CN108921727A - Consider the regional complex energy resource system reliability estimation method of thermic load dynamic characteristic - Google Patents
Consider the regional complex energy resource system reliability estimation method of thermic load dynamic characteristic Download PDFInfo
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Abstract
A kind of regional complex energy resource system reliability estimation method considering thermic load dynamic characteristic：According to selected regional complex energy resource system, input：Electric power, natural gas, space-heating system data, energy hub data, various element failure rates, electricity, air and heat cutting load price data, layering decoupling optimization convergence judgment threshold, reliability index restrain judgment threshold, largest sample number, Indoor environment design temperature；Choose the operating status of all elements；System mode analysis is carried out, tide optimization calculating is carried out to selected system mode, obtains the cutting load amount of electricity under system mode, air and heat；Reliability index is calculated, includes electricity, air and heat expected load is cut down and expected load cuts down frequency；Calculate reliability index convergence criterion；Judge quasi- sequential method frequency in sampling；Export electricity/gas/thermal region integrated energy system reliability index.The present invention can more accurately assess electricity/gas/thermal region integrated energy system reliability.
Description
Technical field
The present invention relates to a kind of regional complex energy resource system reliability estimation methods.More particularly to a kind of consideration thermic load
The regional complex energy resource system reliability estimation method of dynamic characteristic.
Background technique
For the pressure and response environment pollution problem for alleviating lack of energy, improves efficiency of energy utilization, increases renewable energy
Source utilization rate becomes the emphasis of various countries' research.Wherein integrated energy system be collect the production of various energy resources, transmission, distribution, conversion,
Each link is stored and consumed in the energy supply system of one, whole rule are carried out to the energy such as electricity, gas, hot and cold, biological, chemical
It draws and dispatches, effectively improve flexibility, safety, reliability and the economy of energy supply.At present with micro-gas-turbine
Machine is that the Coupling device of representative greatly accelerates the construction progress of integrated energy system.Reliability assessment is by qualitative or fixed
The risk level of the index reflection system energy supply discontinuity of amount, and then the planning, design, operation for instructing system of science, are opened
It opens up integrated energy system reliability assessment and important in inhibiting is run to the construction investment of integrated energy system and decision.
Mainly it is unfolded at present from energy hub direction about the research of integrated energy system reliability, it is determined that comprehensive energy
The content and range of source system Reliability Research, but integrated comprehensive energy network system is furtherd investigate not yet.It is comprehensive
Energy resource system is to carry out scientific optimization and economic tune to energy supply side, transmission equipment side and user side in planning, construction and operation
Degree to reach the maximized integral system of efficiency of energy utilization, comprehensive analysis integrated energy resource system for, it is defeated, etc. each ring
Section is conducive to fully understand the operating status of integrated energy system.Wherein user side part throttle characteristics difference is big, such as electric load, uses
Family is to equipment charges such as electric cars, and when system jam, the electricity input of electric car can not continue to supply, the electricity of user
Workload demand is just not being met at once；And for refrigeration duty, user is freezed using equipment such as air-conditionings, works as system jam
When, the electricity input of air-conditioning can not continue to supply, but user can also meet refrigeration duty demand in one section of longer time at this time.
Therefore, when assessing the utilizability of user's energy, the workload demand for analyzing user is more more significant than the load supply of user.
It is converted in integrated energy system comprising various energy resources, especially in system user side user because of oneself the various energy
Demand produces various load needs to system supply side, transmission equipment side, there are problems that complicated multipotency cooperative compensating.In view of energy
Source coupled relation user side more intensively with it is common, the present invention, which defines, meets the energy of the various life requirements of user directly to use
" load needs " (the LD-Load Demand) at family, that is, the outlet side of energy hub；User is due to workload demand to energy
The energy demand that source supply side and transmission equipment side generate is " load supply " (the LS-Load Supply) of user, that is, energy collection
The input side of line device.The LD of user side is the final purpose that user carries out energy consumption, as the lighting requirements of user, heating need
It asks, charge requirement；The LS of user is the energy supply that generates in order to meet the workload demand of user, such as electric energy supply, thermal energy
Supply, natural gas supply, the air-conditioning equipment that user needs to be supplied with electric energy because of heating, while can also be supplied with thermal energy
Heat sink apparatus.It can be seen that the energy demand key for meeting user is to meet the LD of user.In integrated energy system,
The LD of user is various informative, can be mainly divided into two types according to time response relationship between LD and LS：One is LD-LS without
Inertia loading, closely related between the LD and LS of user, if LS stops supply, the availability of the LD of user stops at once, such as light
According to or TV, when light irradiation apparatus electric energy stops for seasonable, the lighting requirements of user stop at once；Another kind is LD-LS inertia
Load, there are time retardances between the LD and LS of user, if LS stops supply, the availability of the LD of user will not stop at once
Only, such as room temperature heating, when heating equipment electric energy stops supply or the stopping flowing of radiator hot water, the heating demand of user is not
It can stop at once, there is time delay.Reliability is the ability that assessment system is uninterruptedly energized to user, meets the LD of user
Demand is the basic task of energy supply system.The present invention assesses electrical heat integrated energy system reliability and refers to that system meets use
The ability of family LD considers that user's thermic load dynamic characteristic is conducive to the reliability of accurate evaluation system.
After considering the dynamic characteristic of thermic load, when carrying out system state selection, need to consider between system mode when
Between continuity, sequential method be in conventional method analyze sequence problem main method, but sequential method exist calculate the time it is long, meter
In place of the deficiencies of calculation amount is big, so selecting system mode using quasi- sequential method.And at present for point of regional complex energy resource system
Analysis, which calculates, mainly united method and decoupling two class of solving method, and wherein united method needs to establish unified electrical heat optimization
Solving model, the optimized variable being related to is numerous, has the characteristics that computationally intensive, the calculating time is long, is not easy to restrain, is not suitable for application
In reliability state problem analysis.
Summary of the invention
The technical problem to be solved by the invention is to provide one kind being capable of the more accurate evaluation electrical heat regional complex energy
The considerations of system reliability thermic load dynamic characteristic regional complex energy resource system reliability estimation method.
The technical scheme adopted by the invention is that：A kind of regional complex energy resource system considering thermic load dynamic characteristic is reliable
Property appraisal procedure, includes the following steps：
1) according to the selected area comprising electric system, gas burning system, space-heating system and energy hub type
Domain integrated energy system, input：Electric power, natural gas, space-heating system data, energy hub data, various element faults
Rate, electricity, air and heat cutting load price data, layering decoupling optimization convergence judgment threshold, quasi- sequential method calculate reliability index convergence
Judgment threshold, quasi- sequential method largest sample number, Indoor environment design temperature；
2) according to the element failure rate provided according to step 1), the operating status of all elements is chosen according to quasi- sequential method；
3) all elements of identification system are carved at the beginning and be in normal operating condition, and all node temperatures are in interior
Set temperature judges whether system is that load cuts down state, and whether there is the node temperature of thermic load at system mode S
For non-indoor set temperature, if two conditions are unsatisfactory for simultaneously, i.e. load reduction neither occurs for system simultaneously not under system mode S
There are node temperatures to be less than indoor set temperature, then system mode S is complete normal condition, return step 2) reselect shape
State；If two conditions have one to meet or meet simultaneously, step 4) is carried out；
4) system mode analysis is carried out, tide optimization calculating is carried out to selected system mode S, is obtained under system mode S
The cutting load amount of electricity, air and heat；
5) according to the optimum results in step 4), calculate reliability index, include electricity, air and heat expected load cut down and
Expected load cuts down frequency；
6) reliability index convergence criterion δ is calculatedsc, judge whether parameter restrains, if convergence, carries out step 8), it is no
Then, step 7) is carried out；
7) judge quasi- sequential method frequency in sampling NMCWhether maximum value N is reachedmax；If so, carrying out step 8), output reliability
Index, otherwise, return step 2), reselect system mode；
8) electricity/gas/thermal region integrated energy system reliability index is exported, i.e., electricity, air and heat expected load are cut down and it is expected
Load cuts down frequency.
