CN108446865A - Thermo-electrically based on interval method couples multipotency streaming system power methods of risk assessment - Google Patents
Thermo-electrically based on interval method couples multipotency streaming system power methods of risk assessment Download PDFInfo
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Abstract
The present invention relates to a kind of coupled thermomechanics multipotency streaming system power methods of risk assessment based on interval method, belongs to the risk assessment technology field of integrated energy system.The methods of risk assessment of the present invention, establish the stochastic model in the device model of multipotency streaming system, multipotency streaming system, energy storage device in consideration system, flexible unit Optimized Operation probabilistic consumption is acted on, provide the interval method for solving this stochastic problems, calculate the uncertainty of regenerative resource in following one day, the uncertainty of electric user's heat user energy consumption can caused by multipotency streaming system power-balance risk size.This method more accurately calculates the operation risk of electro thermal coupling multipotency streaming system.Be conducive to improve safety in operation, and help to reduce the operating cost of multipotency streaming system.To explore the polynary consumption of regenerative resource, multifunctional system combined dispatching is made that certain contribution.
Description
Technical field
The present invention relates to a kind of, and the thermo-electrically based on interval method couples multipotency streaming system power methods of risk assessment, belongs to
The risk assessment technology field of integrated energy system.
Background technology：
Multipotency streaming system covers that electric, hot, cold, gas etc. is multiple to flow subsystem, and each subsystem passes through cogeneration (CHP/
CCHP), the equipment such as heat pump are converted and are coupled.The rise of energy internet in recent years so that with electric, hot, cold, gas is a variety of
The multipotency network of form transmission energy shows its superiority.On the one hand the foundation of multipotency streaming system has considerable benefit, separately
The coupling of one side multipotency also causes the uncertain factor in each subsystem to interact, and can influence the just common energy of user, very
The overall security of multipotency streaming system can extremely be influenced.Multipotency streaming system risk assessment is exactly before risk case occurs, to possible
Caused by influence and the possibility of loss is assessed, to carry out early warning to the traffic control personnel of multipotency streaming system, so as to
Carry out corresponding control means.Multipotency streaming system risk assessment needs to consider a variety of couplings that can be between stream, needs to consider
The uncertainty of regenerative resource and multipotency stream customer charge, compared to the risk assessment of conventional electric power system, multipotency stream risk
Assessment has high complexity.
One multipotency streaming system generally comprises the renewable energy power generations such as wind-power electricity generation, photovoltaic generation, the work(of these power supplys
Rate changes and is affected with the random variation of weather, this may result in grid power imbalance；Electric load, thermic load itself
There is randomness, unbalanced power may also can be led to by being coupled to grid side；Energy storage device, flexible unit in power grid are quickly adjusted
Output power can reduce the unbalanced probability of grid power.By risk assessment to above-mentioned factor carry out comprehensive analysis after
It can obtain the size of multipotency streaming system operation risk.Traffic control personnel can be instructed to implement control hand when risk is larger
Section improves safety in operation；Multipotency streaming system can be instructed to take relatively radical operation when risk is smaller or no risk
Mode, to reduce the operating cost of multipotency streaming system.
Invention content
Purpose of the present invention is to propose a kind of thermo-electrically coupling multipotency streaming system power risk assessment side based on interval method
Method, establishes the stochastic model in the device model of multipotency streaming system, multipotency streaming system, energy storage device in consideration system, flexibly
The Optimized Operation of unit acts on probabilistic consumption, provides the interval method for solving this stochastic problems, then calculates
The uncertainty of regenerative resource, the uncertainty of electric user's heat user energy consumption can be flat to multipotency streaming system power in one day following
Risk size caused by weighing apparatus.
Thermo-electrically coupling multipotency streaming system power methods of risk assessment proposed by the present invention based on interval method, including with
Lower step：
(1) an electric-thermal coupling multipotency streaming system operation stochastic model is established：
If in electric-thermal coupling multipotency streaming system including S cogeneration units, E electric energy storage device, H hot energy storage
Equipment, L1 electric load, L2 thermic load and P photovoltaic plant, electric-thermal coupling multipotency streaming system pass through interconnection and external electrical
Net is connected；
The active power of the photovoltaic generation of the one day future each scheduling instance t obtained from electric-thermal coupling multipotency streaming system is pre-
The interval prediction data of measured value, electric load active power predicted value and thermic load active power predicted value, establish stochastic model
It is as follows：
Ppvdown(t)≤Ppv(t)≤Ppvup(t)
Pedown(t)≤Pe(t)≤Peup(t)
Phdown1(t)≤Ph,1(t)≤Phup,1(t)
Phdown2(t)≤Ph,2(t)≤Phup,2(t)
...
