CN111313429A - Reliability assessment method and system for comprehensive energy system - Google Patents

Reliability assessment method and system for comprehensive energy system Download PDF

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CN111313429A
CN111313429A CN202010115478.4A CN202010115478A CN111313429A CN 111313429 A CN111313429 A CN 111313429A CN 202010115478 A CN202010115478 A CN 202010115478A CN 111313429 A CN111313429 A CN 111313429A
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energy system
reliability
heat
power
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CN111313429B (en
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王佳伟
李旭霞
王尧
梁燕
荆永明
刘文霞
李守强
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North China Electric Power University
Economic and Technological Research Institute of State Grid Shanxi Electric Power Co Ltd
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Economic and Technological Research Institute of State Grid Shanxi Electric Power Co Ltd
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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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • 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
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—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
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A30/00—Adapting or protecting infrastructure or their operation
    • Y02A30/60—Planning or developing urban green infrastructure
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

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Abstract

The invention provides a reliability evaluation method and a system for an integrated energy system, which comprise the following steps: acquiring data of the comprehensive energy system, constructing a structural model of the comprehensive energy system, and establishing an operating energy flow balance model of a power supply subsystem and a heat supply subsystem according to the structural model of the comprehensive energy system; establishing a load reduction optimization model by taking the minimum value of the equivalent load reduction as an optimization target according to the energy price of the electric load, the heat load and the cold load and the relative importance degree of the electric load; and calculating network parameters, air temperature variables and source load variables of the comprehensive energy system based on the running energy flow balance model and the load reduction model, carrying out load flow calculation on the comprehensive energy system, and determining the reliability index of the comprehensive energy system by using the load flow calculation result and combining a sequential Monte Carlo method. The method and the system consider the difference of network topology, transmission delay, terminal thermal inertia and user reliability requirements, and improve the accuracy of the reliability evaluation of the comprehensive energy system.

Description

Reliability assessment method and system for comprehensive energy system
Technical Field
The invention belongs to the technical field of comprehensive energy systems, and particularly relates to a reliability evaluation method and system for a comprehensive energy system.
Background
In order to realize energy conservation, emission reduction and energy pressure relief, all countries in the world develop Integrated Energy Systems (IES). The IES can break through the system, technology and market barriers of the traditional energy supply system, enhance the overall interaction of various energy flows of a source, a net and a load, and improve the energy supply reliability of the system due to the replaceability and complementarity among the energy flows. Therefore, considering the difference of influence of energy supply interruption of different energy sources on users, the change characteristics of the operating state of each energy system and multi-energy complementation under faults, establishing a comprehensive energy reliability index and researching a reliability evaluation method become problems to be solved urgently in planning and operating the IES system.
Research aiming at IES reliability evaluation mainly relates to the aspects of element reliability modeling, fault state analysis, reliability indexes and evaluation methods.
The prior art adopts the following ways in reliability modeling: a Markov two-state model is adopted to represent the reliability of the distributed power supply and the equipment of the electric, thermal and gas network in the system; and considering the shutdown of the gas unit caused by the self fault of the gas unit and the fault of the gas supply network, and establishing a model of the availability ratio of the gas unit equipment.
For IES fault state analysis, most of the prior art adopts a fault mode consequence analysis method, traverses the influence of all element faults on each load in a system, and establishes a corresponding relation set of expected accidents and fault consequences; other fault state analysis methods are that the priority level of the load supply after the fault is determined according to the difference of the energy source grade, and a load reduction strategy is formulated; the following models exist for the fault load reduction of the integrated energy system: one type is a load reduction optimization model which aims at minimizing the sum of the reduction of electric, gas and heat loads; the other is to optimize the load reduction amount with the aim of minimizing the system operation cost.
The reliability index evaluation methods mainly include these methods: one method is that indexes such as average failure power failure frequency SAIFI and average failure power failure time SAIDI of the traditional power system reliability evaluation are pushed to a heat supply subsystem and a gas supply subsystem to form an IES energy supply reliability index system; on the basis of the former evaluation method, another evaluation method of the reliability index is as follows: and calculating the heat dissipation inertia of cold and heat loads and carrying out heat supply reliability index statistical calculation by taking the hot water temperature of the terminal water tank as a reliability criterion, thereby more accurately describing the energy utilization reliability condition of a user. In the evaluation method of the reliability index, the traditional reliability evaluation of the power system mainly comprises an analytical method and a simulation method; in addition, the access of photovoltaic equipment, energy storage equipment and other equipment in the IES and the correlation between heat load and factors such as weather and temperature are considered, and the IES reliability index is evaluated by adopting a Monte Carlo simulation method.
However, the reliability evaluation method for the energy system in the prior art still has the following technical problems:
firstly, the reliability evaluation method in the existing energy system mostly ignores the actual network topology and the transmission loss of a heat supply network, only simulates the temperature change of a heating power terminal, and cannot reflect the influence of the network transmission performance on the reliability;
secondly, a load reduction strategy is mostly formulated from the perspective of energy suppliers and society, and the difference of the user on the requirement of energy supply reliability is ignored;
thirdly, due to inertia and transmission delay of the heat supply network, the state change of the system at the moment of the fault has time lag, and the state of the system at the moment of the fault is combined with the fault recovery time to determine the change of the heat supply temperature of the user, so that the reliability of the energy system is greatly influenced.
Disclosure of Invention
The embodiment of the invention provides a reliability evaluation method and system for an integrated energy system, which aim to at least solve one technical problem in the prior art.
In a first aspect, an embodiment of the present invention provides a reliability assessment method for an integrated energy system, the method including the following steps:
acquiring data of the comprehensive energy system, constructing a structural model of the comprehensive energy system, and establishing an operating energy flow balance model of a power supply subsystem and a heat supply subsystem according to the structural model of the comprehensive energy system;
establishing a load reduction optimization model by taking the minimum value of the equivalent load reduction as an optimization target according to the energy price of the electric load, the heat load and the cold load and the relative importance degree of the electric load;
and calculating network parameters, air temperature variables and source load variables of the integrated energy system based on the running energy flow balance model and the load reduction optimization model, carrying out load flow calculation on the integrated energy system, and determining the reliability index of the integrated energy system by using the load flow calculation result and combining a sequential Monte Carlo method.