In step 1), the electric power, natural gas, space-heating system data, including route and pipe parameter, load water
Flat, network topology connection relationship；The energy hub data, including device type, place capacity and energy hub
Topological structure；The various element failure rates, including transmission line of electricity, gas pipeline, heat supply pipeline, transformer, compressor with
And all devices in energy hub.
Step 2) includes：
Element k is set in period TpPeriod is in operation or malfunction, in next period section TpBetween in operation
Or malfunction probability Pk：
In formula, λkIt is the rate of transform that k-th of element leaves from state s, if k-th of element is working, λkIt is failure
Rate；It stops transport if k-th of element is in, λkIt is repair rate；
Choose [0,1] be uniformly distributed in random number Rk, utilize RkJudge the operating status of counter element,
If k-th of element is working, λkIt is crash rate
It stops transport if k-th of element is in, λkIt is repair rate
In formula, skIt is the sample mode of element k；RkIt is k-th of element in [0,1] equally distributed random number；PkIt is kth
The state transition probability of a element.
The system mode S for possessing N number of element is determined by vector s
S=(s1,…,si,…,sN) (4)
Step 4) includes：
(1) according to the room temperature T of current system conditions initial time (a upper Optimal State terminates)A_start, optimize energy
The distribution of source hub, wherein first kind energy hub model is by transmission line of electricity (PLe), gas pipeline (PLg), heating tube
Road (PLh), minisize thermoelectric cogeneration facility (CHP), central air conditioner system (AC) composition；Second class energy hub model is by defeated
Electric line (PLe), gas pipeline (PLg), heat supply pipeline (PLh), minisize thermoelectric cogeneration facility (CHP), gas fired-boiler (F) group
At, the coupled relation formula of first kind energy hub and the second class energy hub respectively such as following formula,
In formula, subscript e, g, h respectively correspond electricity, air and heat；C is coupling matrix, and P, L respectively represent the defeated of energy hub
Enter power vector and output power vector；ηPLe、ηPLg、ηPLhIt is transmission line of electricity, gas pipeline, heat supply pipeline efficiency of transmission respectively,
ηACIt is the Energy Efficiency Ratio of central air conditioner system refrigeration and heating, ηFIt is gas fired-boiler transformation efficiency, ηPCHP_eAnd ηCHP_gIt is natural respectively
Gas is converted into the transfer efficiency of electric energy and thermal energy via minisize thermoelectric cogeneration facility；ve_ACIt is the distribution that inputing power distributes to AC
Coefficient, vg_FAnd vg_CHPIt is the distribution coefficient for inputting natural gas and being respectively allocated to F and CHP, wherein 0≤(ve_AC,vg_F,vg_CHP)≤
1, and vg_F+vg_CHP≤1；
The optimization distribution of energy hub is the majorized function that objective function is non-linear, constraint condition is linear, optimization
Model is as follows,
Objective function：
min(αLCEH_e+βLCEH_g+γLCEH_h) (7)
Wherein
Constraint condition：
LEH=CPEH+LCEH (9)
PEH min≤PEH≤PEH max (11)
PC min≤vPEH≤PC max (12)
0≤v≤1 (13)
In formula, α, β, γ are electricity, air and heat cutting load price respectively；LCEH_e、LCEH_g、LCEH_hIt is electrical thermic load respectively
Need cutting load amount；γhighIt is cutting load price when user's thermic load needs room temperature to be higher than setting value, γlowIt is user
Thermic load needs cutting load price of the room temperature lower than setting value when, TA_sIt is indoor design temperature, TA_cIt is indoor receiving temperature
Spend setting value, TA_aIt is outside air temperature, TA_startIt is current system conditions initial time temperature；LEHIt is that energy hub is defeated
The load of side needs vector out, and C is the coupling matrix of energy hub, PEHIt is the load supply arrow of energy hub input side
Amount, LCEHIt is that the load of energy hub input side needs cutting load amount vector；TA_endIt is current system conditions end moment temperature
Degree, ΦnIt is that thermic load node rated heat input, rated heat load needs capacity, LEH_hIt is that thermic load needs capacity under current system conditions, Γ is to work as
Preceding system mode continuous working period, χ are thermic load Focal point building object hot reserve coefficients；PEH max、PEH minIt is energy collection respectively
The bound of line device input terminal load supply；PC max、PC minIt is each equipment input terminal load supply in energy hub respectively
Bound；V is distribution coefficient, and value range is [0,1]；
(2) electric power, natural gas, district heating energy subsystem are reset according to the optimum results of (1) step energy hub
The load resetting of the load of respective nodes, three energy subsystems is as follows
In formula, Ne_EH、Ng_EH、Nh_EHIt is that three energy subsystems and energy hub connected node are numbered respectively；PLe,ir、
PLg,jr、PLh,krIt is three energy subsystems and energy hub connected node load respectively；
(3) according to the load data of the power subsystem of (2) step resetting, electric power system tide, natural gas is separately optimized
System load flow and space-heating system trend；
(4) input power P when calculating energy hub cutting load amount, and resetting the optimization of energy hubEHColumn vector
The upper limit, wherein energy hub cutting load amount is negative as energy supply subsystem and cutting for energy hub connected node
Lotus amount；Simultaneously in optimization process, using the maximal workload of three energy subsystems and energy hub connected node as
Energy hub input power PEHThe upper limit of column vector resets PEHThe equation of the upper limit is as follows,
In formula, PEH_e,if、PEH_g,jf、PEH_h,kfIt is electricity, air and heat input work when current energy hub optimization calculates respectively
The rate upper limit；PEH_emax,if、PEH_gmax,jf、PEH_hmax,kfIt is electricity, air and heat input when energy hub optimization next time calculates respectively
The upper limit of the power；ΔLCe,if、ΔLCg,jf、ΔLCh,kfBe respectively when the optimization of current energy hub calculates three energy subsystems with
Energy hub connected node cutting load amount；
(5) the convergence index for calculating layering decoupling optimization is according to energy hub twice up and down during loop iteration
The difference of the upper limit of input power optimizes convergent criterion as layering decoupling, and calculation formula is as follows,
In formula, δip、δjp、δkpIt is the convergence criterion of energy hub electricity, air and heat respectively, takes wherein maximum value δmaxAs
The convergence criterion of entire layering decoupling optimization；
(6) setting value of convergence criterion is set as δspIf δmax<δspInvalid, calculating does not restrain, and returns to (1) step weight
New optimization；If δmax<δspIt sets up, calculates convergence, circulation terminates, the cutting load amount of output integrated energy resource system electricity, air and heat.