Phdown,L2(t)≤Ph,L2(t)≤Phup,L2(t)
Wherein, t is scheduling instance, t=1,2 ..., T, for distinguishing the following intraday T scheduling instance, Ppv(t) it is
Total generated output of all P photovoltaic plants of t scheduling instances, Pe(t) it is total wattful power of all L1 electric loads of t scheduling instances
Rate, Ph,1(t)、Ph,2(t)、...、Ph,L2(t) power of each thermic load, P are indicated respectivelypvdown(t)、Pedown(t)、Phdown1(t)、
Phdown2(t)、...、Phdown,L2(t) be respectively t scheduling instances photovoltaic generation, electric load, thermic load power interval prediction under
Limit, Ppvup(t)、Peup(t)、Phup,1(t)、Phup,2(t)、...、Phup,L2(t) be respectively t scheduling instances photovoltaic generation power,
The upper limit that electric load power, thermic load power interval are predicted, Ppv(t)、Pe(t)、Ph,1(t)、Ph,2(t)、...、Ph,L2(t) be with
Machine variable；
(2) constraint of electric-thermal coupling multipotency streaming system is established, including：
(2-1) energy storage device runs constraints：
The constraints of E electric energy storage device is：
0≤Pdis(t)≤Pdmax, 0≤Pchar(t)≤Pcmax
0≤SoC(t)≤SoCmax
SoC (t)=SoC (t-1)-Pdis(t)+Pchar(t), t=2,3 ..., T
Wherein, Pdis(t) it is total discharge power of all E electric energy storage devices of t-th of scheduling instance, Pchar(t) it is t
Total charge power of the electric energy storage device of all E of a scheduling instance, PdmaxFor the maximum value of total discharge power, PcmaxAlways to charge
The maximum value of power, SoC (t) are all E total storing up electricity states of electric energy storage device of t-th of scheduling slot, SoCmaxFor stored energy capacitance
Maximum value.Pdis(t)、Pchar(t), SoC (t) is respectively the amount for being scheduled control；
The constraints of H hot energy storage devices is：
0≤Qdis,hh(t)≤Qdmax,hh, 0≤Qchar,hh(t)≤Qcmax,hh,0≤EHhh(t)≤EHmax,hh
EHhh(t)=EHhh(t-1)+Qchar,hh(t)-Qdis,hh(t)
Hh=1,2 ..., H
Wherein, Qdis,hh(t) it is the heat release power of each hot energy storage device of t-th of scheduling instance, Qdmax,hhIt is set for each hot energy storage
Standby heat release power maximum value, Qchar,hh(t) it is each hot energy storage device Endothermic power of t-th of scheduling instance, Qcmax,hhFor each heat storage
The Endothermic power maximum value of energy equipment, EHhh(t) it is the heat accumulation state of each hot energy storage device of t-th of scheduling instance, EHmax,hhIt is each
The maximum value of hot energy storage device heat storage capacity, Qdis,hh(t)、Qchar,hh(t)、EHhh(t) it is respectively the amount for being scheduled control；
(2-2) cogeneration units run constraints：
By the feasible zone of s-th of cogeneration units in S cogeneration units, the D of convex polygonsA vertex is denoted as
(Pd,s,Qd,s), d=1,2...Ds, feasible zone is described with the linear combination on vertex, then a bit (p in feasible zones,qs) full
Foot：
Wherein, the electrical power p of each cogeneration units1(t)、p2(t)、...、pS(t), each cogeneration units
Thermal power q1(t)、q2(t)、...、qS(t)；
Cogeneration units active power climbing constraints be：
Wherein, RAMPs downAnd RAMPs upRespectively upward, the downward climbing speed of s platforms cogeneration units active power
The maximum value of rate, ps(t) and ps(t-1) it is respectively s platforms cogeneration units when t-th of scheduling instance and the t-1 are dispatched
The active power at quarter；
(2-3) obtains the operation constraints of power grid and heat supply network in electric-thermal coupling multipotency streaming system：
From electric-thermal couple multipotency streaming system Energy Management System in, obtain electric-thermal coupling multipotency streaming system in power grid,
The safe operation of heat supply network constrains Ψsc, including：Grid branch active power allows maximum value and grid branch active power to allow most