Further, the objective function of the load shedding optimization model is as follows:
Figure BDA0002391365610000031
wherein v isiIs the relative importance of the electrical load iA coefficient; pE,i、PΦ,j、PC,kThe price of electricity of the electric load i, the price of heat of the heat load j and the price of cold of the cold load k are respectively; rE,i、RΦ,j、RC,kRespectively reducing the electric load, the heat load and the cold load of each node;
establishing equality constraint in load reduction according to the running energy flow balance model of the power supply subsystem and the heat supply subsystem;
Figure BDA0002391365610000032
wherein, PDN,iThe electric power accessed by an electric load node i of the power supply system is referred to; pc,iOutputting electric power for an electric load node i of the gas generator set; ps,iThe photovoltaic power generation power of an electrical load node i; pHP,iHeat pump electrical power for electrical load node i; pEB,iInput electric power of an electric load node i of the electric boiler; pL,iIndicating the conventional electric load of the electric load node i; piInjecting active power into an electric load node i; phiLB,jThe winter heating power of a thermal load node j of the lithium bromide unit; phiL,jIs the thermal load of thermal load node j; phijThe injected thermal power for thermal load node j; cLB,kThe cooling power of a cooling load node k of the lithium bromide unit in summer; rC,kThe cold load of the cold load node is reduced; cL,kIs the cooling load of cooling load node k; ckIs the injected cold power of the cold load node k.
Further, the determining the reliability index of the integrated energy system comprises the following substeps:
sampling the states of all elements of the comprehensive energy system by utilizing a sequential Monte Carlo method based on an element reliability model in a given time span;
load flow calculation, namely performing load flow calculation on the comprehensive energy system according to the running energy flow balance model of the power supply subsystem and the heat supply subsystem and by taking network parameters, temperature variables and source load variables of the comprehensive energy system into account;
acquiring the fault time and state of the comprehensive energy system: determining the fault occurrence time according to the element state sampling result, and obtaining the state of the comprehensive energy system according to the load flow calculation result;
and (3) state analysis: according to the heat supply reliability index of the comprehensive energy system, analyzing the state of the comprehensive energy system to obtain a state analysis result;
load point index statistics: according to the load reduction optimization model and in combination with the state analysis result, counting the reliability indexes of all load points of the comprehensive energy system;
and (3) sampling convergence judgment: when the variance coefficient of the reliability index is smaller than a preset value or the number of times of analog sampling reaches the maximum sampling number, ending the analog cycle and outputting the reliability index of the load point; otherwise, returning to obtain the element state sampling result to perform next sampling;
calculating a system index: and (4) counting the reliability indexes of all the load points, and calculating the reliability of the comprehensive energy system.
Further, the heat supply reliability index of the integrated energy system comprises the average energy supply interruption times of users, the average energy supply interruption time of users and the reliability of energy supply of users.
Further, the reliability evaluation method further includes the steps of: the method comprises the following steps of considering the time delay characteristic of a heat supply network to correct the heat supply reliability index of the comprehensive energy system, and specifically comprises the following substeps:
(1) establishing a solving model of temperature recovery time based on a thermodynamic energy conservation law;
Figure BDA0002391365610000041
Figure BDA0002391365610000042
wherein q is the heat dissipation capacity of the radiator per unit length through natural convection, v is the flow velocity of water flow, and tuFor temperature recovery time, α is the heat loss systemCounting;
(2) calculating the temperature recovery time according to the solution model of the temperature recovery time;
Figure BDA0002391365610000043
(3) and correcting according to the energy supply recovery time, the allowable temperature change time and the temperature recovery time to obtain a corrected calculation model of the user heat supply fault time:
tf＝tr-tp+tu
wherein, tfAnd supplying heat to the corrected user for the fault time.
In a second aspect, embodiments of the present invention provide a reliability assessment system for an integrated energy system, the system comprising a first modeling module, a second modeling module, and a calculation module, wherein;
the first modeling module acquires data of the comprehensive energy system, constructs a structural model of the comprehensive energy system, and establishes an operating energy flow balance model of a power supply subsystem and a heat supply subsystem according to the structural model of the energy system;
the second modeling module establishes a load reduction optimization model by taking the minimum value of the equivalent load reduction as an optimization target according to the energy price of the electric load, the heat load and the cold load and the relative importance degree of the electric load;
the calculation module calculates network parameters, air temperature variables and source load variables of the comprehensive energy system based on the running energy flow balance model and the load reduction optimization model, carries out load flow calculation on the comprehensive energy system, and determines the reliability index of the comprehensive energy system by using the load flow calculation result and combining a sequential Monte Carlo method.
Further, the objective function of the load shedding optimization model is as follows:
Figure BDA0002391365610000051
wherein v isiIs the relative importance of the electrical load iA degree coefficient; pE,i、PΦ,j、PC,kThe price of electricity of the electric load i, the price of heat of the heat load j and the price of cold of the cold load k are respectively; rE,i、RΦ,j、RC,kRespectively reducing the electric load, the heat load and the cold load of each node;
establishing equality constraint in load reduction according to the running energy flow balance model of the power supply subsystem and the heat supply subsystem;
Figure BDA0002391365610000052
wherein, PDN,iThe electric power accessed by an electric load node i of the power supply system is referred to; pc,iOutputting electric power for an electric load node i of the gas generator set; ps,iThe photovoltaic power generation power of an electrical load node i; pHP,iHeat pump electrical power for electrical load node i; pEB,iInput electric power of an electric load node i of the electric boiler; pL,iIndicating the conventional electric load of the electric load node i; piInjecting active power into an electric load node i; phiLB,jThe winter heating power of a thermal load node j of the lithium bromide unit; phiL,jIs the thermal load of thermal load node j; phijThe injected thermal power for thermal load node j; cLB，kThe cooling power of a cooling load node k of the lithium bromide unit in summer; rC,kThe cold load of the cold load node is reduced; cL,kIs the cooling load of cooling load node k; ckIs the injected cold power of the cold load node k.
Further, the calculation module performs the following operations:
sampling the states of all elements of the comprehensive energy system by utilizing a sequential Monte Carlo method based on an element reliability model in a given time span;
load flow calculation, namely performing load flow calculation on the comprehensive energy system according to the running energy flow balance model of the power supply subsystem and the heat supply subsystem and by taking network parameters, temperature variables and source load variables of the comprehensive energy system into account;
acquiring the fault time and state of the comprehensive energy system: determining the fault occurrence time according to the element state sampling result, and obtaining the state of the comprehensive energy system according to the load flow calculation result;
and (3) state analysis: according to the heat supply reliability index of the comprehensive energy system, analyzing the state of the comprehensive energy system to obtain a state analysis result;
load point index statistics: according to the load reduction optimization model and in combination with the state analysis result, counting the reliability indexes of all load points of the comprehensive energy system;
and (3) sampling convergence judgment: when the variance coefficient of the reliability index is smaller than a preset value or the number of times of analog sampling reaches the maximum sampling number, ending the analog cycle and outputting the reliability index of the load point; otherwise, returning to obtain the element state sampling result to perform next sampling;
calculating a system index: and (4) counting the reliability indexes of all the load points, and calculating the reliability of the comprehensive energy system.
