CN115549211A - High-confidence-degree multi-time-scale active optimization regulation and control method for new energy station - Google Patents
High-confidence-degree multi-time-scale active optimization regulation and control method for new energy station Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
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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
- H02J3/241—The oscillation concerning frequency
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/48—Controlling the sharing of the in-phase component
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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
- 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]
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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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
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- 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
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
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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
- 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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Abstract
The invention discloses a high-confidence-degree multi-time-scale active optimization regulation and control method for a new energy station, which belongs to the field of new energy station power grid support and comprises the following steps: dividing a unit into a wind turbine unit, a photovoltaic power generation unit and an energy storage unit; acquiring key frequency modulation parameters of the generator set; obtaining a smoothing time constant of a low-pass filter; constructing a multi-machine system frequency response model and an energy storage smooth wind and light fluctuation model; discretizing the model, and constructing a dynamic frequency safety constraint and a dynamic power smoothness constraint; and constructing a wind power output model considering prediction errors and a high-confidence-degree multi-time-scale active optimization regulation and control model of the new energy station, and using a model solving result as an active optimization regulation and control mode. The method considers the influence of the dynamic frequency response characteristic of the system and the dynamic fluctuation degree of wind and light on the optimal operation of the station, and realizes the coordination optimization of the active support function with high confidence coefficient and multiple time scales of the second-level frequency modulation, the minute-level power smoothing and the hour-level peak modulation of the new energy station.
Description
Technical Field
The invention belongs to the technical field of new energy station power grid support, and particularly relates to a high-confidence-degree multi-time-scale active optimization regulation and control method for a new energy station.
Background
The energy power field is a main battlefield of 'carbon peak reaching and carbon neutralization', and a power grid in China is converted from a traditional power system which takes a synchronous machine as a main body and coal as a main primary energy into a new energy power system which takes a power electronic converter as an interface and complementarily utilizes various energy sources.
Along with the change of electricity utilization structure and the improvement of electrification degree in China and the continuous improvement of the occupation ratio of electricity in the third industry and the life of residents in China, the peak-to-valley difference rate and the peak-to-valley difference absolute value of the load in the day of the electricity utilization side are continuously increased, and the load peak-to-valley difference increases the requirement of the power generation station for providing hour-level peak regulation support for the power grid; meanwhile, due to the weak inertia characteristic of the renewable energy source to the power grid, after high-power shortage disturbance occurs in the power grid, the supporting effect of the renewable energy source to the system frequency is not ideal enough, and even secondary drop of the frequency can be caused, so that the second-level frequency supporting capability of the new energy station to the power grid needs to be improved; secondly, the wind turbine and the photovoltaic power generation have randomness and fluctuation, and in order to improve the quality of electric energy and reduce the power fluctuation of renewable power generation, a station needs to provide a minute-level power smooth supporting function. Under the background that the operation scene is gradually diversified, the problem of how to coordinate and optimize the active support function of different time scales in the station new energy station is to be solved.
At present, foreign scholars study optimization methods of energy storage users participating in multi-application scenes, and from the perspective of users, the study enables the energy storage to simultaneously provide various auxiliary services with different time scales, such as voltage regulation, frequency modulation, power smoothing and the like, for each power plant and each power transmission and distribution network, and auxiliary services with different time scales are stacked by fully utilizing limited energy storage capacity resources, so that the benefit maximization of the users is realized. The method has the defects that the economic efficiency of the optimization scheme for users is only considered, the effects of energy storage supporting power grid frequency modulation, peak regulation and other functions are not considered, and dynamic characteristics of frequency and other related indexes are not depicted. Therefore, how to consider both the economy of new energy station operation and the active support effect of the stations on multiple time scales is to be researched, and how to establish a high-confidence-degree active optimization regulation and control method on multiple time scales for new energy stations is to be researched.
Disclosure of Invention
The invention aims to provide a high-confidence multi-time scale active power optimization regulation and control method for a new energy station, which is characterized by comprising the following steps of:
according to the type of a unit in the wind-solar-energy-storage combined power generation system, the unit is divided into a wind turbine unit, a photovoltaic power generation unit and an energy storage unit, wherein the wind turbine unit and the photovoltaic power generation unit realize conversion and utilization of clean energy through grid-connected power generation, and the energy storage unit is responsible for storage and release of electric energy;
obtaining key frequency modulation parameters of the generator set, wherein the key frequency modulation parameters comprise a wind turbine unit, a photovoltaic power generation unit, an energy storage unit, the total system inertia of a synchronous generator set, a difference adjustment coefficient, a reheating time constant, a high-pressure turbine power fraction, a damping coefficient and a stroking power;
obtaining a smoothing time constant of a first-order low-pass power filter for smoothing wind-light minute-level power fluctuation in the new energy station;
constructing a multi-machine system frequency response model based on a frequency swing equation; constructing an energy storage smooth wind and light fluctuation model based on a low-pass filtering principle;
discretizing the multi-machine system frequency response model and the energy storage smooth wind and light fluctuation model by an Euler method and a forward difference method to respectively obtain a time domain difference equation set of system frequency and unit mechanical power and a time domain difference equation set of wind and light real-time fluctuation power and energy storage compensation power so as to construct dynamic frequency safety constraint and dynamic power smoothness constraint;
and considering the prediction error and the confidence coefficient of the wind and light prediction power, constructing a wind power output model and a new energy station high-confidence-degree multi-time-scale active optimization regulation and control model considering the prediction error, solving the active optimization regulation and control model, and taking the solution result as an active optimization regulation and control mode.
The multi-machine system frequency response model is defined as follows:
where df is the system frequency deviation, f 0 Is a rated frequency, s is a Laplace Las operator,
respectively active adjustment quantity before and after the amplitude limiting link of the synchronous unit i,
respectively the rated power of the synchronous unit i and the amplitude limiting value of the speed regulator,
respectively the active adjustment quantity before and after the energy storage unit i passes through the amplitude limiting link,
is the output margin value of the energy storage unit i,
respectively the active adjustment quantity before and after the wind power unit i passes through the amplitude limiting link,
is the output amplitude limit value of the wind power unit i,
respectively the active adjustment quantity before and after the amplitude limiting link of the photovoltaic unit i,
is the output amplitude limit value, K, of the photovoltaic power generation unit i i 、T R,i 、F H,i 、T t,i 、T r,i Difference adjustment coefficient, reheating time constant, high-pressure turbine power fraction, generator time constant and speed regulator time constant, dP, of synchronous unit i g,i Mechanical power, T, output for the synchronous unit i s,i Is the time constant of the energy storage unit i, T w,i Is the time constant, T, of the wind power unit i v,i Is the time constant of the photovoltaic cell i, R s Is the sag factor, R, of the energy storage cell w Is the droop coefficient, R, of the wind power unit v Is the sag factor of the photovoltaic cell, wherein T R,i ＞＞T t,i ,T R,i ＞＞T r,i Neglecting T t,i 、T r,i Influence on the system frequency response process.