Described in (3) step：
(3.1) optimize electric power system tide, be the tide optimization for carrying out electric system using interior point method, Optimized model is such as
Under,
Objective function：
Constraint condition：
In formula, P is node active power, and Q is node reactive power,It is node voltage phasor, Y is node admittance square
Battle array；LCe,iIt is power system load node ieLLoad reduction, Ne_LIt is load node quantity；PL,ieLIt is power system load
Node ieLLoad power, PL max,ieL、PL min,ieLIt is power system load node i respectivelyeLThe upper and lower limit of load power；Vie
It is electric system node ieVoltage magnitude, Vmax,ie、Vmin,ieIt is electric system node i respectivelyeThe upper and lower limit of voltage magnitude,
Ne_nodeIt is the number of nodes of electric system；
(3.2) it optimizes natural gas system trend, being optimized using interior point method, Optimized model is as follows,
Objective function：
Constraint condition：
(Ag+U)P+ω-ZcFc=0 (30)
Fcom=kcomFmn(pm-pk) (33)
fc min,i≤pm/pk≤fc max,i ic∈Ng_C (36)
In formula, AgIt is node-branch incident matrix；U is compressor-node incidence matrix；F is pipeline power；ω is section
Point injecting power；ZcIt is compressor injecting power knot vector；FcIt is compressor injecting power；FmnIt is pipeline power, kmnIt is pipe
Road constant, pmIt is upstream node pressure, pnIt is downstream node pressure, smnIndicate natural gas node flow direction；kcomIt is to indicate to press
The constant of contracting machine characteristic, pmIt is compressor delivery pressure, pkIt is compressor inlet pressure, FmnIt is the natural gas flow that compressor flows through
Amount；LCg,iIt is natural gas system load bus igLLoad reduction, Ng_LIt is load node quantity；ωigSIt is natural gas system
Supply station igSOutput power, ωmax,igS、ωmin,igSIt is natural gas system supply station i respectivelygSThe upper and lower limit of output power,
Ng_GIt is natural gas system supply station quantity；pigIt is natural gas system node igAir pressure, pmax,ig、pmin,igIt is natural gas respectively
System node igThe upper and lower limit of air pressure, Ng_nodeIt is the number of nodes of natural gas system；pm/pkIt is compressor icStep-up ratio,
fc max,i、fc min,iIt is compressor i respectivelycThe upper and lower limit of step-up ratio, Ng_CIt is natural gas system number of compressors；
(3.3) optimize space-heating system trend, space-heating system system is by hydraulic model and thermodynamic model two
It is grouped as, space-heating system tide optimization model exists hydraulic model and thermodynamic model two parts joint solves and decoupling is asked
Two types are solved, are solved by the way of space-heating system unified Modeling, and using interior point method, Optimized model is as follows,
Objective function：
Wherein
Constraint condition：
CpAsm(Ts-To)-Φ=0 (39)
BhKm | m |=0 (40)
CsTs,L-bs=0 (41)
CrTr,L-br=0 (42)
In formula, LCh,iIt is heating system load bus ihLLoad reduction, Nh_LIt is load node quantity；CpIt is water
Specific heat capacity, AsIt is node-branch incident matrix, m is pipeline hot water flow, TsIt is node for hot side temperature, TaIt is environment temperature,
Φ is node thermal power；BhIt is circuit-branch incidence matrix, K is the impedance factor of pipeline；Cs、CrBe respectively with heating network,
The related matrix of structure and flow of backheat network, bs、brIt is column vector related with heat supply temperature, backheat temperature respectively；
ΦS,ihSIt is heating system heating plant ihSOutput thermal power, ΦS max,ihS、ΦS min,ihSIt is heating system heating plant i respectivelyhS
Output thermal power upper and lower limit, Nh_SIt is the quantity of heating system heating plant；ΦL,ihLIt is heating system load bus ihL's
Thermic load power, ΦL max,ihL、ΦL min,ihLIt is heating system load bus i respectivelyhLThermic load power upper and lower limit, Nh_L
It is the quantity of heating system load bus；milIt is pipeline ilHot water flow, mmax,il、mmin,ilIt is pipeline i respectivelylHot water flow
Upper and lower limit, Nh_pIt is number of tubes；Ts,ihIt is heating system node ihHeat supply temperature, Ts max,ih、Ts min,ihIt is to supply respectively
Hot systems node ihThe upper and lower limit of heat supply temperature, Nh_nodeIt is heating system number of nodes；Tr,ihIt is heating system node ihReturn
Hot temperature, Tr max,ih、Tr min,ihIt is heating system node i respectivelyhThe upper and lower limit of backheat temperature；To,ihLIt is heating system load
Node ihLHot water outlet temperature, To max,ihL、To min,ihLIt is heating system load bus i respectivelyhLHot water outlet temperature
Upper and lower limit；
Wherein, in formula (33)
In formula, L is duct length, and f is pipeline roughness, and D is pipe diameter, and ρ is water density, and g is acceleration of gravity.
Step 5) is calculated using following formula：
In formula, SXIt is the effective status that load occurs and cuts down；C (S) is state influence function, that is, state S electricity,
Air and heat load reduction, NMCIt is quasi- sequential method frequency in sampling, T0It is reliability assessment time interval, 1 year is 8760h；n(S)
It is the number that load occurs and cuts down effective status.
Reliability index convergence criterion calculation method described in step 6) is as follows,
In formula,It is the estimated value of reliability index；It is the variance of reliability estimated value.
The present invention is the regional complex energy resource system reliability estimation method for considering thermic load dynamic characteristic, is based on energy collection
Line device model constructs electricity/gas/thermal region integrated energy system model.The present invention considers the dynamic characteristic of thermic load, Ke Yigeng
Accurately assessment electricity/gas/thermal region integrated energy system reliability.The present invention selects system mode using quasi- sequential method, can be with
In view of the time continuity between system mode, compared to the sequential method of traditional analysis sequence problem, this method calculating time is short,
Calculation amount is small.The present invention has chosen system mode using Decoupling Analysis method research institute, studies selected system using decoupling method
State has higher efficiency and validity.
Detailed description of the invention
Fig. 1 a is the energy hub figure of the first seed type；
Fig. 1 b is the energy hub figure of second of type；
Fig. 2 is quasi- sequential method state transition diagram；
Fig. 3 is electrical heat regional complex energy resource system tide optimization concept map；
Fig. 4 is system mode Time-Series analysis figure；
Fig. 5 is regional complex energy resource system state optimization flow chart；
Fig. 6 is the electrical heat regional complex energy resource system reliability estimation method flow chart for considering thermic load dynamic characteristic；
Fig. 7 is electrical heat regional complex energy resource system example figure；
Fig. 8 a is the comparison diagram that electric load needs node EENS under three kinds of scenes；
Fig. 8 b is the comparison diagram that electric load needs node PLC under three kinds of scenes；
Fig. 9 a is the comparison diagram that gas load needs node EENS under three kinds of scenes；
Fig. 9 b is the comparison diagram that gas load needs node PLC under three kinds of scenes；
Figure 10 a is the comparison diagram that thermic load needs node EENS under three kinds of scenes；
Figure 10 b is the comparison diagram that thermic load needs node PLC under three kinds of scenes.