Further, the heat supply reliability index of the integrated energy system comprises the average energy supply interruption times of users, the average energy supply interruption time of users and the reliability of energy supply of users.
Further, the calculation module is further configured to take into account a heat supply network delay characteristic to correct the heat supply reliability index, including performing the following operations:
establishing a solving model of temperature recovery time based on a thermodynamic energy conservation law;
Figure BDA0002391365610000061
Figure BDA0002391365610000062
wherein q is the heat dissipation capacity of the radiator per unit length through natural convection, v is the flow velocity of water flow, and tuFor temperature recovery time, α is the heat loss coefficient;
calculating the temperature recovery time according to the solution model of the temperature recovery time;
Figure BDA0002391365610000063
and correcting according to the energy supply recovery time, the allowable temperature change time and the temperature recovery time to obtain a corrected calculation model of the user heat supply fault time:
tf＝tr-tp+tu
wherein, tfAnd supplying heat to the corrected user for the fault time.
The reliability evaluation method of the invention considers the difference of network topology, transmission delay, terminal thermal inertia and user reliability requirements, and improves the precision of the IES reliability evaluation of the integrated energy system.
Drawings
Fig. 1 is a schematic flowchart of a method for evaluating reliability of a consumer integrated energy system according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an integrated energy system according to an embodiment of the present invention;
fig. 3 is a schematic flowchart of a process for determining a reliability index according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a reliability evaluation system for an integrated energy system.
Detailed Description
The present invention is described in detail with reference to the embodiments shown in the drawings, but it should be understood that these embodiments are not intended to limit the present invention, and those skilled in the art should understand that functional, methodological, or structural equivalents or substitutions made by these embodiments are within the scope of the present invention.
Example one
Fig. 1 is a schematic flow chart of a reliability assessment method for an integrated energy system according to an embodiment of the present invention, and referring to fig. 1, the reliability assessment method includes the following steps:
s100, acquiring data of the comprehensive energy system, constructing a structural model of the comprehensive energy system, and establishing an operation energy flow balance model of a power supply subsystem and a heat supply subsystem according to the structural model of the energy system;
s200, establishing a load reduction optimization model of the comprehensive energy system by taking the minimum value of the equivalent load reduction as an optimization target according to the energy price of the electric load, the heat load and the cold load and the relative importance degree of the electric load;
and S300, calculating network parameters, air temperature variables and source load variables of the comprehensive energy system based on the running energy flow balance model and the load reduction optimization model of the power supply subsystem and the heat supply subsystem, carrying out load flow calculation on the comprehensive energy system, and determining the reliability index of the comprehensive energy system by utilizing the load flow calculation result and combining a sequential Monte Carlo method.
Referring to fig. 2, fig. 2 is a schematic structural diagram of an integrated energy system according to an embodiment of the present invention, in this embodiment, a core of the integrated energy system is a Combined Cooling, Heating and Power (CCHP) Unit, which includes a gas turbine Generator (GT), a Waste Heat Boiler (WHB), and an absorption Lithium Bromide Unit (LB); further, the integrated energy system is further configured with a Ground Source Heat Pump (HP) and an Electric Boiler (EB), the Ground Source Heat Pump can be used for decoupling cold/Heat and a power supply and flexibly operating the unit, and the Electric Boiler (EB) is used for coping with a peak load and serving as a Heat Source backup under a failure of the combined cooling heating and power unit. Meanwhile, the integrated energy system is connected to distributed Photovoltaic (PV) to consume renewable energy on site.
Specifically, the step S100 includes the following sub-steps S110 to S130:
s110, establishing operation models of a gas generator set, a waste heat boiler, a lithium bromide unit, a ground source heat pump and an electric boiler;
the operation models and the operation strategies of the gas generator set, the waste heat boiler, the lithium bromide unit, the ground source heat pump and the electric boiler in the embodiment are as follows:
a. a gas generator set: the gas generator set generates electricity by using high-temperature and high-pressure flue gas generated by burning natural gas, and the recyclable medium-low temperature heat is absorbed by the waste heat boiler to generate heat, so that an operation model of the gas generator set is established:
Figure BDA0002391365610000081
wherein, Pc、Φc、SGTThe output electric power, the thermal power and the input natural gas flow of the gas generator set are respectively;
Figure BDA0002391365610000082
the power generation coefficient and the heating coefficient of the gas turbine are respectively.
b. Waste heat boiler: exhaust-heat boiler can utilize the waste gas in the gas unit production process to heat water to the uniform temperature to realize thermal recovery, this exhaust-heat boiler's output thermal power as follows:
ΦWHB＝ηWHBΦc(2)
wherein phiWHB、ηWHBRespectively the output thermal power and the heat recovery efficiency of the waste heat boiler.
c. Lithium bromide unit: the heat of each heat source is transmitted to a heating station at the tail end of a pipe network through a primary pipe network, an absorption type lithium bromide unit can be configured in the station, heat exchange between a primary pipe network and a secondary pipe network is directly carried out in the heating period in winter, the absorption type lithium bromide unit can realize heat-cold conversion in the cooling period in summer, and cold is supplied to users through a secondary pipe network, so that corresponding models can be established according to the capacity modes of the lithium bromide unit in different seasons:
Figure BDA0002391365610000083
Figure BDA0002391365610000084
ΦHN＝ΦWHB+ΦHP+ΦEB(5)
wherein phiHNFor input of thermal power, phi, to thermal power stationsLB、CLBRespectively the winter heating power and the summer refrigerating power of the lithium bromide unit,
Figure BDA0002391365610000085
the lithium bromide unit heating energy efficiency ratio and the lithium bromide unit refrigerating energy efficiency ratio are respectively.
d. A ground source heat pump: the ground source heat pump obtains energy through underground soil heat exchange, the combined configuration of the ground source heat pump and the gas generator set can break through the limitation that the traditional fixed-temperature electric output is not easy to adjust, and the relation between the heating power and the consumed electric power of the ground source heat pump can be represented by an equation (6):
ΦHP＝ηHPPHP(6)
wherein phiHP、PHP、ηHPRespectively the output thermal power, the input electric power and the electric heat conversion efficiency of the ground source heat pump.
e. An electric boiler: the electric boiler can balance the heat absorbed and emitted to the terrestrial heat in the heating period and the refrigerating period, and can establish an operation model of the electric boiler according to the electric heat conversion efficiency of the electric boiler:
ΦEB＝ηEBPEB(7)
wherein phiEB、ηEBHeating power, and electric-to-heat conversion efficiency, P, respectively, of an electric boilerEBIs the input electric power of the electric boiler.
