CN109494816B - Risk assessment method and device for electric-thermal coupling multi-energy flow system - Google Patents

Risk assessment method and device for electric-thermal coupling multi-energy flow system Download PDF

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CN109494816B
CN109494816B CN201811628612.XA CN201811628612A CN109494816B CN 109494816 B CN109494816 B CN 109494816B CN 201811628612 A CN201811628612 A CN 201811628612A CN 109494816 B CN109494816 B CN 109494816B
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active power
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CN109494816A (en
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孙宏斌
郭庆来
王彬
车浩田
沈欣炜
王莉
熊文
刘育权
蔡莹
吴任博
华煌圣
曾顺奇
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Tsinghua-Berkeley Shenzhen Institute Preparation Office
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/48—Controlling the sharing of the in-phase component
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract

The invention discloses a risk assessment method, a risk assessment device and a storage medium for an electro-thermal coupling multi-energy flow system. The method comprises the steps of establishing a randomness model of the operation of the electric-thermal coupling multi-energy flow system, generating sn random scenes of the electric-thermal coupling multi-energy flow system according to the randomness model, simultaneously establishing constraints and a cost function of the electric-thermal coupled multi-energy flow system, taking the cost function as an objective function, wherein the constraint condition comprises a heat supply network power flow constraint condition, so that the coupling relation between the active power of the heat supply network and the active power of the power grid can be established, therefore, the minimum value of the objective function of the electro-thermally coupled multi-energy flow system under sn random scenes can be solved through the electro-thermal coupling constraint condition of the electro-thermally coupled multi-energy flow system, and determining the risk value of the electro-thermally coupled multi-energy flow system according to the minimum value of the objective function corresponding to the sn random scenes, so that the solved risk value of the electro-thermally coupled multi-energy flow system is more accurate.

Description

Risk assessment method and device for electric-thermal coupling multi-energy flow system
Technical Field
The embodiment of the invention relates to the technical field of risk assessment of a multi-energy flow system, in particular to a risk assessment method, a risk assessment device and a storage medium of an electric-thermal coupling multi-energy flow system.
Background
The multi-energy flow system comprises multiple energy flow subsystems such as electricity, heat, cold and gas, and all the subsystems are converted and coupled together through equipment such as combined cooling, heating and power, a heat pump and the like. In recent years, the rise of energy internet enables a multi-energy network which transmits energy in various forms of electricity, heat, cold and gas to show superiority. But the multi-energy coupling also causes the interaction of uncertainty factors in each subsystem, influences the normal use energy of users and even influences the overall safety of the multi-energy flow system.
In the prior art, the uncertain factors can be comprehensively analyzed through risk assessment, and the operation risk of the multi-energy flow system is obtained. However, in the prior art, the time scale of the general analysis of the risk assessment considered factors is long, and the risk of the operation of the multi-energy flow system cannot be accurately assessed in time.
Disclosure of Invention
The invention provides a risk evaluation method, a risk evaluation device and a storage medium for an electro-thermal coupling multi-energy flow system, which are used for more accurately analyzing the risk that the electro-thermal coupling multi-energy flow system exceeds an active power threshold value of a grid-connected operation protocol and simultaneously shortening an evaluation period.
In a first aspect, an embodiment of the present invention provides a risk assessment method for an electro-thermal coupling multi-energy flow system, including:
establishing a randomness model of the operation of the electric-thermal coupling multi-energy flow system;
generating sn stochastic scenarios of the electro-thermally coupled multi-energy flow system according to the stochastic model; wherein sn is an integer and sn is greater than 1;
establishing constraints and a cost function of the electro-thermally coupled multi-energy flow system; wherein the constraints comprise heat supply network flow constraints;
taking the cost function as an objective function, and solving the minimum value of the objective function corresponding to the sn random scenes according to the constraint conditions;
and determining the risk value of the electrical-thermal coupled multi-energy flow system according to the minimum value of the objective function corresponding to the sn random scenes.
Specifically, the randomness model of the operation of the electro-thermally coupled multi-energy flow system includes a randomness model of a predicted value of the power generation active power, a randomness model of a predicted value of the electrical load active power and a randomness model of a predicted value of the thermal load active power at each scheduling time of a day in the future of the electro-thermally coupled multi-energy flow system, and the randomness model of the predicted value of the power generation active power and the randomness model of the predicted value of the electrical load active power are specifically:
Epv(t)～N(μpv(t),pv(t)2)
Ee(t)～N(μe(t),e(t)2)
Ppv(t)＝PFpv(t)+Epv(t)
Pe(t)＝PFe(t)+Ee(t)
wherein T is the T-th scheduling time of the future day, T is 1,2pv(t) is a predicted value of the active power generated at the t-th scheduling time, PFe(t) is a predicted value of the active power of the electric load at the t-th scheduling moment, Epv(t) deviation of predicted value of active power generated at the t-th scheduling time, EeFor the t-th scheduling time of the electrical loadDeviation of the predicted value of active power, mupv(t) is an expected value of deviation of the predicted value of the active power generated at the t-th scheduling time,pv(t) is a standard deviation of a predicted deviation of the predicted value of the active power generated at the t-th scheduling time, mue(t) is the expected value of the deviation of the predicted value of the active power of the electric load at the t-th scheduling moment,e(t) is the standard deviation of the predicted deviation of the active power of the electric load at the t-th scheduling moment, Ppv(t) active power generated at the t-th scheduling time, Pe(t) the electric load active power at the tth scheduling moment;
the randomness model of the heat load active power predicted value specifically comprises the following steps:
Ph,1(t)～Beta(A＝10,B＝10(Pmax/Phf,1(t)-1))
Ph,2(t)～Beta(A＝10,B＝10(Pmax/Phf,2(t)-1))
...
Ph,L1(t)～Beta(A＝10,B＝10(Pmax/Phf,L1(t)-1))
wherein, Phf,1(t)、Phf,2(t)、...、Phf,L1(t) the predicted active power at the t-th scheduling time of each thermal load, P, represented by L1 thermal loads in totalh,1(t)、Ph,2(t)、...、Ph,L1(t) the active power at the t-th scheduling time of each thermal load, PmaxIs the maximum power of the total heat load in the electric-thermal coupling multi-energy flow system.
Specifically, the generating sn stochastic scenarios of the electro-thermally coupled multi-energy flow system according to the stochastic model comprises:
determining T schedulable times of day for the electro-thermally coupled multi-energy flow system;
determining the dimension of each random scene according to the number of the schedulable moments and the number of the heat loads of the electrical-thermal coupled multi-energy flow system, wherein the dimension w of each random scene is w-T (2+ L1), T is the number of the schedulable moments in one day, and L1 is the number of the heat loads;
solving a randomness model of the operation of the electric-thermal coupling multi-energy flow system through SN times to form SN random scenes;
and performing scene reduction on the SN random scenes to obtain SN random scenes, wherein SN is an integer and is greater than SN.
Specifically, the heat supply network flow constraint condition is as follows:
Figure BDA0001928504370000031
wherein A is incidence matrix, and the incidence matrix is arranged on the incidence matrix
Figure BDA0001928504370000032
And lower association matrixA：
Figure BDA0001928504370000033
Figure BDA0001928504370000034
Where, i is 1,2 … N, N is the serial number of the node, j is 1,2 … B, B is the serial number of the branch, CpSpecific heat capacity of heat transfer transmitters for heat supply networks, ATFor the transposition of the incidence matrix A, M is the heat flow of one branch in B branches, the subscript of M represents the serial number of the branch, M is the vector formed by the flows of the B branches, TnVector formed by temperatures of nodes of heat supply network, TeVector formed by the temperature at the end of each branch of the heat supply network, TaIs ambient temperature, QJC is a diagonal matrix with the pipe heat loss coefficient of each branch as a diagonal element, λ is the unit length thermal conductivity of each branch of the heat supply network, the subscript of λ represents the branch number, L the length of each branch of the heat supply network, and the subscript of L represents the branch number.