The energy storage smooth wind and light fluctuation model is defined as follows:
in the formula, dP ref For reference power adjustment, dP, after smoothing of the wind w For smoothing the pre-variation of wind power, T smooth Is a smoothing time constant, dP ', of a first-order low-pass power filter' B 、dP B Respectively the power adjustment quantity before and after the stored energy passes through the amplitude limiting link,
respectively the upper and lower power limit values, dP, of the clipping element comb And adjusting the power of wind storage combined output.
The time domain difference equation system of the system frequency and the mechanical power of the unit is as follows:
wherein, Δ f t,n Representing the frequency deviation of the nth step at the moment t, dn being a differential step, G being the total number of synchronous units, S being the total number of energy storage units, W being the total number of wind power units, V being the total number of photovoltaic units, D being a damping coefficient, H being the total inertia of the system,
for synchronizing the rated power, Δ P, of the unit i e,t For system disturbance power, P L,t For the total load on the grid side, K i For synchronizing governor gains of unit i, T s,i Is the time constant of the energy storage unit i, T w,i Is the time constant, T, of the wind power unit i v,i Is the time constant of the photovoltaic cell i, R s Is the sag factor, R, of the energy storage cell w Is the droop coefficient, R, of the wind power unit v Is the droop coefficient of the photovoltaic unit, tau is the differential step length,
respectively represents the active adjustment quantity of the nth step length synchronous unit i at the time t before and after the limiting link of the speed regulator,
are respectively provided withShowing the active adjustment quantity before and after the nth step energy storage i passes through the amplitude limiting link of the speed regulator at the time t,
respectively represents the active adjustment quantity of the nth step length wind turbine unit i at the time t before and after the limiting link of the speed regulator,
respectively representing active adjustment quantity delta P before and after the nth step size photovoltaic unit i passes through the amplitude limiting link of the speed regulator at the time t g,i,t,n The adjustment quantity of the primary frequency modulation mechanical power of the nth step size unit i at the moment t is shown,
in order to synchronize the governor amplitude limiting values of the unit i,
is the amplitude limit value of the output of the energy storage unit i,
is the output amplitude limit value of the wind power unit i,
is the output amplitude limit value, delta P, of the photovoltaic unit i g,i,t,n Mechanical power, T, output by the nth step-size synchronous unit i at time T R,i For synchronizing the reheat time constant of the unit i, F H,i The high-pressure turbine coefficient of the equivalent unit.
The dynamic frequency security constraints are:
-RoCoF max ≤RoCoF t,n ≤RoCoF min (4)
Δf min ≤Δf t,n ≤Δf max (5)
wherein, roCoF t,n For the rate of change of frequency of the system at nth step at time t, roCoF max RoCoF, the maximum limit value of the rate of change of the system frequency min Is the minimum limit value, Δ f, of the rate of change of the system frequency min Is the minimum limit value of the frequency deviation of the system, Δ f max Is the maximum limit value of the deviation of the system frequency.
The time domain difference equation set of the wind-solar real-time fluctuation power and the energy storage compensation power is as follows:
wherein, Δ P ref,t (n) denotes the reference power after smoothing of the wind-solar fluctuation, T smooth Is the smoothing time constant of the first-order low-pass power filter, tau is the difference step length, delta P w,t (n) represents wind power fluctuation power delta P 'of nth step at time t' B,t (n) represents the compensation power before the energy storage limiting of the nth step at the time t, delta P B,t (n) represents the compensation power after the energy storage amplitude limiting of the nth step at the time t, delta P comb,t (n) represents the wind storage combined output of the nth step at the time t,
respectively the upper and lower power limits of the clipping element.
The dynamic power smoothness constraint is:
wherein T is the total number of scheduling periods, N is the total number of differential step lengths in a unit period, P rate For the rated value of the wind power,
the maximum threshold value of the wind power smoothness in T time intervals is shown, T is the scheduling time, n is the serial number of the difference step length at the T-th time, and tau is the difference step length.
The wind power output model considering the prediction error is defined as follows:
ΔP W (t)＝P W (t)-P W,k,t (8)
where α is the confidence, Δ P W (t) is the wind power prediction error at the t-th moment, P W (t) predicted output value at the t moment of wind power, P W,k,t Is the actual output value at the t moment of the wind power,P W,α (t) is the lower limit of the error band,
upper limit of error band, P r Representing the size of the probability;
the confidence coefficient is alpha, and the wind power prediction error band is
The method for constructing the high-confidence multi-time scale active power optimization regulation and control model of the new energy station comprises the following steps:
step 61: establishing an objective function of a station optimization regulation model,
wherein T is the dispatching time, T is the number of power dispatching time segments, S is the energy storage units, S is the total number of the energy storage units, W is the wind power unit, W is the total number of the wind power unit, V is the photovoltaic unit, V is the total number of the photovoltaic unit, G is the total number of the power generation unit, N is the differential step length, and N is the total number of the discrete segments,
for the cost coefficient of the charge and discharge power of the stored energy,
to store the charging power for the period of time t,
in order to store the discharge power during the period t,
for the cost coefficient of the power generated by the fan,
is the generated power of the fan in the time period t,
for the photovoltaic power generation power cost coefficient,
for the generated power of the photovoltaic cell during the period t, C re,i Reserve cost factor for stored energy i, R i,t Reserve power reserved for energy storage i during time t, C ffr To frequency-modulated yield coefficient, C smooth For power smoothing of the yield coefficient, C p Is the time of use, Δ f t,n Representing the frequency deviation of the nth step at time T, c being the highest threshold value of wind power smoothness in T time periods, P level Wind power smoothness level for T periods;
step 62: and (3) constructing conventional constraints of a station optimization regulation model:
the output constraint of the wind turbine unit is as follows:
output restraint of the photovoltaic unit:
energy storage charging and discharging restraint:
energy storage and electric quantity restraint:
and (4) energy storage operation state constraint:
in the formula (I), the compound is shown in the specification,P w,α (t) is the lower limit of the wind power prediction error band,
and is the upper limit of the wind power prediction error band, wherein,P v,α (t) is the lower limit of the photovoltaic prediction error band,
is the upper limit of the photovoltaic prediction error band,
is a variable from 0 to 1 and represents the charging state of the stored energy s in the period t,
is a variable from 0 to 1 and represents the discharge state of the stored energy s in the period t, x s,t Is a variable from 0 to 1 and represents the starting and stopping states of the energy storage s in the period t,
the lower limit of the charging power for the stored energy s,
is the upper limit of the charging power of the stored energy s,
the lower limit of the discharge power of the stored energy s,
discharge power for stored energy sUpper limit, S st Is the SOC value of the stored energy s in the period t, delta represents the self-discharge rate of the stored energy, eta c And η d Respectively representing the charging and discharging efficiencies of the energy storage unit, E s For the capacity of the energy storage unit, S max And S min The upper limit and the lower limit of the charge state of the energy storage unit are respectively.