Small value, electric-thermal, which couples heat supply network node temperature in multipotency streaming system, allows maximum value, heat supply network node temperature to allow minimum value, heat supply network
Bypass flow allows maximum value and heat supply network bypass flow to allow minimum value；
(2-4) heat supply network trend constraint condition is：
Assuming that the flow in heat-net-pipeline remains unchanged, the heat supply network power flow equation for establishing matrix form is as follows：
In above-mentioned heat supply network power flow equation, A is incidence matrix, remembers in heat supply network there is N number of node and B branch, is associated in foundation
MatrixWith lower incidence matrixA：
In above-mentioned heat supply network power flow equation：CpFor the specific heat capacity of heat supply network heat transfer mediator, m is a branch in B branch
Flow, the subscript of m indicate that the serial number of branch, M are the vector of the flow composition of B branch, TnFor each node temperature composition of heat supply network
Vector, TeFor the vector that the temperature of each branch end of heat supply network forms, TaFor environment temperature, QJFor the thermic load of heat supply network node
The vector of composition, C are the diagonal matrixs using the pipeline heat loss coefficient of each branch as diagonal element, and λ is that the unit of each branch of heat supply network is long
Thermal conductivity is spent, the subscript of λ indicates that the serial number of branch, L are the length of each branch of heat supply network, and the subscript of L indicates the serial number of branch；
(3) object function of thermo-electrically coupling multipotency streaming system power-balance risk is established：
When calculating the power least risk value of scheduling instance t, the object function OF of power-balance risklower(t) it is：
OFlower(t)=Ptie(t)
When calculating the power greatest risk value of some t moment, the object function OF of power-balance riskupper(t) it is
OFupper(t)=- Ptie(t)
Wherein, Ptie(t) it is the power on interconnection, the power on interconnection is equal to imbalance power；
(4) the operating cost object function OF of thermo-electrically coupling multipotency streaming system is establishedinner：
Wherein, S is cogeneration units number, and s is the serial number of cogeneration units, Hprices(t) it is s-th of thermoelectricity
The f timesharing prices for sending out unit thermal power of coproduction unit, Eprices(t) it is that s-th cogeneration units send out unit electricity
The timesharing price of power, Ctie(t) it is
Ctie(t)=Price_n (t) * Ptie1(t)+Price_ex*Ptie2(t)
Ptie(t)=Ptie1(t)+Ptie2(t)
0≤Ptie1(t)≤Ptie_limit
0≤Ptie2(t)
Wherein CtieFor interconnection purchases strategies, Ptie1For permitted imbalance power threshold value, Ptie2For more than threshold value
Power, Price_n (t) be imbalance power in threshold value when interconnection on tou power price, Price_ex be more than threshold value
Partial punishment electricity price, Ptie_limitFor the upper limit of the power of interconnection carrying；
(5) using the object function of the thermo-electrically of above-mentioned steps (3) coupling multipotency streaming system power-balance risk as outer layer mesh
Scalar functions, above-mentioned steps (1) are used as outer layer constraints, with the thermo-electricallies of above-mentioned steps (4) couple the operation of multipotency streaming system at
It is internal layer constraints that this object function, which is used as internal layer object function, above-mentioned steps (2), builds a bi-level optimal model, and
Using interior point method, 2T solution is carried out, OF is obtainedlower(t) and OFupper(t), t=1,2 ... T is to get to each scheduling
The upper bound of the thermo-electrically coupling multipotency streaming system power risk at moment and lower bound, realize and couple multipotency streaming system power to thermo-electrically
Risk assessment.