Specifically, the constraint conditions further include co-generation unit operation constraint conditions, where the co-generation unit operation constraint conditions are:
Figure BDA0001928504370000035
Figure BDA0001928504370000036
wherein T is the T-th scheduling time of the future day, T is 1,2, and T, T is the number of scheduling times of the future day, S is the S-th cogeneration unit of the S cogeneration units, Ds is the number of vertices of the feasible region of the S-th cogeneration unit, (P)d,s(t),Qd,s(t)),d＝1,2...DsFor the coordinates of the vertices of the feasible region at the t-th scheduling time of the s-th cogeneration unit, (p)s(t),qs(t)) is a point, k, within the feasible region of the tth scheduling instant of the s-th cogeneration unitd,s(t) is the coordinate (P) of the vertex of the feasible region of the tth scheduling time of the s-th cogeneration unitd,s(t),Qd,s(t)),d＝1,2...DsWeight of p1(t)、p2(t)、...、pS(t) is the electric load active power of each cogeneration unit at the t-th scheduling moment, q1(t)、q2(t)、...、qS(t) is the heat load active power of each cogeneration unit.
Specifically, the constraint conditions of the operation of the cogeneration unit further include a constraint condition of active power ramp of the cogeneration unit, that is:
Figure BDA0001928504370000041
s＝1,2...S
wherein, RAMPs downAnd RAMPs upRespectively the maximum values of the upward and downward climbing rates of the active power of the s-th cogeneration unit, wherein delta t is the time difference between the t-th scheduling time and the t-1-th scheduling time, and ps(t) and ps(t-1) respectively at the t-th scheduling time and the t-1-th scheduling time of the s-th cogeneration unitActive power of (1).
Specifically, the constraints of the electro-thermally coupled multi-energy flow system further include constraints of an electrical energy storage device and a thermal energy storage device in the electro-thermally coupled multi-energy flow system, and are as follows:
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 T is the T-th scheduling time of the future day, T is 1,2, T is the number of scheduling times of the future day, E is the number of electrical energy storage devices, Pdis(t) is the total discharge power, P, of all E electrical energy storage devices at the t-th scheduling momentchar(t) is the total charging power, P, of all E electrical energy storage devices at the t-th scheduling momentdmaxAt the maximum value of the total discharge power, PcmaxFor the maximum value of the total charging power, SoC (t) is the total power storage state of all E electric energy storage devices in the t scheduling period, and SoCmaxThe maximum value of the energy storage capacity of all E electric energy storage devices;
the constraint conditions of the H heat energy storage devices are as follows:
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 H is the number of the heat energy storage equipment, Qdis,hh(t) the heat release power of each thermal energy storage device at the t-th scheduling moment, Qdmax,hhFor maximum heat-release power, Q, of each thermal energy-storage devicechar,hh(t) the heat absorption power of each thermal energy storage device at the t-th scheduling moment, Qcmax,hhFor the maximum value of the heat absorption power, EH, of each thermal energy storage devicehh(t) Heat storage State, EH, of each thermal energy storage device at the tth scheduling timemax,hhThe maximum value of the heat storage capacity of each heat energy storage device.
Specifically, the constraints of the electro-thermally coupled multi-energy flow system further include power flow constraints of a tie line connecting the thermo-electrically coupled multi-energy flow system with an external power grid, specifically:
Ppv(t)+Pe(t)+ps(t)+Ptie(t))＝0
wherein, Ptie(t) is the active power transmitted on the contact line at the tth scheduling moment, pSAnd (t) the electric load active power of the thermoelectric coupling multi-energy flow system cogeneration unit at the t-th scheduling moment.
Specifically, the cost function is:
Figure BDA0001928504370000051
among them, Hprices(t) is the time-sharing price of generating unit thermal power at the tth scheduling time of the cogeneration unit, namely, edges(t) is the time-shared price of the generated unit electric power of the mth scheduling time of the mth cogeneration unit, Ctie(t) 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, CtiePurchasing cost, P, for a tie line connecting the thermo-electric coupling multi-energy flow system with an external power gridtie1An unbalance power threshold value, P, allowed by the tie line when the electric-thermal coupled multi-energy flow system is connected to the gridtie2For partial powers exceeding the tie allowed imbalance power threshold, Price _ n (t) is the share-time Price on the tie when the tie allowed imbalance power is within a threshold, Price _ ex is the penalty Price exceeding the tie allowed imbalance power threshold fraction, Ptie_limitCarrying for the tieUpper power limit of (2).
Specifically, when the cost function is used as an objective function and the minimum value of the objective function corresponding to the sn random scenes is solved according to the constraint conditions, the minimum value of the objective function corresponding to the sn random scenes at different scheduling moments is solved by adopting an interior point method.
Specifically, the determining the risk value of the electro-thermally coupled multi-energy flow system according to the minimum value of the objective function corresponding to the sn random scenarios comprises:
determining a probability of occurrence of the sn random scenes;
determining a risk value of each random scene at a scheduling moment according to the minimum value of the objective function of the sn random scenes at the scheduling moment;
calculating a risk value of a certain scheduling time of the electrical-thermal coupled multi-energy flow system according to the risk values of the sn random scenes and the probability of the sn scenes at the same scheduling time.
In a second aspect, an embodiment of the present invention further provides a risk assessment apparatus for an electro-thermal coupling multi-energy flow system, including:
the randomness model establishing module is used for establishing a randomness model of the operation of the electric-thermal coupling multi-energy flow system;
a stochastic scenario generation module for generating sn stochastic scenarios of the electro-thermally coupled multi-energy flow system according to the stochastic model; wherein sn is an integer and sn is greater than 1;
a constraint and cost function establishing module for establishing a constraint and cost function of the electro-thermally coupled multi-energy flow system; wherein the constraints comprise heat supply network flow constraints;
an objective function solving module, which takes the cost function as an objective function and solves the minimum value of the objective function corresponding to the sn random scenes according to the constraint conditions;
and the risk determination module is used for determining a risk value of the electrical-thermal coupled multi-energy flow system according to the minimum value of the objective function corresponding to the sn random scenes.
In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the risk assessment method for an electrically-thermally coupled multi-energy flow system according to any of the embodiments of the present invention.
The method comprises the steps of establishing a randomness model of the operation of the electric-thermal coupling multi-energy flow system, generating sn random scenes of the electric-thermal coupling multi-energy flow system according to the randomness model, and simultaneously establishing constraint conditions and a cost function of the electric-thermal coupling multi-energy flow system, wherein the constraint conditions comprise thermal network power flow constraint conditions, so that the coupling relation between the active power of a thermal network and the active power of a power grid can be established, the minimum value of an objective function of the electric-thermal coupling multi-energy flow system under the sn random scenes can be solved through the electric-thermal coupling constraint conditions of the electric-thermal coupling multi-energy flow system, and the risk value of the electric-thermal coupling multi-energy flow system is determined according to the minimum value of the objective function corresponding to the sn random scenes, so that the solved risk value that the power interaction between the electric-thermal coupling multi-energy flow system and an external power grid during the operation exceeds the active power threshold value of grid-connected operation agreement And is more accurate. And a randomness model of the electric-thermal coupling multi-energy flow system is established, and the evaluation period is shortened. In addition, the random model of the operation of the electric-thermal coupling multi-energy flow system considers the uncertain influence of the optimal scheduling of the cogeneration unit, the electric energy storage device, the thermal energy storage device, the electric load, the thermal load and the photovoltaic power station on the risk assessment, so that the accuracy of the risk assessment of the electric-thermal coupling multi-energy flow system is improved.
