CN117937575A - Power system reliability assessment method considering wind power space-time uncertainty - Google Patents
Power system reliability assessment method considering wind power space-time uncertainty Download PDFInfo
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Abstract
The invention discloses a power system reliability assessment method considering wind power space-time uncertainty, which comprises the following steps: constructing a wind power plant reliability model based on the wind speed space-time correlation model and the wind turbine random fault repair model; constructing a reliability model of the gas unit and a reliability model of an electric conversion facility; constructing an electric power and natural gas combined system optimization control model considering wind power access and natural gas dynamic characteristics; and evaluating the space-time reliability of the power natural gas system based on an optimal control model of the power natural gas combined system taking wind power access and natural gas dynamic characteristics into consideration and a multi-energy and multi-dimensional space-time reliability index. The reliability analysis method of the power system provided by the invention can be used for clarifying the influence of wind power on reliability in time and space dimensions, and meanwhile, the uncertainty of wind power is absorbed by effectively utilizing the dynamic characteristics of a natural gas system, so that decision-making assistance is provided for combination of a system day-ahead unit, equipment switching, operation scheme formulation and emergency fault management.
Description
Technical Field
The invention belongs to the field of comprehensive energy system operation optimization and reliability analysis, and particularly relates to a reliability evaluation method of a power system considering wind power space-time uncertainty.
Background
The volatility, intermittence and difficulty in predictability of renewable energy power generation present challenges for safe and stable operation of electrical power systems. In denmark, more than half of the power system imbalance scenarios are due to wind power, and failure of the wind turbines themselves can also lead to uncertainty in wind power generation. Therefore, it is important to evaluate the reliability of an electrical power system at high rates of wind penetration.
The existing reliability assessment for the power system under high-proportion wind power penetration has the following defects: in the aspect of uncertainty modeling of wind power generation, an evolution process for wind power space-time uncertainty and relevance is lacked, and the influence analysis of the combined action of wind power space-time uncertainty and wind turbine internal faults on the working condition of a wind power plant is lacking; in the aspect of the treatment means of wind power uncertainty, along with the gradual tight coupling of the power and the natural gas system, the natural gas system can provide powerful support for the power system to cope with the impact caused by the great change of wind power output due to the slower dynamic characteristic of the natural gas system. However, there are few studies on analyzing the reliability influence of the dynamic characteristics of the natural gas system on the power system under wind power access, and in the aspect of reliability evaluation, there are few studies on analyzing the space-time distribution characteristics of the reliability.
Disclosure of Invention
Aiming at the problems in the background method, the invention provides a method for evaluating the reliability of the power system by considering wind power space-time uncertainty, and solves the problem of the method for evaluating the reliability of the power system under the premise of considering wind power space-time uncertainty and natural gas dynamic characteristics.
In order to achieve the above object, the present invention provides a method for evaluating reliability of an electric power system in consideration of wind power space-time uncertainty, comprising:
constructing a wind speed space-time correlation model and a wind turbine random fault repair model;
Constructing a wind power plant reliability model based on the wind speed space-time correlation model and the fan random fault repair model;
constructing a reliability model of the gas unit and a reliability model of an electric conversion facility;
Based on the wind power plant reliability model, the gas turbine unit reliability model and the electric power-to-gas facility reliability model, constructing an electric power and natural gas combined system optimization control model considering wind power access and natural gas dynamic characteristics;
Acquiring space-time reliability indexes of multiple energy sources and multiple dimensions, and evaluating the space-time reliability of the power natural gas system based on the power natural gas combined system optimization control model considering wind power access and natural gas dynamic characteristics and the space-time reliability indexes of the multiple energy sources and the multiple dimensions.
Optionally, constructing the wind speed space-time correlation model includes:
Acquiring a wind speed state set, and randomly taking values based on the wind speed state set to acquire a plurality of wind speeds;
Setting states of a plurality of wind speeds, and acquiring time-varying state probabilities of the wind speeds based on the states of the wind speeds;
Constructing a wind speed time-varying state probability model based on a plurality of wind speed time-varying state probabilities;
expanding the wind speed time-varying state probability model and constructing a wind speed space-time correlation model.