It is proposed by the present invention based on interval method thermo-electrically coupling multipotency streaming system methods of risk assessment, feature and
Advantage is：
Methods of risk assessment proposed by the present invention is established random in the device model of multipotency streaming system, multipotency streaming system
Property model, energy storage device in consideration system, flexible unit Optimized Operation probabilistic consumption is acted on, provide solution this
Then the interval method of stochastic problems calculates the uncertainty of regenerative resource in one day following, electric user's heat user energy consumption
Uncertainty can caused by multipotency streaming system power-balance risk size.Compared to traditional Study of Risk Evaluation Analysis for Power System,
The method of the present invention considers electric-thermal coupled problem, it is also considered that the Optimized Operation of energy storage device, flexible unit is not in system
Deterministic consumption effect, more accurately calculates the operation risk of electric-thermal coupling multipotency streaming system.The method of the present invention is conducive to
Improve safety in operation, and contribute to reduce multipotency streaming system operating cost, for explore regenerative resource it is polynary consumption, it is more
It system combined can dispatch and be made that certain contribution.
Description of the drawings
Fig. 1 is the schematic diagram for the electric-thermal coupling multipotency streaming system that the method for the present invention is related to.
Fig. 2 is feasible zone convex polygon schematic diagram in the cogeneration units operation constraint that the method for the present invention is related to.
Specific implementation mode
Thermo-electrically coupling multipotency streaming system power methods of risk assessment proposed by the present invention based on interval method, including with
Lower step：
(1) an electric-thermal coupling multipotency streaming system operation stochastic model is established：
If in electric-thermal coupling multipotency streaming system including S cogeneration units, E electric energy storage device, H hot energy storage
Equipment, L1 electric load, L2 thermic load and P photovoltaic plant, electric-thermal coupling multipotency streaming system pass through interconnection and external electrical
Net is connected；
Electric-thermal involved in the method for the present invention couples multipotency streaming system as shown in Figure 1, being in multipotency stream garden in its center
Portion, each component part can merge that be integrated into energy source station, renewable energy source station (photovoltaic plant), all kinds of cool and thermal powers negative in garden
Lotus；The part that garden carries out Power Exchange with external electrical network is interconnection.Energy source station is interior including cogeneration units (CHP) and cold
Thermoelectricity energy storage device.For a multipotency stream garden, the electricity of the cogeneration units in garden, which is contributed, to be tended not to meet garden
Electric load, in order to meet energy balance require garden need by the Tie line Power with external electrical network.Thus multipotency
The risk size of stream garden power-balance can be equivalent to the risk size of dominant eigenvalues overrate.
The active power of the photovoltaic generation of the one day future each scheduling instance t obtained from electric-thermal coupling multipotency streaming system is pre-
The interval prediction data of measured value, electric load active power predicted value and thermic load active power predicted value, establish stochastic model
It is as follows：
Ppvdown(t)≤Ppv(t)≤Ppvup(t)
Pedown(t)≤Pe(t)≤Peup(t)
Phdown1(t)≤Ph,1(t)≤Phup,1(t)
Phdown2(t)≤Ph,2(t)≤Phup,2(t)
...
Phdown,L2(t)≤Ph,L2(t)≤Phup,L2(t)
Wherein, t is scheduling instance, t=1,2 ..., T, for distinguishing the following intraday T scheduling instance, Ppv(t) it is
Total generated output of all P photovoltaic plants of t scheduling instances, Pe(t) it is total wattful power of all L1 electric loads of t scheduling instances
Rate, Ph,1(t)、Ph,2(t)、...、Ph,L2(t) power of each thermic load, P are indicated respectivelypvdown(t)、Pedown(t)、Phdown1(t)、
Phdown2(t)、...、Phdown,L2(t) be respectively t scheduling instances photovoltaic generation, electric load, thermic load power interval prediction under
Limit, Ppvup(t)、Peup(t)、Phup,1(t)、Phup,2(t)、...、Phup,L2(t) be respectively t scheduling instances photovoltaic generation power,
The upper limit that electric load power, thermic load power interval are predicted, Ppv(t)、Pe(t)、Ph,1(t)、Ph,2(t)、...、Ph,L2(t) be with
Machine variable；
(2) constraint of electric-thermal coupling multipotency streaming system is established, including：
(2-1) energy storage device runs constraints：
The constraints of E electric energy storage device is：
0≤Pdis(t)≤Pdmax, 0≤Pchar(t)≤Pcmax
0≤SoC(t)≤SoCmax
SoC (t)=SoC (t-1)-Pdis(t)+Pchar(t), t=2,3 ..., T
Wherein, Pdis(t) it is total discharge power of all E electric energy storage devices of t-th of scheduling instance, Pchar(t) it is t