Drawings
Fig. 1 is a flowchart of a risk assessment method of an electro-thermally coupled multi-energy flow system according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of an electro-thermally coupled multi-energy flow system according to an embodiment of the present invention;
fig. 3 is a power diagram illustrating an operation manner of a cogeneration unit according to an embodiment of the present invention;
fig. 4 is a schematic diagram illustrating a result of a stochastic model fitting of a thermal load active power predicted value adopted by an electro-thermal coupling multi-energy flow system according to an embodiment of the present invention;
FIG. 5 is a flow chart of a method for risk assessment of an alternative electro-thermally coupled multi-flow system provided by an embodiment of the present invention;
fig. 6 is a schematic diagram of the active power probability distribution of the tie line of an electro-thermal coupling multi-energy flow system according to an embodiment of the present invention;
FIG. 7 is a flow chart of a method for risk assessment of an alternative electro-thermally coupled multi-flow system provided by an embodiment of the present invention;
fig. 8 is a schematic structural diagram of a risk assessment apparatus of an electro-thermal coupling multi-energy flow system according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
The method and the device can be suitable for evaluating the risk that the active power of the electric-thermal coupling multi-energy flow system exceeds the active power threshold value agreed during grid connection when the electric-thermal coupling multi-energy flow system is in grid connection operation. In particular, when the electro-thermally coupled multi-energy flow system is operated in a grid-connected mode, there is a power interaction between the electro-thermally coupled multi-energy flow system and an external power grid. And a threshold value of active power that will interact with external grid agreement power before the electro-thermally coupled multi-energy flow system is operated on grid-connected. When the power interaction of the electro-thermally coupled multi-energy flow system and the external power grid exceeds the agreed active power threshold value, the electro-thermally coupled multi-energy flow system influences the stability of the external power grid, and meanwhile, the external power grid punishs and adjusts the price of the power of the electro-thermally coupled multi-energy flow system exceeding the agreed active power threshold value. Therefore, the risk evaluation method of the electro-thermal coupling multi-energy flow system is used for carrying out risk evaluation on the electro-thermal coupling multi-energy flow system, so that the risk that the power interaction with an external power grid exceeds the active power threshold value of a grid-connected operation protocol during the operation of the electro-thermal coupling multi-energy flow system is analyzed more accurately, and the analysis period is shortened.
Fig. 1 is a flowchart of a risk assessment method for an electro-thermally coupled multi-energy flow system according to an embodiment of the present invention, where the method may be performed by a risk assessment apparatus for an electro-thermally coupled multi-energy flow system, as shown in fig. 1, and the method specifically includes the following steps:
and S110, establishing a randomness model of the operation of the electric-thermal coupling multi-energy flow system.
The risk assessment method of the electric-thermal coupled multi-energy flow system can also assess the risk of the electric-thermal coupled multi-energy flow system when the number of the cogeneration unit, the electric energy storage device, the thermal energy storage device, the electric load, the thermal load and the photovoltaic power station in the electric-thermal coupled multi-energy flow system changes, taking into account the uncertain influence of optimal scheduling on risk assessment of the cogeneration unit, the electric energy storage device, the thermal energy storage device, the electric load, the thermal load and the photovoltaic power station, when the cogeneration unit, the thermal power generation system, the thermal power extraction power and the photovoltaic power station are connected with the thermal power generation system, so that the risk assessment method of the electric-thermal coupled multi-energy flow system improves the risk assessment of the electric-thermal coupled multi-energy flow system, the thermal power storage device, the thermal energy storage device, the thermal load and the photovoltaic power generation system, and the risk assessment of the thermal power generation system when the thermal power extraction power of the electric-thermal coupled multi-energy flow system is not necessarily equal, and the thermal power extraction power system can be used for the external thermal power generation system when the multi-thermal power extraction power consumption power is more than the random thermal power extraction power consumption power, the random thermal power extraction power generation system, the random thermal power extraction power generation system, the random power extraction power generation power, the random power extraction power is equal, the random power extraction power, the random power extraction.
It should be noted that the numbers of cogeneration units, electrical energy storage devices, thermal energy storage devices, electrical loads, thermal loads, and photovoltaic power plants in fig. 2 are only an example, and are not limited.
In addition, different scheduling time instants may have different loads in a future evaluation period, and thus, one evaluation period may be divided into different scheduling time instants so as to better evaluate risks in different scheduling time instants. In addition, the technical scheme adopted by the invention is to establish a randomness model of the electric-thermal coupling multi-energy flow system, so that the evaluation period can be greatly shortened. For example, the evaluation period may be a day in the future, and the scheduling time may be divided into T scheduling times by having different electrical loads and thermal loads at different scheduling times in the day, for example, each scheduling time corresponds to a stochastic model, so as to improve the accuracy of the stochastic model.
S120, generating sn random scenes of the electric-thermal coupling multi-energy flow system according to a randomness model; wherein sn is an integer and sn is greater than 1.
The selection of the number of random scenes is related to the accuracy of risk assessment. The higher the accuracy requirement for risk assessment, the more random scenes. In general, when the confidence interval of the risk assessment of the electro-thermally coupled multi-energy flow system is 90%, the number of random scenarios may be selected to be 1000.
The random scene is a stochastic simulation of the conditions of the power generation active power, the power load active power and the heat load active power when an electro-thermal coupling multi-energy flow system operates, and therefore, the random scene is not only related to the power generation active power, the power load active power and the heat load active power when the electro-thermal coupling multi-energy flow system operates, but also related to the scheduling time of the operation of the electro-thermal coupling multi-energy flow system. Thus, a stochastic scenario of an electro-thermally coupled multi-energy flow system can be obtained by solving a stochastic model of the electro-thermally coupled multi-energy flow system.
It should be noted that, when there are sn stochastic scenarios of the electro-thermally coupled multi-energy flow system, sn stochastic models of the electro-thermally coupled multi-energy flow system need to be solved. A stochastic scenario can be derived each time for a stochastic model of the electro-thermally coupled multi-energy flow system.
S130, establishing constraint conditions and a cost function of the electric-thermal coupling multi-energy flow system; wherein the constraint condition comprises a heat supply network flow constraint condition.
The constraint condition is a condition of normal operation of the electrical-thermal coupled multi-energy flow system, and the constraint condition of the electrical-thermal coupled multi-energy flow system comprises multiple conditions. Illustratively, the constraint condition may include a heat supply network flow constraint condition, where the heat supply network flow constraint condition is a condition that the heat energy in the heat supply network satisfies during the transmission process, such as a maximum allowable heat supply network node temperature, a minimum allowable heat supply network node temperature, a maximum allowable heat supply network branch flow, a minimum allowable heat supply network branch flow, and the like when the electrical-thermal coupled multi-energy flow system operates normally. In addition, in the electrical-thermal coupled multi-energy flow system, a common node is included between the heat supply network and the power grid, as shown in fig. 2, the dotted line in fig. 2 is a heat supply network line, and the solid line in fig. 2 is a power grid line. The grid power flow also includes constraints, such as a maximum allowed grid branch active power and a minimum allowed grid branch active power, and the total active power of the heat supply network and the grid is equal to the total active power of the electro-thermally coupled multi-energy flow system. Therefore, when the constraint condition comprises a heat supply network power flow constraint condition, a coupling relation between the active power of the heat supply network and the active power of the power grid can be established. The risk of the electro-thermally coupled multi-energy flow system can thus be evaluated by the electro-thermal coupling of the electro-thermally coupled multi-energy flow system.
The cost function of the electro-thermally coupled multi-flow system is the cost of the electro-thermally coupled multi-flow system for generating electrical and thermal power and the cost of the electro-thermally coupled multi-flow system for power interaction with an external power grid when the electro-thermally coupled multi-flow system is operated in a grid-connected mode. Generally, the cost of generating electrical power and thermal power in an electro-thermally coupled multi-flow system is relatively flat. When the power interaction is carried out between the electric-thermal coupled multi-energy flow system and an external power grid during grid-connected operation, when the power of the interaction between the electric-thermal coupled multi-energy flow system and the external power grid is within the threshold range of the agreed active power, the cost generated when the power interaction is carried out between the electric-thermal coupled multi-energy flow system and the external power grid during grid-connected operation is relatively stable. When the interactive power of the electric-thermal coupling multi-energy flow system and the external power grid exceeds the agreed active power threshold range, the external power grid can punish and adjust the price of the power of the electric-thermal coupling multi-energy flow system exceeding the agreed active power threshold range, the cost generated when the electric-thermal coupling multi-energy flow system is subjected to power interaction with the external power grid during grid-connected operation can be increased, meanwhile, the electric-thermal coupling multi-energy flow system can influence the stability of the external power grid, and therefore the risk that the power interaction of the electric-thermal coupling multi-energy flow system and the external power grid during operation exceeds the agreed active power threshold of the grid-connected operation can be analyzed through the value of the cost function.
And S140, solving the minimum value of the objective functions corresponding to the sn random scenes by taking the cost function as the objective function according to the constraint conditions.