And S120, respectively establishing an operation energy flow balance model of the power supply subsystem and the heat supply subsystem based on the operation models of the gas generator set, the waste heat boiler, the lithium bromide unit, the ground source heat pump and the electric boiler and considering the transmission balance of energy flows of electric loads, heat loads and cold load nodes.
The comprehensive energy system in the embodiment takes the power supply subsystem as a core, and is connected with an external power grid through a medium-voltage line to ensure the power supply reliability when the comprehensive energy system fails, so that the operation mode of fixing the power by heat is adopted. Due to the difference of the operation modes of heating in winter and cooling in summer, a balance model of the heat energy flow in winter and the cooling energy flow in summer needs to be established respectively.
Figure BDA0002391365610000091
Wherein, PDN,iThe electric power accessed by an electric load node i of the power supply system is referred to; pL,iIndicating the conventional electric load of the electric load node i; phiL,jIs the thermal load of thermal load node j; cL,kIs the cooling load of cooling load node k; pi、Φj、CkThe injected active power of the electrical load node i, the injected thermal power of the thermal load node j, and the injected cold power of the cold load node k are respectively.
And S130, calculating network parameters, air temperature variables and source load variables of the comprehensive energy system according to the running energy flow balance model of the power supply subsystem and the heat supply subsystem to perform comprehensive power flow calculation on the comprehensive energy system.
Because the state of the heat supply network has time sequence correlation, the operation simulation of the comprehensive energy system is needed to determine the operation state of the comprehensive energy system at the fault moment so as to analyze the energy supply reliability index.
In this embodiment, the state of the integrated energy system at each time interval is calculated by adopting the electric-thermal combined power flow, and the state quantity includes: node voltage, branch power, node supply and return water temperature, pipeline flow, water pressure and the like.
Under the operation mode of the comprehensive energy system with the heat for power generation, the comprehensive power flow calculation of the comprehensive energy system comprises the following substeps:
s131, calculating the power flow of the heat supply subsystem to obtain the flow distribution and the supply and return water temperature of the heat supply network;
s132, calculating heat power of a heat source required for maintaining the temperature of the hot water;
s133, obtaining the equivalent electric load size of the electricity-to-heat in the power supply subsystem at the electric-to-heat coupling node through a power model of the electricity-to-heat equipment;
and S134, carrying out load flow calculation of the power supply subsystem.
The comprehensive power flow calculation of the comprehensive energy system can be divided into three parts, namely heat supply network power flow calculation, electric heat power flow coupling node power calculation and power grid power flow calculation, wherein:
a. flow of heat supply network
The flow model of the heat supply network in the embodiment comprises a hydraulic model and a thermal model, the heat supply medium of the heat supply network considered in the embodiment is hot water, and the supply and return water networks can be equivalent to a pipe network for hydraulic calculation by neglecting the small flow change of the supply and return water pipes caused by the water temperature difference.
The hydraulic model comprises the flow balance of each node and the pressure balance of a loop, reflects the distribution situation of the pipeline flow and the water pressure change in the heat supply network, and is shown in the following formulas (9-11):
Am＝mq(9)
BΔhf＝0 (10)
Δhf＝Km|m| (11)
wherein, A is the incidence matrix of the branch and the node of the pipe network, B is the incidence matrix of the branch and the loop of the pipe network, m is the flow of the pipeline, m is the flow rate of the pipelineqInjecting flow into the node; Δ hfK is the resistance coefficient of the pipeline.
The thermodynamic model analyzes the supply water temperature and the return water temperature of each node, and reflects the temperature condition of each node of the heat supply network under the transmission loss. The node temperature of the water supply network depends on the water supply temperature of a superior node, the environment temperature, the pipeline flow and the transmission loss characteristics of the pipeline, and the transmission loss of the heat supply network is modeled as follows:
Figure BDA0002391365610000101
wherein, Tsi、Tsj、TenRespectively the temperature of the beginning end and the end of the water supply pipeline and the outdoor environment temperature; lambda is the heat conductivity coefficient of a unit pipeline, and lambda can be 0.05W/m.K; dijFor the length of the pipe, CpIs the specific heat capacity of water, mijIs the flow rate of the pipeline.
In the same way, a node temperature model of the water return network can be obtained:
Figure BDA0002391365610000102
wherein, Tri、TrjRespectively the temperature of the beginning end and the temperature of the tail end of the water return pipeline.
The hot water flowing into the load point flows out after heat exchange and enters a water return network, the water temperature change caused by the heat exchange process is mainly related to the size and the flow of the heat load and can be represented by a specific heat capacity model:
ΦL,j＝Cpmq(Tsj-Tri) (14)
for a node which has multiple ports and is subjected to flow mixing in the network, the mixed water temperature can be calculated by a heat conservation law, and is represented by an equation (15):
(∑mout)Tout＝∑(minTin) (15)
wherein m isin、moutRespectively inflow and outflow of the heat load node, Tin、ToutRespectively inflow temperature and outflow temperature.
And obtaining the return water temperature flowing back to the heat source according to the flow of the heat supply network, and heating the circulating water by using heat source equipment to keep the stability of the water supply temperature. It should be noted that, the calculation method of the return water temperature flowing back to the heat source is the prior art, and is not described herein again.
b. Electric heat tidal current coupling node power
The configuration nodes of the cogeneration unit, the ground source heat pump and the electric boiler are coupling nodes of electricity and heat flow, the electricity-to-heat equipment can be regarded as loads of the power subsystem, and when the heat generation power of each equipment is obtained through energy flow balance and heat supply network flow calculation, the electric power consumed by each electricity-to-heat equipment can be obtained through the models shown in the formulas (3) and (4), and the electric power consumed by each electricity-to-heat equipment is regarded as equivalent loads of the power distribution network.
c. Power flow of the grid
The power supply comprises a large power grid and a photovoltaic, and the electric load comprises a conventional load and a coupling device equivalent electric load.
In the embodiment, a node connected with a large power grid is used as a balance node, photovoltaic equivalence is used as a load with negative electric power, and a rectangular coordinate form of power flow calculation of a power system is adopted:
Figure BDA0002391365610000111
wherein, Pi、QiRespectively injecting active power and reactive power into the electrical load node i; e.g. of the typei、fiRespectively a real part and an imaginary part of the voltage of the electric load node; gij、BijRespectively corresponding conductance and susceptance in the admittance matrix of the electrical load node.