Optionally, establishing the fan random fault repair model includes:
Predicting the wind speed under the consideration of the space-time correlation based on the wind speed space-time correlation model to obtain a predicted wind speed;
Acquiring available power generation capacity of a fan of the wind power plant based on the predicted wind speed;
acquiring a normal running state and a fan fault state of a fan;
And constructing a random fan fault repair model based on the available power generation capacity of the fan of the wind power plant, the normal running state of the fan and the fan fault state.
Optionally, based on the wind speed space-time correlation model and the fan random fault repair model, constructing the wind power plant reliability model includes:
Acquiring the available power generation capacity of the wind power plant based on the wind speed space-time correlation model and the fan random fault restoration model;
and characterizing the available power generation capacity of the wind farm, and constructing a wind farm reliability model.
Optionally, constructing the reliability model of the gas turbine unit includes:
constructing a gas unit fault model and a gas unit repair model;
Acquiring the state quantity of the gas units of the nodes, and acquiring an available power generation capacity set of the gas units based on the gas unit fault model, the gas unit repair model and the state quantity of the gas units of the nodes;
randomly taking values of the available power generation capacity sets of the gas turbine sets to obtain the available power generation capacity of the gas turbine sets;
And based on the available power generation capacity of the gas turbine set, obtaining the actual available power generation capacity of the gas turbine set, and constructing the reliability model of the gas turbine set.
Optionally, constructing the reliability model of the electric power conversion facility includes:
Constructing an electric conversion gas module, and acquiring a natural gas production available capacity set of the electric conversion gas model based on the electric conversion gas model;
randomly taking values of the natural gas production available capacity set of the electric gas conversion model to obtain the natural gas production available capacity of a natural gas facility;
and acquiring the natural gas production available capacity of the electric conversion facility based on the natural gas production available capacity of the natural gas facility, and constructing an operation reliability model of the electric conversion facility.
Optionally, based on the wind power plant reliability model, the gas turbine unit reliability model and the electric gas conversion facility reliability model, constructing an electric power and natural gas combined system optimization control model considering wind power access and natural gas dynamic characteristics includes:
Acquiring dynamic characteristics of a natural gas system based on the wind power plant reliability model, the gas turbine unit reliability model and the wind power plant reliability model;
acquiring initial conditions of natural gas and initial conditions of a natural gas pipeline, and acquiring a partial differential equation based on the initial conditions of the natural gas and the initial conditions of the natural gas pipeline;
And constructing an electric power and natural gas combined system optimization control model considering wind power access and natural gas dynamic characteristics based on the partial differential equation and the dynamic characteristics of the natural gas system.
Optionally, the power and natural gas combined system optimization control model considering wind power access and natural gas dynamic characteristics obeys the constraint condition of a power system and the constraint condition of a natural gas system.
Optionally, the evaluating the space-time reliability of the power natural gas system based on the power natural gas combined system optimization control model considering wind power access and natural gas dynamic characteristics and the multi-energy and multi-dimensional space-time reliability index comprises:
based on the power and natural gas combined system optimization control model considering wind power access and natural gas dynamic characteristics, sampling wind speed, random faults and repair of equipment by adopting a time sequence Monte Carlo method, and obtaining a sampling sample;
and evaluating the space-time reliability of the electric power natural gas system based on the multi-energy source, the multi-dimensional space-time reliability index and the sampling sample.
Optionally, the multi-energy, multi-dimensional spatio-temporal reliability index includes expected load interruption and system failure risk.
The invention has the following beneficial effects:
The reliability analysis method of the power system provided by the invention can be used for clarifying the influence of wind power on reliability in time and space dimensions, and meanwhile, the uncertainty of wind power is absorbed by effectively utilizing the dynamic characteristics of a natural gas system, so that a decision basis is provided for combination of a system day-ahead unit, equipment switching, operation scheme formulation and emergency fault management.