Specific embodiment
It can below with reference to regional complex energy resource system of the embodiment and attached drawing to consideration thermic load dynamic characteristic of the invention
It is described in detail by property appraisal procedure.
The regional complex energy resource system reliability estimation method of consideration thermic load dynamic characteristic of the invention, as shown in fig. 6,
Include the following steps：
1) according to the selected area comprising electric system, gas burning system, space-heating system and energy hub type
Domain integrated energy system, input：Electric power, natural gas, space-heating system data, energy hub data, various element faults
Rate, electricity, air and heat cutting load price data, layering decoupling optimization convergence judgment threshold, quasi- sequential method calculate reliability index convergence
Judgment threshold, quasi- sequential method largest sample number, Indoor environment design temperature；
The electric power, natural gas, space-heating system data, including route and pipe parameter, load level, network are opened up
Flutter connection relationship；The energy hub data, including device type, place capacity and energy hub topology structure；
The various element failure rates, including transmission line of electricity, gas pipeline, heat supply pipeline, transformer, compressor and energy line concentration
All devices in device.
2) according to the element failure rate provided according to step 1), the operating status of all elements is chosen according to quasi- sequential method；Packet
It includes：
Element k is set in period TpPeriod is in operation or malfunction, in next period section TpBetween in operation
Or malfunction probability Pk：
In formula, λkIt is the rate of transform that k-th of element leaves from state s, if k-th of element is working, λkIt is failure
Rate；It stops transport if k-th of element is in, λkIt is repair rate；
Choose [0,1] equally distributed random number Rk, utilize RkJudge the operating status of counter element, as shown in Figure 2.
If k-th of element is working, λkIt is crash rate
It stops transport if k-th of element is in, λkIt is repair rate
In formula, skIt is the sample mode of element k；RkIt is k-th of element in [0,1] equally distributed random number；PkIt is kth
The state transition probability of a element.
The system mode S for possessing N number of element is determined by vector s
S=(s1,…,si,…,sN) (4)
3) all elements of identification system are carved at the beginning and be in normal operating condition, and all node temperatures are in interior
Set temperature judges whether system is that load cuts down state, and whether there is the node temperature of thermic load at system mode S
For non-indoor set temperature, if two conditions are unsatisfactory for simultaneously, i.e. load reduction neither occurs for system simultaneously not under system mode S
There are node temperatures to be less than indoor set temperature, then system mode S is complete normal condition, return step 2) reselect shape
State；If two conditions have one to meet or meet simultaneously, step 4) is carried out；
4) system mode analysis as shown in Figure 3, Figure 4, is carried out, tide optimization calculating is carried out to selected system mode S, is obtained
Electric, air and heat cutting load amount under to system mode S；
Specific method is as shown in figure 5, include：
(1) according to the room temperature T of current system conditions initial time (a upper Optimal State terminates)A_start, optimize energy
The distribution of source hub, energy hub model are for describing integrated energy system coupled relation, for existing in system
Different coupled relations, need to establish different energy hub models respectively and described.Wherein first kind energy line concentration
Device model is as shown in Figure 1a, is by transmission line of electricity (PLe), gas pipeline (PLg), heat supply pipeline (PLh), minisize thermoelectric coproduction
Equipment (CHP), central air conditioner system (AC) composition；Second class energy hub model is as shown in Figure 1 b, is by transmission line of electricity
(PLe), gas pipeline (PLg), heat supply pipeline (PLh), minisize thermoelectric cogeneration facility (CHP), gas fired-boiler (F) composition, first
The coupled relation formula of class energy hub and the second class energy hub respectively such as following formula,
In formula, subscript e, g, h respectively correspond electricity, air and heat；C is coupling matrix, and P, L respectively represent the defeated of energy hub
Enter power vector and output power vector；ηPLe、ηPLg、ηPLhIt is transmission line of electricity, gas pipeline, heat supply pipeline efficiency of transmission respectively,
ηACIt is the Energy Efficiency Ratio of central air conditioner system refrigeration and heating, ηFIt is gas fired-boiler transformation efficiency, ηPCHP_eAnd ηCHP_gIt is natural respectively
Gas is converted into the transfer efficiency of electric energy and thermal energy via minisize thermoelectric cogeneration facility；ve_ACIt is the distribution that inputing power distributes to AC
Coefficient, vg_FAnd vg_CHPIt is the distribution coefficient for inputting natural gas and being respectively allocated to F and CHP, wherein 0≤(ve_AC,vg_F,vg_CHP)≤
1, and vg_F+vg_CHP≤1；
The optimization distribution of energy hub is the majorized function that objective function is non-linear, constraint condition is linear, optimization
Model is as follows,
Objective function：
min(αLCEH_e+βLCEH_g+γLCEH_h) (7)
Wherein
Constraint condition：
LEH=CPEH+LCEH (9)
PEH min≤PEH≤PEH max (11)
PC min≤vPEH≤PC max (12)
0≤v≤1 (13)
In formula, α, β, γ are electricity, air and heat cutting load price respectively；LCEH_e、LCEH_g、LCEH_hIt is electrical thermic load respectively
Need cutting load amount；γhighIt is cutting load price when user's thermic load needs room temperature to be higher than setting value, γlowIt is user
Thermic load needs cutting load price of the room temperature lower than setting value when, TA_sIt is indoor design temperature, TA_cIt is indoor receiving temperature
Spend setting value, TA_aIt is outside air temperature, TA_startIt is current system conditions initial time temperature；LEHIt is that energy hub is defeated
The load of side needs vector out, and C is the coupling matrix of energy hub, PEHIt is the load supply arrow of energy hub input side
Amount, LCEHIt is that the load of energy hub input side needs cutting load amount vector；TA_endIt is current system conditions end moment temperature
Degree, ΦnIt is that thermic load node rated heat input, rated heat load needs capacity, LEH_hIt is that thermic load needs capacity under current system conditions, Γ is to work as
Preceding system mode continuous working period, χ are thermic load Focal point building object hot reserve coefficients；PEH max、PEH minIt is energy collection respectively
The bound of line device input terminal load supply；PC max、PC minIt is each equipment input terminal load supply in energy hub respectively
Bound；V is distribution coefficient, and value range is [0,1]；
(2) electric power, natural gas, district heating energy subsystem are reset according to the optimum results of (1) step energy hub
The load of respective nodes, the load supply (LS) of energy hub input side are all to carry out energy confession by corresponding energy subsystem
It answers.Therefore, the load resetting of three energy subsystems is as follows
In formula, Ne_EH、Ng_EH、Nh_EHIt is that three energy subsystems and energy hub connected node are numbered respectively；PLe,ir、
PLg,jr、PLh,krIt is three energy subsystems and energy hub connected node load respectively；
(3) according to the load data of the power subsystem of (2) step resetting, electric power system tide, natural gas is separately optimized