When the cost function is used as an objective function, a feasible domain of the objective function is formed through constraint conditions, and the cost function of each random scene is optimally solved, namely the minimum value of the cost function of each random scene is solved, and at the moment, each random scene in the electrical-thermal coupling multi-energy flow system is the condition with the best operation safety. Correspondingly, sn random scenes need to solve sn cost functions. When the power interaction between the electrically-thermally coupled multi-energy flow system and the external power grid during operation is equal to the active power threshold of the grid-connected operation protocol, the cost value corresponding to the cost function can be used as a risk judgment condition for judging that the power interaction between the electrically-thermally coupled multi-energy flow system and the external power grid exceeds the active power threshold of the grid-connected operation protocol. Namely, when the minimum value of the objective function corresponding to the random scene solved according to the constraint condition is larger than the judgment condition, the risk that the power interaction with the external power grid during the operation of the electric-thermal coupling multi-energy flow system under the random scene exceeds the active power threshold value of the grid-connected operation protocol is considered.
And S150, determining the risk value of the electrical-thermal coupled multi-energy flow system according to the minimum value of the objective function corresponding to the sn random scenes.
Specifically, after the cost function is used as an objective function to solve the optimal values of sn random scenes, the number of risks that power interaction between the random scenes and an external power grid has an active power threshold exceeding a grid-connected operation agreement when the power interaction between the electricity-thermal coupled multi-energy flow system and the external power grid is operated under the condition of the best operation safety and the probability of occurrence of different random scenes are determined in the sn random scenes according to the minimum value of the objective function corresponding to the sn random scenes, and the risk value of the electricity-thermal coupled multi-energy flow system is determined according to the probability and the number of occurrence of the random scenes.
According to the technical scheme of the embodiment, the randomness model of the operation of the electro-thermal coupling multi-energy flow system is established, sn random scenes of the electro-thermal coupling multi-energy flow system are generated according to the randomness model, and the constraint conditions and the cost functions of the electro-thermal coupling multi-energy flow system are simultaneously established, wherein the constraint conditions comprise the hot network power flow constraint conditions, so that the coupling relation between the active power of the hot network and the active power of the power grid can be established, therefore, the minimum value of the objective function of the electro-thermal coupling multi-energy flow system under the sn random scenes can be solved through the electro-thermal coupling constraint conditions of the electro-thermal coupling multi-energy flow system, and the risk value of the electro-thermal coupling multi-energy flow system is determined according to the minimum value of the objective function corresponding to the sn random scenes, so that the solved power interaction between the electro-thermal coupling multi-energy flow system and the external power grid during the operation exceeds the active power of a grid-parallel The risk value of the rate threshold is more accurate. And a randomness model of the electric-thermal coupling multi-energy flow system is established, and the evaluation period is shortened. In addition, the random model of the operation of the electric-thermal coupling multi-energy flow system considers the uncertain influence of the optimal scheduling of the cogeneration unit, the electric energy storage device, the thermal energy storage device, the electric load, the thermal load and the photovoltaic power station on the risk assessment, so that the accuracy of the risk assessment of the electric-thermal coupling multi-energy flow system is improved.
On the basis of the above technical solutions, the randomness model of the operation of the electro-thermally coupled multi-energy flow system includes a predicted value of the active power generated at each scheduling time of a future day and a predicted value of the active power of the electrical load of the electro-thermally coupled multi-energy flow system, and specifically includes:
Epv(t)～N(μpv(t),pv(t)2) (1.1)
Ee(t)～N(μe(t),e(t)2) (1.2)
Ppv(t)＝PFpv(t)+Epv(t) (1.3)
Pe(t)＝PFe(t)+Ee(t) (1.4)
wherein T is the T-th scheduling time of the future day, T is 1,2pv(t) is a predicted value of the active power generated at the t-th scheduling time, PFe(t) is a predicted value of the active power of the electric load at the t-th scheduling moment, Epv(t) is the predicted value of the active power generated at the t-th scheduling momentDeviation of (E)eFor deviations of the predicted values of the active power of the electrical load at the t-th scheduling instant, mupv(t) is an expected value of deviation of the predicted value of the active power generated at the t-th scheduling time,pv(t) is a standard deviation of a predicted deviation of the predicted value of the active power generated at the t-th scheduling time, mue(t) is the expected value of the deviation of the predicted value of the active power of the electric load at the t-th scheduling moment,e(t) is the standard deviation of the predicted deviation of the active power of the electric load at the t-th scheduling moment, Ppv(t) active power generated at the t-th scheduling time, PeAnd (t) is the active power of the electric load at the t-th scheduling moment.
Specifically, the formula (1.1) is a deviation of the predicted value of the power generation active power at the t-th scheduling time of the electro-thermally coupled multi-energy flow system in the evaluation period, and the formula (1.2) is a deviation of the predicted value of the power load active power at the t-th scheduling time of the electro-thermally coupled multi-energy flow system in the evaluation period. Equation (1.1) and equation (1.2) follow a Beta distribution. The formula (1.3) is used for evaluating the generating active power at the t-th scheduling time of the electro-thermally coupled multi-energy flow system in the period, and can be obtained by evaluating the deviation between the predicted value of the generating active power and the predicted value of the generating active power at the t-th scheduling time of the electro-thermally coupled multi-energy flow system in the period, and the formula (1.4) is used for evaluating the electrical load active power at the t-th scheduling time of the electro-thermally coupled multi-energy flow system in the period, and can be obtained by evaluating the deviation between the predicted value of the electrical load active power and the predicted value of the electrical load active power at the t-th scheduling time of the electro-thermally coupled multi-energy flow system in the period. Therefore, the generating active power and the electric load active power at the t-th scheduling moment of the electro-thermally coupled multi-energy flow system in the evaluation period can be obtained in advance so as to be used for evaluating the risk value of the electro-thermally coupled multi-energy flow system in the evaluation period. In addition, the active power P is generated at the t-th scheduling timepv(t) may be the generated active power of the photovoltaic power generation output in the electro-thermal coupled multi-energy flow system.
In addition, the randomness model may further include a predicted value of the active power of the thermal load, specifically:
Ph,1(t)～Beta(A＝10,B＝10(Pmax/Phf,1(t)-1))
Ph,2(t)～Beta(A＝10,B＝10(Pmax/Phf,2(t)-1))
...
Ph,L1(t)～Beta(A＝10,B＝10(Pmax/Phf,L1(t)-1))
wherein, Phf,1(t)、Phf,2(t)、...、Phf,L1(t) the predicted active power at the t-th scheduling time of each thermal load, P, represented by L1 thermal loads in totalh,1(t)、Ph,2(t)、...、Ph,L1(t) the active power at the t-th scheduling time of each thermal load, PmaxFor maximum power of the total heat load in the electro-thermally coupled multi-energy flow system, the heat load follows a Beta profile with parameters (a, B). The thermal load power at the t-th scheduling moment of the electric-thermal coupling multi-energy flow system in the evaluation period can be obtained in advance through the randomness model of the thermal load so as to be used for evaluating the risk value of the electric-thermal coupling multi-energy flow system in the evaluation period.
Specifically, fig. 4 is a schematic diagram of a result of fitting a stochastic model of a predicted value of the active power of the thermal load in the electro-thermal coupling multi-energy flow system according to the embodiment of the present invention. As shown in fig. 4, the thermal load data of the electrical-thermal coupling multi-energy flow system includes thermal load data for a period of time, for example, 6-9 months, while the thermal load data for each day is represented by bar graphs at different scheduling times, and the thermal load data for four scheduling times of the day is exemplarily shown in fig. 4. And fitting and analyzing the heat load data of the electric-thermal coupling multi-energy flow system by using a random model of the heat load active power predicted value, and standardizing the heat load data to obtain a fitting curve 102. As can be seen from fig. 4, the fitting curve 102 obtained by fitting the thermal load data of the electro-thermal coupling multi-energy flow system by using the stochastic model of the predicted value of the thermal load active power is better fitted with the thermal load data of the electro-thermal coupling multi-energy flow system, so that the accuracy of the stochastic model of the predicted value of the thermal load active power provided by the embodiment of the invention is higher.
On the basis of the foregoing embodiments, fig. 5 is a flowchart of another risk assessment method for an electro-thermally coupled multi-energy flow system according to an embodiment of the present invention, and as shown in fig. 5, step S120 in fig. 1 may include:
and S121, determining T schedulable time moments included in the electrical-thermal coupling multi-energy flow system in one day.