And step 63: adding the dynamic frequency constraint and the dynamic smoothness constraint into a station optimization regulation model, and adopting a large M method to carry out linearization treatment on an amplitude limiting link in a power smooth model as follows:
linearization treatment:
in the formula, z 1 、z 2 、u 1 、u 2 Is a 0-1 auxiliary variable introduced during linearization, M is a constant, Δ P' B,t (n) represents the compensation power before the energy storage limiting of the nth step at the time t, delta P B,t (n) represents the compensation power after the energy storage amplitude limitation of the nth step at the time t,
respectively the upper and lower power limits of the clipping element.
The utility model provides a high-confidence-degree many time scales active power optimization regulation and control system of new forms of energy station which characterized in that includes:
the system comprises an acquisition unit, a smoothing unit and a control unit, wherein the acquisition unit is used for acquiring key frequency modulation parameters of a generator set, the key frequency modulation parameters comprise a wind turbine unit, a photovoltaic power generation unit, an energy storage unit, the total system inertia of a synchronous generator set, a difference adjustment coefficient, a reheating time constant, a high-pressure turbine power fraction, a damping coefficient and a stroking power, and the smoothing time constant of a first-order low-pass power filter for smoothing wind and light minute-level power fluctuation in a new energy station is acquired;
and the regulating and controlling unit is used for performing active optimization regulation and control on the new energy station by using the wind power output model considering the prediction error and the solving result of the high-confidence-degree multi-time-scale active optimization regulation and control model of the new energy station.
The invention has the beneficial effects that:
the invention considers the active coordination optimization method of the multiple time scales of the station under the influence of the dynamic frequency response characteristic and the dynamic wind-light fluctuation characteristic, the frequency safety index and the power smoothness index can still be stabilized in a normal range under certain frequency disturbance and uncertain fluctuation of wind-light power, the reasonable configuration of the multiple time scale rotation standby of the wind-light storage station is further guided, and the system economy is considered. The active optimization regulation and control method with high confidence degree and multiple time scales for the new energy station is different from the prior art in that the optimization is only carried out from the control angle, and the prior art realizes better control effect by controlling parameter optimization; the invention optimizes the frequency modulation and power smoothing backup with different time scales from the optimization angle on the premise of fixing control parameters, achieves the balance of economy and control effect on the premise of ensuring safety, high confidence and multiple time scales, realizes the cooperative optimization of second-level and multiple time scale control functions, and reasonably distributes the limited energy storage power to the frequency modulation and power smoothing.
Drawings
Fig. 1 is a flow chart of an implementation of the high-confidence multi-time-scale active power optimization regulation and control method for a new energy station according to the present invention;
FIG. 2 is a frequency response curve comparison diagram before and after the wind and light storage station participating system frequency modulation under 10% load disturbance of the high-confidence multi-time scale active optimization regulation and control model of the new energy station;
fig. 3 is a comparison curve diagram before and after the wind power smoothness of the high-confidence multi-time scale active power optimization regulation and control model of the new energy station in a 15-minute time period;
fig. 4 is a peak regulation effect diagram of the high-confidence multi-time-scale active power optimization regulation and control model of the new energy station.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The embodiment of the invention shown in fig. 1 discloses a high-confidence multi-time-scale active power optimization regulation and control method for a new energy station, which specifically comprises the following steps:
according to the type of a unit in the wind-solar-energy-storage combined power generation system, the unit is divided into a wind turbine unit W, a photovoltaic power generation unit V and an energy storage unit S, wherein the wind turbine unit and the photovoltaic power generation unit realize conversion and utilization of clean energy through grid-connected power generation, and the energy storage unit is responsible for storage and release of electric energy;
obtaining key frequency modulation parameters of the generator set, including total system inertia H, difference adjustment coefficient K and reheating time constant T of the wind turbine unit, the photovoltaic power generation unit, the energy storage unit and the synchronous generator set R High pressure turbine power fraction F H Damping coefficient D and power P step ；
Obtaining power smoothing key parameters:
obtaining a smoothing time constant T of a first-order low-pass power filter used for smoothing wind and light minute-level power fluctuation in a new energy station smooth ；
Considering a nonlinear amplitude limiting link of the speed regulator, and constructing a multi-machine system frequency response model based on a frequency swing equation; an energy storage smoothing wind and light fluctuation model based on a low-pass filtering principle is constructed by considering an amplitude limiting link of energy storage charging and discharging, and the method specifically comprises the following steps:
the basis of the system frequency response model is the swing equation of the generator:
where H is the total system inertia, D, df,
f 0 ,P L the damping coefficient, the system frequency deviation, the first derivative of the system frequency deviation, the rated frequency and the total load of the system are respectively.dP m Adjusting the total amount of mechanical power of all participating primary frequency modulation units: dP e For active disturbance of the system, modeled here as 10% L 。
Starting from a swing equation of the generator, taking account of dead zones and amplitude limiting links of a speed regulator, and finely modeling a frequency response model of the wind-solar storage station multi-machine system:
where df is the system frequency deviation, f 0 Is a rated frequency, s is a Laplace Las operator,
respectively the active adjustment quantity before and after the amplitude limiting link of the synchronous unit i,
respectively the rated power of the synchronous machine set i and the amplitude limiting value of the speed regulator,
respectively the active adjustment quantity of the energy storage unit i before and after the amplitude limiting link,
is the amplitude limit value of the output of the energy storage unit i,
respectively the active adjustment quantity before and after the amplitude limiting link of the wind power unit i,
is the output amplitude limit value of the wind power unit i,
respectively the active adjustment quantity before and after the amplitude limiting link of the photovoltaic unit i,
is the output amplitude limit value, K, of the photovoltaic power generation unit i i 、T R,i 、F H,i 、T t,i 、T r,i Difference adjustment coefficient, reheating time constant, high-pressure turbine power fraction, generator time constant and speed regulator time constant, dP, of synchronous unit i g,i Mechanical power, T, output for the synchronous unit i s,i Is the time constant of the energy storage unit i, T w,i Is the time constant, T, of the wind power unit i v,i Is the time constant of the photovoltaic cell i, R s Is the sag factor, R, of the energy storage cell w Is the droop coefficient, R, of the wind power unit v Is the sag factor of the photovoltaic cell, wherein T R,i ＞＞T t,i ,T R,i ＞＞T r,i Neglecting T t,i 、T r,i Influence on the system frequency response process.