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The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:
FIG. 1 is a schematic flow chart of a reliability evaluation method of a power system considering wind power space-time uncertainty in an embodiment of the invention;
FIG. 2 is a schematic diagram of a power system and a natural gas system according to an embodiment of the present invention;
FIG. 3 is a graph of wind speed data according to an embodiment of the present invention;
FIG. 4 is a graph of wind power, cut load and running cost in a typical scenario presented by an embodiment of the present invention;
FIG. 5 is a graph of natural gas production, natural gas consumption of a gas turbine set, and node barometric pressure for an electrical gas conversion facility in a typical scenario presented by an embodiment of the present invention;
FIG. 6 is a diagram of expected load interruption and system failure risk for a power system according to an embodiment of the present invention;
FIG. 7 is a diagram of expected load interruption of a node in an operation period according to an embodiment of the present invention
Fig. 8 is a diagram of expected load interruption of a node at t=24h according to an embodiment of the present invention
FIG. 9 is a fault risk diagram of a node system in an operation period according to an embodiment of the present invention
Fig. 10 is a fault risk diagram of a node system at t=24h according to an embodiment of the present invention
Detailed Description
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The application will be described in detail below with reference to the drawings in connection with embodiments.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.
As shown in fig. 1, in this embodiment, a method for evaluating reliability of a power system in consideration of wind power space-time uncertainty is provided, including:
step one, a wind power plant reliability model is obtained through coupling by establishing a space-time correlation model of wind speed and a random fault repair model of a wind turbine;
step two, establishing a reliability model of coupling elements such as a gas unit, an electric conversion facility and the like;
step three, an electric power and natural gas combined system optimization control model which is obtained by considering wind power access and natural gas dynamic characteristics is established;
And step four, expanding the traditional reliability index into a multi-energy and multi-dimensional space-time reliability index, and evaluating the space-time reliability of the electric power natural gas system by a time sequence Monte Carlo method.
The case of an embodiment implemented according to the complete method of the present disclosure is as follows:
First, parameters of the power system and the natural gas combined system are initialized, a schematic structural diagram of the power system and the natural gas combined system is shown in fig. 2, and the following modifications are made in this embodiment:
At node 18 of the power system, replacing a 400MW nuclear power unit with a wind power unit of the same capacity; at the node 7 of the power system, a 100MW unit is replaced by a wind turbine unit with the same capacity; at node 22 of the power system, two 50MW units are replaced with wind turbines of the same capacity. The fuel-fired power generation units on the power nodes 2, 13, 14 and 15 are replaced by gas-fired power units of the same capacity. Three electrical gas conversion facilities are installed at the natural gas nodes 7, 10 and 16, respectively, with a natural gas production capacity of 0.5mm 3/day. The natural gas pressure is limited to between 0.95 and 1.05 times its normal value.
The present case is intended to illustrate the cut-off and operating conditions of power and natural gas systems by given wind speed conditions.
Wind speed data wind speed history data was obtained from National Oceanic and Atmospheric Administration (NOAA) agency over the past decade in texas, and 4 scenarios were compared, as shown in fig. 3. It can be seen that the wind speed probability densities for the 4 regions exhibit similar characteristics, which show the spatial-temporal correlation of wind speeds. Wind speed is aggregated into eight states. In the simulation, the effect of wind speed on the operation of the power system, and the indirect effect of the electricity to the natural gas system through the electricity to gas facility, are illustrated by a typical scenario.
As shown in fig. 4, it can be seen that a high proportion of wind power access may threaten the normal safe operation of the power system even though other gensets are operating properly. At 3:00 and 7:00, a small cut load of 0.25MW and 0.89MW will also be generated before a 400MW unit failure; by 11:00 generator failure, the power cut load rises sharply, although the wind speeds are near 12:00-17:00 and 3:00-7:00. This indicates that the power system is very vulnerable to possible generator faults at varying wind permeabilities.