When the elements of the integrated energy system fail and the energy supply cannot satisfy all the loads, load reduction is required to restore the system to a stable operation. On the premise of obtaining better system operation economy, the power supply of the conventional electric load can be ensured by reducing the operation output of the electric-to-heat equipment, namely reducing the equivalent electric load of the coupling equipment; meanwhile, the difference of the relative importance degrees of different types of electrical loads is considered, and the electrical reliability of the load point with higher importance level is preferentially ensured when the system carries out load reduction, so that the objective function and the constraint condition for establishing the comprehensive energy system load reduction optimization model in the step S200 are as follows:
(1) objective function
The objective function f takes into account the relative importance of the electricity, heat, and cold energy prices and the electrical load, and takes the economy of the equivalent load reduction amount as an optimization target, that is, the minimum value of the equivalent load reduction amount as an optimization target.
Figure BDA0002391365610000121
Wherein v isiIs the relative importance coefficient of the electrical load i; pE,i、PΦ,j、PC,kThe price of electricity of the electric load i, the price of heat of the heat load j and the price of cold of the cold load k are respectively; rE,i、RΦ,j、RC,kRespectively for the electric load reduction, the heat load reduction and the cold load of each nodeThe amount is reduced.
(2) Constraint conditions
Based on the operating energy flow balance model of the power and heat supply subsystems in step 100, the equality constraints of the load shedding model can be established.
Figure BDA0002391365610000122
Wherein, PDN,iThe electric power accessed by an electric load node i of the power supply system is referred to; pC,iOutputting electric power for an electric load node i of the gas generator set; ps,iThe photovoltaic power generation power of an electrical load node i; pHP,iHeat pump electrical power for electrical load node i; pEB,iInput electric power of an electric load node i of the electric boiler; pL,iIndicating the conventional electric load of the electric load node i; piInjecting active power into an electric load node i;
ΦLB,jthe winter heating power of a thermal load node j of the lithium bromide unit; phiL,jIs the thermal load of thermal load node j; phijThe injected thermal power for thermal load node j; cLB,kThe cooling power of a cooling load node k of the lithium bromide unit in summer; rC,kThe cold load of the cold load node is reduced;
CL,kis the cooling load of cooling load node k; ckIs the injected cold power of the cold load node k.
In addition, the load reduction optimization model further includes upper and lower limit constraints such as voltage of each node, branch power, load reduction amount, unit output and the like, and since the upper and lower limit constraints are prior art, the detailed description is omitted here.
Before determining the reliability index of the integrated energy system, the method further comprises the following steps: and establishing an element reliability model of the comprehensive energy system.
The two-state reliability model (i.e. normal and fault state reliability models) is widely applied to modeling of power system reliability evaluation elements, elements of the power supply and heat supply subsystems in the integrated energy system of the embodiment both adopt the two-state reliability model, and the normal and fault states of the two-state reliability model are respectively represented by two parameters, namely an element fault rate lambda and a repair rate mu.
The operation-failure Time sequence of The element can be obtained by simulating The Time To Failure (TTF) and The Repair Time (TTR).
The sample values of the state durations generally take the form of an exponential distribution;
the formulas of the non-fault continuous working time TTF and the repairing time TTR are as follows:
Figure BDA0002391365610000131
Figure BDA0002391365610000132
wherein, β1、β2Is a random number of 0 to 1.
The specific implementation of step S300 will be described with reference to fig. 3, and step S300 includes the following sub-steps S310 to S370:
s310, obtaining element state sampling results, namely sampling all element states of the comprehensive energy system by utilizing a sequential Monte Carlo method in a given time span to obtain the element state sampling results;
specifically, in a large number of sampling years, a sampling time span is determined, random number generation and state duration sampling are repeatedly performed on each element of the integrated energy system, and a time sequence state transition process of the integrated energy system in a given time span is obtained by combining state transition processes of all the elements.
And S320, performing load flow calculation on the comprehensive energy system according to the running energy flow balance model of the power supply subsystem and the heat supply subsystem and by taking the network parameters, the air temperature variable and the source load variable of the comprehensive energy system into account, wherein the specific process of the specific load flow calculation can be referred to the specific implementation method of each substep S131-S134 in S130.
S330, acquiring the fault time and state of the comprehensive energy system: determining the shortest element without fault working time of the comprehensive energy system according to the element state sampling result and recording the fault occurrence time and the element fault occurrence time; obtaining the state of the comprehensive energy system at the corresponding moment according to the load flow calculation result;
s340, analyzing the fault state of the system: according to the heat supply reliability index of the comprehensive energy system, analyzing the state of the comprehensive energy system to obtain a state analysis result, wherein the state analysis result comprises an energy supply balance analysis result of the multi-energy flow and a load reduction optimization result of the comprehensive energy system;
s350, load point index statistics: according to the load reduction optimization model and in combination with the state analysis result, counting the reliability indexes of all load points of the comprehensive energy system;
s360, sampling convergence judgment: when the variance coefficient of the reliability index is smaller than a preset value or the number K of times of analog sampling reaches the maximum sampling number, ending the analog cycle and outputting the reliability index of the load point; otherwise, returning to obtain the element state sampling result to perform next sampling so as to analyze the state of the comprehensive energy system;
s370, calculating system indexes: and (4) counting the reliability indexes of all the load points, and calculating the reliability of the comprehensive energy system.
Optionally, in an embodiment, the system fault state analysis in step S340 further includes the following sub-steps:
s341, failure analysis of power supply subsystem
When an element of the power supply subsystem fails, traversing all the electric loads in the comprehensive energy system by adopting a minimum path method, and analyzing the power supply state of each load point under the failure condition, wherein the two conditions are specifically included;
a. the faulty element being located in the smallest path of the load
According to connectivity analysis, the fault of the element causes load power supply interruption, and a branch circuit where the fault element is located needs to be isolated through switching operation, and the operation causes load power failure until the fault element is repaired; when a transfer is considered, the load can be transferred to the spare line via the tie switch.
b. The faulty element being located in a non-minimum path of the load
The nearest circuit breaker is searched upstream of the faulty element, and when the circuit breaker is located on the minimum path of the load, the isolation operation of the faulty element will cause a short-term power outage effect on the load, otherwise the faulty element will not cause a power outage of the load.
Heating system fault analysis taking thermal inertia into account S342
To know the reliability state of the thermal load in the heating subsystem, the temperature variation process of the load needs to be analyzed. The change rule of the indoor average temperature along with time is analyzed by using an unsteady state heat conduction lumped parameter model, and the energy conservation relation of the building set is shown by the following formula.
Figure BDA0002391365610000141
Wherein rho is the indoor air density, c is the air specific heat capacity, V is the indoor air volume, h is the surface heat transfer coefficient of the convection heat transfer between the building and the outdoor air, and A is the building surface area.