Specifically, a day may be an evaluation period of the electro-thermally coupled multi-energy flow system, active power of a power grid and active power of a heat supply grid of the electro-thermally coupled multi-energy flow system are different at different scheduling time of the day, and therefore active power in the electro-thermally coupled multi-energy flow system can be scheduled more accurately by dividing the day into T schedulable time.
And S122, determining the dimension of each random scene according to the number of schedulable moments and the number of thermal loads of the electrical-thermal coupling multi-energy flow system, wherein the dimension w of each random scene is W-T (2+ L1), T is the number of schedulable moments in one day, and L1 is the number of thermal loads.
In particular, the stochastic scenario is related to not only the power generation active power, the electrical load active power and the thermal load active power when the electro-thermally coupled multi-energy flow system is operating, but also the scheduling time of the operation of the electro-thermally coupled multi-energy flow system. Each scheduling instant may have different power generation active power, power load active power and heat load active power, and thus, the dimension of each random scenario may be w.
S123, solving the randomness model of the operation of the electric-thermal coupling multi-energy flow system through SN times to form SN random scenes.
The stochastic model of the operation of the electro-thermally coupled multi-energy flow system can be solved, specifically, the stochastic model of the operation of the electro-thermally coupled multi-energy flow system can be solved by generating random numbers, one stochastic scenario can be solved by (2+ L1) formulas of the stochastic model at different scheduling moments, therefore, one stochastic scenario can be obtained by solving the stochastic model by w random numbers
Figure BDA0001928504370000121
As a random scenario, in which,
Figure BDA0001928504370000122
a random distribution function, rand, discretized according to the requirements of computational accuracy, of a stochastic model operating for an electro-thermally coupled multi-energy flow system1，rand2,...,randwAnd for w random numbers, a random scene is obtained by bringing the random numbers into the cumulative distribution function for solving. In general, random scenes SC1The first T elements in (a) may represent the power generation active power of T scheduling time instants, the last T elements represent the power load active power of T scheduling time instants, the last T elements represent the active power of the first thermal load of T scheduling time instants, the last T elements represent the active power of the second thermal load of T scheduling time instants, and so on. And repeatedly solving the SN times of the randomness model operated by the electric-thermal coupling multi-energy flow system by generating SN times of w random numbers to obtain SN random scenes. Generally, a random scene generated by a random model for solving the operation of the electric-thermal coupled multi-energy flow system through random numbers has randomness, so that a part of the random scene is not representative. In order to obtain SN representative random scenes and meet the accuracy of risk assessment, the SN random scene numbers are generated to be larger than the SN random scene numbers, so that the SN representative random scenes can be screened conveniently.
S124, scene reduction is carried out on the SN random scenes to obtain the SN random scenes, wherein SN is an integer and is greater than SN.
Specifically, when SN random scenes are reduced, a clustering method may be adopted to reduce scenes, so as to obtain SN representative random scenes, and the SN random scenes obtained have different probabilities in the SN random scenes. Exemplarily, the probabilities of sn random scenes obtained after scene reduction by adopting a clustering method are p respectively1，p2，···psn. In addition, in order to meet the accuracy requirement of risk assessment, representative risk assessment is requiredSN random scenes, SN random scenes generated randomly need to be sufficiently large. Illustratively, when 1000 random scenes are required to be representative, the randomly generated random scenes may be 10000.
On the basis of the technical schemes, the flow constraint conditions of the heat supply network are as follows:
Figure BDA0001928504370000131
wherein A is incidence matrix, and the incidence matrix is arranged on the incidence matrix
Figure BDA0001928504370000132
And lower association matrixA：
Figure BDA0001928504370000133
Figure BDA0001928504370000134
Where, i is 1,2 … N, N is the serial number of the node, j is 1,2 … B, B is the serial number of the branch, CpSpecific heat capacity of heat transfer transmitters for heat supply networks, ATFor the transposition of the incidence matrix A, M is the heat flow of one branch in B branches, the subscript of M represents the serial number of the branch, M is the vector formed by the flows of the B branches, TnVector formed by temperatures of nodes of heat supply network, TeVector formed by the temperature at the end of each branch of the heat supply network, TaIs ambient temperature, QJC is a diagonal matrix with the pipe heat loss coefficient of each branch as a diagonal element, λ is the unit length thermal conductivity of each branch of the heat supply network, the subscript of λ represents the branch number, L the length of each branch of the heat supply network, and the subscript of L represents the branch number.
Specifically, when the electric-thermal coupled multi-energy flow system works, the vector T formed by the temperatures of all nodes of the heat supply networknVector T formed by temperatures at tail ends of branches of heat supply networkeThe vector M composed of the flow of B branches is a controllable quantityThe control of the active power of the heat supply network is realized by controlling three quantities. In addition, the vector Q of the heat load of each node of the heat supply networkJRelated to the active power of the hot-spot cogeneration unit and the grid, thus, the vector Q formed by the thermal loads of the nodes of the thermal gridJAn electro-thermal coupling of a multi-energy flow system capable of electro-thermal coupling is achieved.
On the basis of the above technical solutions, the constraint conditions may further include an operation constraint condition of the cogeneration unit, and the operation constraint condition of the cogeneration unit is as follows:
Figure BDA0001928504370000141
Figure BDA0001928504370000142
wherein T is the T-th scheduling time of the future day, T is 1,2, T is the number of scheduling times of the future day, S is the S-th cogeneration unit of the S cogeneration units, Ds is the number of vertexes of the feasible region of the S-th cogeneration unit, (P)d,s(t),Qd,s(t)),d＝1,2...DsCoordinates of the vertex of the feasible region at the t-th scheduling time of the s-th cogeneration unit, (p)s(t),qs(t)) is a point, k, within the feasible region of the tth scheduling time of the s-th cogeneration unitd,s(t) is a coordinate (P) of a vertex of a feasible region at the tth scheduling time of the s-th cogeneration unitd,s(t),Qd,s(t)),d＝1,2...DsWeight of p1(t)、p2(t)、...、pS(t) is the electric load active power of each cogeneration unit at the t-th scheduling moment, q1(t)、q2(t)、...、qSAnd (t) the heat load active power of each cogeneration unit.
In particular, in a cogeneration unit, each cogeneration unit has a number of constraints, such as the maximum active power that the cogeneration unit generates. The constraint condition of the cogeneration unit is represented by a feasible domain, and each edge of the feasible domain is a constraint condition of the cogeneration unit. Therefore, the points of the feasible region formed by the constraint conditions of the cogeneration unit all satisfy the constraint conditions of the cogeneration unit.
In addition, considering that the active power change of the co-generation unit is a process in actual operation, the constraint conditions of the co-generation unit operation may further include a constraint condition of the co-generation unit active power ramp, that is:
Figure BDA0001928504370000143
s＝1,2...S
wherein, RAMPs downAnd RAMPs upRespectively the maximum values of the upward and downward climbing rates of the active power of the s-th cogeneration unit, wherein delta t is the time difference between the t-th scheduling time and the t-1-th scheduling time, and ps(t) and psAnd (t-1) respectively representing the active power of the s-th combined heat and power generation unit at the t-th scheduling moment and the t-1-th scheduling moment.
On the basis of the above technical solutions, the constraint conditions of the electro-thermally coupled multi-energy flow system further include constraint conditions of an electrical energy storage device and a thermal energy storage device in the electro-thermally coupled multi-energy flow system, and the constraint conditions are as follows:
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 T is the T-th scheduling time of the future day, T is 1,2, T is the number of scheduling times of the future day, E is the number of electrical energy storage devices, Pdis(t) is the total discharge power, P, of all E electrical energy storage devices at the t-th scheduling momentchar(t) is the total charging power, P, of all E electrical energy storage devices at the t-th scheduling momentdmaxAt the maximum value of the total discharge power, PcmaxFor the maximum value of the total charging power, SoC (t) is the total power storage state of all E electric energy storage devices in the t scheduling period, and SoCmaxThe maximum value of the energy storage capacity of all E electrical energy storage devices.
The constraint conditions of the H heat energy storage devices are as follows:
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 H is the number of the heat energy storage equipment, Qdis,hh(t) the heat release power of each thermal energy storage device at the t-th scheduling moment, Qdmax,hhFor maximum heat-release power, Q, of each thermal energy-storage devicechar,hh(t) the heat absorption power of each thermal energy storage device at the t-th scheduling moment, Qcmax,hhFor the maximum value of the heat absorption power, EH, of each thermal energy storage devicehh(t) Heat storage State, EH, of each thermal energy storage device at the tth scheduling timemax,hhThe maximum value of the heat storage capacity of each heat energy storage device.