From a natural gas system perspective, we observe that the natural gas system provides effective support for the power system, as shown in fig. 5. During periods of weaker wind speeds, the electrical conversion facility produced 0.13Mm 3 of natural gas by consuming electricity; in the case of power generation shortage, the gas turbine set increases the power generation power by 24.16% to meet the power load. Although natural gas injection from an electrical gas conversion facility produces some small fluctuations in the node gas pressure of the natural gas system, it is always maintained within a safe and controllable range.
Spatio-temporal reliability analysis
In this case, space-time reliability is calculated, and EID and RSOL of the power system during operation are shown in fig. 6. All elements are in normal operation at the initial time, so EID and RSOL are both 0 at the initial time. With the state transitions of wind speeds and other genset elements, EID and RSOL have grown to 2.58MW and 0.023, respectively.
The EID and RSOL during operation are further refined to nodes as shown in fig. 7-10. It can be seen that the power nodes 10, 9, 5 have the highest EID and RSOL. In particular, for power node 10, the RSOL is much higher than other power nodes, indicating that the power load on that power node is more susceptible to shedding.
The implementation process is as follows:
step one, obtaining a wind power plant reliability model through coupling by establishing a space-time correlation model of wind speed and a random fault repair model of a wind turbine
Calculating time-varying state probabilities of wind speed
The reliability of a wind farm is mainly characterized by the available capacity of its power generation, which is related to two factors, the wind speed and the state of the wind turbine, respectively. The wind speed is essentially determined by the weather system, which varies not only over time, but also over space. Therefore, the wind speed is predicted from two dimensions of space and time, and the prediction accuracy is greatly improved.
During the run time period, a markov process is typically used to describe the change in wind speed over time in a single space. To establish a markov process, wind speeds are first aggregated into a finite set of states, the number of which is flexibly selectable, depending on the requirements for the accuracy of the predictions. It is assumed that the wind speeds encountered by different fans are the same for the same wind park. In general, we assume that wind speeds at wind farm j are aggregated asIndividual states, then during operation, wind speeds are from the aggregateAnd taking a random value. Assuming that the state of the wind speed at the time t=0 is h 0, the time-varying state probability Pr h (t) of the wind speed in each state can be calculated by solving for the market-off:
wherein, The state transition rate of the wind speed from the state h to the state h', and w is the superscript indicating the wind speed related physical quantity.
Predicting wind speed under consideration of spatio-temporal correlation
In order to characterize the relevance of wind speeds in different spaces, the Markov model is further expanded into a space-time Markov process.
Considering two wind farms j 1 and j 2 for a given wind speed dataset, the state probability of a transition from wind speed state h 1 of wind farm j 1 to state h 2 of wind farm j 2 can be calculated as:
wherein, For the number of times the wind speed of wind farm j 1 is in state h 1,/>The wind speeds for wind farms j 1 and j 2 are in states h 1 and h 2, respectively. When j 1＝j2, the transition probability/>Defined as the probability of autorotation in a normal markov process; when j 1≠j2, we define the cross transition probability, which describes the effect of wind speed at other spatial locations on wind speed at a given spatial location.
Repeating the calculation until all the state transition probabilities are obtained, and then obtaining a state transition matrix between any two wind farmsCan be expressed as:
And repeating the calculation to obtain a state transition matrix between any two wind power plants. Given wind speed information for all spatial locations over a current period, wind speeds for all locations thereafter can be predicted based on a time-sequential Monte Carlo method by a state transition matrix. Nevertheless, for wind speed conditions at the same spatial location, the wind speeds predicted by wind farms at different locations may be different, and we set these predictions as reference values. And the wind speed prediction result of the final wind power plant j in the period k The mean of these reference values under different weights can be calculated as:
where k 1,j is a weight coefficient when wind farm j is predicted from wind farm 1, v 1,j,k is a wind speed value of wind farm j predicted from wind farm 1 in period k, v j′,j,k is a wind speed value of wind farm j predicted from wind farm j 'in period k, NW is the number of wind farms, k j′,j is a weight coefficient when wind farm j is predicted from wind farm j', k NW,j is a weight coefficient when wind farm j is predicted from wind farm NW, and v NW,j,k is a wind speed value of wind farm j predicted from wind farm NW in period k.