Total charge power of the electric energy storage device of all E of a scheduling instance, PdmaxFor the maximum value of total discharge power, PcmaxAlways to charge
The maximum value of power, SoC (t) are all E total storing up electricity states of electric energy storage device of t-th of scheduling slot, SoCmaxFor stored energy capacitance
Maximum value.Pdis(t)、Pchar(t), SoC (t) is respectively the amount for being scheduled control；
The constraints of H hot energy storage devices is：
0≤Qdis,hh(t)≤Qdmax,hh, 0≤Qchar,hh(t)≤Qcmax,hh,0≤EHhh(t)≤EHmax,hh
EHhh(t)=EHhh(t-1)+Qchar,hh(t)-Qdis,hh(t)
Hh=1,2 ..., H
Wherein, Qdis,hh(t) it is the heat release power of each hot energy storage device of t-th of scheduling instance, Qdmax,hhIt is set for each hot energy storage
Standby heat release power maximum value, Qchar,hh(t) it is each hot energy storage device Endothermic power of t-th of scheduling instance, Qcmax,hhFor each heat storage
The Endothermic power maximum value of energy equipment, EHhh(t) it is the heat accumulation state of each hot energy storage device of t-th of scheduling instance, EHmax,hhIt is each
The maximum value of hot energy storage device heat storage capacity, Qdis,hh(t)、Qchar,hh(t)、EHhh(t) it is respectively the amount for being scheduled control；
(2-2) cogeneration units run constraints：
There are certain relationship, the i.e. feasible zone of point (p, q) between the generated output p and heat supply output q of cogeneration units
There are boundary, cogeneration units to be divided into back pressure type unit and extraction condensing type unit, the feasible zone of (p, q) of two kinds of units is presented
Convex polygon, by s-th of cogeneration of heat and power machine in S cogeneration units, the cogeneration units that the method for the present invention is related to are transported
Row constraint convex polygon schematic diagram is as shown in Figure 2, wherein Fig. 2 (a) is the feasible zone of back pressure type unit, and Fig. 2 (b) is extraction condensing type
The feasible zone of unit, the D of convex polygonsA vertex is denoted as (Pd,s,Qd,s), d=1,2...Ds, retouched with the linear combination on vertex
Feasible zone is stated, then a bit (p in feasible zones,qs) meet：
Wherein, the electrical power p of each cogeneration units1(t)、p2(t)、...、pS(t), each cogeneration units
Thermal power q1(t)、q2(t)、...、qS(t)；
Cogeneration units active power climbing constraints be：
Wherein, RAMPs downAnd RAMPs upRespectively upward, the downward climbing speed of s platforms cogeneration units active power
The maximum value of rate, ps(t) and ps(t-1) it is respectively s platforms cogeneration units when t-th of scheduling instance and the t-1 are dispatched
The active power at quarter；
(2-3) obtains the operation constraints of power grid and heat supply network in electric-thermal coupling multipotency streaming system：
From electric-thermal couple multipotency streaming system Energy Management System in, obtain electric-thermal coupling multipotency streaming system in power grid,
The safe operation of heat supply network constrains Ψsc, including：Grid branch active power allows maximum value and grid branch active power to allow most
Small value, electric-thermal, which couples heat supply network node temperature in multipotency streaming system, allows maximum value, heat supply network node temperature to allow minimum value, heat supply network
Bypass flow allows maximum value and heat supply network bypass flow to allow minimum value；
(2-4) heat supply network trend constraint condition is：
Assuming that the flow in heat-net-pipeline remains unchanged, the heat supply network power flow equation for establishing matrix form is as follows：
In above-mentioned heat supply network power flow equation, A is incidence matrix, in order to describe the thermodynamic behaviour of heating network, needs pipeline
Topological relation, remember in heat supply network there is a N number of node and B branch, incidence matrix in foundationWith lower incidence matrixA：
In above-mentioned heat supply network power flow equation：CpFor the specific heat capacity of heat supply network heat transfer mediator, m is a branch in B branch
Flow, the subscript of m indicate that the serial number of branch, M are the vector of the flow composition of B branch, TnFor each node temperature composition of heat supply network
Vector, TeFor the vector that the temperature of each branch end of heat supply network forms, TaFor environment temperature, QJFor the thermic load of heat supply network node
The vector of composition, C are the diagonal matrixs using the pipeline heat loss coefficient of each branch as diagonal element, and λ is that the unit of each branch of heat supply network is long
Thermal conductivity is spent, the subscript of λ indicates that the serial number of branch, L are the length of each branch of heat supply network, and the subscript of L indicates the serial number of branch；
The vector M of above-mentioned each bypass flow composition of heat supply network, the vector T of each node temperature composition of heat supply networknWith each branch of heat supply network
The vector T of last temperature compositioneFor the variable of control can be scheduled.