Based on the principle of a first-order low-pass filter, after considering the energy storage charge and discharge power amplitude limiting link, an energy storage smooth wind and light fluctuation model is obtained by derivation as follows:
in the formula, dP ref For reference power adjustment, dP, after smoothing of the wind w For smoothing the pre-variation of wind power, T smooth Is the smoothing time constant of the first-order low-pass power filter, s is the Laplacian Lass operator, dP' B 、dP B Respectively the power adjustment quantity before and after the stored energy passes through the amplitude limiting link,
respectively the upper and lower power limit values, dP, of the clipping element comb And adjusting the power of wind storage combined output.
Discretizing the multi-machine system frequency response model and the energy storage smooth wind-light fluctuation model by an Euler method and a forward difference method to respectively obtain a time domain difference equation set of the system frequency and the unit mechanical power and a time domain difference equation set of the wind-light real-time fluctuation power and the energy storage compensation power so as to construct a dynamic frequency safety constraint and a dynamic power smoothness constraint.
In this embodiment, the discretized multi-machine system frequency response model and the energy storage smooth wind and light fluctuation model are as follows:
firstly, carrying out difference approximation processing on a first derivative in a multi-machine frequency response model according to an Euler method:
wherein dn is a difference step, t is a scheduling period, df t,n Indicating the frequency deviation of the nth step at time t.
Then, the above formula is substituted into a multi-machine frequency response model to obtain a time domain difference equation set of the system frequency and the machine set mechanical power:
wherein, Δ f t,n Denotes the frequency deviation of the nth step at time t, dn is the difference step, f 0 Is rated frequency, G is total number of synchronous units, S is total number of energy storage units, W is total number of wind power units, V is total number of photovoltaic units, D is damping coefficient, H is total inertia of the system,
for synchronizing the rated power, Δ P, of the unit i e,t For the system disturbance power, P L,t For the total load on the grid side, K i For synchronizing the governor gain, T, of the unit i s,i Is the time constant, T, of the energy storage unit i w,i Is the time constant, T, of the wind power unit i v,i Is the time constant of the photovoltaic cell i, R s Is the sag factor, R, of the energy storage cell w Is the droop coefficient, R, of the wind power unit v Is the droop coefficient of the photovoltaic unit, tau is the differential step length,
respectively represents the active adjustment quantity of the nth step length synchronous unit i at the time t before and after the limiting link of the speed regulator,
respectively representing active adjustment quantity before and after the nth step energy storage i passes through the amplitude limiting link of the speed regulator at the time t,
respectively represents the active adjustment quantity of the nth step length wind turbine unit i at the time t before and after the limiting link of the speed regulator,
respectively representing active adjustment quantity delta P before and after the nth step size photovoltaic unit i passes through the amplitude limiting link of the speed regulator at the time t g,i,t,n The adjustment quantity of the primary frequency modulation mechanical power of the nth step size unit i at the moment t is shown,
in order to synchronize the governor amplitude limiting values of the unit i,
is the output margin value of the energy storage unit i,
is the output amplitude limit value of the wind power unit i,
is the output amplitude limit value, delta P, of the photovoltaic unit i g,i,t,n Mechanical power, T, output by the nth step-size synchronous unit i at time T R,i For the reheat time constant of the synchronous train i, F H,i The high-pressure turbine coefficient of the equivalent unit.
Limiting the key indexes reflecting the transient frequency characteristics to obtain the dynamic frequency safety constraint under the nth step length of t time intervals, as follows:
-RoCoF max ≤RoCoF t,n ≤RoCoF min (23)
Δf min ≤Δf t,n ≤Δf max (24)
wherein, roCoF t,n For the rate of change of frequency of the system at nth step at time t, roCoF max RoCoF, the maximum limit value of the rate of change of the system frequency min Is the minimum limit value, Δ f, of the rate of change of the system frequency min Is the minimum limit value of the frequency deviation of the system, Δ f max Is the maximum limit value of the deviation of the system frequency.
In a similar way, the formula (5) is substituted into the model of energy storage smooth wind and light fluctuation to obtain a time domain difference equation set of the wind and light real-time fluctuation power and the energy storage compensation power:
wherein, Δ P ref,t (n) denotes the reference power after smoothing of the wind-solar fluctuation, T smooth Is the smoothing time constant of the first-order low-pass power filter, tau is the difference step length, delta P w,t (n) represents wind power fluctuation power delta P 'of nth step at time t' B,t (n) represents the compensation power before the energy storage limiting of the nth step at the time t, delta P B,t (n) represents the compensation power after the energy storage amplitude limiting of the nth step at the time t, delta P comb,t (n) represents the wind storage combined output of the nth step at the time t,
respectively the upper and lower power limits of the clipping element.
The smoothness is used as an index for quantifying the wind power fluctuation smoothness degree, and is specifically defined as follows:
wherein, P level For the smoothness level of the wind power over T periods, P rate For rated power of wind power, P g For wind power outputThe ratio;
discretizing the smoothness index by adopting an equation (5) to obtain dynamic power smoothness constraint of the wind power in T smooth time periods as follows:
wherein T is the total number of scheduling periods, N is the total number of differential steps in a unit period, and P rate For the rated value of the wind power,
the maximum threshold value of the wind power smoothness in T time intervals is shown, T is the scheduling time, n is the serial number of the difference step length at the T-th time, and tau is the difference step length.
Therefore, the construction of the dynamic frequency safety constraint and the dynamic power smoothness constraint is completed.
And considering the prediction error and the confidence coefficient of the wind and light prediction power, constructing a wind power output model and a new energy station high-confidence-degree multi-time-scale active power optimization regulation and control model considering the prediction error, and solving the active power optimization regulation and control model by using a commercial solver.