To calculate the appropriate weight coefficients, we build an optimization model to minimize the prediction error for a given data set as follows:
where v j,k is the wind speed of the wind farm j over period k in a given dataset.
Calculating the available power generation capacity of a wind farm
Based on the predicted wind speed, the available power generation capacity of wind turbine l of wind farm j during period kIt can be calculated as:
wherein, And/>For the cut-in wind speed, rated wind speed and cut-out speed of the wind turbine l of the wind farm j, A j,l、Bj,l and C j,l are parameters of the corresponding wind turbines respectively,/>And rated power generation power of the fan.
On the other hand, the running state of the fan is one of the other important factors for determining the output of the fan, and the reliability of the fan can be represented by a two-state model. Thus, the available power generation capacity of wind farm j during period kIt can be calculated as:
wherein, Is a variable of 0-1,/>And 0 respectively represent a normal running state and a fault state, and NL j is the number of fans in the wind farm j.
Step two, establishing a reliability model of coupling elements such as a gas unit, an electric conversion facility and the like
Operational reliability model of gas turbine unit
The operational reliability model of the gas turbine unit is characterized by a multi-state model, which means that it can be partially failed, its available power generation capacity can be reduced by a portion of the values, or a complete failure can occur, its available power generation capacity is reduced to 0. The number of states of the gas unit j of the node i can be expressed asThus, during the period of operation, the available power generation capacity of the gas turbine set due to its random failure and repair is at/>Is randomly valued.
The available power generation capacity of a gas turbine is also dependent on the sufficiency of its natural gas supply and is thus further affected by the natural gas system. Thus, the actual available power generation capacity of the gas turbine unit j on node iIt can be calculated as:
wherein, For the power generation capacity of the gas turbine set j of the node i in the state h, a i,j、bi,j and c i,j are the operation parameters of the gas turbine set j of the node i, HV is the heat value of natural gas,/>Is the natural gas injection amount of the gas unit in the operation process.
Operational reliability model of electric conversion facility
The operational reliability model of an electrical transfer facility is also dependent on its own state transitions and power supply conditions, with electrical transfer modules typically operating in parallel in the electrical transfer facility, with their respective states being independent of each other. The reliability of each electric conversion module can be characterized by a two-state model, and the natural gas production available capacity of the electric conversion module j of the node i is in the running processIs randomly valued. Thus, the natural gas production available capacity of the entire natural gas facility is:
wherein, Is a variable of 0-1,/>And 0, the number of electrical switching modules of node i is NP i, which represents the normal operating state and the complete fault state, respectively.
The natural gas production available capacity of an electrical conversion facility is also constrained by the abundance of power supplies at its nodes, and is further affected by the operating conditions of the electrical power system. Thus, the natural gas production available capacity of an electrical conversion facility can be calculated as:
wherein, Natural gas production available capacity for electric gas conversion facilities of node i,/>For the power supply of the electricity to the node i during operation,/>For electrical conversion efficiency, H g is the heating value of natural gas.
Step three, establishing an electric power and natural gas combined system optimization control model obtained by considering wind power access and natural gas dynamic characteristics
Establishing an optimized control model
The goal of the optimal control of the electric natural gas cogeneration system is to minimize the electricity generation cost, the gas production cost, and the load shedding cost, wherein the load shedding cost is described by a user loss function. This optimal control is performed over a given time domain due to consideration of the dynamics of the natural gas system. Thus, it can be described by the following formula:
Where J is the objective function of the optimization control, K is the ordinal number of the time step, K is the total number of time steps, For generating power of a gas unit j on a node i in a time step k,/>For the gas production rate of the electrical gas transfer facility at node i at time step k, CDF i is the user loss function of node i, lc i,k is the load shedding of node i at time step k.