System load flow and space-heating system trend；Described in wherein：
(3.1) optimize electric power system tide, be the tide optimization for carrying out electric system using interior point method, Optimized model is such as
Under,
Objective function：
Constraint condition：
In formula, P is node active power, and Q is node reactive power,It is node voltage phasor, Y is node admittance square
Battle array；LCe,iIt is power system load node ieLLoad reduction, Ne_LIt is load node quantity；PL,ieLIt is power system load
Node ieLLoad power, PL max,ieL、PL min,ieLIt is power system load node i respectivelyeLThe upper and lower limit of load power；Vie
It is electric system node ieVoltage magnitude, Vmax,ie、Vmin,ieIt is electric system node i respectivelyeThe upper and lower limit of voltage magnitude,
Ne_nodeIt is the number of nodes of electric system；
(3.2) it optimizes natural gas system trend, being optimized using interior point method, Optimized model is as follows,
Objective function：
Constraint condition：
(Ag+U)P+ω-ZcFc=0 (23)
Fcom=kcomFmn(pm-pk) (26)
fc min,i≤pm/pk≤fc max,iic∈Ng_C (29)
In formula, AgIt is node-branch incident matrix；U is compressor-node incidence matrix；F is pipeline power；ω is section
Point injecting power；ZcIt is compressor injecting power knot vector；FcIt is compressor injecting power；FmnIt is pipeline power, kmnIt is pipe
Road constant, pmIt is upstream node pressure, pnIt is downstream node pressure, smnIndicate natural gas node flow direction；kcomIt is to indicate to press
The constant of contracting machine characteristic, pmIt is compressor delivery pressure, pkIt is compressor inlet pressure, FmnIt is the natural gas flow that compressor flows through
Amount；LCg,iIt is natural gas system load bus igLLoad reduction, Ng_LIt is load node quantity；ωigSIt is natural gas system
Supply station igSOutput power, ωmax,igS、ωmin,igSIt is natural gas system supply station i respectivelygSThe upper and lower limit of output power,
Ng_GIt is natural gas system supply station quantity；pigIt is natural gas system node igAir pressure, pmax,ig、pmin,igIt is natural gas respectively
System node igThe upper and lower limit of air pressure, Ng_nodeIt is the number of nodes of natural gas system；pm/pkIt is compressor icStep-up ratio,
fc max,i、fc min,iIt is compressor i respectivelycThe upper and lower limit of step-up ratio, Ng_CIt is natural gas system number of compressors；
(3.3) optimize space-heating system trend, space-heating system system is by hydraulic model and thermodynamic model two
It is grouped as, space-heating system tide optimization model exists hydraulic model and thermodynamic model two parts joint solves and decoupling is asked
Two types are solved, are solved by the way of space-heating system unified Modeling, and using interior point method, Optimized model is as follows,
Objective function：
Wherein
Constraint condition：
CpAsm(Ts-To)-Φ=0 (32)
BhKm | m |=0 (33)
CsTs,L-bs=0 (34)
CrTr,L-br=0 (35)
In formula, LCh,iIt is heating system load bus ihLLoad reduction, Nh_LIt is load node quantity；CpIt is water
Specific heat capacity, AsIt is node-branch incident matrix, m is pipeline hot water flow, TsIt is node for hot side temperature, TaIt is environment temperature,
Φ is node thermal power；BhIt is circuit-branch incidence matrix, K is the impedance factor of pipeline；Cs、CrBe respectively with heating network,
The related matrix of structure and flow of backheat network, bs、brIt is column vector related with heat supply temperature, backheat temperature respectively；
ΦS,ihSIt is heating system heating plant ihSOutput thermal power, ΦS max,ihS、ΦS min,ihSIt is heating system heating plant i respectivelyhS
Output thermal power upper and lower limit, Nh_SIt is the quantity of heating system heating plant；ΦL,ihLIt is heating system load bus ihL's
Thermic load power, ΦL max,ihL、ΦL min,ihLIt is heating system load bus i respectivelyhLThermic load power upper and lower limit, Nh_L
It is the quantity of heating system load bus；milIt is pipeline ilHot water flow, mmax,il、mmin,ilIt is pipeline i respectivelylHot water flow
Upper and lower limit, Nh_pIt is number of tubes；Ts,ihIt is heating system node ihHeat supply temperature, Ts max,ih、Ts min,ihIt is to supply respectively
Hot systems node ihThe upper and lower limit of heat supply temperature, Nh_nodeIt is heating system number of nodes；Tr,ihIt is heating system node ihReturn
Hot temperature, Tr max,ih、Tr min,ihIt is heating system node i respectivelyhThe upper and lower limit of backheat temperature；To,ihLIt is heating system load
Node ihLHot water outlet temperature, To max,ihL、To min,ihLIt is heating system load bus i respectivelyhLHot water outlet temperature
Upper and lower limit；
Wherein, in formula (33)
In formula, L is duct length, and f is pipeline roughness, and D is pipe diameter, and ρ is water density, and g is acceleration of gravity.
(4) input power P when calculating energy hub cutting load amount, and resetting the optimization of energy hubEHColumn vector
The upper limit, wherein energy hub cutting load amount is negative as energy supply subsystem and cutting for energy hub connected node
Lotus amount；Simultaneously in optimization process, using the maximal workload of three energy subsystems and energy hub connected node as
Energy hub input power PEHThe upper limit of column vector resets PEHThe equation of the upper limit is as follows,
In formula, PEH_e,if、PEH_g,jf、PEH_h,kfIt is electricity, air and heat input work when current energy hub optimization calculates respectively
The rate upper limit；PEH_e max,if、PEH_g max,jf、PEH_h max,kfIt is that electricity, air and heat are defeated when energy hub optimization next time calculates respectively
Enter the upper limit of the power；ΔLCe,if、ΔLCg,jf、ΔLCh,kfIt is three energy subsystems when current energy hub optimization calculates respectively
With energy hub connected node cutting load amount；
(5) the convergence index for calculating layering decoupling optimization is according to energy hub twice up and down during loop iteration
The difference of the upper limit of input power optimizes convergent criterion as layering decoupling, and calculation formula is as follows,
In formula, δip、δjp、δkpIt is the convergence criterion of energy hub electricity, air and heat respectively, takes wherein maximum value δmaxAs
The convergence criterion of entire layering decoupling optimization；
(6) setting value of convergence criterion is set as δspIf δmax<δspInvalid, calculating does not restrain, and returns to (1) step weight
New optimization；If δmax<δspIt sets up, calculates convergence, circulation terminates, the cutting load amount of output integrated energy resource system electricity, air and heat.
5) according to the optimum results in step 4), reliability index is calculated, includes electricity, the reduction of air and heat expected load
(EENS) and expected load cuts down frequency (PLC)；It is to be calculated using following formula：
In formula, SXIt is the effective status that load occurs and cuts down；C (S) is state influence function, that is, state S electricity,
Air and heat load reduction, NMCIt is quasi- sequential method frequency in sampling, T0It is reliability assessment time interval, 1 year is 8760h；n(S)
It is the number that load occurs and cuts down effective status.
6) reliability index convergence criterion δ is calculatedsc, judge whether parameter restrains, if convergence, carries out step 8), it is no
Then, step 7) is carried out；The reliability index convergence criterion calculation method is as follows,
In formula,It is the estimated value of reliability index；It is the variance of reliability estimated value；Formula (54) is reliable
The calculation method of property index estimated value, formula (55) is the calculation method of reliability estimated value variance.