In particular, whether it is an electrical energy storage device or a thermal energy storage device, each energy storage device has its maximum energy storage capacity, and therefore the constraint condition of each energy storage device includes its energy storage being less than or equal to its maximum energy storage capacity. And the total energy storage of the E electric energy storage devices is smaller than the sum of the maximum energy storage capacity of each electric energy storage device, and the total energy storage of the H heat energy storage devices is smaller than the sum of the maximum energy storage capacity of each heat energy storage device.
In addition, the constraints may also include power flow constraints of the tie lines connecting the thermo-electrically coupled multi-energy flow system with the external grid. Assuming that the sign of the active power flowing into the thermo-electric coupling multi-energy flow system from the external power grid is positive, the sign of the active power flowing into the external power grid from the thermo-electric coupling multi-energy flow system is negative, and the sum of the active power flowing into the thermo-electric coupling multi-energy flow system from the external power grid through the tie line and the active power flowing into the external power grid from the thermo-electric coupling multi-energy flow system is zero, that is, the sum is zero
Ppv(t)+Pe(t)+ps(t)+Ptie(t))＝0
Wherein, Ptie(t) is the active power transmitted on the contact line at the t-th scheduling time, Ppv(t) generating active power P for the t scheduling moment of the thermal-electric coupling multi-energy flow systeme(t) electric load active power at the t scheduling moment of the thermoelectric coupling multi-energy flow system, pSAnd (t) the active power of the electric load at the t-th scheduling moment of each cogeneration unit. And, Ptie(t) is a controllable amount. Thus, by adjusting Ptie(t) achieving active power balance within the thermo-electrically coupled multi-energy flow system.
On the basis of the technical schemes, the cost function is as follows:
Figure BDA0001928504370000161
among them, Hprices(t) is the time-sharing price of generating unit heat power at the tth scheduling time of the s-th cogeneration unit, namely, the epochs(t) is the time-sharing price of the generated unit electric power of the s-th combined heat and power generation unit at the t-th scheduling time, Ctie(t) 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, CtieCost of purchasing electricity for a line connecting a thermo-electric coupling multi-energy flow system to an external power grid, Ptie1Unbalanced power threshold, P, allowed by the tie-line when connecting the multi-energy flow system to the gridtie2For partial power exceeding the tie-line allowed imbalance power threshold, Price _ n (t) is the share-line time-of-use tariff when the tie-line allowed imbalance power is within the threshold, Price _ ex is the penalty tariff exceeding the tie-line allowed imbalance power threshold, Ptie_limitIs the upper power limit carried by the tie.
Specifically, the active power on the tie line between the thermal-electrical coupling multi-energy flow system and the external power grid is provided with a threshold, when the active power on the tie line between the thermal-electrical coupling multi-energy flow system and the external power grid exceeds the threshold, the external power grid can perform punishment price adjustment on the active power exceeding the threshold, the value of the cost function can be increased, meanwhile, the electrical-thermal coupling multi-energy flow system can affect the stability of the external power grid, and therefore the risk that the power interaction between the electrical-thermal coupling multi-energy flow system and the external power grid during operation exceeds the active power threshold of a grid-connected operation agreement can be analyzed through the value of the cost function. In addition, the cost function is used as an objective function, and the minimum value of the objective function at different scheduling time corresponding to sn random scenes can be solved by adopting an interior point method. Illustratively, the solution of the minimum value of the objective function at different scheduling time instants corresponding to sn random scenes is
1(t),2(t),...,sn(t)，t＝1,2,...,T
Wherein the content of the first and second substances,1(t),2(t),...,snthe active power components of the Tie lines in (t) are sequentially recorded as Tie1(t)，Tie2(t)，...，Tiesn(t), namely the active power on the connecting line of the electro-thermal coupling multi-energy flow system corresponding to the sn random scenes and the external power grid when the sn random scenes obtain the minimum value in the objective function.
In addition, the electric-thermal coupled multi-energy flow system risk assessment method is used for obtaining the connecting line active power probability distribution situation of the electric-thermal coupled multi-energy flow system at each scheduling time, and the connecting line active power probability distribution situation can be represented by adopting a confidence interval. Fig. 6 is a schematic diagram of an active power probability distribution of a tie line of an electro-thermal coupling multi-energy flow system according to an embodiment of the present invention, as shown in fig. 6, active power probability distributions on the tie line at the same scheduling time are different in different confidence intervals.
Based on the above technical solution, fig. 7 is a flowchart of another risk assessment method for an electro-thermal coupling multi-energy flow system according to an embodiment of the present invention, as shown in fig. 7, the step S150 in fig. 1 may specifically include:
and S151, determining the occurrence probability of sn random scenes.
Specifically, sn random scenes are representative scenes, and include random scenes on sunny days and cloudy days, for example. The probability of different random scenes occurring is also not equal, so it is necessary to determine the probability of different random scenes occurring. Illustratively, the probability of occurrence of different random scenes can be determined by a statistical method, and the probability of occurrence of sn random scenes is p11，p12，···p1sn。
S152, determining the risk value of sn random scenes at a certain scheduling time according to the minimum value of the objective function of the sn random scenes at the scheduling time.
Specifically, when the minimum value of the objective function of a random scene at a certain scheduling moment is solved, the active power on a connecting line connecting a thermo-electric coupling multi-energy current system and an external power grid is obtained when the minimum value is obtained through solving. Illustratively, the solution of the minimum value of the objective function at different scheduling instants for n random scenes1(t),2(t),...,snThe active power components of the Tie lines in (t) are sequentially recorded as Tie1(t)，Tie2(t)，...，Tiesn(t) of (d). In general, the active power on the tie line connecting the thermo-electric coupling multi-energy flow system and the external power grid is related to the risk value of the thermo-electric coupling multi-energy flow system, so that the risk value of the thermo-electric coupling multi-energy flow system at a certain scheduling time in a certain random scene can be calculated through the active power on the tie line connecting the thermo-electric coupling multi-energy flow system and the external power grid corresponding to the minimum value of the objective function. Illustratively, the relationship between the active power on the Tie line connecting the thermo-electrically coupled multi-energy flow system with the external grid and the Risk value of the thermo-electrically coupled multi-energy flow system is Risk (Tie)i(t)), wherein the Risk is a function, for example, a direct proportional function, so that the Risk value of the thermo-electrically coupled multi-energy flow system at a certain scheduling time in a random scene corresponding to the functional power on the connecting line connecting the thermo-electrically coupled multi-energy flow system with the external power grid can be obtained by using the Risk function. Because the probability of different random scenes is different, when calculating the risk value of a random scene at a scheduling momentIt needs to be multiplied by the probability of occurrence of a certain scene corresponding thereto. That is, the Risk value of a random scene at a scheduling time is Risk (Tie)i(t))·p1iWherein i is 1,2, sn.
S153, calculating the risk value of the electric-thermal coupling multi-energy flow system at a certain scheduling time according to the risk values of the sn random scenes and the probability of the sn scenes at the same scheduling time.
Specifically, after the risk value of each random scene at a certain scheduling time is calculated, the risk values of each random scene at a certain scheduling time are summed, and the risk value of the electrical-thermal coupling multi-energy flow system at a certain scheduling time can be obtained. I.e. the risk value Rsk at a certain scheduling instant of the electro-thermally coupled multi-energy flow systemtie(t)＝∑Risk(Tiei(t))·piAnd the left side of the equation is the risk value of the electrical-thermal coupled multi-energy flow system at a certain scheduling moment, and the right side of the equation is the sum of the risk values of each random scene at a certain scheduling moment. In addition, the risk values may be different for different scheduling times, so that different scheduling times need to be calculated respectively.
The embodiment of the invention also provides a risk assessment device of the electric-thermal coupling multi-energy flow system. Fig. 8 is a schematic structural diagram of a risk assessment apparatus of an electro-thermal coupling multi-energy flow system according to an embodiment of the present invention, as shown in fig. 8, the apparatus includes:
and a randomness model building module 10 for building a randomness model of the operation of the electro-thermally coupled multi-energy flow system.