The temperature change process of the load is mainly influenced by factors such as building envelope, outdoor temperature and the like to the heat transfer process, wherein the influence of the envelope on the temperature change time can be simplified into the influence of a building thermal reserve coefficient x on the temperature change time, and a model of the temperature change process is shown as follows.
Figure BDA0002391365610000151
The larger the reserve coefficient x is, the better the heat storage performance of the material is, and the stronger the temperature fluctuation resistance of the material is, so that the heat network terminal shows the thermal inertia of temperature reduction in winter retardation chamber and temperature rise in summer retardation chamber, and the actual energy supply failure time of the user side lags behind the element failure time.
The parameter χ is a reference value of a heat reserve coefficient of a representative building envelope of a heating area with 4 temperature intervals defined by dividing the outdoor calculation temperature of the heating area into 4 intervals according to the climate zoning standard of the building in China, and is shown in tables B1, B2 and B3.
Table B1 unit equipment reliability parameters
Table B1 Reliability parameters of equipments
Figure BDA0002391365610000152
TABLE B2 thermal Reserve coefficients χ for buildings at different outdoor temperatures
Table B2 Building thermal reserve coefficienχin different outdoortemperature
Figure BDA0002391365610000153
TABLE B3 operating parameters
Table B3 Operating parameter
Figure BDA0002391365610000154
Figure BDA0002391365610000161
According to the formula (21), the lowest standard temperature and the highest standard temperature are respectively set for winter centralized heating and summer centralized cooling, the time required for the indoor temperature to change to the standard temperature can be calculated by utilizing the heat storage coefficients of different loads and the outdoor temperature thereof so as to reflect the fault delay caused by the thermal inertia of the building, and the time is defined as the allowable temperature change time tp。
Figure BDA0002391365610000162
Wherein, TpIs the standard temperature (the lowest standard temperature is taken for heating in winter and the highest standard temperature is taken for cooling in summer), TinIs the initial indoor temperature of the building.
The heat network is mainly composed of heatThe system comprises a source, a pipeline, a valve and a compensator, wherein when heat source equipment fails to cause insufficient heat supply power, heat load reduction is needed, and when the pipeline and the valve fail, the heat supply energy flow of users at the downstream of a superior valve is interrupted. The fault location and repair work of the element is started from the fault occurrence time, the fault is repaired after the element repair time TTF, and the heat supply energy flows through the time delay tcThen transmitted to the user, and the heat supply of the user is recovered at the moment. The calculation model of the energy supply recovery time is shown as a formula (24).
Figure BDA0002391365610000163
Wherein, trFor energy supply recovery time, L is the length of the pipeline, vwIs the water flow rate.
If the energy supply recovers for time trLess than the allowable temperature change time tpIf the temperature of the user does not deviate from the standard temperature range when the energy supply is recovered, the load point is considered to be in a normal state, otherwise, the load point is recorded as a fault.
Figure BDA0002391365610000164
Wherein S isL,iThe reliability of the load i is 1 is normal, and 0 is failure.
In this embodiment, Average power supply Interruption Times (AITC), Average power supply Interruption Times (AIHC), and user power supply Reliability (Reliability on Service, RS) are mainly used as the heat supply Reliability indexes of the integrated energy system, and the specific examples are as follows:
Figure BDA0002391365610000165
Figure BDA0002391365610000166
Figure BDA0002391365610000167
wherein, tfi、NfiThe duration and the number of the affected users of each fault are respectively, and N is the total number of the users of the comprehensive energy system.
Optionally, the heat supply reliability index of the comprehensive energy system is corrected by considering the delay characteristic of the heat supply network;
due to the fact that the component fault consequence of the heat supply network in the integrated energy system IES has obvious time delay characteristics, the indexes cannot be counted by utilizing the sampling values; on the one hand, because the heat supply network is thermally inert; on the other hand, the cold/hot energy flow is different from the transmission speed of the electric energy, and the delay of the user energy supply recovery moment is also brought.
In the embodiment, the influences of factors such as the heat storage performance of the building and the weather are taken into consideration, and the heat supply reliability index of the comprehensive energy system is corrected based on the time delay characteristic.
After the heat supply of the user is recovered, the indoor temperature is gradually recovered and maintained within a normal range, and the process can establish a solving model of the temperature recovery time by using a thermodynamic energy conservation law.
Figure BDA0002391365610000171
Figure BDA0002391365610000172
Wherein q is the heat dissipation capacity of the radiator per unit length through natural convection, v is the flow velocity of water flow, and tuFor temperature recovery time, α is the heat loss coefficient.
The combination type (29-30) can obtain the temperature recovery time tuAn explicit expression of (2).
Figure BDA0002391365610000173
Therefore, when the heat supply is interrupted due to the element fault of the integrated energy system IES, the time when the indoor temperature of the user actually deviates from the standard temperature range is taken as the statistical data of the reliability index, which mainly comprises the energy supply recovery time, the allowable temperature change time and the temperature recovery time, and the calculation model of the corrected user heat supply fault time is shown as the formula (32).
tf＝tr-tp+tu(32)
Wherein, tfAnd supplying heat to the corrected user for the fault time.
In the embodiment, the time that the actual indoor temperature of the user deviates from the standard temperature range is taken as the statistical data of the reliability index, and the corrected user heat supply fault time is obtained according to the reliability indexes such as energy supply recovery time, allowable temperature change time, temperature recovery time and the like, so that the actual network topology and heat supply network transmission loss are also considered, and the influence of the network transmission performance on the reliability can be better reflected;
in addition, the embodiment considers the relative importance degree of electricity, heat and cold energy consumption prices and the electric load, takes the economy of equivalent load reduction as an optimization target, and considers the difference of the requirement of a user on the energy supply reliability, so the reliability obtained by calculation is more accurate;
according to the method, the operation of the comprehensive energy system is simulated, and the state of the heating subsystem in the fault state (namely the corrected temperature recovery time obtained by the formula 32) is obtained, so that the granularity of the finally obtained reliability evaluation result is finer.
Example two
Fig. 4 is a schematic structural diagram of a reliability evaluation system for an integrated energy system according to an embodiment of the present invention, referring to fig. 4, the system includes a first modeling module, a second modeling module, and a calculation module, wherein;
the first modeling module acquires data of the comprehensive energy system, constructs a structural model of the comprehensive energy system, and establishes an operating energy flow balance model of a power supply subsystem and a heat supply subsystem according to the structural model of the energy system;
the second modeling module establishes a load reduction optimization model by taking the minimum value of the equivalent load reduction as an optimization target according to the energy price of the electric load, the heat load and the cold load and the relative importance degree of the electric load;
the calculation module calculates network parameters, air temperature variables and source load variables of the comprehensive energy system based on the running energy flow balance model and the load reduction optimization model, carries out load flow calculation on the comprehensive energy system, and determines the reliability index of the comprehensive energy system by using the load flow calculation result and combining a sequential Monte Carlo method.