7) judge quasi- sequential method frequency in sampling NMCWhether maximum value N is reachedmax；If so, carrying out step 8), output reliability
Index, otherwise, return step 2), reselect system mode；
8) electricity/gas/thermal region integrated energy system reliability index is exported, i.e., electricity, air and heat expected load cut down (EENS)
Frequency (PLC) is cut down with expected load.
The regional complex energy resource system reliability estimation method of consideration thermic load dynamic characteristic of the invention, realizes realization
Electrical heat regional complex energy resource system reliability assessment.
For the embodiment of the present invention, first electrical heat regional complex energy resource system parameter, 33 node system of IEEE is inputted
The active power and reactive power of middle load cell, the impedance value of circuit element, network topology connection relationship and mode bit, input
The gentle source node supply power of 14 node natural gas system load bus power, channel factor, compressor characteristics constant and compression
Than, network topology connection relationship and mode bit, inputs 32 node region heating system load bus power and heat supply tiny node supplies
Power, the supply and return water temperature of user's input/output, heat supply pipeline parameters, network topology connection relationship and mode bit are answered, it is defeated
Enter the capacity and network topology connection relationship of 4 energy hubs, wherein EH1, EH2 are a type energy hubs, and EH2, EH3 are
Two type energy hubs, example structure is as shown in fig. 7, detail parameters are shown in Table one (a), table one (b)-table four；Input each element
Crash rate and repair time, input electricity, air and heat cutting load price, each equipment efficiency of transmission and transformation efficiency, layering decoupling optimization
Judgment threshold and largest sample number, Indoor environment design temperature, acceptable temperature, outdoor temp are restrained with reliability index
Degree；Finally it is arranged in operational process, the bound of each decision variable and state variable, concrete condition is detailed in each five-table of table six.
Executing the computer hardware environment that reliability assessment calculates is Intel (R) Core (TM) CPU i5-7400, dominant frequency
For 3.00GHz, 8GB is inside saved as；Software environment is 7 operating system of Windows.Simulation analysis is carried out using MATLAB2016a.
The influence of energy Coupling device and thermic load dynamic characteristic to the comprehensive analysis of system, present invention design are considered for analysis
Three situations carry out the influence of systems reliability analysis：First is that not considering Coupling device, while thermic load dynamic is not considered yet
Characteristic, the reliability index of computing system；Second is that considering Coupling device, but thermic load dynamic characteristic is not considered, computing system
Reliability index；Third is that considering Coupling device, while considering the thermic load dynamic characteristic of system, the reliability of computing system refers to
Mark.While computing system reliability, record system reliability calculates time, calculated result such as table seven.Comparative analysis three
Under example, the reliability index of regional complex energy resource system electricity, air and heat, when considering Coupling device, system electricity, heat are negative
Lotus needs it is expected that reduction is decreased obviously, and system gas load needs it is expected that reduction obviously rises, this shows carrying out comprehensive energy
When the systems reliability analysis of source, due to the effect of user side Coupling device, in system jam, the electric load of user is needed
Want, thermic load need cannot by electric system and therrmodynamic system supply meet, user can reduce gas load need with meet electricity with
Thermic load needs, to enhance integrated energy system power supply, reliability of heat-supply system, but also reduces the gas load of system accordingly
Reliability.When further considering thermic load dynamic characteristic, since it is specified not to be able to satisfy user in the development of heat system failure
Thermic load needs, but due to the effect of thermal inertia, the room temperature of user slowly declines, and for a user, is stopping supplying
Will not be using the way of heat supply of dying, therefore in hot short period, integrated energy system supplies reliability relative to not considering heat
Dynamic load characteristic increased, and for reliability of heat-supply system relative to not considering that thermic load dynamic characteristic is declined, system is negative to heat
The degree of priority decline that lotus needs.
By analysis system node reliability, further analysis considers that Coupling device and thermic load dynamic characteristic can to system
By the influence of property.Analysis chart 8, the node users electric load for connecting Coupling device need it is expected that reduction is decreased obviously, show
The electric load that increase energy Coupling device has substantially change the user node at user node needs reliability；And whether consider heat
Dynamic load characteristic, which needs reliability not to the electric load of system node, to be influenced, it is meant that is considering thermic load dynamic characteristic meter
When calculation system electric load needs reliability, there is no occurring, user node temperature is too low, needs the case where cutting electric thermal protection generation, because
This, user's electric load needs reliability to remain unchanged.Analysis chart 9, breaking down in integrated energy system causes node load to need
, due to the presence of Coupling device, user cannot be shifted will electrically or thermally load supply by corresponding energy supplying system for seasonable
Into natural gas system, and then cause the distribution of natural gas trend and change, needs system interior joint natural gas load reliable
Property occur significant change, by taking natural gas system node 14 as an example, node 14 is not connected with Coupling device, consideration Coupling device meter
When calculating integrated energy system reliability, 14 reliability of node is substantially reduced, this is with Coupling device connected node because of other from day
Significant changes occur for the gas load supply that right gas system needs, and then influence the distribution of natural gas system trend, make the day of node 14
Right gas load supply is affected, and then affects the natural gas load needs of node 14.Analysis chart 10, meter and energy coupling are set
When standby, node thermic load needs reliability to be obviously improved, since Coupling device via switching node and then influences corresponding energy supply
System load flow distribution, makes the thermic load of non-coupled node that reliability be needed also to be obviously improved.And considering thermic load
After dynamic characteristic, system interior joint thermic load needs reliability to reduce many again, this is caused by the thermal inertia of thermic load, when
When system jam, it is contemplated that the thermic load of user needs, i.e., room temperature slowly declines, in the early stage, the heat of user
Load needs can be still satisfied, and user is not in appearance the case where cutting gas thermal protection or cut electric thermal protection, the heat of user at this time
Load needs it is expected to cut down frequency relative to not considering that thermic load dynamic characteristic dramatically increases, and is considering that thermic load dynamic is special
Property can be further according to real situation accurate evaluation system reliability.