Subject to constraints (12) - (18), and related constraints of the natural gas system.
fij,k＝(θi,k-θi,k)/Xij (17)
Wherein,And/>The electricity transfer facility j for node i is the natural gas production and electricity consumption during period k.Is the price of natural gas. g i,j,k is the generated energy of the traditional non-gas turbine set j of the node i in the period k, cst i,j is the power generation cost function of the traditional non-gas turbine set, GB, EB natural gas and power node set, NG i and/>Set of traditional non-gas units and gas units respectively being node i,/>And/>Is the lower limit of the power generation power of the traditional non-gas unit and gas unit,/>Generating power for a wind farm, NW i is the number of wind farms at node i,/>For the power load of node i,/>For the collection of power lines connecting node i, f ij,k is the power flow from node i to node j, θ i,k is the phase angle of node i during period k, X ij is the reactance of power line ij,/>As the upper limit of the transmission capacity of the power line ij,Upper limit of power generation of non-gas unit j of node i,/>For the upper limit of the generated power of wind farm j on node i at time step k,/>For the upper limit of the schedulable gas production rate of the electric gas conversion facility of the node i, NG i is the number of non-gas units on the node i.
In addition to the constraints described above, relevant constraints for natural gas systems are set forth in step 3.2.
Establishing a natural gas system dynamic characteristic model
Reconstruction of natural gas dynamic model formula
Two partial differential equations, including a continuity equation and a motion equation, are used to describe a natural gas dynamic model in a natural gas pipeline. In a horizontally insulated natural gas pipeline, this is denoted:
Wherein q and p are natural gas flow and natural gas pressure respectively, A is the cross-sectional area of the pipeline, B is the wave velocity in the natural gas, ρ 0 is the density of the natural gas under standard conditions, D is the pipeline diameter, and F is the van der Waals transmission coefficient.
The partial differential equation described above may be discretized by the following Wendroff equation:
wherein p m+1,k+1 is the air pressure of the natural gas pipeline segment m+1 at the time step k+1, p m,k+1 is the air pressure of the natural gas pipeline segment m at the time step k+1, p m+1,k is the air pressure of the natural gas pipeline segment m+1 at the time step k, p m,k is the air pressure of the natural gas pipeline segment m at the time step k, q m+1,k+1 is the natural gas flow of the natural gas pipeline segment m+1 at the time step k+1, q m,k+1 is the natural gas flow of the natural gas pipeline segment m at the time step k+1, q m+1,k is the natural gas flow of the natural gas pipeline segment m+1 at the time step k, q m,k is the natural gas flow of the natural gas pipeline segment m at the time step k, Δx and Δt are the space and time step, m is the sequence number of the natural gas pipeline segment, ψ represents the flow direction of the natural gas flow, ψ=sgn (p i-pj), sgn is a sign function, and is defined by (23).
Assuming the natural gas flow direction is unchanged during operation, then (23) may be further relaxed to a second order cone constraint:
Initial conditions and boundary conditions
Since the natural gas dynamic characteristic model is described by partial differential equation, initial conditions and boundary conditions need to be given, and the initial conditions are determined by calculation results of steady-state optimization power flow of the electric power natural gas in a normal state. The model is as follows, with the objective of minimizing the operating costs by optimizing control of the power generation and natural gas production plans of traditional non-gas units, gas units and natural gas sources:
obeying constraints (26) - (29) and power system constraints (12) - (18):
Wherein w i is the natural gas production of the natural gas source of the node i, g i,j is the power generation of the non-gas unit j on the node i, For the power generation of the gas turbine unit j on the node i, C IEGS is the running cost,/>For natural gas price of node i,/>And/>Respectively the upper and lower limits of the natural gas productionFor natural gas load of node i,/>Natural gas consumption rate of gas unit j for node i,/>For the collection of natural gas pipes connecting node i, q ij is the natural gas flow from node i to node j, C ij is the characteristic parameter of natural gas pipe ij, Γ ij is the direction of natural gas flow of pipe ij, p i is the natural gas pressure of node i,/>Is the transmission capacity of the natural gas pipeline ij.