Optionally, the objective function of the load shedding optimization model is as follows:
Figure BDA0002391365610000181
wherein v isiIs the relative importance coefficient of the electrical load i; pE,i、PΦ,j、PC,kThe price of electricity of the electric load i, the price of heat of the heat load j and the price of cold of the cold load k are respectively; rE,i、RΦ,j、RC,kRespectively reducing the electric load, the heat load and the cold load of each node;
establishing equality constraint in load reduction according to the running energy flow balance model of the power supply subsystem and the heat supply subsystem;
Figure BDA0002391365610000182
wherein R isDN,iThe electric power accessed by an electric load node i of the power supply system is referred to; rc,iOutputting electric power for an electric load node i of the gas generator set; ps,iThe photovoltaic power generation power of an electrical load node i; pHP,iHeat pump electrical power for electrical load node i; pEB,iInput electric power of an electric load node i of the electric boiler; pL,iIndicating the conventional electric load of the electric load node i; piInjecting active power into an electric load node i;
ΦLB,jthe winter heating power of a thermal load node j of the lithium bromide unit; phiL,jIs the thermal load of thermal load node j; phijNotes for Heat load node jHeating power; cLB，kThe cooling power of a cooling load node k of the lithium bromide unit in summer; rC,kThe cold load of the cold load node is reduced;
CL,kis the cooling load of cooling load node k; ckIs the injected cold power of the cold load node k.
Optionally, the calculation module performs the following operations:
sampling the states of all elements of the comprehensive energy system by utilizing a sequential Monte Carlo method based on an element reliability model in a given time span;
load flow calculation, namely performing load flow calculation on the comprehensive energy system according to the running energy flow balance model of the power supply subsystem and the heat supply subsystem and by taking network parameters, temperature variables and source load variables of the comprehensive energy system into account;
acquiring the fault time and state of the comprehensive energy system: determining the fault occurrence time according to the element state sampling result, and obtaining the state of the comprehensive energy system according to the load flow calculation result;
and (3) state analysis: according to the heat supply reliability index of the comprehensive energy system, analyzing the state of the comprehensive energy system to obtain a state analysis result;
load point index statistics: according to the load reduction optimization model and in combination with the state analysis result, counting the reliability indexes of all load points of the comprehensive energy system;
and (3) sampling convergence judgment: when the variance coefficient of the reliability index is smaller than a preset value or the number of times of analog sampling reaches the maximum sampling number, ending the analog cycle and outputting the reliability index of the load point; otherwise, returning to obtain the element state sampling result to perform next sampling;
calculating a system index: and (4) counting the reliability indexes of all the load points, and calculating the reliability of the comprehensive energy system.
Optionally, the heat supply reliability index of the integrated energy system includes the average energy supply interruption times of the users, the average energy supply interruption time of the users and the reliability of the energy supply of the users.
Optionally, the calculation module is further configured to take into account a heat supply network delay characteristic to correct the heat supply reliability index, including performing the following operations:
establishing a solving model of temperature recovery time based on a thermodynamic energy conservation law;
Figure BDA0002391365610000191
Figure BDA0002391365610000192
wherein q is the heat dissipation capacity of the radiator per unit length through natural convection, v is the flow velocity of water flow, and tuFor temperature recovery time, α is the heat loss coefficient;
calculating the temperature recovery time according to the solution model of the temperature recovery time;
Figure BDA0002391365610000201
and correcting according to the energy supply recovery time, the allowable temperature change time and the temperature recovery time to obtain a corrected calculation model of the user heat supply fault time:
tf＝tr-tp+tu
wherein, tfAnd supplying heat to the corrected user for the fault time.
The reliability evaluation system in this embodiment is substantially the same as the reliability evaluation method in the first embodiment, and is not described herein again.
In summary, the invention has the following advantages:
the reliability evaluation method of the invention considers the difference of network topology, transmission delay, terminal thermal inertia and user reliability requirements, and improves the precision of the IES reliability evaluation of the integrated energy system.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (10)

1. A reliability assessment method for an integrated energy system, the method comprising the steps of:
acquiring data of the comprehensive energy system, constructing a structural model of the comprehensive energy system, and establishing an operating energy flow balance model of a power supply subsystem and a heat supply subsystem according to the structural model of the comprehensive energy system;
establishing a load reduction optimization model by taking the minimum value of the equivalent load reduction as an optimization target according to the energy price of the electric load, the heat load and the cold load and the relative importance degree of the electric load;
and calculating network parameters, air temperature variables and source load variables of the integrated energy system based on the running energy flow balance model and the load reduction optimization model, carrying out load flow calculation on the integrated energy system, and determining the reliability index of the integrated energy system by using the load flow calculation result and combining a sequential Monte Carlo method.
2. The reliability assessment method according to claim 1, wherein the objective function of the load shedding optimization model is:
Figure FDA0002391365600000011
wherein v isiIs the relative importance coefficient of the electrical load i; pE,i、PΦ,j、PC,kThe price of electricity of the electric load i, the price of heat of the heat load j and the price of cold of the cold load k are respectively; rE,i、RΦ,j、RC,kRespectively reducing the electric load, the heat load and the cold load of each node;
establishing equality constraint in load reduction according to the running energy flow balance model of the power supply subsystem and the heat supply subsystem;
Figure FDA0002391365600000012
wherein, PDN,iThe electric power accessed by an electric load node i of the power supply system is referred to; pc,iOutputting electric power for an electric load node i of the gas generator set; ps,iThe photovoltaic power generation power of an electrical load node i; pHP,iHeat pump electrical power for electrical load node i; pEB,iInput electric power of an electric load node i of the electric boiler; pL,iIndicating the conventional electric load of the electric load node i; piInjecting active power into an electric load node i; phiLB,jThe winter heating power of a thermal load node j of the lithium bromide unit; phiL,jIs the thermal load of thermal load node j; phijThe injected thermal power for thermal load node j; cLB,kThe cooling power of a cooling load node k of the lithium bromide unit in summer; pC,kThe cold load of the cold load node is reduced; cL,kIs the cooling load of cooling load node k; ckIs the injected cold power of the cold load node k.
