CN112989576A - Method for evaluating reliability of energy information coupling system based on real-time demand response - Google Patents
Method for evaluating reliability of energy information coupling system based on real-time demand response Download PDFInfo
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Abstract
The invention relates to the technical field of comprehensive energy, and aims to provide a method for evaluating the reliability of an energy information coupling system based on real-time demand response, which comprises the following steps: s1: constructing a comprehensive energy information physical system framework and analyzing a real-time demand response mechanism of the comprehensive energy information physical system framework, wherein the comprehensive energy information physical system comprises an energy input end, an energy storage device and an energy output end of a physical system; s2: establishing an information transmission reliability model, sampling the element state by adopting a mixed Monte Carlo method, and generating a multi-period fault scene; s3: bringing element state variables into energy balance constraint, and establishing an optimal load reduction model by taking the lowest energy purchase cost, load reduction cost and demand response cost as targets; s4: and determining the reliability evaluation index and the evaluation flow of the energy information physical system.
Description
Technical Field
The invention relates to the field of comprehensive energy, in particular to a method for evaluating the reliability of an energy information coupling system based on real-time demand response.
Background
In recent years, Energy systems such as electricity, gas, heat, and the like are gradually changed from an independent Energy supply mode to a multi-source coupling and multi-Energy complementary mode, and Integrated Energy Systems (IES) have been developed. The rapid development and application of IES need an efficient information system as a support, a plurality of demonstration projects are established at home and abroad by means of advanced information technology, and the efficient operation of an energy system, the rapid and precise control of distributed demand response resources and the intelligent interaction and open sharing of the energy system are realized. With the rapid development of information technologies such as big data, internet of things and the like, the IES realizes the deep coupling of information streams and physical streams, and forms a Cyber Physical System (CPS). The comprehensive energy CPS is a user-oriented high-efficiency energy sharing system, a physical layer core network of the system is a regional comprehensive energy system, the deep coupling of energy flow and information flow is realized by integrating technologies such as communication, control, distributed computation and the like into an information layer, the reliability of the comprehensive energy CPS is influenced by not only the reliability of elements of the physical layer but also elements of an information domain, and the traditional IES reliability evaluation method is not applicable any more.
Currently, most of research on the reliability of the integrated energy system focuses on the physical system level. The supply and demand balance mechanism of the energy CPS is the key of reliability evaluation, the comprehensive demand response becomes an important means for adjusting multi-energy power balance along with the continuous deepening of the coupling degree of an energy system and an information system, and the real-time Demand Response (DR) has a remarkable effect on the real-time power balance after the fault due to the rapidity of the DR, so that the energy supply reliability is influenced. At present, research of comprehensive demand response focuses on improving economic benefits or environmental benefits, and the reliability improvement benefits brought by real-time comprehensive demand response are less concerned.
However, the following problems still exist in the existing research: firstly, reliability evaluation of the comprehensive energy system is centralized on a physical level, and interaction influence of a physical domain and an information domain of the comprehensive energy system is less considered; secondly, the research of comprehensive demand response at present focuses on the economic dispatching aspect of the physical layer in the non-fault state, and the influence of real-time comprehensive demand response on the reliability of the energy CPS after the fault is less concerned.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method for evaluating the reliability of an energy information coupling system based on real-time demand response. Firstly, a comprehensive energy CPS structure is briefly described, and a real-time comprehensive demand response mechanism is analyzed from two aspects of real-time response and multipotency complementation; secondly, constructing an information domain topology model and a reliability analysis series model, and summarizing an information link reliability analysis flow; thirdly, taking the two-state Markov model as a reliability modeling basis of the CPS element, and completing multi-period fault scene sampling by adopting a mixed Monte Carlo method; then, constructing an energy CPS optimal load reduction model considering real-time demand response to analyze fault consequences; finally, summarizing an energy CPS operation mechanism, and evaluating the reliability of the energy CPS through three indexes of the lack of supply energy expectation, the lack of energy time expectation and the importance.
The method is realized by the following technical scheme: the method for evaluating the reliability of the energy information coupling system based on the real-time demand response comprises the following steps:
s1: constructing a comprehensive energy information physical system framework and analyzing a real-time demand response mechanism of the comprehensive energy information physical system framework, wherein the comprehensive energy information physical system comprises an energy input end, an energy storage device and an energy output end of a physical system;
s2: establishing an information transmission reliability model, sampling the element state by adopting a mixed Monte Carlo method, and generating a multi-period fault scene;
s3: bringing element state variables into energy balance constraint, and establishing an optimal load reduction model by taking the lowest energy purchase cost, load reduction cost and demand response cost as targets;
s4: and determining the reliability evaluation index and the evaluation flow of the energy information physical system.
Preferably, the demand response mechanism comprises a response quantity constraint and a scheduling time constraint, and the specific calculation formula is
In the formula, the period Lambda participating in the response, the unit period responsive load quantity
And compensating for the price
sd is a responsive load identifier; subscript j is an energy identifier, including electric energy, heat energy and cold energy; α is a user identifier;
and
the upper and lower limits of the response quantity of the jth class load of the contract-defined user alpha in the t period;
the response state variable of the j-th energy period t is 1, which indicates a response state, 0 which indicates an unresponsive state, and the number of users who make a contract in the energy CPS is α0The maximum load amount that can participate in real-time demand response in the time period t is
In the formula: alpha is alpha0The number of users who make a contract;
and
the actual reduction amount and the reduction limit value of the j-th load in the t period are respectively.