In this embodiment, the wind power predicted output model considering uncertainty is specifically as follows:
ΔP W (t)＝P W (t)-P W,k,t (28)
where α is the confidence, Δ P W (t) is the wind power prediction error at the t-th moment, P W (t) predicted power value at t moment of wind power, P W,k,t Is the actual output value of the wind power at the t moment,P W,α (t) is the lower limit of the error band,
upper limit of error band, P r Representing the size of the probability;
the wind power prediction error band with the confidence coefficient of alpha is
Constructing a station high-confidence multi-time scale active power optimization regulation and control model:
establishing an objective function of a station optimization regulation model:
wherein T is the dispatching time, T is the number of power dispatching time segments, S is the energy storage units, S is the total number of the energy storage units, W is the wind power unit, W is the total number of the wind power unit, V is the photovoltaic unit, V is the total number of the photovoltaic unit, G is the total number of the power generation unit, N is the differential step length, and N is the total number of the discrete segments,
for the cost coefficient of the charge and discharge power of the stored energy,
to store the charging power for the time period t,
in order to store the discharge power during the period t,
for the cost coefficient of the power generated by the fan,
is the generated power of the fan in the time period t,
for the photovoltaic power generation power cost coefficient,
for the generated power of the photovoltaic cell during the period t, C re,i Reserve cost factor, R, for stored energy i i,t Reserve power reserved for energy storage i during time t, C ffr To frequency-modulated yield coefficient, C smooth For power smoothing of the yield coefficient, C p Is the time of use, Δ f t,n Frequency deviation representing nth step at time T, c is the highest threshold value of wind power smoothness in T time periods, P level Is the wind power smoothness level for T periods.
The method comprises the steps of optimizing an objective function of a regulation and control model of the station, balancing generation cost, standby cost, system frequency modulation benefit, peak regulation benefit and power smoothing benefit of a station unit, realizing optimized distribution of station generation capacity resources among services by introducing the generation cost of the station and the benefit of the station for providing services of peak regulation, frequency modulation and power smoothing, and improving the economy of station operation under the goal of maximizing the total benefit of the station.
Constructing conventional constraints of a site optimization regulation model:
and (3) output constraint of the wind turbine unit:
output restraint of the photovoltaic unit:
energy storage charge-discharge restraint:
energy storage and electric quantity restraint:
and (4) energy storage operation state constraint:
in the formula (I), the compound is shown in the specification,P w,α (t) is the lower limit of the wind power prediction error band,
and is the upper limit of the wind power prediction error band, wherein,P v,α (t) is the lower limit of the photovoltaic prediction error band,
is the upper limit of the photovoltaic prediction error band,
is a variable from 0 to 1 and represents the charging state of the stored energy s in the period t,
is a variable from 0 to 1 and represents the discharge state of the stored energy s in the period t, x s,t Is a variable from 0 to 1 and represents the starting and stopping states of the energy storage s in the period t,
the lower limit of the charging power for the stored energy s,
is the upper limit of the charging power of the stored energy s,
the lower limit of the discharge power of the stored energy s,
upper limit of discharge power for stored energy S, S st Is the SOC value of the stored energy s in the period t, delta represents the self-discharge rate of the stored energy, eta c And η d Respectively representing the charging and discharging efficiencies of the energy storage unit, E s For the capacity of the energy storage unit, S max And S min Respectively charge state of energy storage unitUpper and lower limit of state.
Adding the dynamic frequency constraint and the dynamic smoothness constraint in the formulas (6) to (9) into a station optimization regulation model, and adopting a large M method to carry out linearization treatment on an amplitude limiting link in a power smoothing model as follows:
and (3) linearization treatment:
in the formula, z 1 、z 2 、u 1 、u 2 Is a 0-1 auxiliary variable introduced during linearization, M is a constant, Δ P' B,t (n) represents the compensation power before the energy storage amplitude limiting of the nth step at the time t, delta P B,t (n) represents the compensation power after the energy storage amplitude limit of the nth step at the time t,
respectively the upper and lower power limits of the clipping element.
Therefore, the linearization processing of the high-confidence-degree multi-time-scale active optimization regulation and control model of the new energy station, which comprises the dynamic frequency constraint and the dynamic smoothness constraint, is completed, the model is solved through commercial software GUROBI, and the unit combination and the dispatching plan of each type of unit in the new energy station are obtained.
In order to verify the effectiveness and the correctness of the high-confidence-level multi-time-scale active optimization regulation and control method for the new energy station, a GUROBI commercial solver is adopted to solve a high-confidence-level multi-time-scale active optimization regulation and control model of the station, and a system frequency response curve is drawn in MATLAB software; in the simulation case, the relevant specific parameters are set as follows: damping coefficient D =10400, inertia of synchronous generator set H =104000 and reheating time constant T R =10, high-pressure turbine power fraction F H =0.3, power of stroking P step ＝10％P L (ii) a The effect of the frequency response is improved by adding frequency safety dynamic constraints. As shown in fig. 2, under 10% load disturbance power, the wind and light storage station participates in the system frequency response curve comparison before and after frequency modulation at the system side, and the result shows that: before the high-confidence multi-time-scale active optimization regulation and control model of the new energy station is optimized, the lowest point of the transient frequency at the system side is 49.79Hz; after the optimization regulation and control model is adopted for optimization, the lowest point of the transient frequency of the system is 49.88Hz, the lowest point of the frequency is obviously improved, and the frequency characteristic of the system side is improved to a certain extent.
Simulating a smooth front and back comparison curve of the wind power within a 15-minute time period by using MATLAB Simulink; the smoothing time constant T =0.4s of the first-order low-pass power filter adopted in simulation; the smoothing time constant of the first-order low-pass power filter can be increased to improve the smoothing degree of the smoothed wind power and improve the power smoothing effect. As shown in fig. 3, the results of the comparison curve before and after the wind power of the optimized regulation model is smoothed within 15 minutes show that: the maximum fluctuation range of the wind power of the new energy station before optimization can reach +/-2.8 multiplied by 10 5 w; the maximum fluctuation range of the wind power of the new energy station is reduced to +/-0.6 multiplied by 10 after optimization 5 w; the smoothness of the output power of the station is greatly improved.
Solving the active optimization regulation and control model with high confidence coefficient and multiple time scales of the field station by adopting a GUROBI commercial solver, and drawing a comparison curve before and after peak regulation as shown in figure 4 in MATLAB software; the optimization regulation and control model of the invention adopts a time-of-use electricity price mode to stimulate a new energy station to participate in power grid side peak regulation, and the parameter setting of the time-of-use electricity price in a simulation case is shown in a table 1:
TABLE 1 parameter settings for electricity time of use prices
| Time period (h) | 0-5 | 6-8 | 9-12 | 13-16 | 17-19 | 20-23 |
| Electricity price (Wanyuan) | 0.1 | 0.4 | 0.9 | 0.4 | 0.8 | 0.1 |
The peak clipping and valley filling effects of the new energy field station can be improved by increasing the peak-valley electricity price difference value. As shown in fig. 4, the comparison of the effects before and after the optimized regulation model participates in the peak load measurement and regulation of the power grid shows that: the load peak-valley difference can reach 66MW before optimizing the operation of the power grid side; and after optimization, the load peak-valley difference during the operation of the power grid side is reduced to 25MW, and a relatively obvious peak clipping and valley filling effect is realized.
According to the simulation result, the influence of the dynamic frequency response characteristic and the wind and light dynamic fluctuation degree of the system on the optimal operation of the station is considered, the coordination optimization of the high-confidence-degree multi-time-scale active support function of the second-level frequency modulation, the minute-level power smoothing and the hour-level peak modulation of the new energy station is realized, the frequency safety index and the power smoothness index can still be stabilized in a normal range under certain frequency disturbance and wind and light power uncertain fluctuation, the reasonable configuration of the wind and light storage station multi-time-scale rotating standby is further guided, and the system economy is considered.
The active optimization regulation and control method with high confidence degree and multiple time scales for the new energy station is different from the prior art in that the optimization is only carried out from the control angle, and the prior art realizes better control effect by controlling parameter optimization; the invention optimizes the frequency modulation and power smoothing backup with different time scales from the optimization angle on the premise of fixing control parameters, achieves the balance of economy and control effect on the premise of ensuring safety, high confidence and multiple time scales, realizes the cooperative optimization of second-level and multiple time scale control functions, and reasonably distributes the limited energy storage power to the frequency modulation and power smoothing. The method has the following specific beneficial effects:
(1) Safety: by introducing frequency safety constraint into a multi-machine system frequency response model in a high-confidence multi-time-scale active optimization regulation and control model of the new energy station, important frequency indexes can be limited within a safety range, and frequency safety is ensured.
(2) High confidence: the wind power prediction error band with the confidence coefficient alpha can be modeled by considering the uncertain wind power prediction output model, and the high-confidence-degree multi-time-scale active optimization regulation and control model of the new energy station is guaranteed to have a higher confidence coefficient level by improving the confidence coefficient alpha level under the condition of considering wind and light uncertain fluctuation.
(3) Multiple time scales: in a high-confidence-degree multi-time-scale active power optimization regulation and control model of a new energy station, t scheduling time intervals are divided, time-of-use electricity prices under different scheduling time intervals are set, hour-level peak regulation with the scheduling time intervals as basic units is achieved, finite difference is conducted on a multi-machine frequency response model and a power smoothing model by setting difference step length in each scheduling time interval, hour-level peak regulation, second-level frequency modulation and minute-level power smoothing can be achieved, and therefore collaborative optimization of a multi-time-scale regulation and control function of the station is achieved.
(4) The economic efficiency is as follows: in an objective function of a high-confidence multi-time-scale active optimization regulation and control model of a new energy station, by introducing station power generation cost and gains of providing peak regulation, frequency modulation and power smoothing for the station, under the aim of maximizing the total gains of the station, the optimal allocation of station power generation capacity resources among services is realized, and the economical efficiency of station operation is improved.
The second embodiment of the invention discloses a high-confidence multi-time scale active power optimization regulation and control system for a new energy station, which comprises:
the system comprises an acquisition unit, a smoothing unit and a control unit, wherein the acquisition unit is used for acquiring key frequency modulation parameters of a generator set, the key frequency modulation parameters comprise a wind turbine unit, a photovoltaic power generation unit, an energy storage unit, the total system inertia of a synchronous generator set, a difference adjustment coefficient, a reheating time constant, a high-pressure turbine power fraction, a damping coefficient and a stroking power, and the smoothing time constant of a first-order low-pass power filter used for smoothing wind and light minute-level power fluctuation in a new energy station is acquired;
and the regulating and controlling unit is used for performing active optimization regulation and control on the new energy station by using the wind power output model considering the prediction error and the solving result of the high-confidence-degree multi-time-scale active optimization regulation and control model of the new energy station.
The foregoing embodiment is also applicable to the high-confidence multi-time-scale active optimization regulation and control system for the new energy station provided in this embodiment, and is not described in detail in this embodiment. The foregoing embodiments and the advantages thereof are also applicable to the present embodiment, and therefore, the description of the same parts is omitted.
A third embodiment of the present invention discloses a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a high-confidence multi-time-scale active optimization regulation and control method for a new energy station according to the present invention, and specifically includes the following steps:
according to the type of a unit in the wind-solar-energy-storage combined power generation system, the unit is divided into a wind turbine unit, a photovoltaic power generation unit and an energy storage unit, wherein the wind turbine unit and the photovoltaic power generation unit realize conversion and utilization of clean energy through grid-connected power generation, and the energy storage unit is responsible for storage and release of electric energy;
obtaining key frequency modulation parameters of the generator set, wherein the key frequency modulation parameters comprise a wind turbine unit, a photovoltaic power generation unit, an energy storage unit, the total system inertia of a synchronous generator set, a difference adjustment coefficient, a reheating time constant, a high-pressure turbine power fraction, a damping coefficient and a stroking power;
obtaining a smoothing time constant of a first-order low-pass power filter used for smoothing wind and light minute-level power fluctuation in the new energy station;
constructing a multi-machine system frequency response model based on a frequency swing equation; constructing an energy storage smooth wind-light fluctuation model based on a low-pass filtering principle;
discretizing the multi-machine system frequency response model and the energy storage smooth wind and light fluctuation model by an Euler method and a forward difference method to respectively obtain a time domain difference equation set of system frequency and unit mechanical power and a time domain difference equation set of wind and light real-time fluctuation power and energy storage compensation power so as to construct dynamic frequency safety constraint and dynamic power smoothness constraint;
and considering the prediction error and the confidence coefficient of the wind and light prediction power, constructing a wind power output model and a new energy station high-confidence-degree multi-time-scale active optimization regulation and control model considering the prediction error, solving the active optimization regulation and control model, and taking the solution result as an active optimization regulation and control mode.
A fourth embodiment of the present invention discloses a computer device, which includes a memory, a processor, and a computer program that is stored in the memory and can be run on the processor, wherein the processor implements the active optimal regulation and control method for high-confidence multi-time scale in a new energy field station according to the present invention when executing the program, and the specific steps are the same as those in the third embodiment of the present invention, and are not described herein again.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The scheme in the embodiment of the application can be implemented by adopting various computer languages, such as object-oriented programming language Java and transliterated scripting language JavaScript.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (10)
1. A high-confidence-level multi-time-scale active power optimization regulation and control method for a new energy station is characterized by comprising the following steps:
according to the type of a unit in the wind-solar-energy-storage combined power generation system, the unit is divided into a wind turbine unit, a photovoltaic power generation unit and an energy storage unit, wherein the wind turbine unit and the photovoltaic power generation unit realize conversion and utilization of clean energy through grid-connected power generation, and the energy storage unit is responsible for storage and release of electric energy;
obtaining key frequency modulation parameters of the generator set, wherein the key frequency modulation parameters comprise a wind turbine unit, a photovoltaic power generation unit, an energy storage unit, the total system inertia of a synchronous generator set, a difference adjustment coefficient, a reheating time constant, a high-pressure turbine power fraction, a damping coefficient and a stroking power;
obtaining a smoothing time constant of a first-order low-pass power filter for smoothing wind-light minute-level power fluctuation in the new energy station;
constructing a multi-machine system frequency response model based on a frequency swing equation; constructing an energy storage smooth wind and light fluctuation model based on a low-pass filtering principle;
discretizing the multi-machine system frequency response model and the energy storage smooth wind and light fluctuation model by an Euler method and a forward difference method to respectively obtain a time domain difference equation set of system frequency and unit mechanical power and a time domain difference equation set of wind and light real-time fluctuation power and energy storage compensation power so as to construct dynamic frequency safety constraint and dynamic power smoothness constraint;
and considering the prediction error and the confidence coefficient of the wind and light prediction power, constructing a wind power output model and a new energy station high-confidence-degree multi-time-scale active optimization regulation and control model considering the prediction error, solving the active optimization regulation and control model, and taking the solution result as an active optimization regulation and control mode.
2. The active power optimization regulation and control method for the new energy station with high confidence level and multiple time scales according to claim 1, wherein the definition of the frequency response model of the multi-machine system is as follows:
where df is the system frequency deviation, f 0 Is a rated frequency, s is a Laplace Las operator,
respectively the active adjustment quantity before and after the amplitude limiting link of the synchronous unit i,
respectively the rated power of the synchronous machine set i and the amplitude limiting value of the speed regulator,
respectively the active adjustment quantity of the energy storage unit i before and after the amplitude limiting link,
is the amplitude limit value of the output of the energy storage unit i,
respectively the active adjustment quantity before and after the wind power unit i passes through the amplitude limiting link,
is the output amplitude limit value of the wind power unit i,
respectively the active adjustment quantity before and after the amplitude limiting link of the photovoltaic unit i,
is the output amplitude limit value, K, of the photovoltaic power generation unit i i 、T R,i 、F H,i 、T t,i 、T r,i Difference adjustment coefficient, reheating time constant, high-pressure turbine power fraction, generator time constant and speed regulator time constant, dP, of synchronous unit i g,i Mechanical power, T, output for the synchronous unit i s,i Is the time constant of the energy storage unit i, T w,i Is the time constant, T, of the wind power unit i v,i Is the time constant of the photovoltaic cell i, R s Is the sag factor, R, of the energy storage cell w Is the droop coefficient, R, of the wind power unit v Is the sag factor of the photovoltaic cell, wherein T R,i >>T t,i ,T R,i >>T r,i Neglecting T t,i 、T r,i Response to system frequencyThe impact of the process.
3. The active power optimization regulation and control method for the new energy station with high confidence and multiple time scales according to claim 1, wherein the energy storage smooth wind and light fluctuation model is defined as:
in the formula, dP ref For the reference power adjustment, dP, after smoothing of the wind power w For smoothing the pre-variation of wind power, T smooth Is a smoothing time constant, dP ', of a first-order low-pass power filter' B 、dP B Respectively the power adjustment quantity before and after the stored energy passes through the amplitude limiting link,
respectively the upper and lower power limit values, dP, of the clipping element comb And adjusting the power of wind storage combined output.
4. The active power optimization regulation and control method for the new energy station with high confidence and multiple time scales according to claim 1 is characterized in that a time domain difference equation system of the system frequency and the mechanical power of the unit is as follows:
wherein, Δ f t,n Representing the frequency deviation of the nth step at the moment t, dn being a differential step, G being the total number of synchronous units, S being the total number of energy storage units, W being the total number of wind power units, V being the total number of photovoltaic units, D being a damping coefficient, H being the total inertia of the system,
for synchronizing the rated power, Δ P, of the unit i e,t For system disturbance power, P L,t For the total load on the grid side, K i For synchronizing governor gains of unit i, T s,i Is the time constant, T, of the energy storage unit i w,i Is the time constant, T, of the wind power unit i v,i Is the time constant of the photovoltaic cell i, R s Is the sag factor, R, of the energy storage cell w Is the droop coefficient, R, of the wind power unit v Is the droop coefficient of the photovoltaic unit, tau is the differential step length,
respectively represents the active adjustment quantity of the nth step length synchronous unit i at the time t before and after the limiting link of the speed regulator,
ΔP s,i,t,n respectively representing active adjustment quantity before and after the nth step energy storage i passes through the amplitude limiting link of the speed regulator at the time t,
ΔP w,i,t,n respectively represents the active adjustment quantity of the nth step length wind turbine unit i at the time t before and after the limiting link of the speed regulator,
ΔP v,i,t,n respectively representing active adjustment quantity delta P of the nth step length photovoltaic unit i at the time t before and after the amplitude limiting link of the speed regulator g,i,t,n The adjustment quantity of the primary frequency modulation mechanical power of the nth step size unit i at the moment t is shown,
in order to synchronize the governor amplitude limiting values of the unit i,
is the amplitude limit value of the output of the energy storage unit i,
is the output amplitude limit value of the wind power unit i,
is the output margin value, delta P, of the photovoltaic cell i g,i,t,n Mechanical power, T, output by the nth step-size synchronous unit i at time T R,i For synchronizing the reheat time constant of the unit i, F H,i The high-pressure turbine coefficient of the equivalent unit.
5. The active power optimization regulation and control method for the new energy station with high confidence level and multiple time scales according to claim 1, wherein the dynamic frequency safety constraint is as follows:
-RoCoF max ≤RoCoF t,n ≤RoCoF min (4)
Δf min ≤Δf t,n ≤Δf max (5)
wherein, roCoF t,n For the rate of change of frequency of the system at nth step at time t, roCoF max RoCoF, the maximum limit value of the rate of change of the system frequency min Is the minimum limit value, Δ f, of the rate of change of the system frequency min Is the minimum limit value of the frequency deviation of the system, Δ f max Is the maximum limit value of the frequency deviation of the system.
6. The active power optimization regulation and control method for the new energy station with high confidence and multiple time scales according to claim 1, wherein the time-domain difference equation set of the wind-solar real-time fluctuation power and the energy storage compensation power is as follows:
wherein, Δ P ref,t (n) represents the reference power after smoothing of the wind-solar fluctuation, T smooth Is the smoothing time constant of the first-order low-pass power filter, tau is the difference step length, delta P w,t (n) represents the wind power fluctuation power delta P 'of the nth step at the moment t' B,t (n) represents the compensation power before the energy storage amplitude limiting of the nth step at the time t, delta P B,t (n) represents the compensation power after the energy storage amplitude limiting of the nth step at the time t, delta P combt (n) represents the wind storage combined output of the nth step at the time t,
respectively the upper and lower power limits of the clipping element.
7. The active power optimization regulation and control method for the new energy station with high confidence level and multiple time scales according to claim 1, wherein the dynamic power smoothness constraint is as follows:
wherein T is the total number of scheduling periods, N is the total number of differential steps in a unit period, and P rate The wind power is a rated value of the wind power, C is a highest threshold value of wind power smoothness in T time periods, T is a scheduling time, n is a serial number of a difference step length at the T-th time, and tau is the difference step length.
8. The active power optimization regulation and control method for the new energy station with the high confidence level and the multiple time scales according to claim 1, wherein the wind power output model considering the prediction error is defined as follows:
ΔP W (t)＝P W (t)-P W,k,t (8)
where α is the confidence, Δ P W (t) is the wind power prediction error at the t-th moment, P W (t) predicted output value at the t moment of wind power, P W,k,t Is the actual output value of the wind power at the t moment,P W,α (t) is the lower limit of the error band,
upper limit of error band, P r Representing the size of the probability;
the confidence coefficient is alpha, and the wind power prediction error band is
9. The active optimization regulation and control method for the high-confidence multi-time scale of the new energy station according to claim 1, wherein the step of constructing the active optimization regulation and control model for the high-confidence multi-time scale of the new energy station comprises the following steps:
step 61: establishing an objective function of the station optimization regulation model,
in the formula, T is the scheduling time, T is the number of power scheduling periods, S is the energy storage unit, S is the total number of the energy storage unit, W is the wind power unit, W is the total number of the wind power unit, V is the photovoltaic unit, V is the total number of the photovoltaic unit, G is the total number of the power generation unit, N is the differential step length, N is the total number of discrete sections, C Sc For the cost coefficient of the charge and discharge power of the stored energy,
to store the charging power for the time period t,
discharge power for storing energy during time t, C Wc For the cost coefficient of the power generated by the fan,
is the generated power of the fan in the time period t, C Vc For the photovoltaic power generation power cost coefficient,
for the generated power of the photovoltaic cell during the period t, C re,i Reserve cost factor for stored energy i, R i,t Reserve power reserved for energy storage i during time t, C ffr To the frequency modulation gain factor, C smooth For power smoothing of the yield coefficient, C p Is the time of use, Δ f t,n Representing the frequency deviation of the nth step at time T, c being the highest threshold value of wind power smoothness in T time periods, P level Wind power smoothness level for T periods;
step 62: constructing conventional constraints of a site optimization regulation model:
and (3) output constraint of the wind turbine unit:
output restraint of the photovoltaic unit:
energy storage charging and discharging restraint:
energy storage and electric quantity restraint:
and (4) energy storage operation state constraint:
in the formula (I), the compound is shown in the specification,P w,α (t) is the lower limit of the wind power prediction error band,
and is the upper limit of the wind power prediction error band, wherein,P v,α (t) is the lower limit of the photovoltaic prediction error band,
is the upper limit of the photovoltaic prediction error band,
is a variable from 0 to 1 and represents the charging state of the stored energy s in the period t,
is a variable of 0 to 1Representing the discharge state of the stored energy s during the period t, x s,t Is a variable from 0 to 1 and represents the starting and stopping states of the energy storage s in the period t,
the lower limit of the charging power for the stored energy s,
is the upper limit of the charging power of the stored energy s,
the lower limit of the discharge power of the stored energy s,
upper limit of discharge power for stored energy S, S st Is the SOC value of the stored energy s in the period t, delta represents the self-discharge rate of the stored energy, eta c And η d Respectively representing the charging and discharging efficiencies of the energy storage unit, E s To the capacity of the energy storage unit, S max And S min The upper limit and the lower limit of the charge state of the energy storage unit are respectively set;
and step 63: adding the dynamic frequency constraint and the dynamic smoothness constraint into a station optimization regulation model, and adopting a large M method to carry out linearization treatment on an amplitude limiting link in a power smooth model as follows:
and (3) linearization treatment:
in the formula, z 1 、z 2 、u 1 、u 2 Is a 0-1 auxiliary variable introduced during linearization, M is a constant, Δ P' B,t (n) represents the compensation power before the energy storage amplitude limit of the nth step at the time t,ΔP B,t (n) represents the compensation power after the energy storage amplitude limit of the nth step at the time t,
respectively the upper and lower limit values of the power of the amplitude limiting link.
10. A high-confidence-level multi-time-scale active power optimization regulation and control system for a new energy station is characterized by comprising:
the system comprises an acquisition unit, a smoothing unit and a control unit, wherein the acquisition unit is used for acquiring key frequency modulation parameters of a generator set, the key frequency modulation parameters comprise a wind turbine unit, a photovoltaic power generation unit, an energy storage unit, the total system inertia of a synchronous generator set, a difference adjustment coefficient, a reheating time constant, a high-pressure turbine power fraction, a damping coefficient and a stroking power, and the smoothing time constant of a first-order low-pass power filter used for smoothing wind and light minute-level power fluctuation in a new energy station is acquired;
and the regulating and controlling unit is used for performing active optimization regulation and control on the new energy station by using the wind power output model considering the prediction error and the solving result of the high-confidence-degree multi-time-scale active optimization regulation and control model of the new energy station.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116154877A (en) * | 2023-04-24 | 2023-05-23 | 华北电力大学 | Method for optimizing frequency modulation parameters of new energy station cluster |
| CN116191473A (en) * | 2023-03-20 | 2023-05-30 | 华北电力大学 | Primary frequency modulation standby optimization method considering random-extreme disturbance |
| CN117638990A (en) * | 2023-12-05 | 2024-03-01 | 华北电力大学 | Calculation method for decoupling frequency modulation parameter adjustable range of new energy power system |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116191473A (en) * | 2023-03-20 | 2023-05-30 | 华北电力大学 | Primary frequency modulation standby optimization method considering random-extreme disturbance |
| CN116191473B (en) * | 2023-03-20 | 2023-09-15 | 华北电力大学 | Primary frequency modulation standby optimization method considering random-extreme disturbance |
| CN116154877A (en) * | 2023-04-24 | 2023-05-23 | 华北电力大学 | Method for optimizing frequency modulation parameters of new energy station cluster |
| CN117638990A (en) * | 2023-12-05 | 2024-03-01 | 华北电力大学 | Calculation method for decoupling frequency modulation parameter adjustable range of new energy power system |
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