3. The reliability assessment method according to claim 1, wherein said determining a reliability indicator of said integrated energy system comprises the sub-steps of:
sampling the states of all elements of the comprehensive energy system by utilizing a sequential Monte Carlo method based on an element reliability model in a given time span;
load flow calculation, namely performing load flow calculation on the comprehensive energy system according to the running energy flow balance model of the power supply subsystem and the heat supply subsystem and by taking network parameters, temperature variables and source load variables of the comprehensive energy system into account;
acquiring the fault time and state of the comprehensive energy system: determining the fault occurrence time according to the element state sampling result, and obtaining the state of the comprehensive energy system according to the load flow calculation result;
and (3) state analysis: according to the heat supply reliability index of the comprehensive energy system, analyzing the state of the comprehensive energy system to obtain a state analysis result;
load point index statistics: according to the load reduction optimization model and in combination with the state analysis result, counting the reliability indexes of all load points of the comprehensive energy system;
and (3) sampling convergence judgment: when the variance coefficient of the reliability index is smaller than a preset value or the number of times of analog sampling reaches the maximum sampling number, ending the analog cycle and outputting the reliability index of the load point; otherwise, returning to obtain the element state sampling result to perform next sampling;
calculating a system index: and (4) counting the reliability indexes of all the load points, and calculating the reliability of the comprehensive energy system.
4. The reliability assessment method according to claim 3, wherein the heat supply reliability indicators of the integrated energy system comprise user average power supply interruption times, user average power supply interruption time and user power supply reliability.
5. The reliability assessment method according to any one of claims 1 to 4, further comprising the steps of: the method comprises the following steps of considering the time delay characteristic of a heat supply network to correct the heat supply reliability index of the comprehensive energy system, and specifically comprises the following substeps:
(1) establishing a solving model of temperature recovery time based on a thermodynamic energy conservation law;
Figure FDA0002391365600000021
Figure FDA0002391365600000022
wherein q is the heat dissipation capacity of the radiator per unit length through natural convection, v is the flow velocity of water flow, and tuFor temperature recovery time, α is the heat loss coefficient;
(2) calculating the temperature recovery time according to the solution model of the temperature recovery time;
Figure FDA0002391365600000023
(3) and correcting according to the energy supply recovery time, the allowable temperature change time and the temperature recovery time to obtain a corrected calculation model of the user heat supply fault time:
tf＝tr-tp+tu
wherein, tfAnd supplying heat to the corrected user for the fault time.
6. A reliability assessment system for an integrated energy system, the system comprising a first modeling module, a second modeling module, and a calculation module, wherein;
the first modeling module acquires data of the comprehensive energy system, constructs a structural model of the comprehensive energy system, and establishes an operating energy flow balance model of a power supply subsystem and a heat supply subsystem according to the structural model of the energy system;
the second modeling module establishes a load reduction optimization model by taking the minimum value of the equivalent load reduction as an optimization target according to the energy price of the electric load, the heat load and the cold load and the relative importance degree of the electric load;
the calculation module calculates network parameters, air temperature variables and source load variables of the comprehensive energy system based on the running energy flow balance model and the load reduction optimization model, carries out load flow calculation on the comprehensive energy system, and determines the reliability index of the comprehensive energy system by using the load flow calculation result and combining a sequential Monte Carlo method.
7. The reliability assessment system according to claim 6, wherein the objective function of the load shedding optimization model is:
Figure FDA0002391365600000031
wherein v isiIs the relative importance coefficient of the electrical load i; pE,i、PΦ,j、PC,kThe price of electricity of the electric load i, the price of heat of the heat load j and the price of cold of the cold load k are respectively; rE,i、RΦ,j、RC,kRespectively reducing the electric load, the heat load and the cold load of each node;
establishing equality constraint in load reduction according to the running energy flow balance model of the power supply subsystem and the heat supply subsystem;
Figure FDA0002391365600000032
wherein, PDN,iThe electric power accessed by an electric load node i of the power supply system is referred to; pc,iOutputting electric power for an electric load node i of the gas generator set; ps,iThe photovoltaic power generation power of an electrical load node i; pHP,iHeat pump electrical power for electrical load node i; pEB,iInput electric power of an electric load node i of the electric boiler; pL,iIndicating the conventional electric load of the electric load node i; piInjecting active power into an electric load node i; phiLB,jThe winter heating power of a thermal load node j of the lithium bromide unit; phiL,jIs the thermal load of thermal load node j; phijThe injected thermal power for thermal load node j; cLB，kThe cooling power of a cooling load node k of the lithium bromide unit in summer; rC,kThe cold load of the cold load node is reduced; cL,kIs the cooling load of cooling load node k; ckIs the injected cold power of the cold load node k.
8. The reliability evaluation system of claim 6, wherein the calculation module performs the following operations:
sampling the states of all elements of the comprehensive energy system by utilizing a sequential Monte Carlo method based on an element reliability model in a given time span;
load flow calculation, namely performing load flow calculation on the comprehensive energy system according to the running energy flow balance model of the power supply subsystem and the heat supply subsystem and by taking network parameters, temperature variables and source load variables of the comprehensive energy system into account;
acquiring the fault time and state of the comprehensive energy system: determining the fault occurrence time according to the element state sampling result, and obtaining the state of the comprehensive energy system according to the load flow calculation result;
and (3) state analysis: according to the heat supply reliability index of the comprehensive energy system, analyzing the state of the comprehensive energy system to obtain a state analysis result;
load point index statistics: according to the load reduction optimization model and in combination with the state analysis result, counting the reliability indexes of all load points of the comprehensive energy system;
and (3) sampling convergence judgment: when the variance coefficient of the reliability index is smaller than a preset value or the number of times of analog sampling reaches the maximum sampling number, ending the analog cycle and outputting the reliability index of the load point; otherwise, returning to obtain the element state sampling result to perform next sampling;
calculating a system index: and (4) counting the reliability indexes of all the load points, and calculating the reliability of the comprehensive energy system.
9. The reliability evaluation system of claim 8, wherein the heating reliability indicators of the integrated energy system include a user average power outage time, a user average power outage duration, and a user power reliability.
10. The reliability evaluation system of any one of claims 6-9, wherein the calculation module is further configured to account for a heat network delay characteristic to modify a heat supply reliability indicator, comprising:
establishing a solving model of temperature recovery time based on a thermodynamic energy conservation law;
Figure FDA0002391365600000041
Figure FDA0002391365600000042
wherein q is the heat dissipation capacity of the radiator per unit length through natural convection, v is the flow velocity of water flow, and tuFor temperature recovery time, α is the heat loss coefficient;
calculating the temperature recovery time according to the solution model of the temperature recovery time;
Figure FDA0002391365600000051
and correcting according to the energy supply recovery time, the allowable temperature change time and the temperature recovery time to obtain a corrected calculation model of the user heat supply fault time:
tf＝tr-tp+tu
wherein, tfAnd supplying heat to the corrected user for the fault time.
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