Table one (a) power system load parameter
Table one (b) circuit on power system parameter
Table two (a) natural gas system load and gas source parameter
Table two (b) natural gas system gas pipeline parameter
Table two (c) natural gas system compressor parameter
Table three (a) heating system load and heating plant parameter
Table three (b) heating system heat supply pipeline parameter
Four energy hub parameter of table
The fault parameter of five equipment of table
| Equipment | Crash rate λ (occ./year) | Repair time MTTR (h) |
| Transmission line of electricity PLe (per km) | 0.065 | 5 |
| Gas pipeline PLg (per km) | 0.065 | 5 |
| Heat supply pipeline PLh (per km) | 0.065 | 5 |
| Central air-conditioning AC | 0.03 | 200 |
| Minisize thermoelectric cogeneration facility CHP | 0.03 | 200 |
| Gas fired-boiler F | 0.025 | 300 |
Other parameters involved in six reliability assessment of table
Seven reliability results of table
Claims (7)
1. a kind of regional complex energy resource system reliability estimation method for considering thermic load dynamic characteristic, which is characterized in that including
Following steps：
1) comprehensive according to the selected region comprising electric system, gas burning system, space-heating system and energy hub type
Close energy resource system, input：Electric power, natural gas, space-heating system data, energy hub data, various element failure rates,
Electricity, air and heat cutting load price data, layering decoupling optimization convergence judgment threshold, quasi- sequential method calculate reliability index convergence and sentence
Disconnected threshold value, quasi- sequential method largest sample number, Indoor environment design temperature；
2) according to the element failure rate provided according to step 1), the operating status of all elements is chosen according to quasi- sequential method；
3) all elements of identification system are carved at the beginning and be in normal operating condition, and all node temperatures are in indoor setting
Temperature judges whether system is that load cuts down state, and it is non-for whether there is the node temperature of thermic load at system mode S
Indoor set temperature, if two conditions are unsatisfactory for simultaneously, i.e. system neither occurs load reduction while being not present under system mode S
Node temperature is less than indoor set temperature, then system mode S is complete normal condition, return step 2) reselect state；If
Two conditions have one to meet or meet simultaneously, then carry out step 4)；
4) carry out system mode analysis, tide optimization calculating carried out to selected system mode S, obtain under system mode S electricity,
The cutting load amount of air and heat；
5) according to the optimum results in step 4), reliability index is calculated, includes electricity, air and heat expected load is cut down and expectation
Load cuts down frequency；
6) reliability index convergence criterion δ is calculatedsc, judge whether parameter restrains, if convergence, carries out step 8), otherwise, into
Row step 7)；
7) judge quasi- sequential method frequency in sampling NMCWhether maximum value N is reachedmax；If so, step 8) is carried out, output reliability index,
Otherwise, return step 2), reselect system mode；
8) electricity/gas/thermal region integrated energy system reliability index is exported, i.e. electricity, the reduction of air and heat expected load and expected load
Cut down frequency.
2. the regional complex energy resource system reliability estimation method according to claim 1 for considering thermic load dynamic characteristic,
It is characterized in that, in step 1), the electric power, natural gas, space-heating system data, including it is route and pipe parameter, negative
Lotus level, network topology connection relationship；The energy hub data, including device type, place capacity and energy collection
Line device topological structure；The various element failure rates, including transmission line of electricity, gas pipeline, heat supply pipeline, transformer, compression
All devices in machine and energy hub.
3. the regional complex energy resource system reliability estimation method according to claim 1 for considering thermic load dynamic characteristic,
It is characterized in that, step 2) includes：
Element k is set in period TpPeriod is in operation or malfunction, in next period section TpBetween in operation or therefore
Hinder state probability Pk：
In formula, λkIt is the rate of transform that k-th of element leaves from state s, if k-th of element is working, λkIt is crash rate；Such as
K-th of element of fruit, which is in, stops transport, then λkIt is repair rate；
Choose [0,1] be uniformly distributed in random number Rk, utilize RkJudge the operating status of counter element,
If k-th of element is working, λkIt is crash rate
It stops transport if k-th of element is in, λkIt is repair rate
In formula, skIt is the sample mode of element k；RkIt is k-th of element in [0,1] equally distributed random number；PkIt is k-th yuan
The state transition probability of part.
The system mode S for possessing N number of element is determined by vector s
S=(s1,…,si,…,sN) (4)。
4. the regional complex energy resource system reliability estimation method according to claim 1 for considering thermic load dynamic characteristic,
It is characterized in that, step 4) includes：
(1) according to the room temperature T of current system conditions initial time (a upper Optimal State terminates)A_start, optimize energy line concentration
The distribution of device, wherein first kind energy hub model is by transmission line of electricity (PLe), gas pipeline (PLg), heat supply pipeline
(PLh), minisize thermoelectric cogeneration facility (CHP), central air conditioner system (AC) composition；Second class energy hub model is by transmitting electricity
Route (PLe), gas pipeline (PLg), heat supply pipeline (PLh), minisize thermoelectric cogeneration facility (CHP), gas fired-boiler (F) composition,
The coupled relation formula of first kind energy hub and the second class energy hub respectively such as following formula,
In formula, subscript e, g, h respectively correspond electricity, air and heat；C is coupling matrix, and P, L respectively represent the input work of energy hub
Rate vector sum output power vector；ηPLe、ηPLg、ηPLhIt is transmission line of electricity, gas pipeline, heat supply pipeline efficiency of transmission, η respectivelyACIt is
The Energy Efficiency Ratio of central air conditioner system refrigeration and heating, ηFIt is gas fired-boiler transformation efficiency, ηPCHP_eAnd ηCHP_gIt is natural gas warp respectively
The transfer efficiency of electric energy and thermal energy is converted by minisize thermoelectric cogeneration facility；ve_ACIt is the distribution system that inputing power distributes to AC
Number, vg_FAnd vg_CHPIt is the distribution coefficient for inputting natural gas and being respectively allocated to F and CHP, wherein 0≤(ve_AC,vg_F,vg_CHP)≤1,
And vg_F+vg_CHP≤1；
The optimization distribution of energy hub is the majorized function that objective function is non-linear, constraint condition is linear, Optimized model
It is as follows,
Objective function：
min(αLCEH_e+βLCEH_g+γLCEH_h) (7)
Wherein
Constraint condition：
LEH=C PEH+LCEH (9)
PEH min≤PEH≤PEH max (11)
PC min≤v PEH≤PC max (12)
0≤v≤1 (13)
In formula, α, β, γ are electricity, air and heat cutting load price respectively；LCEH_e、LCEH_g、LCEH_hIt is electrical thermic load needs respectively
Cutting load amount；γhighIt is cutting load price when user's thermic load needs room temperature to be higher than setting value, γlowIt is that user's heat is negative
Lotus needs cutting load price of the room temperature lower than setting value when, TA_sIt is indoor design temperature, TA_cIt is that interior receives temperature and sets
Definite value, TA_aIt is outside air temperature, TA_startIt is current system conditions initial time temperature；LEHIt is energy hub outlet side
Load need vector, C is the coupling matrix of energy hub, PEHIt is the load supply vector of energy hub input side,
LCEHIt is that the load of energy hub input side needs cutting load amount vector；TA_endIt is current system conditions end moment temperature,
ΦnIt is that thermic load node rated heat input, rated heat load needs capacity, LEH_hIt is that thermic load needs capacity under current system conditions, Γ is current
System mode continuous working period, χ are thermic load Focal point building object hot reserve coefficients；PEH max、PEH minIt is energy line concentration respectively
The bound of device input terminal load supply；PC max、PC minIt is the upper of each equipment input terminal load supply in energy hub respectively
Lower limit；V is distribution coefficient, and value range is [0,1]；
(2) corresponding according to the optimum results of (1) step energy hub resetting electric power, natural gas, district heating energy subsystem
The load resetting of the load of node, three energy subsystems is as follows
In formula, Ne_EH、Ng_EH、Nh_EHIt is that three energy subsystems and energy hub connected node are numbered respectively；PLe,ir、PLg,jr、
PLh,krIt is three energy subsystems and energy hub connected node load respectively；
(3) according to the load data of the power subsystem of (2) step resetting, electric power system tide, natural gas system is separately optimized
Trend and space-heating system trend；
(4) input power P when calculating energy hub cutting load amount, and resetting the optimization of energy hubEHThe upper limit of column vector,
Wherein, using energy hub cutting load amount as the cutting load amount of energy supply subsystem and energy hub connected node；
Simultaneously in optimization process, using the maximal workload of three energy subsystems and energy hub connected node as energy collection
Line device input power PEHThe upper limit of column vector resets PEHThe equation of the upper limit is as follows,
In formula, PEH_e,if、PEH_g,jf、PEH_h,kfIt is electricity when current energy hub optimization calculates respectively, in air and heat input power
Limit；PEH_emax,if、PEH_gmax,jf、PEH_hmax,kfIt is electricity, air and heat input power when energy hub optimization next time calculates respectively
The upper limit；ΔLCe,if、ΔLCg,jf、ΔLCh,kfIt is three energy subsystems and the energy when current energy hub optimization calculates respectively
Hub connected node cutting load amount；
(5) the convergence index for calculating layering decoupling optimization is according to energy hub inputs twice up and down during loop iteration
The difference of the upper limit of power optimizes convergent criterion as layering decoupling, and calculation formula is as follows,
In formula, δip、δjp、δkpIt is the convergence criterion of energy hub electricity, air and heat respectively, takes wherein maximum value δmaxAs entire
The convergence criterion of layering decoupling optimization；
(6) setting value of convergence criterion is set as δspIf δmax<δspInvalid, calculating does not restrain, and it is again excellent to return to (1) step
Change；If δmax<δspIt sets up, calculates convergence, circulation terminates, the cutting load amount of output integrated energy resource system electricity, air and heat.