Preferably, in S2, the integrated energy information physical system framework includes an information acquisition device, an information transmission device, and a data processing center, information elements in the information acquisition device, the information transmission device, and the data processing center are equivalent to nodes, a topological connection relationship between the information elements is equivalent to edges, an energy information element connection topological structure can be formed, and a model of topological reliability is CPS
Wherein m is the source node, n is the sink node, z1And z2Respectively the number of transmission paths between m-n and the r-th1The number of information nodes contained in each transmission path;
is the r1A state of the strip transmission path; u. ofc(r2) Is the r2The state of each information node is a normal state when 1 is selected and a fault state when 0 is selected, psim-nLink topology reliability.
Preferably, in step S3, the multi-target ac/dc distribution network load recovery model includes two objective functions, where the objective function 1 targets that the load interruption duration is minimum, and the objective function 2 targets that the electric quantity shortage is minimum.
Preferably, the multi-period fault scenario in S2 is
In the formula:
respectively representing state variables of a superior power grid and a superior gas grid in a time period t;
and
the state variables of the s-th combined heat and power generation unit, the electric energy storage unit, the gas energy storage unit, the heat energy storage unit, the cold energy storage unit, the photovoltaic unit, the wind power generation unit, the electric refrigerator, the electric boiler, the ice storage air conditioner, the gas boiler and the absorption refrigerator in the t period are respectively, when the physical domain element is in a non-failure state and the information transmission of the element is effective at the moment, the element is considered to be capable of normally working, 1 is measured corresponding to the state variable of the element, and otherwise 0 is taken.
Preferably, in S3, the load reduction model is
In the formula: t is the research period; j is the number of the types of energy sources of the output port, and comprises 3 energy sources of electricity, heat and cold, and when J is 1, 2 and 3 in sequence, the energy sources respectively correspond to electric energy, heat energy and cold energy; q is input energy type, including 2 kinds of energy of electricity, gas; c is an identifier that does not participate in the demand response load;
compensating the price for the unit of the jth type responsive load t period;
the reduction amount of the jth type responsivity load in the t period; pij,tAnd
respectively unit reduction cost and reduction amount of the jth type conventional load in the t period; p is a radical ofq,tAnd Pq,tRespectively the purchase price and the power of the q-th type energy source in the t period.
Preferably, in S4, the reliability evaluation mechanism of the energy information physical system includes the following steps:
and S61, information acquisition: the sensor samples the state and electric quantity information of each unit of the comprehensive energy CPS physical layer to form an information packet S. Wherein the information packet of the key element is
S＝[SDG SLD,x SES,x SEC,l]
In the formula, SDG、SLD,x、SES,xAnd SEC,lInformation packets respectively representing the distributed power supply, the load, the energy storage and the energy conversion device; sLD,xThe method comprises the steps that user side electricity utilization information and user side real-time response electricity quantity information are contained; l is the type of energy conversion equipment, and corresponds to the type of the device in section 1 respectively;
and S62, information uploading: the information packet is transmitted to a control center through a communication network, and the information transmission reliability of the section 3 needs to be considered during transmission;
s63, information processing decision: generating an operation strategy of the comprehensive energy CPS according to the acquired information;
s64: and (3) command issuing: the decision result is sent to each controlled unit controller through an information network, and the controller generates a control instruction; information transmission reliability also needs to be considered in the command issuing process;
s65: and command execution: and each device in the comprehensive energy system completes the actions of output adjustment, load reduction and the like according to the issued instruction.
The invention has the beneficial effects that:
(1) the energy CPS physical domain and information domain weak links can be effectively identified, and references are provided for economic operation and strengthening construction of the energy CPS physical domain and information domain weak links;
(2) the fact that different elements have different influences on the reliability of the energy CPS is proved, and the real-time demand response can effectively improve the operation economy and reliability of the energy CPS.
Drawings
FIG. 1 is a schematic diagram of a method for evaluating reliability of an energy information coupling system based on real-time demand response according to the present invention
FIG. 2 is a diagram of the integrated energy CPS structure of the present invention;
FIG. 3 is a diagram of an exemplary information element connection topology;
FIG. 4 is a flow chart of comprehensive energy CPS reliability evaluation;
FIG. 5 is an integrated energy system physical layer architecture;
FIG. 6 is a power supply importance index histogram;
FIG. 7 is a histogram of heat supply importance indicators;
FIG. 8 is a histogram of cooling importance indicators;
FIG. 9 is a histogram of the integrated energy supply importance indicators;
FIG. 10 is a histogram of year average cost for some of the different scenarios;
FIG. 11 is a histogram of the annual average cost for partially different scenarios
FIG. 12 is a graph of element failure rate impact lines;
FIG. 13 is an analysis of the rate of energy contribution due to starvation;
fig. 14 is a diagram of an information element connection topology.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to fig. 1 to 14 of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, but not all embodiments. All other implementations made by those of ordinary skill in the art based on the embodiments of the present invention are obtained without inventive efforts.
In the description of the present invention, it is to be understood that the terms "counterclockwise", "clockwise", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate orientations or positional relationships based on those shown in the drawings, and are used for convenience of description only, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be considered as limiting.
Example 1:
specifically, the comprehensive energy CPS structure is a user-oriented high-efficiency energy sharing system, a physical layer core network of the system is a regional comprehensive energy system, and deep coupling of energy streams and information streams is achieved by integrating technologies such as communication, control and distributed computing in an information layer. Fig. 2 is a typical integrated energy CPS architecture. The energy input end of the physical system comprises electric energy, natural gas, distributed wind power, photovoltaic and the like; the energy conversion device includes a Combined Heat and Power (CHP), an electric boiler (EF), a gas boiler (GF), an Absorption Chiller (AC), an Electric Chiller (EC), and an Ice Storage Air Conditioner (ISAC); energy storage devices include Electrical Storage Systems (ESS), Gas Storage Systems (GSS), Heat Storage Systems (HSS), and Cold Storage Systems (CSS); the output terminals include electrical, thermal and cold loads. The energy CPS information system is composed of units such as an information acquisition device (a sensor), an information transmission device (a communication line, a switch and the like), a data processing center and the like, wherein CC is a control center, and SW is a switch. The sensor and controller modules of the physical elements are packaged to form the information terminal unit of the corresponding device in fig. 2. The sensor is responsible for collecting state information (output power, node voltage and the like) of the physical unit and uploading the state information to a superior information network; the controller is responsible for receiving and executing the control command issued by the information layer.