(3) object function of thermo-electrically coupling multipotency streaming system power-balance risk is established：
The part of thermo-electrically coupling multipotency streaming system unbalanced power is provided by interconnection, therefore the size of power risk can be with
It is indicated with dominant eigenvalues.
When calculating the power least risk value of scheduling instance t, the object function OF of power-balance risklower(t) it is：
OFlower(t)=Ptie(t)
When calculating the power greatest risk value of some t moment, the object function OF of power-balance riskupper(t) it is
OFupper(t)=- Ptie(t)
Wherein, Ptie(t) it is the power on interconnection, the power on interconnection is equal to imbalance power；
(4) the operating cost object function OF of thermo-electrically coupling multipotency streaming system is establishedinner：
It is all uncommon when thermo-electrically coupling multipotency streaming system does not have power-balance risk and when emergent power imbalance risk
Hope operating cost as low as possible, it is assumed that unbalanced power is in certain threshold value on thermo-electrically coupling multipotency streaming system interconnection
When be considered as there is no power-balance risk,
Wherein, S is cogeneration units number, and s is the serial number of cogeneration units, Hprices(t) it is s-th of thermoelectricity
The f timesharing prices for sending out unit thermal power of coproduction unit, Eprices(t) it is that s-th cogeneration units send out unit electricity
The timesharing price of power, Ctie(t) it is
Ctie(t)=Price_n (t) * Ptie1(t)+Price_ex*Ptie2(t)
Ptie(t)=Ptie1(t)+Ptie2(t)
0≤Ptie1(t)≤Ptie_limit
0≤Ptie2(t)
Wherein CtieFor interconnection purchases strategies, Ptie1For permitted imbalance power threshold value, Ptie2For more than threshold value
Power, Price_n (t) be imbalance power in threshold value when interconnection on tou power price, Price_ex be more than threshold value
Partial punishment electricity price, Ptie_limitFor the upper limit of the power of interconnection carrying；
(5) using the object function of the thermo-electrically of above-mentioned steps (3) coupling multipotency streaming system power-balance risk as outer layer mesh
Scalar functions, above-mentioned steps (1) are used as outer layer constraints, with the thermo-electricallies of above-mentioned steps (4) couple the operation of multipotency streaming system at
It is internal layer constraints that this object function, which is used as internal layer object function, above-mentioned steps (2), builds a bi-level optimal model, and
Using interior point method, 2T solution is carried out, OF is obtainedlower(t) and OFupper(t), t=1,2 ... T is to get to each scheduling
The upper bound of the thermo-electrically coupling multipotency streaming system power risk at moment and lower bound, realize and couple multipotency streaming system power to thermo-electrically
Risk assessment.
For t moment, all it is unfavorable in the photovoltaic generation power at 96 moment of whole day, electric load power, thermic load power
Under the scene for cutting down t moment dominant eigenvalues, (each scheduling mode all wraps the optimal scheduling mode for obtaining for t moment
Operation plan containing 96 moment), the dominant eigenvalues under this scheduling mode of this scene are exactly the upper bound of dominant eigenvalues；It is similar
, be all conducive to cut down t moment interconnection in the photovoltaic generation power at 96 moment of whole day, electric load power, thermic load power
Under the scene of power, the optimal scheduling mode for t moment is obtained, the dominant eigenvalues under this scheduling mode of this scene are just
It is the lower bound of dominant eigenvalues.The bound for having obtained dominant eigenvalues has also just obtained the thermo-electrically coupling multipotency stream system of t moment
System power risk.