After solving the above-described optimization problem, w i、gij and w i、gij can be obtainedIs a value of (2).
During operation, the gas pressure p ij,m,k of the natural gas pipeline segment m during period k needs to be controlled within a safe range:
(1-Y)pij,m≤pij,m,k≤(1+γ)pij,m (30)
Where γ is the relative allowable range of other fluctuations in natural gas and p ij,m is the gas pressure of segment m of pipeline ij.
Thus, the initial conditions of natural gas pressure and natural gas flow can be given by:
qij,m,0＝qij (32)
Where p ij,m,0 is the initial value of the air pressure of segment m of pipe ij, q ij,m,0 is the initial value of the natural gas flow of segment m of pipe ij, and L ij is the length of pipe ij.
In a natural gas system, the boundary conditions of the pipeline satisfy the following relationship:
/>
wherein, For the air pressure of segment 0 of pipeline ij 1 in the k period, j 1 is the node number,/>The air pressure of a segment M ij of a pipeline j 2i in the k period is represented by j 2, which is the node sequence number,/>For the natural gas flow of segment M ij of pipe ij at time k, M ij is the number of pipe segments that pipe ij contains.
Expanding the traditional reliability index into a multi-energy and multi-dimensional space-time reliability index, and evaluating the space-time reliability of the electric power natural gas system by using a time sequence Monte Carlo method
The space-time reliability assessment of a power system aims at predicting the reliability level of the system and node users under certain given system boundary conditions. The time sequential Monte Carlo method is typically used for sampling wind speed, random failure and repair of equipment, and calculation of reliability metrics. Expected load outages (expected interruption of demand, EID) and system failure risk (risk of system overload, RSOL), as shown in the following, are used to evaluate the reliability of the power system. It is noted that both of these metrics are extended to time-varying operational reliability metrics, as well as refined to node reliability metrics. This enables it to more flexibly characterize the impact between the space-time characteristics of wind power and the reliability of the power system.
Where n and NS are the sequence number and total number of simulation times. The convergence index of the time sequence Monte Carlo method is as follows:
Wherein, EID i (t) is the EID of node i at time t, lc i is the power cut load of node i, τ is time, RSOL i (t) is RSOL of node i at time t, flag is a sign function, calculated by (38), var is variance, and ζ is a convergence criterion.
The present application is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present application are intended to be included in the scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.
Claims (10)
1. The power system reliability evaluation method taking wind power space-time uncertainty into consideration is characterized by comprising the following steps of:
constructing a wind speed space-time correlation model and a wind turbine random fault repair model;
Constructing a wind power plant reliability model based on the wind speed space-time correlation model and the fan random fault repair model;
constructing a reliability model of the gas unit and a reliability model of an electric conversion facility;
Based on the wind power plant reliability model, the gas turbine unit reliability model and the electric power-to-gas facility reliability model, constructing an electric power and natural gas combined system optimization control model considering wind power access and natural gas dynamic characteristics;
Acquiring space-time reliability indexes of multiple energy sources and multiple dimensions, and evaluating the space-time reliability of the power natural gas system based on the power natural gas combined system optimization control model considering wind power access and natural gas dynamic characteristics and the space-time reliability indexes of the multiple energy sources and the multiple dimensions.
2. The power system reliability assessment method considering wind power space-time uncertainty as claimed in claim 1, wherein constructing the wind speed space-time correlation model comprises:
Acquiring a wind speed state set, and randomly taking values based on the wind speed state set to acquire a plurality of wind speeds;
Setting states of a plurality of wind speeds, and acquiring time-varying state probabilities of the wind speeds based on the states of the wind speeds;
Constructing a wind speed time-varying state probability model based on a plurality of wind speed time-varying state probabilities;
expanding the wind speed time-varying state probability model and constructing a wind speed space-time correlation model.