The energy CPS is provided with a high-grade measuring system, and a precondition is provided for real-time interaction of network load when a reliable accident occurs. When the energy CPS has energy supply shortage, the multi-element energy storage and the cold, heat and electricity flexible load can respond quickly, and the loss of the supply shortage energy is reduced; the energy conversion device can perform energy supply adjustment by changing the energy output ratio and the output size, realize multi-energy complementation and reduce energy supply interruption loss. The invention regards the holders of the energy conversion device, the energy storage device and other equipment as energy CPS operators, and after the energy CPS fails, the control center can directly adjust the output behavior of the energy CPS operators through the controller. The flexible load has the control authority only after the user authorizes the flexible load, so that the method emphasizes modeling on real-time demand response of the flexible load after the fault.
The energy CPS operator contracts with residents, industrial or commercial users capable of bearing energy supply interruption for a certain time without influencing the working life of the users to form emergency demand response resources. The comprehensive real-time demand response contract content for the single user comprises a response participation period Lambda and a unit period response load quantity according to the power demand response
And compensating for the price
The constraints mainly comprise response quantity constraints and scheduling time constraints, as shown in formulas (1) and (2).
In the formula: sd is a responsive load identifier; subscript j is an energy identifier, including electric energy, heat energy and cold energy; α is a user identifier;
and
the upper and lower limits of the response quantity of the jth class load of the contract-defined user alpha in the t period;
the response state variable of the j-th energy source period t is in a response state when being 1, and is in an unresponsive state when being 0.
In order to improve the execution flexibility of the contract and adapt to the fluctuation behavior of the user response intention, the CPS collects the response intention information of the user in all time periods of the next day before the energy CPS
Is [0,1 ]]The closer to 1, the stronger the willingness of the user α to participate in the response during the period t is considered. Comprehensively considering the response desire of the user and the necessity of calling resources, the energy CPS returns the information epsilon of the next-day emergency calling period to the userα,j,tTo pair
A correction is made to specify the time period during which the user alpha is the next day as an emergency demand response resource (if the user is required to be the emergency demand response resource during that time period, the value epsilon is returnedα,j,tIs 1, otherwise is 0.
Through the information interaction and the confirmation of the two parties, when a reliability accident occurs, the control center can directly execute load reduction of authorized users through the intelligent control device, and the reliability of the energy CPS is improved. When the number of users who make a contract in the energy CPS is alpha0In time, the maximum load amount that can participate in the real-time demand response in the period t is as described in formulas (3) and (4):
in the formula: alpha is alpha0The number of users who make a contract;
and
the actual reduction amount and the reduction limit value of the j-th load in the t period are respectively.
Regarding the reliability analysis of information transmission, during the operation of the energy CPS, if the information terminal loses contact with the control center, the physical domain elements may lose control, and then the supply and demand balance of the energy CPS is affected. Therefore, in consideration of the reliability of information transmission in the energy CPS, information elements such as a control center, an information terminal, a switch, and a communication line are equivalent to nodes, and the topological connection relationship between the information elements is equivalent to edges, so that an energy CPS information element connection topological graph can be formed, as shown in fig. 3. In fig. 3, node 1 corresponds to the control center in fig. 3; node 3 corresponds to switch SW0 in fig. 3; the communication line between the control center and the switch SW0 is equivalent to node 2; nodes 17, 18, 19, 20 correspond to information terminal units of PV, ESS, WT, GSS, respectively, and the nature of the information transmission reliability analysis is to determine whether network information at an information source node (source node) in the information domain can be reliably transmitted to a destination node (sink node). An information element connection topological graph is established, namely, the reliability of information transmission can be analyzed by graph theory accessibility and the like. Regarding the reliability analysis tandem model, if a certain node or some nodes in the information transmission path have a fault or the transmission delay and the error rate of the communication line node exceed the threshold, the uploading or the issuing of the information may face the risk of transmission interruption, therefore, the tandem model can be used to describe the real-time transmission reliability between the source node m and the sink node n,
Am-n＝Ψm-nΩm-n,iΦm-n,i (5)
in the formula: a. them-nReliability between m-n; Ψm-nLink topology reliability; omegam-n,iDetermining the delay connectivity of the link i for m-n; phim-n,iAnd determining the error reliability of the link i for the m-n. The link m-n data transmission is efficient if and only if the above-mentioned class 3 reliability fulfils the requirements.
The topology, delay, error reliability models are as follows:
1) a topological reliability model.
If z is between the source node m and the sink node n1And when the main transmission path fails, information can be transmitted through other paths in a connected state, so that the topological reliability can be expressed as:
in the formula: z is a radical of1And z2Respectively the number of transmission paths between m-n and the r-th1The number of information nodes contained in each transmission path;
is the r1A state of the strip transmission path; u. ofc(r2) Is the r2The state of each information node is a normal state when 1 is taken and a fault state when 0 is taken.
Equation (9) shows that information transmission is unreliable only if all paths between m-n fail; the expression (10) indicates that the transmission path can be reliably operated when all the elements in one transmission path are in a normal state.
uc(r2) The state is determined by equation (11):
in the formula: mu.sr2And λr2Are respectively the r-th2The repair rate and the failure rate of each information node; theta denotes obedience 0,1]Uniformly distributed random numbers.
