348343B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011193581.7A CN112348343B (en) 2020-10-30 2020-10-30 Uncertainty-considered multi-energy flow distribution network operation cost evaluation method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011193581.7A CN112348343B (en) 2020-10-30 2020-10-30 Uncertainty-considered multi-energy flow distribution network operation cost evaluation method

Publications (2)

Publication Number Publication Date
CN112348343A true CN112348343A (en) 2021-02-09
CN112348343B CN112348343B (en) 2022-11-29

Family

ID=74356187

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011193581.7A Active CN112348343B (en) 2020-10-30 2020-10-30 Uncertainty-considered multi-energy flow distribution network operation cost evaluation method

Country Status (1)

Country Link
CN (1) CN112348343B (en)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108734350A (en) * 2018-05-17 2018-11-02 燕山大学 A kind of independent method for solving with combined dispatching of the power distribution network containing micro-capacitance sensor
CN109711601A (en) * 2018-11-28 2019-05-03 国网浙江省电力有限公司电力科学研究院 The hot integrated energy system distributed optimization dispatching method of electric-gas-and device
CN109934450A (en) * 2018-12-29 2019-06-25 国网天津市电力公司电力科学研究院 More scene active distribution network planning appraisal methods based on Demand Side Response
CN110245878A (en) * 2019-06-27 2019-09-17 华翔翔能电气股份有限公司 The Distributed Integration energy demand of intelligent building group responds cooperative optimization method
CN111476470A (en) * 2020-03-30 2020-07-31 广西电网有限责任公司崇左供电局 Multi-main-body investment cost allocation method based on asymmetric Nash negotiation model

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108734350A (en) * 2018-05-17 2018-11-02 燕山大学 A kind of independent method for solving with combined dispatching of the power distribution network containing micro-capacitance sensor
CN109711601A (en) * 2018-11-28 2019-05-03 国网浙江省电力有限公司电力科学研究院 The hot integrated energy system distributed optimization dispatching method of electric-gas-and device
CN109934450A (en) * 2018-12-29 2019-06-25 国网天津市电力公司电力科学研究院 More scene active distribution network planning appraisal methods based on Demand Side Response
CN110245878A (en) * 2019-06-27 2019-09-17 华翔翔能电气股份有限公司 The Distributed Integration energy demand of intelligent building group responds cooperative optimization method
CN111476470A (en) * 2020-03-30 2020-07-31 广西电网有限责任公司崇左供电局 Multi-main-body investment cost allocation method based on asymmetric Nash negotiation model

Also Published As

Publication number Publication date
CN112348343B (en) 2022-11-29

Similar Documents

Publication Publication Date Title
CN108596453B (en) Day-ahead optimization scheduling method and system of comprehensive energy system considering network dynamic characteristics
CN110263966B (en) Electric-thermal comprehensive energy system optimal scheduling method considering dynamic heat transfer process
CN110348602B (en) Comprehensive energy system optimization method considering natural gas pipe network and heat power pipe network characteristics
CN111815042B (en) Electric heating comprehensive energy system optimization scheduling method considering refinement heat supply network model
CN109377008B (en) Electric-thermal coupling comprehensive energy system risk assessment method
CN106844895B (en) Decoupling calculation method for combined cooling heating and power micro-grid energy flow
CN110728032B (en) Quick power flow calculation method for electricity-heat interconnection comprehensive energy system considering ring network
CN111082417A (en) State estimation method based on comprehensive energy system electric and heat combined network
CN109523076A (en) A kind of energy storage participate under integrated energy system steady-state optimization analysis method a few days ago
CN110765622A (en) Energy flow obtaining system, equipment and medium of natural gas pipeline model
CN113036819A (en) Electric heating comprehensive energy system robust scheduling method considering source-load bilateral uncertainty
CN112989612B (en) Electric heating comprehensive energy system linear modeling method based on Mickey envelope
CN113806972B (en) Comprehensive energy system reliability analysis method considering supply and demand bilateral flexibility
CN112862158B (en) Day-ahead optimal scheduling method and system for comprehensive energy system
Shaposhnikov et al. Increasing efficiency of CCP-based TPP with injection of dry saturated steam from recovery boiler into regenerator
CN114169727A (en) Multi-energy-flow comprehensive energy low-carbon scheduling method considering carbon capture and electricity-to-gas coordination
Man et al. State estimation for integrated energy system containing electricity, heat and gas
CN113886761A (en) Energy efficiency analysis and evaluation method for comprehensive energy system
CN112348343B (en) Uncertainty-considered multi-energy flow distribution network operation cost evaluation method
CN115859686A (en) Comprehensive energy system low-carbon scheduling method and system considering expanded carbon emission flow
Self et al. Effects of oxy-fuel combustion on performance of heat recovery steam generators
CN114626587A (en) Gas-electricity combined system operation optimization method considering natural gas pipeline hydrogen doping
CN112686447A (en) Multi-energy flow coupling load prediction method for offshore oil and gas field development
CN111969588B (en) Solving method, storage medium and equipment for optimal energy flow of multi-energy flow system
CN115906587B (en) Method for establishing dynamic coupling model of heat supply pipe network of comprehensive energy system

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant