CN113629733A - Rapid frequency response reserve capacity planning method considering risk preference - Google Patents

Rapid frequency response reserve capacity planning method considering risk preference Download PDF

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CN113629733A
CN113629733A CN202111068406.XA CN202111068406A CN113629733A CN 113629733 A CN113629733 A CN 113629733A CN 202111068406 A CN202111068406 A CN 202111068406A CN 113629733 A CN113629733 A CN 113629733A
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risk
frequency
cost
gamma
capacity
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李卫东
李梓锋
张明理
张娜
徐熙林
潘霄
赵琳
程孟增
商文颖
杨博
满林坤
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STATE GRID LIAONING ECONOMIC TECHNIQUE INSTITUTE
Dalian University of Technology
State Grid Corp of China SGCC
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STATE GRID LIAONING ECONOMIC TECHNIQUE INSTITUTE
Dalian University of Technology
State Grid Corp of China SGCC
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/04—Power grid distribution networks
    • G—PHYSICS
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
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    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
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    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
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    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

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Abstract

A rapid frequency response reserve capacity planning method considering risk preference sets a reserve planning scheme for threatening system frequency safety events by adding rapid frequency response reserve capacity so as to deal with the new situation of power system frequency stability. And focusing the transient change process and the quantitative risk attention range of the post-accident system, and avoiding underestimating the accident deterioration risk based on the power shortage of the full accident set. Simulating the equipment state based on a random production simulation method, and capturing second-level dynamic change by taking frequency dynamic simulation analysis as a means on the basis of a traditional cost benefit analysis method; the attention degree of accidents of different levels is realized according to the risk preference consideration; and checking the feasibility of the scheme in the aspect of checking the cost and obtaining a corresponding confidence index. The method is oriented to the rapid frequency response standby planning requirement, takes the transient change process after an accident into consideration, considers the risk preference, brings the subjective analysis capability of a decision maker into the consideration factor of the planning process, can adjust the planning scheme according to the running condition of the power grid and the requirement of the regional safety level, and has higher flexibility.

Description

Rapid frequency response reserve capacity planning method considering risk preference
Technical Field
The invention belongs to the field of power grid frequency stability control requirements, relates to a rapid frequency corresponding reserve planning problem, and particularly relates to a rapid frequency response reserve capacity planning method considering risk preference.
Background
The power system reserves the capacity of the equipment added to ensure the power balance requirement under extreme conditions such as equipment failure. Along with the high permeability of clean energy and the turning of a grid structure to long-distance high-pressure transmission of power supply, the proportion of a conventional unit is reduced and accidents are upgraded. In the past, the thermal power base number is large, the occupation ratio is high, the system inertia is sufficient, and the rough spare reserving mode of the fixed coefficient naturally meets the frequency adjusting requirement. The emergency reserve is oriented to various requirements such as primary frequency modulation, AGC (automatic Generation control) and peak shaving, and the reserve taking amount of specific requirements is not independently calculated. However, the current situation of 'double low' under the background of 'double carbon' of the existing power system leads to the steep increase of frequency response requirement, the original standby leaving mode cannot ensure the stable frequency of the system, the standby needs to be classified finely, and the rapid frequency response standby is added.
Aiming at the problems of the traditional standby reservation means, such as roughness and mismatching with the modern power grid safety situation, the invention researches the rapid frequency corresponding standby planning problem in the aspect of the power grid frequency stability requirement under the new situation. In the process of planning the spare capacity, the frequency change track of the system after an accident is tracked by taking simulation analysis as a means, a differential planning scheme is made by changing the risk preference of a specific accident range concerned by an operator, and the feasibility verification is carried out on the planning result from the perspective of the total cost, so that the rationality of the planning scheme provided by the invention is verified.
Disclosure of Invention
In view of the above, the present invention provides a fast frequency response spare capacity plan considering risk preference.
The technical scheme adopted by the invention is as follows:
a method for fast frequency response spare capacity planning with risk preferences taken into account, comprising the steps of:
step 1: risk preference cost benefit analysis, the concerned accident severity can reflect the risk preference of a decision maker, the accurate risk range is quantified, and the risk range can be suddenly changedThe effect of serious accidents is achieved, and the accidents are prevented from being submerged in a large number of common accidents with high incidence. The risk value of an extreme event should be reevaluated, the more extreme the event concerned, the higher the corresponding risk value. All events causing losses exceeding the safety threshold are high risk and low probability events, which are characterized by the possibility of causing severe system frequency problems and thus potentially large-scale blackout risks. In order to reflect the influence effect of the security events threatening the system frequency, the concerned average risk is converted into a specific risk, the risk range is quantified by defining a risk factor gamma, different gammas correspond to specific low-frequency load-shedding penalty threshold TV. P is satisfied between the capacity r for the equipment and the corresponding low-frequency load shedding cost V of the systemvAnd (x, r) probability distribution, wherein x is a random variable representing loss caused by system faults, the concerned risk range approaches to extreme accidents threatening frequency safety when the value of gamma tends to 0, and the concerned risk range approaches to the average influence of all accidents when the value of gamma tends to 1. Under different risk factors gamma, the difference between V (0) and V (r) before and after the spare r is added is the risk preference cost benefit. Then there are:
Figure BDA0003259474200000021
Figure BDA0003259474200000022
Figure BDA0003259474200000023
wherein:
Figure BDA0003259474200000024
representation and low frequency load shedding penalty threshold TvThe corresponding risk factor function at the reserve capacity r,
Figure BDA0003259474200000025
representing the inverse of the function of the risk factor, i.e.A punishment threshold value for low-frequency load shedding, wherein V (gamma, r) represents the system low-frequency load shedding cost corresponding to the reserve capacity r under the risk factor gamma; e (V | V ≧ TV) Indicating that the low-frequency deloading cost V exceeds the low-frequency deloading penalty threshold TvAn expected value of the distribution;
step 2: and simulating the state of the system element.
Analyzing the reasonability of the setting of the rapid frequency response spare capacity, and firstly carrying out state simulation on the running state of system equipment by adopting a sequential Monte Carlo method. Because the system load and the new energy output have obvious time sequence characteristics and uncertainty, the running state of system equipment needs to be simulated by adopting a sequential Monte Carlo method, and a conventional unit and an extra-high voltage direct current transmission line are described by adopting two state models according to the respective running characteristics of system elements:
Figure BDA0003259474200000026
wherein: tau is1、τ2The two-state model has continuous normal operation time and repair time, lambda2、μ2For the normal to failure transfer rate, failure repair rate of a two-state model element, u is the compliance [0, 1]A distributed random number.
The wind turbine generator set adopts a more detailed three-state model:
Figure BDA0003259474200000027
in the formula: tau is12、τ13Transition into the healthy running time before outage and derating for the three-state model21、τ31The derating and outage time before the healthy operation is recovered. The transition rate of the running state from the healthy state to the shutdown state and the derating state is omega1And ω2(ii) a The transition rate from the shutdown state and the derating state to the healthy operation state is xi1And xi2。
And finally, filtering the faults which cannot trigger the low-frequency load shedding action, and only storing an effective fault set with frequency security threat capability.
And step 3: and constructing a power system frequency response model containing hydroelectric power, thermal power and gas turbine units.
The difference adjustment coefficients of various units represent the primary frequency modulation capability of the system, the inertia time constants represent the initial power impact resistance capability of the system, namely, the system frequency difference signals after the system is subjected to power disturbance act on the unit speed regulator, the frequency response link corresponds to the physical significance of parameters of various units, and a response block diagram can be obtained after various resources are connected in parallel and accumulated, wherein the response block diagram is shown in figure 1. The system is characterized in that a hydroelectric power unit, a thermal power unit and a gas turbine unit in the system provide frequency response support in a conventional and spontaneous mode, planning and research of frequency response reserve capacity are carried out by starting with comprehensive response capability of various frequency modulation resources, and the response process of the frequency modulation resources and the disturbance quantity of the system in a frequency response model of the system are represented by step impact response. And (3) inputting the system element state simulated in the step (2) into a system frequency simulation model, so that a frequency change track after the system is disturbed can be obtained, the duration of primary frequency modulation is about 20s, and the simulation duration covers 20s, so that system frequency tracking and lowest point capture can be realized on power disturbance.
And 4, step 4: revenue analysis based on risk preferences.
4.1 Standby Integrated cost calculation analysis
The standby related cost c (r) mainly comprises two parts of investment and calling, and the investment cost I (r) is mostly modeled by adopting an equal-year-value method in a planning problem; the invocation cost D (r) is related to the number m of effective lost loads in the sampling year and the invocation cost P of the unit electric quantity.
c(r)＝I(r)+D(r) (6)
Figure BDA0003259474200000031
Figure BDA0003259474200000032
In the formula: a represents discount rate, J represents correlationLife cycle of the spare resource, NnFor the number of sampling years, IΣ(r) is the sum of spare investment costs.
4.2 reliability benefit computational analysis
According to the system frequency downward exploration degree, a plurality of rounds of low-frequency load shedding are arranged on a system corresponding protection mechanism, and related calculation analysis is performed on the basis of random production simulation, and E in the nth yeariRepresenting the ith load loss, a and b are low-frequency load shedding proportionality coefficients and unit penalty values under different frequency sounding adverse levels, NmFor effective equivalent of the number of times of the occurrence of the load loss accident, formula (9) Vn(r) represents the sum of the low frequency load shedding losses of the system at year n at the reserve capacity r.
Figure BDA0003259474200000033
By combining the dynamic tracking of multi-scene frequency tracks and the low-frequency load shedding action mechanism under the condition of system load shedding aggregation, the loss distribution condition under the full risk range can be calculated, the rapid frequency response reserve under the risk factor gamma by additionally arranging the capacity r can be obtained by quantizing the risk range, and the reliability benefit brought by the system is as follows:
e(γ,r)＝V(γ,r)-V(γ,0)(10)
in the formula: e (gamma, r) is the system reliability gain by adding the capacity r under the risk factor gamma, V (gamma, r) is the system low-frequency load shedding loss after adding the spare capacity r under the risk factor gamma, and V (gamma, 0) is the system low-frequency load shedding loss without spare capacity under the risk factor gamma.
4.3 Overall revenue computational analysis
The system reliability is improved by the aid of the standby addition, the low-frequency load shedding loss is reduced as reliability benefits, and investment and calling of the standby addition are comprehensive costs. The relationship between reliability gains and spare integrated costs is traded off. Namely, on the basis of 4.1 standby comprehensive cost calculation analysis and 4.2 reliability benefit calculation analysis, an overall calculation benefit analysis model is established, so that the overall benefit is maximum:
F(r)＝max{e(γ,r)-c(r)} (11)
where F (r) is the overall revenue model at spare capacity r.
And (4) performing model analysis based on the formulas (6) to (11) and judging according to a marginal cost benefit analysis principle, namely when the change slope of the backup comprehensive cost is equal to the change slope of the reliability benefit, obtaining the optimal backup capacity under the corresponding risk level.
And 5: and the feasibility verification of the scheme is carried out, because the focusing degree of an operator on serious accidents is reflected in the setting process of different risk grades, and the risk preference analysis essentially has subjective intervention factors, the feasibility verification of the standby optimization result is necessary to obtain the confidence coefficient of the planning scheme. For the total cost under the same risk level, including the spare related cost c (r) formed by investment and calling in step 4 and the reliability cost V (r) for resisting the system risk under a specific risk, the sum of the two parts c (r) and V (r) is defined as the verification cost:
C(r)＝c(r)+V(r) (12)
analogy of p between r and V as described in the first sectionv(x, r) probability distribution, accordingly, let p be satisfied between r and CCProbability distribution of (z, r), different risk classes corresponding to a specific total cost check threshold TCThe distribution satisfies, z is a random variable representing the reliability cost of the system:
Figure BDA0003259474200000041
Figure BDA0003259474200000042
Figure BDA0003259474200000043
wherein:
Figure BDA0003259474200000044
presentation and total cost check threshold TCThe corresponding risk factor function at the reserve capacity r,
Figure BDA0003259474200000045
the inverse function representing the risk factor function is the total cost check threshold, and C (gamma, r) represents the system total check cost corresponding to the spare capacity r under the risk factor gamma; e (C | C ≧ TC) Indicating that the total verification cost C exceeds the total cost verification threshold TCAn expected value of the distribution;
in order to quantitatively describe and verify the feasibility of the scheme, a risk concern range gamma is taken as a variable, the variation trend of the system verification cost before and after the optimal standby investment is taken as a reference, and a scheme confidence index eta is defined.
Figure BDA0003259474200000051
Figure BDA0003259474200000052
Wherein: eta (gamma, r)o) Representing the spare capacity r at the risk factor gammaoCorresponding scheme confidence index, C (gamma, r)o) Representing the spare capacity r at the risk factor gammaoThe method comprises the steps that corresponding system total check cost is obtained, C (gamma, 0) represents the corresponding system total check cost before spare capacity is added under a risk factor gamma, M represents the relation between the confidence degree of a spare planning scheme and the check cost, 3 conditions can occur along with the change of a risk concern range gamma, and under all risk levels corresponding to the conditions, the cost after the spare is added is too high, and the corresponding spare planning scheme cannot be adopted. (b) In one case critical points are selected for the recipe. (c) Under all the corresponding risk levels in the situation, the backup addition is meaningful, and the farther the confidence index is away from the selection critical point value, the better the effect brought by the planning is proved, and the more the scheme is worth being selected.
Step 6: and based on the 5 steps, obtaining a rapid frequency response reserve capacity planning process considering the risk preference.
The method is oriented to the rapid frequency response standby planning requirement, takes the transient change process after an accident into consideration, takes the risk preference into consideration, namely, the subjective analysis capability of a decision maker is taken into consideration in the planning process, can adjust the planning scheme according to the power grid operation condition and the regional safety level requirement, and has higher flexibility.
Simulating the equipment state based on a random production simulation method, and capturing second-level dynamic change by taking frequency dynamic simulation analysis as a means on the basis of a traditional cost benefit analysis method; the attention degree of accidents of different levels is realized according to the risk preference consideration; and checking the feasibility of the scheme in the aspect of checking the cost and obtaining a corresponding confidence index.
By adding the quick frequency response spare capacity, a spare planning scheme is specially set for threatening the system frequency safety event so as to deal with the new situation of power system frequency stability. And focusing the transient change process and the quantitative risk attention range of the post-accident system, and avoiding underestimating the accident deterioration risk based on the power shortage of the full accident set. The rapid frequency response reserve capacity planning scheme considering the risk preference can reflect the attention degree of operators to different events in the decision process, effectively avoids the risk homogenization phenomenon of the traditional planning scheme based on the whole accident set, and the more the attention range of the events tends to be rare, the higher the rapid frequency response reserve demand is.
Furthermore, in the step 6, second-level tracking of a frequency dynamic change process after an accident is realized on the basis of frequency dynamic simulation analysis, quantitative analysis is performed on the system facing the load loss risk, a standby planning scheme with risk differentiation can be made according to safety level requirements of different regions by operators according to local conditions, and finally the feasibility of the planning scheme is verified in terms of verifying the total cost.
The invention has the beneficial effects that: the rapid frequency response reserve capacity planning scheme considering the risk preference can reflect the attention degree of operators to different events in the decision process, effectively avoids the risk homogenization phenomenon of the traditional planning scheme based on the whole accident set, and the more the attention range of the events tends to be rare, the higher the rapid frequency response reserve demand is.
Drawings
In order to more clearly illustrate the implementation of the present invention and the technical solution of capacity allocation, the drawings used will be briefly described below.
FIG. 1 is a block diagram of a system frequency response provided by the present invention;
FIG. 2 is a frequency response curve of a system under different levels of spare capacity provided by the present invention;
FIG. 3 is a back-up optimization process based on average risk provided by the present invention;
FIG. 4 is a partial backup optimization result with a higher risk preference provided by the present invention;
FIG. 5 is a spare optimization result under a part of risk factors provided by the present invention;
fig. 6 is a block diagram illustrating the feasibility verification of the solution provided by the present invention.
Detailed Description
The invention will be further described with reference to the accompanying drawings and specific embodiments, but is not intended to be limited thereto.
A rapid frequency response reserve capacity planning considering risk preference aims at the problems that the traditional reserve reservation means is rough and unmatched with the safety situation of a modern power grid, the problem of rapid frequency corresponding reserve planning is researched facing to the requirement of power grid frequency stability under the new situation, and a table 1 shows the physical significance of the unit parameters provided by the invention:
TABLE 1
Figure BDA0003259474200000061
The specific strategy of the invention comprises the following steps:
step 1: the risk preference cost benefit analysis shows that the concerned accident severity can reflect the risk preference of a decision maker, accurately quantizes the risk range, can highlight the influence effect of serious accidents and avoids the serious accidents from being submerged in a large number of high-incidence common accidents. The risk value of an extreme event should be reevaluated, the more extreme the event concerned, the higher the corresponding risk value.All events causing losses exceeding the safety threshold are high risk and low probability events, which are characterized by the possibility of causing severe system frequency problems and thus potentially large-scale blackout risks. P is satisfied between the capacity r for the equipment and the corresponding low-frequency load shedding cost V of the systemvAnd (x, r) probability distribution, wherein x is a random variable representing loss caused by system faults, the concerned risk range approaches to extreme accidents threatening frequency safety when the value of gamma tends to 0, and the concerned risk range approaches to the average influence of all accidents when the value of gamma tends to 1. Under different risk factors gamma, the difference between V (0) and V (r) before and after the spare r is added is the risk preference cost benefit. Then there are:
Figure BDA0003259474200000071
Figure BDA0003259474200000072
Figure BDA0003259474200000073
wherein:
Figure BDA0003259474200000074
representation and low frequency load shedding penalty threshold TvThe corresponding risk factor function at the reserve capacity r,
Figure BDA0003259474200000075
an inverse function representing a risk factor function is a low-frequency load shedding penalty threshold, and V (gamma, r) represents the system low-frequency load shedding cost corresponding to the spare capacity r under the risk factor gamma; e (V | V ≧ TV) Indicating that the low-frequency deloading cost V exceeds the low-frequency deloading penalty threshold TvAn expected value of the distribution;
step 2: and simulating the state of the system element.
Analyzing the reasonability of the setting of the rapid frequency response spare capacity, and firstly carrying out state simulation on the running state of system equipment by adopting a sequential Monte Carlo method. Because the system load and the new energy output have obvious time sequence characteristics and uncertainty, the running state of system equipment needs to be simulated by adopting a sequential Monte Carlo method, and a conventional unit and an extra-high voltage direct current transmission line are described by adopting two state models according to the respective running characteristics of system elements:
Figure BDA0003259474200000076
wherein: tau is1、τ2The two-state model has continuous normal operation time and repair time, lambda2、μ2For the normal to failure transfer rate, failure repair rate of a two-state model element, u is the compliance [0, 1]A distributed random number.
The wind turbine generator set adopts a more detailed three-state model:
Figure BDA0003259474200000081
in the formula: tau is12、τ13Transition into the healthy running time before outage and derating for the three-state model21、τ31The derating and outage time before the healthy operation is recovered. The transition rate of the running state from the healthy state to the shutdown state and the derating state is omega1And ω2(ii) a The transition rate from the shutdown state and the derating state to the healthy operation state is xi1And xi2。
And finally, filtering the faults which cannot trigger the low-frequency load shedding action, and only storing an effective fault set with frequency security threat capability.
And step 3: and constructing a power system frequency response model containing hydroelectric power, thermal power and gas turbine units.
The difference adjustment coefficients of various units represent the primary frequency modulation capability of the system, the inertia time constants represent the initial power impact resistance capability of the system, namely, the system frequency difference signals after the system is subjected to power disturbance act on the unit speed regulator, the frequency response link corresponds to the physical significance of parameters of various units, and a response block diagram can be obtained after various resources are connected in parallel and accumulated, wherein the response block diagram is shown in figure 1. The system is characterized in that a hydroelectric power unit, a thermal power unit and a gas turbine unit in the system provide frequency response support in a conventional and spontaneous mode, planning and research of frequency response reserve capacity are carried out by starting with comprehensive response capability of various frequency modulation resources, and the response process of the frequency modulation resources and the disturbance quantity of the system in a frequency response model of the system are represented by step impact response. And (3) inputting the system element state simulated in the step (2) into a system frequency simulation model, so that a frequency change track after the system is disturbed can be obtained, the duration of primary frequency modulation is about 20s, and the simulation duration covers 20s, so that system frequency tracking and lowest point capture can be realized on power disturbance.
And 4, step 4: revenue analysis based on risk preferences.
4.1 Standby Integrated cost calculation analysis
The standby related cost c (r) mainly comprises two parts of investment and calling, and the investment cost I (r) is mostly modeled by adopting an equal-year-value method in a planning problem; the invocation cost D (r) is related to the number m of effective lost loads in the sampling year and the invocation cost P of the unit electric quantity.
c(r)＝I(r)+D(r) (6)
Figure BDA0003259474200000082
Figure BDA0003259474200000083
In the formula: a represents the discount rate, J represents the life cycle of the relevant standby resource, NnFor the number of sampling years, IΣ(r) is the sum of spare investment costs.
4.2 reliability benefit computational analysis
According to the system frequency downward exploration degree, a plurality of rounds of low-frequency load shedding are arranged on a system corresponding protection mechanism, and related calculation analysis is performed on the basis of random production simulation, and E in the nth yeariRepresenting the ith loss of loadThe quantity a and b are low-frequency load shedding proportionality coefficient and unit penalty value under different frequency probing adverse levels, NmFor effective equivalent of the number of times of the occurrence of the load loss accident, formula (9) Vn(r) represents the sum of the low frequency load shedding losses of the system at year n at the reserve capacity r.
Figure BDA0003259474200000091
By combining the dynamic tracking of multi-scene frequency tracks and the low-frequency load shedding action mechanism under the condition of system load shedding aggregation, the loss distribution condition under the full risk range can be calculated, the rapid frequency response reserve under the risk factor gamma by additionally arranging the capacity r can be obtained by quantizing the risk range, and the reliability benefit brought by the system is as follows:
e(γ,r)＝V(γ,r)-V(γ,0) (10)
in the formula: e (gamma, r) is the system reliability gain by adding the capacity r under the risk factor gamma, V (gamma, r) is the system low-frequency load shedding loss after adding the spare capacity r under the risk factor gamma, and V (gamma, 0) is the system low-frequency load shedding loss without spare capacity under the risk factor gamma.
4.3 Overall revenue computational analysis
The system reliability is improved by the aid of the standby addition, the low-frequency load shedding loss is reduced as reliability benefits, and investment and calling of the standby addition are comprehensive costs. The relationship between reliability gains and spare integrated costs is traded off. Namely, on the basis of 4.1 standby comprehensive cost calculation analysis and 4.2 reliability benefit calculation analysis, an overall calculation benefit analysis model is established, so that the overall benefit is maximum:
F(r)＝max{e(γ,r)-c(r)} (11)
where F (r) is the overall revenue model at spare capacity r.
And (4) performing model analysis based on the formulas (6) to (11) and judging according to a marginal cost benefit analysis principle, namely when the change slope of the backup comprehensive cost is equal to the change slope of the reliability benefit, obtaining the optimal backup capacity under the corresponding risk level.
And 5: and the feasibility verification of the scheme is carried out, because the focusing degree of an operator on serious accidents is reflected in the setting process of different risk grades, and the risk preference analysis essentially has subjective intervention factors, the feasibility verification of the standby optimization result is necessary to obtain the confidence coefficient of the planning scheme. For the total cost under the same risk level, including the spare related cost c (r) formed by investment and calling in step 5 and the reliability cost V (r) for resisting the system risk under a specific risk, the sum of the two parts c (r) and V (r) is defined as the verification cost:
C(r)＝c(r)+V(r) (12)
analogy of p between r and V as described in the first sectionv(x, r) probability distribution, accordingly, let p be satisfied between r and CCProbability distribution of (z, r), different risk classes corresponding to a specific total cost check threshold TCThe distribution satisfies, z is a random variable representing the reliability cost of the system:
Figure BDA0003259474200000101
Figure BDA0003259474200000102
Figure BDA0003259474200000103
wherein:
Figure BDA0003259474200000104
presentation and total cost check threshold TCThe corresponding risk factor function at the reserve capacity r,
Figure BDA0003259474200000105
the inverse function representing the risk factor function is the total cost check threshold, and C (gamma, r) represents the system total check cost corresponding to the spare capacity r under the risk factor gamma; e (C | C ≧ TC) Indicates that the total verification cost C exceedsTotal cost check threshold TCAn expected value of the distribution;
in order to quantitatively describe and verify the feasibility of the scheme, a risk concern range gamma is taken as a variable, the variation trend of the system verification cost before and after the optimal standby investment is taken as a reference, and a scheme confidence index eta is defined.
Figure BDA0003259474200000106
Figure BDA0003259474200000107
Wherein: eta (gamma, r)o) Representing the spare capacity r at the risk factor gammaoCorresponding scheme confidence index, C (gamma, r)o) Representing the spare capacity r at the risk factor gammaoThe method comprises the steps that corresponding system total check cost is obtained, C (gamma, 0) represents the corresponding system total check cost before spare capacity is added under a risk factor gamma, M represents the relation between the confidence degree of a spare planning scheme and the check cost, 3 conditions can occur along with the change of a risk concern range gamma, and under all risk levels corresponding to the conditions, the cost after the spare is added is too high, and the corresponding spare planning scheme cannot be adopted. (b) In one case critical points are selected for the recipe. (c) Under all the corresponding risk levels in the situation, the backup addition is meaningful, and the farther the confidence index is away from the selection critical point value, the better the effect brought by the planning is proved, and the more the scheme is worth being selected.
Step 6: and based on the 5 steps, obtaining a rapid frequency response reserve capacity planning process considering the risk preference.
The method is oriented to the rapid frequency response standby planning requirement, takes the transient change process after an accident into consideration, takes the risk preference into consideration, namely, the subjective analysis capability of a decision maker is taken into consideration in the planning process, can adjust the planning scheme according to the power grid operation condition and the regional safety level requirement, and has higher flexibility.
Simulating the equipment state based on a random production simulation method, and capturing second-level dynamic change by taking frequency dynamic simulation analysis as a means on the basis of a traditional cost benefit analysis method; the attention degree of accidents of different levels is realized according to the risk preference consideration; and checking the feasibility of the scheme in the aspect of checking the cost and obtaining a corresponding confidence index.
The rapid frequency response reserve capacity planning scheme considering the risk preference can reflect the attention degree of operators to different events in the decision process, effectively avoids the risk homogenization phenomenon of the traditional planning scheme based on the whole accident set, and the more the attention range of the events tends to be rare, the higher the rapid frequency response reserve demand is.
According to the specific calculation example, a nuclear power unit with the capacity of 800MW and a thermal power unit with the capacity of 350MW in an IEEE-RTS79 system are replaced by a 787.5MW wind power unit with the equivalent capacity and a clean energy extra-high voltage transmission channel with the transmission margin of 500MW, wherein the type, the capacity and the parameters of a conventional unit are shown in a table 2, and the parameters of the fault rate, the repair time and the like of related elements are shown in the literature. The spare capacity planning object is a comprehensive frequency modulation resource with response delay of 0.5s, and the corresponding investment and calling costs are respectively $ 2/kW and $ 0.12/kW. The standby life cycle is 30 years, and the discount rate is 10%. The triggering conditions and the cost parameters of the low-frequency load shedding mechanism are shown in a table 3, and the values of the parameters of the simulation system set built by Simulink are shown in a table 4.
TABLE 2 system element parameter Table
Figure BDA0003259474200000111
TABLE 3 Low-frequency deloading trigger condition and fee parameter table
Figure BDA0003259474200000112
TABLE 4 system unit parameter values
Figure BDA0003259474200000113
If the accident magnitude is the power shortage of the unit with the frequency regulation capacity of 0.2p.u., the frequency response curves of the regional power grid under different levels of spare capacity are shown in the graph 2, and the specific interception effect on the system frequency is shown in the table 5. The quick frequency response has obvious support to the system frequency, can effectively reduce the frequency of low-frequency load shedding accidents and reduce the impact of power disturbance on the system stability.
TABLE 5 System frequency interception Effect at different Standby capacities
Figure BDA0003259474200000121
The spare increasing benefit is measured through fault simulation and simulation analysis, a spare planning scheme based on average risk can be obtained by taking the value 1 of gamma, and most of accidents with low threat can be intercepted due to the fact that high-loss and low-loss events in the system are more and relatively small spare capacity. As shown in fig. 3, as the speed increase of the reliability benefit e becomes slower, the speed increase is the same as that of the integrated cost c when r is 50MW, and the marginal cost benefit analysis rule is satisfied, that is, the corresponding benefit is the largest at the capacity, and the capacity is the optimal capacity allocation scheme at the average risk level. The average risk analysis method based on the whole accident set can confuse high-risk rare accidents and low-loss high-incidence accidents, and particularly fuzzes the events triggering the frequency stabilization protection mechanism, so that the sensitivity of operators to extreme accidents is reduced, and part of the extremely important risk perception capability is lost.
The optimal capacity distribution conditions under different risk concern ranges can be obtained by enumerating the risk factor γ, fig. 4 and 5 respectively enumerate the backup optimization results of the risk preference analysis, table 6 corresponds to the optimal backup cost and income data under different risk factors, the difference between the reliability income and the comprehensive backup cost is the risk preference income, and the backup benefit is optimal when the value is maximum.
TABLE 6 optimal Capacity cost benefit under different Risk factors
Figure BDA0003259474200000122
The smaller the gamma is, the more sensitive the operator is to the system risk, the higher the extreme accident value corresponding to the small probability and high risk is, the greater the risk preference gain and the reliability gain are correspondingly, when the gamma value is 0.01, the operator pays attention to 1% of the most serious accidents in the severe condition of the system possibly occurring accidents, at this time, the benefit brought to the system by the spare addition can be 1.27 x 107$, when the gamma value is 1, the average influence of all accidents of the system is paid attention to, at this time, the benefit brought to the system by the spare addition can be 1.90 x 103$, and it can be known through data comparison that, as the sensitivity degree of the operator on the risk is reduced, the benefit confidence of the spare addition is influenced, and further, the significance of the spare planning of part of the power grid with higher requirements on the system operation safety level is underestimated.
As can be seen from Table 6, as the risk factor decreases, the scope of the accident concerned by the decision maker tends to be more extreme, and the spare capacity optimization result changes from 50MW to 900 MW. As γ decreases, the reliability gains e from adding unit spare capacity change significantly, i.e., the losses that can be avoided by adding spare capacity are considerable, so the demand of grid operators for fast frequency response spare capacity continues to increase. For the self safety level of the power grid, an operator can evaluate the acceptable risk range of the operator and complete the capacity selection of the rapid frequency response backup planning according to the sensitivity degree of the risk.
Taking an optimization result of which the optimal capacity is 550MW when γ is 0.2 as an example, that is, 20% of the most serious accidents in the severe focusing system may occur, as shown in fig. 6, the verification cost before and after the 550MW standby input corresponds to the left coordinate axis value, and the scheme confidence index corresponding to the standby input corresponds to the right coordinate axis value. The above contents are depicted in the same graph for better embodying the solution decision threshold.
In fig. 6, when γ is 0.676, a critical state occurs in the verification cost before and after the standby is put into use, and the corresponding scheme confidence index η is 0, and a boundary point at which transition occurs is selected for the scheme. When eta meets the condition of (a) in the M corresponding relation, namely the risk quantization range gamma is larger than 0.676, the total cost caused by standby investment is too high, and the standby capacity planning scheme is rejected; when η is equal to (c) in the M correspondence, i.e. the risk quantification range γ is less than 0.676, the spare capacity is acceptable, and the smaller γ is, the higher the acceptable level is. The 550MW backup confidence indicators for different risk factors are calculated as shown in table 7, and the confidence indicator η ═ 0.659 for the 550MW backup solution under γ ═ 0.2 matches the case (c) by checking the solution confidence.
TABLE 7 550MW Standby confidence indicators for different risk factors
Figure BDA0003259474200000131
It should be understood by those skilled in the art that the method can be implemented by those skilled in the art by combining the prior art and the above-mentioned scheme, and the detailed description is not repeated herein.
The preferred method of the present invention is described above. It is to be understood that this invention is not limited to the particular embodiments described above, and that equipment and structures not described in detail are to be understood as being practiced in a manner common to the art; those skilled in the art can make many possible variations and modifications to the disclosed methods and techniques, or modify the equivalents thereof, without departing from the spirit and scope of the invention. Therefore, any simple modification, equivalent change and modification of the above method according to the technical spirit of the present invention will still fall within the protection scope of the technical method of the present invention, unless the technical essence of the present invention departs from the content of the technical method of the present invention.

Claims (1)

1. A method for fast frequency response spare capacity planning with risk preferences taken into account, comprising the steps of:
step 1: risk preference cost benefit analysis
In order to reflect the influence effect of the security events threatening the system frequency, the concerned average risk is converted into a specific risk, the risk range is quantified by defining a risk factor gamma, different gammas correspond to specific low-frequency load-shedding penalty threshold TV(ii) a DeviceSatisfies p between capacity r and corresponding system low-frequency load shedding cost Vv(x, r), wherein x is a random variable representing loss caused by system faults, the concerned risk range approaches to extreme accidents threatening frequency safety when the value of gamma tends to 0, and the concerned risk range approaches to the average influence of all accidents when the value of gamma tends to 1; under different risk factors gamma, the difference value of V (0) and V (r) before and after the spare r is added is the risk preference cost benefit; then there are:
Figure FDA0003259474190000011
Figure FDA0003259474190000012
Figure FDA0003259474190000013
wherein:
Figure FDA0003259474190000014
representation and low frequency load shedding penalty threshold TvThe corresponding risk factor function at the reserve capacity r,
Figure FDA0003259474190000015
an inverse function representing a risk factor function is a low-frequency load shedding penalty threshold, and V (gamma, r) represents the system low-frequency load shedding cost corresponding to the spare capacity r under the risk factor gamma; e (V | V ≧ TV) Indicating that the low-frequency deloading cost V exceeds the low-frequency deloading penalty threshold TvAn expected value of the distribution;
step 2: simulating the state of the system element;
firstly, performing state simulation on the running state of system equipment by adopting a sequential Monte Carlo method, and analyzing the reasonability of the rapid frequency response spare capacity setting; according to respective operating characteristics of system elements, a conventional unit and an extra-high voltage direct current transmission line are described by adopting two state models:
Figure FDA0003259474190000016
wherein: tau is1、τ2The two-state model has continuous normal operation time and repair time, lambda2、μ2For the normal to failure transfer rate, failure repair rate of a two-state model element, u is the compliance [0, 1]A distributed random number;
secondly, the wind turbine generator adopts a more detailed three-state model:
Figure FDA0003259474190000017
in the formula: tau is12、τ13Transition into the healthy running time before outage and derating for the three-state model21、τ31Derating and outage time before recovery of healthy operation; the transition rate of the running state from the healthy state to the shutdown state and the derating state is omega1And ω2(ii) a The transition rate from the shutdown state and the derating state to the healthy operation state is xi1And xi2；
Finally, filtering the faults which cannot trigger the low-frequency load shedding action, and only storing an effective fault set with frequency safety threat capability;
and step 3: constructing a power system frequency response model containing hydroelectric power, thermal power and gas turbine units;
the difference adjustment coefficients of various units represent the primary frequency modulation capability of the system, and the inertia time constants represent the initial power impact resistance capability of the system, namely, the system frequency difference signals after the system suffers power disturbance act on the unit speed regulator; the system is characterized in that hydropower, thermal power and gas turbine units in the system provide frequency response support in a conventional and spontaneous mode, planning and research of frequency response reserve capacity are carried out by starting with comprehensive response capacity of various frequency modulation resources, and the response process and the system disturbance quantity of the frequency modulation resources in a frequency response model of the system are represented by step impact response; inputting the system element state simulated in the step (2) into a system frequency simulation model to obtain a frequency change track after the system is disturbed, and covering a primary frequency modulation duration by the simulation duration to realize system frequency tracking and lowest point capture of the power disturbance;
and 4, step 4: revenue analysis based on risk preferences;
4.1 Standby Integrated cost calculation analysis
The standby related cost c (r) mainly comprises two parts of investment and calling, and the investment cost I (r) is mostly modeled by adopting an equal-year-value method in a planning problem; the calling cost D (r) is related to the number m of effective lost loads in the sampling year and the calling cost P of the unit electric quantity;
c(r)＝I(r)+D(r) (6)
Figure FDA0003259474190000021
Figure FDA0003259474190000022
in the formula: a represents the discount rate, J represents the life cycle of the relevant standby resource, NnFor the number of sampling years, IΣ(r) is the sum of spare investment costs;
4.2 reliability benefit computational analysis
According to the system frequency downward exploration degree, a plurality of rounds of low-frequency load shedding are arranged on a system corresponding protection mechanism, and related calculation analysis is performed on the basis of random production simulation, and E in the nth yeariRepresenting the ith load loss, a and b are low-frequency load shedding proportionality coefficients and unit penalty values under different frequency sounding adverse levels, NmFor effective equivalent of the number of times of the occurrence of the load loss accident, formula (9) Vn(r) represents the sum of the low frequency load shedding losses of the system in the nth year under the spare capacity r;
Figure FDA0003259474190000023
combining a multi-scene frequency track dynamic tracking and a low-frequency load shedding action mechanism under the condition of system load loss aggregation, calculating the loss distribution condition under the full risk range, and obtaining a rapid frequency response standby under the risk factor gamma by adding the capacity r through quantifying the risk range, wherein the reliability benefit brought to the system is as follows:
e(γ,r)＝V(γ,r)-V(γ,0) (10)
in the formula: e (gamma, r) is the system reliability gain by adding the capacity r under the risk factor gamma, V (gamma, r) is the system low-frequency load shedding loss after the spare capacity r under the risk factor gamma is added, and V (gamma, 0) is the system low-frequency load shedding loss without the spare capacity under the risk factor gamma;
4.3 Overall revenue computational analysis
The system reliability is improved by the aid of the spare addition, the reduced low-frequency load shedding loss is the reliability benefit, and investment and calling of the spare addition are the comprehensive cost; the relationship between the reliability benefit and the standby comprehensive cost needs to be balanced; namely, on the basis of the calculation and analysis of the standby comprehensive cost in the step 4.1 and the calculation and analysis of the reliability benefit in the step 4.2, an overall calculation benefit analysis model is established, so that the overall benefit is the maximum:
F(r)＝max{e(γ,r)-c(r)} (11)
wherein, F (r) is an overall profit model under the spare capacity r;
model analysis based on the formulas (6) to (11) is carried out, judgment is carried out according to the marginal cost benefit analysis principle, and when the comprehensive cost of the backup is equal to the change slope of the reliability benefit, the optimal backup capacity under the corresponding risk level is obtained;
and 5: verifying the feasibility of the scheme;
for the total cost under the same risk level, including the spare related cost c (r) formed by investment and calling in step 4 and the reliability cost V (r) for resisting the system risk under a specific risk, the sum of the two parts c (r) and V (r) is defined as the verification cost:
C(r)＝c(r)+V(r) (12)
analogy of p between r and V as described in the first sectionv(x, r) probability distribution, accordingly, let p be satisfied between r and CCProbability distribution of (z, r), different risk classes corresponding to a specific total cost check threshold TCThe distribution satisfies, z is a random variable representing the reliability cost of the system:
Figure FDA0003259474190000031
Figure FDA0003259474190000032
Figure FDA0003259474190000033
wherein:
Figure FDA0003259474190000034
presentation and total cost check threshold TCThe corresponding risk factor function at the reserve capacity r,
Figure FDA0003259474190000035
the inverse function representing the risk factor function is the total cost check threshold, and C (gamma, r) represents the system total check cost corresponding to the spare capacity r under the risk factor gamma; e (C | C ≧ TC) Indicating that the total verification cost C exceeds the total cost verification threshold TCAn expected value of the distribution;
in order to quantitatively describe and verify the feasibility of the scheme, a risk concern range gamma is taken as a variable, the variation trend of the system verification cost before and after the optimal standby input is taken as a reference, and a scheme confidence index eta is defined;
Figure FDA0003259474190000041
Figure FDA0003259474190000042
wherein: eta (gamma, r)o) Representing the spare capacity r at the risk factor gammaoCorresponding scheme confidence index, C (gamma, r)o) Representing the spare capacity r at the risk factor gammaoCorresponding system total verification cost, C (γ,0) represents corresponding system total verification cost before the spare capacity is added under the risk factor γ, M represents a relationship between the confidence degree of the spare planning scheme and the verification cost, and 3 situations may occur along with the change of the risk attention range γ: (a) under all risk levels corresponding to the species situation, the cost after the backup addition is too high, and the corresponding backup planning scheme should not be adopted; (b) in one case, selecting a critical point for the recipe; (c) under all the corresponding risk levels under the conditions, the backup addition is meaningful, and the farther the confidence index is away from the selected critical point value, the better the effect brought by the planning is proved, and the corresponding scheme is selected;
step 6: based on the 5 steps, obtaining a rapid frequency response reserve capacity planning process considering risk preference;
the method comprises the steps of facing to a rapid frequency response standby planning requirement, considering a transient change process after an accident and considering risk preference, namely taking subjective analysis capability of a decision maker into consideration factors of the planning process, and adjusting a planning scheme according to the operation condition of a power grid and the requirement of regional safety level; simulating the equipment state based on a random production simulation method, and capturing second-level dynamic change by taking frequency dynamic simulation analysis as a means on the basis of a traditional cost benefit analysis method; the attention degree of accidents of different levels is realized according to the risk preference consideration; and checking the feasibility of the scheme in the aspect of checking the cost and obtaining a corresponding confidence index.
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