2) And (5) a delay reliability model.
If the transmission delay in a certain information transmission path exceeds a threshold value, information on the time section is lost, and information transmission is unreliable. The delay reliability can be expressed as:
in the formula: tau is0Is a specified delay threshold; tau ism-n,iThe total delay for the transmission of the m-n path i can be determined by equation (13)[23]。
In the formula: xi is obey 0,1]A distributed random number; tau isminIs the minimum value of end-to-end delay; beta is affected by the load factor and is taken to be 30, 20 and 10 under heavy, normal and light load operating conditions, respectively.
3) An error code reliability model.
In information transmission, under the influence of line length and noise, error codes may occur, and when the error code rate exceeds a controllable range, an information transmission error may be caused. The error reliability can be characterized by equation (14) (15):
in the formula: z'1The path i contains the number of communication line nodes;
is r'1Error code reliability of each communication line node;
is r'1The bit error rate of each communication line node; sigma0Is the allowed error threshold.
As to the flow of the information transmission reliability analysis,
in summary, the link m-n reliability analysis flow is as follows:
1) establishing an information node reliability model and an information element connection topological graph, and searching a transmission path of a link m-n according to the topological graph: if the node m or n fails, the information transmission of the link m-n fails, and the process goes to 5); otherwise, the next step is carried out.
2) Calculating transmission path topology reliability according to equation (10), and calculating link topology reliability according to equation (9): if t ism-nIf the result is 1, the next step is carried out; otherwise, judging that the link transmission is failed, and entering 5).
3) Calculating the transmission delay of the link according to the formula (13), and judging the reliability of the delay according to the formula (12): deletion of omegam-n,iIf no path meeting the delay reliability exists, the link m-n fails to transmit, and the link m-n enters 5); otherwise, the next step is carried out.
4) The judgment is made according to the equations (14) and (15): if the path meets the reliability of the error code, the output link m-n can carry out reliable information transmission, and the process is finished; otherwise go to 5).
5) And the m-n information transmission of the output link fails.
With respect to the sampling of the energy source CPS fault scenario,
the energy CPS element relates to physical domain and information domain simultaneously, and adopts two-state Markov model[7]Describing element states, sampling physical domain elements by sequential Monte Carlo method, and sampling information domain elements by non-sequential Monte Carlo method[22]. After the simulation is completed, a multi-period system state is finally formed:
in the formula:
respectively representing state variables of a superior power grid and a superior gas grid in a time period t;
and
the state variables of the s th cogeneration unit, the electric energy storage, the gas energy storage, the heat energy storage, the cold energy storage, the photovoltaic unit, the wind power unit, the electric refrigerator, the electric boiler, the ice storage air conditioner, the gas boiler and the absorption refrigerator in the t period are respectively. When the physical domain element is in a non-fault state and the information transmission of the element is effective at the moment, the element is considered to be capable of normally working, and the corresponding element state variable is taken as 1, otherwise, the corresponding element state variable is taken as 0.
CPS optimal load reduction model for comprehensive energy
And establishing an optimal load reduction model, analyzing the fault scene of the comprehensive energy CPS, and counting various load reduction conditions at each time period to obtain a reliability index. The objective function takes the minimum sum of the demand response cost, the energy purchasing cost and the load reduction cost as the objective function to construct an optimal load reduction model,
in the formula: t is the research period; j is the number of the types of energy sources of the output port, and comprises 3 energy sources of electricity, heat and cold, and when J is 1, 2 and 3 in sequence, the energy sources respectively correspond to electric energy, heat energy and cold energy; q is input energy type, including 2 kinds of energy of electricity, gas; c is an identifier that does not participate in the demand response load (hereinafter referred to as a regular load);
compensating the price for the unit of the jth type responsive load t period;
the reduction amount of the jth type responsivity load in the t period; pij,tAnd
respectively unit reduction cost and reduction amount of the jth type conventional load in the t period; p is a radical ofq,tAnd Pq,tRespectively the purchase price and the power of the q-th type energy source in the t period. The constraint conditions comprise demand response constraint and power balance constraint, specifically, the flexible loads participating in real-time demand response can be adjusted in energy utilization behavior according to external excitation, and when the energy source CPS is insufficient in energy supply, the flexible loads can actively participate in energy utilization reduction under the assistance of the controller. The constraint conditions are as described in formulas (1) to (4), and the electric power balance constraint, the gas power balance constraint, the thermal power balance constraint and the cold power balance constraint of the energy CPS are as follows:
1) electrical power balance constraints.
In the formula: k(·)Indicating the number of corresponding devices; es is an electrical storage identifier; ge is a power generation identifier;
representing the power generation amount of the s-th cogeneration unit in the t period;
and
the discharge power and the charging power are respectively the electricity energy storage of the s station; pt PVAnd Pt WTRespectively the output of distributed photovoltaic power and wind power in a time period t; etaTThe transformer efficiency; pt EThe purchased electric quantity is t time period;
an electrical load for a period of t;
and
the power consumption of the s-th electric refrigerator, the electric boiler and the ice storage air conditioner in the t time period is respectively; pt grid,maxAnd the maximum power supply power is the maximum power supply power of the superior power grid in the period t.
2) The balance of breathing power is constrained.
In the formula: gs is a gas energy storage identifier; pt gasThe gas purchasing quantity in the t period;
and
the input power of the s th cogeneration unit and the gas boiler in the t period is respectively;
and
respectively storing the deflation power and the inflation power of the stored energy of the s-th gas; pt gas,maxThe maximum air supply quantity of the upper air network in the t period.
3) And (4) heat power balance constraint.
In the formula: gh is a heat generation identifier; hs is a thermal energy storage identifier;
and
respectively representing the heat production quantity of the s-th cogeneration unit, the electric boiler and the gas boiler in the t period;
and
respectively the heat release power and the heat charging power of the s-th heat energy storage;
the thermal power consumed by the s-th absorption refrigerator in the t period is represented;
representing the thermal load over time t.
4) Cold power balance constraints.
In the formula: cs is a cold storage identifier;
and
the cold power output by the s-th electric refrigerator, the absorption refrigerator and the ice storage air conditioner in the t period respectively;
and
respectively storing the discharge power and the charge power of the s-th station cold energy;
is the cooling load for the period t.
5) Demand side balance constraints.
In the formula:
and
the electric, thermal and cold load amounts before reduction are respectively.
5.2.3 energy conversion device output constraint
The energy conversion device needs to satisfy the following constraints:
1) and (4) input and output equation constraint.
The energy conversion device is the key of multi-energy coupling at the physical side of the energy CPS, the output power of the energy conversion device is equal to the product of the input power and the conversion efficiency, and the constraint of each device is expanded as follows:
in the formula: etas,(·)The conversion efficiency of the s-th corresponding equipment is shown.
2) And (5) restraining an upper limit and a lower limit of the output.
In the formula:
and
respectively, the minimum and maximum output electric power of the s-th CHP;
and
minimum and maximum power consumption of the s-th station EC, respectively;
and
minimum and maximum power consumption of the s-th station EF, respectively;
and
minimum and maximum power consumption of the station ISAC;
and
the minimum and maximum heat production of the s-th GF;
and
respectively the minimum and maximum heat production of the s-th AC.
With respect to the energy storage device constraints,
the energy storage device can realize the transfer of electricity, heat, gas and cold energy from the surplus time period to other time periods, and the reliability of the CPS is improved. The invention simultaneously considers 4 energy storage forms of electricity storage, gas storage, heat storage and cold storage. The energy storage forms of the devices are different, but the energy transfer is similar in nature, and the following general constraints need to be met during operation: equation (40) is a charge/discharge constraint, equations (41) and (42) are charge/discharge power limit constraints, and equations (43) to (45) are capacity constraints.
In the formula: x represents the energy type of energy storage, can bring into es, hs, cs and gs, correspond to electric energy storage, heat energy storage, cold energy storage and gas energy storage above respectively;
and
the variable is 0-1 and respectively represents the charging state and the discharging state of the energy storage device, 0 represents the non-working state, and 1 represents the working state;
and
the lower limit and the upper limit of the stored energy are respectively set;
and
respectively the lower limit and the upper limit of the discharge of the stored energy;
and
respectively the charging and discharging energy power of the x-type energy storage devices of the s station;
and
respectively the charging efficiency and the discharging efficiency of the x-type energy storage device of the station s;
and
the energy of the x-type energy storage device of the s station in t +1 and t periods respectively;
and
the lowest and highest energy limits to be met by stored energy are respectively;
and
energy is stored for the final time period and the initial time period respectively.
With regard to the mechanism by which the energy source CPS operates,
the energy CPS carries out operation strategy decision according to the collected distributed power supply, load, energy conversion equipment and switch information to ensure the balance of supply and demand of the comprehensive power supply park, and the operation mechanism is as follows[24]：
1) Information acquisition: the sensor samples the state and electric quantity information of each unit of the comprehensive energy CPS physical layer to form an information packet S. The information packet of the key element is shown as formula (46).
S＝[SDG SLD,x SES,x SEC,l] (46)
In the formula: sDG、SLD,x、SES,xAnd SEC,lInformation packets respectively representing the distributed power supply, the load, the energy storage and the energy conversion device; sLD,xThe method comprises the steps that user side electricity utilization information and user side real-time response electricity quantity information are contained; and l is the type of energy conversion equipment, and corresponds to the type of the device in section 1.
2) And (3) information uploading: the information packet is transmitted to the control center through the communication network, and the information transmission reliability in section 3 needs to be considered during transmission.
3) And (3) information processing decision: and generating an operation strategy of the comprehensive energy CPS according to the acquired information.
4) And (3) command issuing: and issuing the decision result to each controlled unit controller through an information network, and generating a control instruction by the controller. Information transmission reliability also needs to be considered in the command issuing process.
5) And command execution: and each device in the comprehensive energy system completes the actions of output adjustment, load reduction and the like according to the issued instruction.
With regard to the reliability index,
the invention establishes two indexes of shortage energy and importance degree to represent the reliability of the CPS, which is shown as formula (47) -49:
in the formula: j represents three types of energy sources of electricity, heat and cold; rEENS,jThe energy shortage expectation of the j-th energy source; rSAIDI,jThe energy shortage time expectation of the j energy source; t isMFor simulating a period(ii) a Delta t is 1 h; u shapec,j,tThe j-th energy t period is in a reduction state, 1 is taken for reduction, and 0 is taken for reduction on the contrary;
is a component
Importance to class j energy sources;
is a component
The annual average energy deficit of the class j energy source caused by the fault.
With regard to the reliability evaluation flow, it is,
the reliability evaluation of the comprehensive energy CPS mainly comprises 3 links of fault sampling, energy CPS operation scene simulation and reliability index statistics, and the specific evaluation flow is shown in FIG. 4. In FIG. 4, ysIn order to set the simulation age, the section applies the proposed energy CPS reliability analysis method to the energy CPS in the section 1, and verifies the validity of the algorithm. The physical layer architecture of the energy CPS is shown in fig. 5, the configurations of energy conversion devices CHP, EC, EF, ISAC, GF, and AC are not shown, the configurations of 4 energy storage devices are not shown, typical solar power generation of wind power and photovoltaic are not shown, and power generation of an upper-level power grid and a gas grid is not shown. The invention adopts a typical daily load curve to represent the time sequence characteristics of electric, cold and heat loads, and multiplies the maximum value of various loads by the load coefficient of each hour to obtain the electric, hot and cold real-time loads[25]Time-by-time coefficients of each energy source in different seasons. The reliability parameters of each device are shown in appendix A, Table A4[11,24]. The information network load rate is set to 50% (normal) and the information domain element connection topology is shown in fig. 14.
In the examples, the cost for reducing the electric, thermal and cooling loads was set to 50, 40 and 45 yuan/(kWh · h), respectively. The price of the electric load compensation, the cold load compensation and the heat load compensation participating in the real-time response is adjusted on the basis of the prior art and is respectively set to 10 yuan/(kW & h), 13 yuan and 14 yuan/(kWh & h). The number of users participating in electricity, cold and heat real-time demand response in each time interval on all typical days is 15% of the total number of users (the users participating in response in each time interval are different), and the accumulated electricity, heat and heat responsive load amount in each time interval is 10% of the real-time load of the system.
4 scenes are set, and the influence of real-time demand response and information domain on the reliability of the energy CPS is researched, as shown in Table 1
TABLE 1 energy CPS research scenario
Note: "x" indicates that the influence of this factor on the reliability of the energy source CPS is not considered; "√" indicates the influence of this factor on the reliability of the energy source CPS.
The energy shortage expectation and the energy shortage time expectation of the electric, thermal and cold loads in 4 scenes are shown in table 2, and the information system fault influence ratio before and after considering the demand response is shown in table 3.
As shown in table 2, comparing scene 1 and scene 2, scene 3 and scene 4, respectively, the following conclusions can be drawn: the influence of the information domain fault on the energy supply reliability of different energy sources in the energy source CPS is different, but the influence is large in proportion, so that the influence of the information domain cannot be ignored when the reliability analysis of the energy source CPS is carried out. Comparing scene 1 with scene 3, and scene 2 with scene 4, it can be known that after the demand response is implemented, the reliability of power supply, heat supply and cold supply is improved to a great extent. Compared with scene 2, in scene 4, the expectation of the power shortage and the power shortage is respectively reduced by 68.89% and 93.42%, the expectation of the heat shortage and the heat shortage is respectively reduced by 99.10% and 99.55%, the expectation of the cold shortage and the cold shortage is respectively reduced by 71.45% and 89.22%, and the expectation of the comprehensive power shortage and the energy shortage is respectively reduced by 77.19% and 95.16%.
As shown in Table 3, when considering the real-time demand of multiple energy sourcesAfter response, the information domain fault influence ratios of the lack heat energy expectation and the heat energy shortage time expectation are respectively increased to 91.38% and 93.16% from 31.55% and 31.44%, because the heat load in scene 3 and scene 4 is greatly prevented from being cut off after the demand response is considered, the heat supply reliability index is small, and even if the reliability increment caused by the information domain fault is small, the reliability increment is only 101Of order of magnitude, but the duty ratio can be high.
TABLE 2 energy supply reliability index under different scenes
TABLE 3 information System Fault impact ratio
To analyze the importance of different elements in the physical domain, the element importance index in the 4 scenes is calculated
The calculation results of the scene 2 are shown in fig. 6 to 9, wherein the element numbers 1 to 14 respectively represent a superior power grid, a superior air grid, a cogeneration unit, an electric refrigerator, an electric boiler, an ice storage air conditioner, an absorption refrigerator, an air boiler, electric energy storage, air energy storage, heat energy storage, cold energy storage, a photovoltaic unit and a wind turbine unit.
As can be seen from fig. 6, the components having a large influence on the power supply reliability have the following orders from high to low: the superior power grid, the cogeneration unit, the wind turbine unit and the photovoltaic unit. The influence of the upper-level gas network on the power supply reliability is small because the fault rate is low and the borne power supply tasks are few, and the influence of the gas energy on the power supply reliability is mainly embodied by the CHP unit with the high fault rate.
As can be seen from fig. 7, the elements having a large influence on the heat supply reliability have the following influence degrees in the order from high to low: the power generation system comprises an electric boiler, a gas boiler, a wind turbine generator, a superior power grid, a superior gas grid, a photovoltaic unit and a cogeneration unit. Wherein, the combined heat and power generation unit influences heat supply reliability relatively less, because the heat supply task that it undertakes is less, and the heat energy upper limit that provides under the non-fault condition is 480kW, when CHP broke down and other components normally worked, can supply partial heat by electric boiler and gas boiler to reduce the heat supply shortage and can be known from figure 8, in the great component of influence to cooling reliability, influence degree sequencing is respectively by high to low: an electric refrigerator, an absorption refrigerator, an ice storage air conditioner and cold energy storage. As can be seen from table a4 in appendix a, the influence of the upper-level power grid and the air grid on cooling reliability is small because the failure rate is far lower than that of energy conversion devices such as EC, ISAC, AC, and the like, and the influence of the power grid and the air grid on cooling reliability is reflected by the energy conversion devices.
As can be seen from fig. 9, the reliability impact of each element on the integrated power supply is ranked from high to low as follows: the system comprises a superior power grid, an electric refrigerator, an absorption refrigerator, a wind turbine, a cogeneration unit, an electric boiler, a gas boiler, an ice storage air conditioner, a photovoltaic unit, a superior gas grid, electric energy storage, cold energy storage, gas energy storage and heat energy storage.
The results are consistent with those in the test data, and the correctness of the method is verified. In addition, according to experimental data, the energy supply shortage caused by partial element faults can be greatly supplemented by the demand response, so that the importance index is reduced, but the importance ranking of the rest elements is consistent with that before the demand response is implemented.
According to the analysis, the energy CPS can drive a user to adjust energy utilization behaviors through economic compensation and participate in demand response, so that the energy supply reliability of various types of energy is effectively improved. To analyze the economy, the annual average value of each type of cost for scene 2 and scene 4 was calculated, and the result is shown in fig. 10. The calculation results of scene 1 and scene 3 are shown in fig. 11.
As can be seen from fig. 10, compared with scenario 2, the system operation cost and the load shedding cost in scenario 4 are both significantly reduced, where the total cost is reduced by 2.22% and the load shedding cost is reduced by 76.23%.
Influence of element faults of different information domains on reliability of energy CPS (control performance simulator)Supposing that the original failures of the information terminal, the communication line and the exchange are respectively lambda1、λ2And λ3Respectively increasing the failure rate of 3 information elements to 10 times, and observing various energy sources REENSThe change of the index. Failure rate of various components to various energy sources REENSAs shown in FIG. 12, the influence of the index R is shown in FIG. 12EENSThe method is sensitive to the fault rate change of the information terminal and the switchboard, and is less sensitive to the fault rate change of the communication line.
Calculating the failure rate of 3-type information elements of the exchanger, the information terminal and the communication line by test data to obtain REENSThe sensitivity of the index was 1.2191X 105、2.1209×104、3.5500×103(kW. h)/(sub. a). Therefore, under the configuration condition of the energy CPS information network frame, the switch has the largest influence on the reliability of energy supply, because the switch is a junction node of a plurality of communication paths, when the switch fails, the communication of the terminal equipment connected with the switch is disabled, and the influence range is far larger than that of a single terminal equipment. Further, the communication line failure rate pair REENSThe sensitivity is low, which shows that the standby path of the ring-shaped communication network frame reduces the influence of the communication line on the energy CPS.
On the basis, different fault pairs R are further analyzedEENSThe contribution ratio of the index is shown in fig. 13. As can be seen from fig. 13, in the information domain failure, the contribution rate of the information terminal device is the largest, and the number of switches is the second, because the failure rate of the information terminal in the energy CPS is relatively high and numerous, and the physical unit controlled by the energy CPS directly fails after failure, the supply energy shortage is large, while the switch has the highest sensitivity, but has high reliability in normal operation, low failure rate, and less energy supply shortage than the information terminal.
By combining the analysis, when the information layer of the energy CPS is built, the information terminal and the switch can be properly backed up, so that the influence of the information system fault on the reliability of the energy CPS is reduced. Reliability indexes before and after backup of the switch and the information terminal are obtained after actual test, and it can be seen from test data that the power supply, heat supply and cold supply reliability of the energy CPS after information element backup is obviously improved, and the validity of an element backup strategy is verified (the test data is not shown).
Reliability influence of information network load rate on energy CPS
The information network load rate influences the information delay, in order to analyze the influence of the load rate on the reliability of the energy CPS, the following 2 scenes are set, and the calculation result is shown in Table 4.
Scene 5: the load factor takes 20% (light load) in consideration of physical element failure, information element failure, and demand response. Scene 6: considering physical element failure, information element failure, demand response, and load rate of 80% (heavy load)
As can be seen from table 4, the reliability index is reduced in light load compared to normal load operation (scenario 4). When the information network is heavily loaded, the communication delay has obvious influence on the reliability of the energy CPS, and the conclusion is consistent with that of the prior art.
Under the background that the coupling degree of an information network and a physical network is continuously deepened, the invention explores an energy CPS real-time demand response strategy and researches a reliability evaluation method of the energy CPS on the basis. The results of the above examples show that:
1) the influence of the information network on the energy CPS cannot be ignored, the influence degree of different information elements on the reliability of the energy CPS is different, and the backup of key elements can be enhanced according to reliability indexes so as to improve the reliability of the energy CPS.
2) The multi-energy user participates in real-time comprehensive demand response, so that the reliability of the energy CPS can be effectively improved, the total operation cost of the system is reduced, and the load reduction cost is greatly reduced.
3) The reliability evaluation method provided by the invention can provide reference basis for identifying weak links of the system, and has guiding significance for implementing the strengthening path of the physical layer and the information layer.
Claims (6)
1. The method for evaluating the reliability of the energy information coupling system based on real-time demand response is characterized by comprising the following steps of:
s1: constructing a comprehensive energy information physical system framework and analyzing a real-time demand response mechanism of the comprehensive energy information physical system framework, wherein the comprehensive energy information physical system comprises an energy input end, an energy storage device and an energy output end of a physical system;
s2: establishing an information transmission reliability model, sampling the element state by adopting a mixed Monte Carlo method, and generating a multi-period fault scene;
s3: bringing element state variables into energy balance constraint, and establishing an optimal load reduction model by taking the lowest energy purchase cost, load reduction cost and demand response cost as targets;
s4: and determining the reliability evaluation index and the evaluation flow of the energy information physical system.
2. The method for evaluating the reliability of the energy information coupling system based on the real-time demand response as claimed in claim 1, wherein the demand response mechanism comprises a response quantity constraint and a scheduling time constraint, and the specific calculation formula is
In the formula, the period Lambda participating in the response, the unit period responsive load quantity
And compensating for the price
sd is a responsive load identifier; subscript j is an energy identifier, including electric energy, heat energy and cold energy; α is a user identifier;
and
the upper and lower limits of the response quantity of the jth class load of the contract-defined user alpha in the t period;
the response state variable of the j-th energy period t is 1, which indicates a response state, 0 which indicates an unresponsive state, and the number of users who make a contract in the energy CPS is α0The maximum load amount that can participate in real-time demand response in the time period t is
In the formula: alpha is alpha0The number of users who make a contract;
and
the actual reduction amount and the reduction limit value of the j-th load in the t period are respectively.