Claims (1)
1. a kind of thermo-electrically based on interval method couples multipotency streaming system power methods of risk assessment, it is characterised in that this method
Include the following steps：
(1) an electric-thermal coupling multipotency streaming system operation stochastic model is established：
If include in electric-thermal coupling multipotency streaming system S cogeneration units, E electricity energy storage device, a hot energy storage devices of H,
L1 electric load, L2 thermic load and P photovoltaic plant, electric-thermal coupling multipotency streaming system pass through interconnection and external electrical network phase
Even；
The active power predicted value of the photovoltaic generation of the one day future each scheduling instance t obtained from electric-thermal coupling multipotency streaming system,
The interval prediction data of electric load active power predicted value and thermic load active power predicted value, it is as follows to establish stochastic model：
Ppvdown(t)≤Ppv(t)≤Ppvup(t)
Pedown(t)≤Pe(t)≤Peup(t)
Phdown1(t)≤Ph,1(t)≤Phup,1(t)
Phdown2(t)≤Ph,2(t)≤Phup,2(t)
...
Phdown,L2(t)≤Ph,L2(t)≤Phup,L2(t)
Wherein, t is scheduling instance, t=1,2 ..., T, for distinguishing the following intraday T scheduling instance, Ppv(t) it is t tune
Spend total generated output of moment all P photovoltaic plants, Pe(t) it is total active power of all L1 electric loads of t scheduling instances,
Ph,1(t)、Ph,2(t)、...、Ph,L2(t) power of each thermic load, P are indicated respectivelypvdown(t)、Pedown(t)、Phdown1(t)、
Phdown2(t)、...、Phdown,L2(t) be respectively t scheduling instances photovoltaic generation, electric load, thermic load power interval prediction under
Limit, Ppvup(t)、Peup(t)、Phup,1(t)、Phup,2(t)、...、Phup,L2(t) be respectively t scheduling instances photovoltaic generation power,
The upper limit that electric load power, thermic load power interval are predicted, Ppv(t)、Pe(t)、Ph,1(t)、Ph,2(t)、...、Ph,L2(t) be with
Machine variable；
(2) constraint of electric-thermal coupling multipotency streaming system is established, including：
(2-1) energy storage device runs constraints：
The constraints of E electric energy storage device is：
0≤Pdis(t)≤Pdmax, 0≤Pchar(t)≤Pcmax
0≤SoC(t)≤SoCmax
SoC (t)=SoC (t-1)-Pdis(t)+Pchar(t), t=2,3 ..., T
Wherein, Pdis(t) it is total discharge power of all E electric energy storage devices of t-th of scheduling instance, Pchar(t) it is t-th of scheduling
Total charge power of all E of moment electric energy storage device, PdmaxFor the maximum value of total discharge power, PcmaxFor total charge power
Maximum value, SoC (t) are all E total storing up electricity states of electric energy storage device of t-th of scheduling slot, SoCmaxFor the maximum of stored energy capacitance
Value.Pdis(t)、Pchar(t), SoC (t) is respectively the amount for being scheduled control；
The constraints of H hot energy storage devices is：
0≤Qdis,hh(t)≤Qdmax,hh, 0≤Qchar,hh(t)≤Qcmax,hh,0≤EHhh(t)≤EHmax,hh
EHhh(t)=EHhh(t-1)+Qchar,hh(t)-Qdis,hh(t)
Hh=1,2 ..., H
Wherein, Qdis,hh(t) it is the heat release power of each hot energy storage device of t-th of scheduling instance, Qdmax,hhFor each hot energy storage device
Heat release power maximum value, Qchar,hh(t) it is each hot energy storage device Endothermic power of t-th of scheduling instance, Qcmax,hhIt is set for each hot energy storage
Standby Endothermic power maximum value, EHhh(t) it is the heat accumulation state of each hot energy storage device of t-th of scheduling instance, EHmax,hhFor each heat storage
The maximum value of energy equipment heat storage capacity, Qdis,hh(t)、Qchar,hh(t)、EHhh(t) it is respectively the amount for being scheduled control；
(2-2) cogeneration units run constraints：
By the feasible zone of s-th of cogeneration units in S cogeneration units, the D of convex polygonsA vertex is denoted as (Pd,s,
Qd,s), d=1,2...Ds, feasible zone is described with the linear combination on vertex, then a bit (p in feasible zones,qs) meet：
Wherein, the electrical power p of each cogeneration units1(t)、p2(t)、...、pS(t), the hot merit of each cogeneration units
Rate q1(t)、q2(t)、...、qS(t)；
Cogeneration units active power climbing constraints be：
Wherein, RAMPs downAnd RAMPs upRespectively upward, the downward creep speed of s platforms cogeneration units active power
Maximum value, ps(t) and ps(t-1) it is respectively s platforms cogeneration units in t-th of scheduling instance and the t-1 scheduling instance
Active power；