A stochastic scenario generation module 20 for generating sn stochastic scenarios of the electro-thermally coupled multi-energy flow system according to a stochastic model; wherein sn is an integer and sn is greater than 1.
A constraint and cost function establishing module 30 for establishing a constraint and cost function of the electro-thermally coupled multi-energy flow system; wherein the constraint condition comprises a heat supply network power flow constraint condition;
and the objective function solving module 40 is used for solving the minimum value of the objective functions corresponding to the sn random scenes according to the constraint conditions by taking the cost function as the objective function.
In particular, the constraint and cost function establishing module 30 may include a constraint establishing module and a cost function establishing module, the constraint establishing module may include a plurality of sub-modules, and different constraints may be established.
A risk determination module 50, configured to determine a risk value of the electro-thermally coupled multi-energy flow system according to a minimum value of the objective function corresponding to the sn random scenarios.
According to the technical scheme of the embodiment, a randomness model of the operation of the electric-thermal coupling multi-energy current system is established through a randomness model establishing module, sn random scenes of the electric-thermal coupling multi-energy current system are generated through a random scene generating module according to the randomness model, and constraint conditions and cost functions of the electric-thermal coupling multi-energy current system are established through a constraint condition and a cost function establishing module, wherein the constraint conditions comprise a heat network power flow constraint condition, so that the coupling relation between the active power of a heat network and the active power of a power network can be established, therefore, the minimum value of the objective function of the electric-thermal coupling multi-energy current system under the sn random scenes can be solved through the electric-thermal coupling constraint condition of the electric-thermal coupling multi-energy current system through an objective function solving module, and the minimum value of the objective function corresponding to the sn random scenes is determined through a risk determining module And the risk value of the system is more accurate, so that the solved risk value that the power interaction of the electric-thermal coupled multi-energy flow system and an external power grid exceeds the active power threshold value of a grid-connected operation protocol during operation is more accurate. And a randomness model of the electric-thermal coupling multi-energy flow system is established, and the evaluation period is shortened. In addition, the random model of the operation of the electric-thermal coupling multi-energy flow system considers the uncertain influence of the optimal scheduling of the cogeneration unit, the electric energy storage device, the thermal energy storage device, the electric load, the thermal load and the photovoltaic power station on the risk assessment, so that the accuracy of the risk assessment of the electric-thermal coupling multi-energy flow system is improved.
The embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the method for risk assessment of any electrical-thermal coupling multi-energy flow system provided in the embodiment of the present invention is implemented.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims (12)

1. A method for risk assessment of an electro-thermally coupled multi-energy flow system, comprising:
establishing a randomness model of the operation of the electric-thermal coupling multi-energy flow system;
generating sn stochastic scenarios of the electro-thermally coupled multi-energy flow system according to the stochastic model; wherein sn is an integer and sn is greater than 1;
establishing constraints and a cost function of the electro-thermally coupled multi-energy flow system; wherein the constraints comprise heat supply network flow constraints;
taking the cost function as an objective function, and solving the minimum value of the objective function corresponding to the sn random scenes according to the constraint conditions;
determining a risk value of the electro-thermally coupled multi-energy flow system according to a minimum value of an objective function corresponding to the sn random scenes;
the randomness model of the operation of the electric-thermal coupling multi-energy flow system comprises a randomness model of a predicted value of the generating active power, a randomness model of a predicted value of the electrical load active power and a randomness model of a predicted value of the thermal load active power at each scheduling time of a day in the future of the electric-thermal coupling multi-energy flow system, wherein the randomness model of the predicted value of the generating active power and the randomness model of the predicted value of the electrical load active power are specifically as follows:
Epv(t)～N(μpv(t)，pv(t)2)
Ee(t)～N(μe(t)，e(t)2)
Ppv(t)＝PFpv(t)+Epv(t)
Pe(t)＝PFe(t)+Ee(t)
wherein T is the T-th scheduling time of the future day, T is 1,2pv(t) is a predicted value of the active power generated at the t-th scheduling time, PFe(t) is a predicted value of the active power of the electric load at the t-th scheduling moment, Epv(t) deviation of predicted value of active power generated at the t-th scheduling time, EeFor deviations of the predicted values of the active power of the electrical load at the t-th scheduling instant, mupv(t) is an expected value of deviation of the predicted value of the active power generated at the t-th scheduling time,pv(t) is a standard deviation of a predicted deviation of the predicted value of the active power generated at the t-th scheduling time, mue(t) is the expected value of the deviation of the predicted value of the active power of the electric load at the t-th scheduling moment,e(t) is the standard deviation of the predicted deviation of the active power of the electric load at the t-th scheduling moment, Ppv(t) active power generated at the t-th scheduling time, Pe(t) the electric load active power at the tth scheduling moment;
the randomness model of the heat load active power predicted value specifically comprises the following steps:
Ph,1(t)～Beta(A＝10,B＝10(Pmax/Phf,1(t)-1))
Ph,2(t)～Beta(A＝10,B＝10(Pmax/Phf,2(t)-1))
...
Ph,L1(t)～Beta(A＝10,B＝10(Pmax/Phf,L1(t)-1))
wherein, Phf,1(t)、Phf,2(t)、...、Phf,L1(t) each represents a totalPredicted active power, P, at the t-th scheduling time of each thermal load of L1h,1(t)、Ph,2(t)、...、Ph,L1(t) the active power at the t-th scheduling time of each thermal load, PmaxIs the maximum power of the total heat load in the electric-thermal coupling multi-energy flow system.
2. The method for risk assessment of an electro-thermally coupled multi-energy flow system according to claim 1, wherein said generating sn stochastic scenarios of said electro-thermally coupled multi-energy flow system according to said stochastic model comprises:
determining T schedulable times of day for the electro-thermally coupled multi-energy flow system;
determining the dimension of each random scene according to the number of the schedulable moments and the number of the heat loads of the electrical-thermal coupled multi-energy flow system, wherein the dimension w of each random scene is w-T (2+ L1), T is the number of the schedulable moments in one day, and L1 is the number of the heat loads;
solving a randomness model of the operation of the electric-thermal coupling multi-energy flow system through SN times to form SN random scenes;
and performing scene reduction on the SN random scenes to obtain SN random scenes, wherein SN is an integer and is greater than SN.
3. The risk assessment method of an electro-thermally coupled multi-energy flow system according to claim 1, wherein the heat network flow constraints are:
Figure FDA0002495717930000021
wherein A is incidence matrix, and the incidence matrix is arranged on the incidence matrix
Figure FDA0002495717930000022
And lower association matrixA：
Figure FDA0002495717930000023
Figure FDA0002495717930000024
Where, i is 1,2 … N, N is the serial number of the node, j is 1,2 … B, B is the serial number of the branch, CpSpecific heat capacity of heat transfer transmitters for heat supply networks, ATFor the transposition of the incidence matrix A, M is the heat flow of one branch in B branches, the subscript of M represents the serial number of the branch, M is the vector formed by the flows of the B branches, TnVector formed by temperatures of nodes of heat supply network, TeVector formed by the temperature at the end of each branch of the heat supply network, TaIs ambient temperature, QJC is a diagonal matrix with the pipe heat loss coefficient of each branch as a diagonal element, λ is the unit length thermal conductivity of each branch of the heat supply network, the subscript of λ represents the branch number, L the length of each branch of the heat supply network, and the subscript of L represents the branch number.
4. The risk assessment method of an electro-thermally coupled multi-energy flow system according to claim 1, wherein said constraints further comprise cogeneration unit operating constraints, said cogeneration unit operating constraints being:
Figure FDA0002495717930000031
Figure FDA0002495717930000032
wherein T is the T-th scheduling time of the future day, T is 1,2, and T, T is the number of scheduling times of the future day, S is the S-th cogeneration unit of the S cogeneration units, Ds is the number of vertices of the feasible region of the S-th cogeneration unit, (P)d，s(t)，Qd，s(t))，d＝1，2...DsFor the s th cogeneration unitCoordinates of the vertex of the feasible region at the t-th scheduling time, (p)s(t),qs(t)) is a point, k, within the feasible region of the tth scheduling instant of the s-th cogeneration unitd,s(t) is the coordinate (P) of the vertex of the feasible region of the tth scheduling time of the s-th cogeneration unitd，s(t)，Qd，s(t))，d＝1，2...DsWeight of p1(t)、p2(t)、...、pS(t) is the electric load active power of each cogeneration unit at the t-th scheduling moment, q1(t)、q2(t)、...、qS(t) is the heat load active power of each cogeneration unit.