3. The method for evaluating reliability of a power system taking wind power space-time uncertainty into consideration as claimed in claim 2, wherein establishing the fan random fault repair model comprises:
Predicting the wind speed under the consideration of the space-time correlation based on the wind speed space-time correlation model to obtain a predicted wind speed;
Acquiring available power generation capacity of a fan of the wind power plant based on the predicted wind speed;
acquiring a normal running state and a fan fault state of a fan;
And constructing a random fan fault repair model based on the available power generation capacity of the fan of the wind power plant, the normal running state of the fan and the fan fault state.
4. A method of evaluating reliability of a power system in consideration of wind power space-time uncertainty as claimed in claim 3 wherein constructing a wind farm reliability model based on said wind speed space-time correlation model and said wind turbine random fault repair model comprises:
Acquiring the available power generation capacity of the wind power plant based on the wind speed space-time correlation model and the fan random fault restoration model;
and characterizing the available power generation capacity of the wind farm, and constructing a wind farm reliability model.
5. The power system reliability assessment method considering wind power space-time uncertainty as claimed in claim 1, wherein constructing the gas turbine unit reliability model comprises:
constructing a gas unit fault model and a gas unit repair model;
Acquiring the state quantity of the gas units of the nodes, and acquiring an available power generation capacity set of the gas units based on the gas unit fault model, the gas unit repair model and the state quantity of the gas units of the nodes;
randomly taking values of the available power generation capacity sets of the gas turbine sets to obtain the available power generation capacity of the gas turbine sets;
And based on the available power generation capacity of the gas turbine set, obtaining the actual available power generation capacity of the gas turbine set, and constructing the reliability model of the gas turbine set.
6. The method for evaluating reliability of a power system taking into account wind power space-time uncertainty as claimed in claim 1, wherein constructing the electric power plant reliability model comprises:
Constructing an electric conversion gas module, and acquiring a natural gas production available capacity set of the electric conversion gas model based on the electric conversion gas model;
randomly taking values of the natural gas production available capacity set of the electric gas conversion model to obtain the natural gas production available capacity of a natural gas facility;
and acquiring the natural gas production available capacity of the electric conversion facility based on the natural gas production available capacity of the natural gas facility, and constructing an operation reliability model of the electric conversion facility.
7. The method for evaluating reliability of a power system considering wind power space-time uncertainty as claimed in claim 1, wherein constructing an electric power natural gas combined system optimization control model considering wind power access and natural gas dynamic characteristics based on the wind power plant reliability model, the gas turbine unit reliability model and the electric power to gas facility reliability model comprises:
Acquiring dynamic characteristics of a natural gas system based on the wind power plant reliability model, the gas turbine unit reliability model and the wind power plant reliability model;
acquiring initial conditions of natural gas and initial conditions of a natural gas pipeline, and acquiring a partial differential equation based on the initial conditions of the natural gas and the initial conditions of the natural gas pipeline;
And constructing an electric power and natural gas combined system optimization control model considering wind power access and natural gas dynamic characteristics based on the partial differential equation and the dynamic characteristics of the natural gas system.
8. The method for evaluating the reliability of the power system considering wind power space-time uncertainty as claimed in claim 7, wherein the power natural gas combined system optimization control model considering wind power access and natural gas dynamic characteristics is subject to the constraint condition of the power system and the constraint condition of the natural gas system.
9. The method for evaluating the reliability of the power system considering the wind power space-time uncertainty according to claim 1, wherein evaluating the space-time reliability of the power natural gas system based on the power natural gas combined system optimization control model considering wind power access and natural gas dynamic characteristics and the multi-energy and multi-dimensional space-time reliability index comprises:
based on the power and natural gas combined system optimization control model considering wind power access and natural gas dynamic characteristics, sampling wind speed, random faults and repair of equipment by adopting a time sequence Monte Carlo method, and obtaining a sampling sample;
and evaluating the space-time reliability of the electric power natural gas system based on the multi-energy source, the multi-dimensional space-time reliability index and the sampling sample.
10. The method for evaluating reliability of a power system taking into account wind power space-time uncertainty as defined in claim 9, wherein said multi-energy, multi-dimensional space-time reliability index comprises expected load interruption and system failure risk.
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