(2-3) obtains the operation constraints of power grid and heat supply network in electric-thermal coupling multipotency streaming system：
From the Energy Management System that electric-thermal couples multipotency streaming system, power grid, heat supply network in electric-thermal coupling multipotency streaming system are obtained
Safe operation constrain Ψsc, including：It is minimum that grid branch active power allows maximum value and grid branch active power to allow
Value, electric-thermal, which couples heat supply network node temperature in multipotency streaming system, allows maximum value, heat supply network node temperature to allow minimum value, heat supply network branch
Road flow allows maximum value and heat supply network bypass flow to allow minimum value；
(2-4) heat supply network trend constraint condition is：
Assuming that the flow in heat-net-pipeline remains unchanged, the heat supply network power flow equation for establishing matrix form is as follows：
In above-mentioned heat supply network power flow equation, A is incidence matrix, remembers there is N number of node and B branch in heat supply network, incidence matrix in foundation
With lower incidence matrixA：
In above-mentioned heat supply network power flow equation：CpFor the specific heat capacity of heat supply network heat transfer mediator, m is the flow of a branch in B branch, m
Subscript indicate the serial number of branch, M is the vector of the flow composition of B branch, TnFor the vector of heat supply network each node temperature composition,
TeFor the vector that the temperature of each branch end of heat supply network forms, TaFor environment temperature, QJFor heat supply network node thermic load form to
Amount, C is the diagonal matrix using the pipeline heat loss coefficient of each branch as diagonal element, and λ is the unit length thermal conductivity of each branch of heat supply network,
The subscript of λ indicates that the serial number of branch, L are the length of each branch of heat supply network, and the subscript of L indicates the serial number of branch；
(3) object function of thermo-electrically coupling multipotency streaming system power-balance risk is established：
When calculating the power least risk value of scheduling instance t, the object function OF of power-balance risklower(t) it is：
OFlower(t)=Ptie(t)
When calculating the power greatest risk value of some t moment, the object function OF of power-balance riskupper(t) it is
OFupper(t)=- Ptie(t)
Wherein, Ptie(t) it is the power on interconnection, the power on interconnection is equal to imbalance power；
(4) the operating cost object function OF of thermo-electrically coupling multipotency streaming system is establishedinner：
Wherein, S is cogeneration units number, and s is the serial number of cogeneration units, Hprices(t) it is s-th of cogeneration of heat and power machine
The f timesharing prices for sending out unit thermal power of group, Eprices(t) it is that s-th cogeneration units send out specific electric power
Timesharing price, Ctie(t) it is
Ctie(t)=Price_n (t) * Ptie1(t)+Price_ex*Ptie2(t)
Ptie(t)=Ptie1(t)+Ptie2(t)
0≤Ptie1(t)≤Ptie_limit
0≤Ptie2(t)
Wherein CtieFor interconnection purchases strategies, Ptie1For permitted imbalance power threshold value, Ptie2For the power more than threshold value,
Price_n (t) be imbalance power in threshold value when interconnection on tou power price, Price_ex be more than threshold portion
Punish electricity price, Ptie_limitFor the upper limit of the power of interconnection carrying；
(5) using the object function of the thermo-electrically of above-mentioned steps (3) coupling multipotency streaming system power-balance risk as outer layer target letter
Number, above-mentioned steps (1) are used as outer layer constraints, and the operating cost mesh of multipotency streaming system is coupled with the thermo-electrically of above-mentioned steps (4)
For scalar functions as internal layer object function, above-mentioned steps (2) are internal layer constraints, build a bi-level optimal model, and use
Interior point method carries out 2T solution, obtains OFlower(t) and OFupper(t), t=1,2 ... T is to get to each scheduling instance
Thermo-electrically coupling multipotency streaming system power risk the upper bound and lower bound, realize to thermo-electrically couple multipotency streaming system power wind
Danger assessment.
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