3. The method for assessing the reliability of an energy information coupling system based on real-time demand response as claimed in claim 1, wherein in S2, the integrated energy information physical system framework includes an information acquisition device, an information transmission device and a data processing center, information elements in the information acquisition device, the information transmission device and the data processing center are equivalent to nodes, topological connection relationships among the information elements are equivalent to edges, an energy CPS information element connection topology structure can be formed, and the topological reliability is modeled as
Wherein m is the source node, n is the sink node, z1And z2Respectively the number of transmission paths between m-n and the r-th1The number of information nodes contained in each transmission path;
is the r1A state of the strip transmission path; u. ofc(r2) Is the r2The state of each information node is a normal state when 1 is selected and a fault state when 0 is selected, psim-nLink topology reliability.
4. The method for assessing the reliability of an energy information coupling system based on real-time demand response as claimed in claim 1, wherein the multi-period fault scenario in S2 is
In the formula:
respectively representing state variables of a superior power grid and a superior gas grid in a time period t;
and
the state variables of the s-th combined heat and power generation unit, the electric energy storage unit, the gas energy storage unit, the heat energy storage unit, the cold energy storage unit, the photovoltaic unit, the wind power generation unit, the electric refrigerator, the electric boiler, the ice storage air conditioner, the gas boiler and the absorption refrigerator in the t period are respectively, when the physical domain element is in a non-failure state and the information transmission of the element is effective at the moment, the element is considered to be capable of normally working, 1 is measured corresponding to the state variable of the element, and otherwise 0 is taken.
5. The method for estimating reliability of an energy information coupling system based on real-time demand response as claimed in claim 1, wherein in the step S3, the load reduction model is
In the formula: t is the research period; j is the number of the types of energy sources of the output port, and comprises 3 energy sources of electricity, heat and cold, and when J is 1, 2 and 3 in sequence, the energy sources respectively correspond to electric energy, heat energy and cold energy; q is input energy type, including 2 kinds of energy of electricity, gas; c is an identifier that does not participate in the demand response load;
compensating the price for the unit of the jth type responsive load t period;
the reduction amount of the jth type responsivity load in the t period; pij,tAnd
respectively unit reduction cost and reduction amount of the jth type conventional load in the t period; p is a radical ofq,tAnd Pq,tRespectively the purchase price and the power of the q-th type energy source in the t period.
6. The method for assessing the reliability of an energy information coupling system based on real-time demand response as claimed in claim 1, wherein the reliability assessment mechanism of the energy information physical system in S4 comprises the following steps:
and S61, information acquisition: the sensor samples the state and electric quantity information of each unit of the comprehensive energy CPS physical layer to form an information packet S. Wherein the information packet of the key element is
S＝[SDG SLD,x SES,x SEC,l]
In the formula, SDG、SLD,x、SES,xAnd SEC,lInformation packets respectively representing the distributed power supply, the load, the energy storage and the energy conversion device; sLD,xThe method comprises the steps that user side electricity utilization information and user side real-time response electricity quantity information are contained; l is the type of energy conversion equipment, and corresponds to the type of the device in section 1 respectively;
and S62, information uploading: the information packet is transmitted to a control center through a communication network, and the information transmission reliability of the section 3 needs to be considered during transmission;
s63, information processing decision: generating an operation strategy of the comprehensive energy CPS according to the acquired information;
s64: and (3) command issuing: the decision result is sent to each controlled unit controller through an information network, and the controller generates a control instruction; information transmission reliability also needs to be considered in the command issuing process;
s65: and command execution: and each device in the comprehensive energy system completes the actions of output adjustment, load reduction and the like according to the issued instruction.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113642165A (en) * | 2021-07-29 | 2021-11-12 | 西安理工大学 | Distributed optimization method of electricity-gas coupling distribution network considering uncertainty of demand side |
| CN115935711A (en) * | 2023-01-31 | 2023-04-07 | 西华大学 | Reliability evaluation method for multi-energy complementary active distribution network system based on graph theory |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110417053A (en) * | 2019-07-29 | 2019-11-05 | 重庆大学 | Meter and the multi-energy system reliability estimation method of integration requirement response |
| CN111313429A (en) * | 2020-02-25 | 2020-06-19 | 国网山西省电力公司经济技术研究院 | Reliability assessment method and system for comprehensive energy system |
| CN112070374A (en) * | 2020-08-25 | 2020-12-11 | 天津大学 | Regional energy Internet energy supply reliability assessment method |
-
2021
- 2021-02-19 CN CN202110195668.6A patent/CN112989576A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110417053A (en) * | 2019-07-29 | 2019-11-05 | 重庆大学 | Meter and the multi-energy system reliability estimation method of integration requirement response |
| CN111313429A (en) * | 2020-02-25 | 2020-06-19 | 国网山西省电力公司经济技术研究院 | Reliability assessment method and system for comprehensive energy system |
| CN112070374A (en) * | 2020-08-25 | 2020-12-11 | 天津大学 | Regional energy Internet energy supply reliability assessment method |
Non-Patent Citations (1)
| Title |
|---|
| 王澜灵: "计及实时需求响应的综合能源信息物理系统可靠性分析", 《电力建设》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113642165A (en) * | 2021-07-29 | 2021-11-12 | 西安理工大学 | Distributed optimization method of electricity-gas coupling distribution network considering uncertainty of demand side |
| CN113642165B (en) * | 2021-07-29 | 2024-04-12 | 西安理工大学 | Distributed optimization method of electric-gas coupling distribution network considering uncertainty of demand side |
| CN115935711A (en) * | 2023-01-31 | 2023-04-07 | 西华大学 | Reliability evaluation method for multi-energy complementary active distribution network system based on graph theory |
| CN115935711B (en) * | 2023-01-31 | 2023-10-20 | 西华大学 | Multi-energy complementary active distribution network system reliability assessment method based on graph theory |
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