5. The risk assessment method of an electro-thermally coupled multi-energy flow system according to claim 4, wherein the cogeneration unit operating constraints further comprise constraints of active power ramp-up of the cogeneration unit, namely:
-RAMPs down·Δt≤ps(t)-ps(t-1)≤RAMPs up·Δt
s＝1,2...S
wherein, RAMPs downAnd RAMPs upRespectively the maximum values of the upward and downward climbing rates of the active power of the s-th cogeneration unit, wherein delta t is the time difference between the t-th scheduling time and the t-1-th scheduling time, and ps(t) and psAnd (t-1) respectively representing the active power of the s-th combined heat and power generation unit at the t-th scheduling moment and the t-1-th scheduling moment.
6. The method for risk assessment of an electro-thermally coupled multi-energy flow system according to claim 1, wherein the constraints of said electro-thermally coupled multi-energy flow system further comprise constraints of electrical and thermal energy storage devices in said electro-thermally coupled multi-energy flow system, being:
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 T is the T-th scheduling time of the future day, T is 1,2, T is the number of scheduling times of the future day, E is the number of electrical energy storage devices, Pdis(t) is the total discharge power, P, of all E electrical energy storage devices at the t-th scheduling momentchar(t) is the total charging power, P, of all E electrical energy storage devices at the t-th scheduling momentdmaxAt the maximum value of the total discharge power, PcmaxFor the maximum value of the total charging power, SoC (t) is the total power storage state of all E electric energy storage devices in the t scheduling period, and SoCmaxThe maximum value of the energy storage capacity of all E electric energy storage devices;
the constraint conditions of the H heat energy storage devices are as follows:
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 H is the number of the heat energy storage equipment, Qdis,hh(t) the heat release power of each thermal energy storage device at the t-th scheduling moment, Qdmax,hhFor maximum heat-release power, Q, of each thermal energy-storage devicechar,hh(t) the heat absorption power of each thermal energy storage device at the t-th scheduling moment, Qcmax,hhFor the maximum value of the heat absorption power, EH, of each thermal energy storage devicehh(t) Heat storage State, EH, of each thermal energy storage device at the tth scheduling timemax,hhThe maximum value of the heat storage capacity of each heat energy storage device.
7. The method for risk assessment of an electro-thermally coupled multi-energy flow system according to claim 1, characterized in that the constraints of the electro-thermally coupled multi-energy flow system further comprise power flow constraints of a tie line connecting the electro-thermally coupled multi-energy flow system with an external power grid, in particular:
Ppv(t)+Pe(t)+ps(t)+Ptie(t))＝0
wherein, Ptie(t) is the active power transmitted on the contact line at the tth scheduling moment, pS(t) is the electric load active power P of the t scheduling moment of the electric-thermal coupling multi-energy flow system cogeneration unitpv(t) active power generated at the t-th scheduling time, PeAnd (t) is the active power of the electric load at the t-th scheduling moment.
8. The method for risk assessment of an electro-thermally coupled multi-energy flow system according to claim 4, characterized in that said cost function is:
Figure FDA0002495717930000041
among them, Hprices(t) is the time-sharing price of generating unit thermal power at the tth scheduling time of the cogeneration unit, namely, edges(t) is the time-shared price of the generated unit electric power of the mth scheduling time of the mth cogeneration unit, Ctie(t) 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, CtiePurchasing cost, P, for a tie line connecting the electro-thermally coupled multi-energy flow system to an external power gridtie1An unbalance power threshold value, P, allowed by the tie line when the electric-thermal coupled multi-energy flow system is connected to the gridtie2For partial powers exceeding the tie allowed imbalance power threshold, Price _ n (t) is the share-time Price on the tie when the tie allowed imbalance power is within a threshold, Price _ ex is the penalty Price exceeding the tie allowed imbalance power threshold fraction, Ptie_limitAn upper power limit carried for the tie.
9. The method according to claim 8, wherein the cost function is used as an objective function, and when the minimum value of the objective function corresponding to the sn stochastic scenes is solved according to the constraint conditions, the minimum value of the objective function corresponding to the sn stochastic scenes at different scheduling time points is solved by using an interior point method.
10. The method for risk assessment of an electro-thermally coupled multi-energy flow system according to claim 9, wherein said determining a risk value of said electro-thermally coupled multi-energy flow system according to a minimum of said sn stochastic scenario corresponding objective functions comprises:
determining a probability of occurrence of the sn random scenes;
determining a risk value of each random scene at a scheduling moment according to the minimum value of the objective function of the sn random scenes at the scheduling moment;
calculating a risk value of a certain scheduling time of the electrical-thermal coupled multi-energy flow system according to the risk values of the sn random scenes and the probability of the sn scenes at the same scheduling time.
11. A risk assessment device for an electro-thermally coupled multi-energy flow system, comprising:
the randomness model establishing module is used for establishing a randomness model of the operation of the electric-thermal coupling multi-energy flow system; the randomness model of the operation of the electric-thermal coupling multi-energy flow system comprises a randomness model of a predicted value of the generating active power, a randomness model of a predicted value of the electrical load active power and a randomness model of a predicted value of the thermal load active power at each scheduling time of a day in the future of the electric-thermal coupling multi-energy flow system, wherein the randomness model of the predicted value of the generating active power and the randomness model of the predicted value of the electrical load active power are specifically as follows:
Epv(t)～N(μpv(t)，pv(t)2)
Ee(t)～N(μe(t)，e(t)2)
Ppv(t)＝PFpv(t)+Epv(t)
Pe(t)＝PFe(t)+Ee(t)
wherein T is the T-th scheduling time of the future day, T is 1,2pv(t) is a predicted value of the active power generated at the t-th scheduling time, PFe(t) is a predicted value of the active power of the electric load at the t-th scheduling moment, Epv(t) deviation of predicted value of active power generated at the t-th scheduling time, EeFor deviations of the predicted values of the active power of the electrical load at the t-th scheduling instant, mupv(t) is an expected value of deviation of the predicted value of the active power generated at the t-th scheduling time,pv(t) is a standard deviation of a predicted deviation of the predicted value of the active power generated at the t-th scheduling time, mue(t) is the expected value of the deviation of the predicted value of the active power of the electric load at the t-th scheduling moment,e(t) is the standard deviation of the predicted deviation of the active power of the electric load at the t-th scheduling moment, Ppv(t) active power generated at the t-th scheduling time, Pe(t) the electric load active power at the tth scheduling moment;
the randomness model of the heat load active power predicted value specifically comprises the following steps:
Ph,1(t)～Beta(A＝10,B＝10(Pmax/Phf,1(t)-1))
Ph,2(t)～Beta(A＝10,B＝10(Pmax/Phf,2(t)-1))
...
Ph,L1(t)～Beta(A＝10,B＝10(Pmax/Phf,L1(t)-1))
wherein, Phf,1(t)、Phf,2(t)、...、Phf,L1(t) the predicted active power at the t-th scheduling time of each thermal load, P, represented by L1 thermal loads in totalh,1(t)、Ph,2(t)、...、Ph,L1(t) the active power at the t-th scheduling time of each thermal load, PmaxMaximum power for total heat load in an electro-thermally coupled multi-energy flow system;
a stochastic scenario generation module for generating sn stochastic scenarios of the electro-thermally coupled multi-energy flow system according to the stochastic model; wherein sn is an integer and sn is greater than 1;
a constraint and cost function establishing module for establishing a constraint and cost function of the electro-thermally coupled multi-energy flow system; wherein the constraints comprise heat supply network flow constraints;
an objective function solving module, which takes the cost function as an objective function and solves the minimum value of the objective function corresponding to the sn random scenes according to the constraint conditions;
and the risk determination module is used for determining a risk value of the electrical-thermal coupled multi-energy flow system according to the minimum value of the objective function corresponding to the sn random scenes.
12. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a method for risk assessment of an electro-thermally coupled multi-energy flow system according to any one of claims 1-10.
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