5. the regional complex energy resource system reliability estimation method according to claim 4 for considering thermic load dynamic characteristic,
It is characterized in that, described in (3) step：
(3.1) optimizing electric power system tide, be the tide optimization for carrying out electric system using interior point method, Optimized model is as follows,
Objective function：
Constraint condition：
In formula, P is node active power, and Q is node reactive power,It is node voltage phasor, Y is node admittance matrix；
LCe,iIt is power system load node ieLLoad reduction, Ne_LIt is load node quantity；PL,ieLIt is power system load section
Point ieLLoad power, PL max,ieL、PL min,ieLIt is power system load node i respectivelyeLThe upper and lower limit of load power；Vie
It is electric system node ieVoltage magnitude, Vmax,ie、Vmin,ieIt is electric system node i respectivelyeThe upper and lower limit of voltage magnitude,
Ne_nodeIt is the number of nodes of electric system；
(3.2) it optimizes natural gas system trend, being optimized using interior point method, Optimized model is as follows,
Objective function：
Constraint condition：
(Ag+U)P+ω-ZcFc=0 (30)
Fcom=kcomFmn(pm-pk) (33)
fc min,i≤pm/pk≤fc max,i ic∈Ng_C (36)
In formula, AgIt is node-branch incident matrix；U is compressor-node incidence matrix；F is pipeline power；ω is node injection
Power；ZcIt is compressor injecting power knot vector；FcIt is compressor injecting power；FmnIt is pipeline power, kmnIt is pipeline constant,
pmIt is upstream node pressure, pnIt is downstream node pressure, smnIndicate natural gas node flow direction；kcomIt is to indicate compressor spy
The constant of property, pmIt is compressor delivery pressure, pkIt is compressor inlet pressure, FmnIt is the gas discharge that compressor flows through；
LCg,iIt is natural gas system load bus igLLoad reduction, Ng_LIt is load node quantity；ωigSIt is that natural gas system supplies
Give station igSOutput power, ωmax,igS、ωmin,igSIt is natural gas system supply station i respectivelygSThe upper and lower limit of output power, Ng_G
It is natural gas system supply station quantity；pigIt is natural gas system node igAir pressure, pmax,ig、pmin,igIt is natural gas system respectively
Node igThe upper and lower limit of air pressure, Ng_nodeIt is the number of nodes of natural gas system；pm/pkIt is compressor icStep-up ratio, fc max,i、fc min,iIt is compressor i respectivelycThe upper and lower limit of step-up ratio, Ng_CIt is natural gas system number of compressors；
(3.3) optimize space-heating system trend, space-heating system system is by hydraulic model and thermodynamic model two parts group
At space-heating system tide optimization model, which exists that hydraulic model and thermodynamic model two parts joint are solved and decoupled, solves two
Seed type is solved by the way of space-heating system unified Modeling, and using interior point method, and Optimized model is as follows,
Objective function：
Wherein
Constraint condition：
CpAsm(Ts-To)-Φ=0 (39)
BhKm | m |=0 (40)
CsTs,L-bs=0 (41)
CrTr,L-br=0 (42)
In formula, LCh,iIt is heating system load bus ihLLoad reduction, Nh_LIt is load node quantity；CpIt is the specific heat of water
Hold, AsIt is node-branch incident matrix, m is pipeline hot water flow, TsIt is node for hot side temperature, TaIt is environment temperature, Φ is
Node thermal power；BhIt is circuit-branch incidence matrix, K is the impedance factor of pipeline；Cs、CrIt is respectively and heating network, backheat
The related matrix of structure and flow of network, bs、brIt is column vector related with heat supply temperature, backheat temperature respectively；ΦS,ihSIt is
Heating system heating plant ihSOutput thermal power, ΦS max,ihS、ΦS min,ihSIt is heating system heating plant i respectivelyhSHeat outputting
The upper and lower limit of power, Nh_SIt is the quantity of heating system heating plant；ΦL,ihLIt is heating system load bus ihLThermic load function
Rate, ΦL max,ihL、ΦL min,ihLIt is heating system load bus i respectivelyhLThermic load power upper and lower limit, Nh_LIt is heat supply
The quantity of system loading node；milIt is pipeline ilHot water flow, mmax,il、mmin,ilIt is pipeline i respectivelylHot water flow it is upper,
Lower limit, Nh_pIt is number of tubes；Ts,ihIt is heating system node ihHeat supply temperature, Ts max,ih、Ts min,ihIt is heat supply system respectively
System node ihThe upper and lower limit of heat supply temperature, Nh_nodeIt is heating system number of nodes；Tr,ihIt is heating system node ihBackheat temperature
Degree, Tr max,ih、Tr min,ihIt is heating system node i respectivelyhThe upper and lower limit of backheat temperature；To,ihLIt is heating system load bus
ihLHot water outlet temperature, To max,ihL、To min,ihLIt is heating system load bus i respectivelyhLHot water outlet temperature it is upper and lower
Limit；
Wherein, in formula (33)
In formula, L is duct length, and f is pipeline roughness, and D is pipe diameter, and ρ is water density, and g is acceleration of gravity.
6. the regional complex energy resource system reliability estimation method according to claim 1 for considering thermic load dynamic characteristic,
It is characterized in that, step 5) is calculated using following formula：
In formula, SXIt is the effective status that load occurs and cuts down；C (S) is state influence function, that is, negative in state S electricity, air and heat
Lotus reduction, NMCIt is quasi- sequential method frequency in sampling, T0It is reliability assessment time interval, 1 year is 8760h；N (S) is to occur to bear
The number of lotus reduction effective status.
7. the regional complex energy resource system reliability estimation method according to claim 1 for considering thermic load dynamic characteristic,
It is characterized in that, reliability index convergence criterion calculation method described in step 6) is as follows,
In formula,It is the estimated value of reliability index；It is the variance of reliability estimated value.
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