CN107230974A - The stable output control method of photovoltaic power based on storage energy operation state - Google Patents
The stable output control method of photovoltaic power based on storage energy operation state 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
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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
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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
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- H02J3/383—
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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
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E70/00—Other energy conversion or management systems reducing GHG emissions
- Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
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Abstract
The invention discloses a kind of stable output control method of photovoltaic power based on storage energy operation state, including：S1, selected research object time cross-section length of window y and its service data P (t)；S2, desired output desired value P is determined based on best power output modelG；S3, setting population dimension D, maximum iteration Mmax, convergence precision Cσ, while initializing population position x and speed v；S4, according to discharge and recharge strategy, calculate the fitness value of each particleAnd by its own particle extreme value piAnd global example extreme value pgCompare, if fitness value is smaller, update piAnd pg, particle rapidity V is updated if notidAnd position Xid；S5, calculating Δ σ2And judge whether to meet the condition of convergenceIf so, then obtaining optimal stored energy capacitance WO；If it is not, release example sets up new group, and repeat step S4 again.The present invention can effectively stabilize grid-connected power swing and accurate adjustment energy-storage system state-of-charge simultaneously.
Description
Technical Field
The invention relates to the technical field of photovoltaic power generation, in particular to a photovoltaic power stable output control method based on an energy storage running state.
Background
Photovoltaic power generation, which is an important component of the national energy strategy, has been rapidly developed in recent years, and delivers a large amount of clean energy to the power grid. However, due to the inherent characteristics of volatility, intermittence and uncontrollable performance, the installed capacity and permeability of the photovoltaic power generation system are continuously improved, and meanwhile, many negative effects are brought to the safe and stable operation of the power system, such as poor peak regulation capacity, large impact on a power grid, the need of increasing the rotating reserve capacity and the like. Therefore, the photovoltaic electric field is provided with the energy storage system with a certain capacity, so that the fluctuation of the photovoltaic output power can be effectively stabilized, the electric energy quality of the system is improved, and the friendly access to the power grid is realized.
In recent years, scholars at home and abroad carry out relevant research on the configuration problem of energy storage, and a plurality of research results are obtained, and the prior art discloses the following technical scheme:
the stabilization of the power fluctuation of the wind-solar combined power generation system is respectively realized through the real-time battery SOC feedback regulation control;
decomposing a photovoltaic output power signal by a wavelet packet method, combining the cycle life performance of different types of energy storage, and adjusting the charge and discharge power of energy type energy storage and power type energy storage in real time through fuzzy adaptive control on the power type energy storage SOC;
based on the state of charge (SOC) of the storage battery energy storage system, the filtering time constant of the filter is adjusted in real time through relevant rules, so that the aim of controlling the SOC to be stabilized in a normal working state is fulfilled;
based on photovoltaic output and short-term load prediction errors, a capacity configuration function of the energy storage equipment is obtained by using an interval estimation method, and prediction error variances of the energy storage capacity under distributed configuration and centralized configuration are compared to achieve a better power compensation effect;
carrying out spectrum analysis on renewable energy sources by utilizing discrete Fourier transform to determine an energy storage compensation range, and further providing a capacity determination method meeting requirements;
the method comprises the steps of establishing a capacity optimization model by taking an independent wind, light, diesel and energy storage microgrid system as a research object, and discussing the optimal capacity configuration of each power supply in the system under a given scheduling strategy by using a genetic algorithm and taking the lowest comprehensive cost as an optimization target.
The research is more considered on the fluctuation problem of the grid-connected power of the photovoltaic power station, but the influence of the running state of the energy storage body on the fluctuation of the photovoltaic power is not considered, so that the capacity requirement of the method on the energy storage is large.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a photovoltaic power stable output control method based on an energy storage running state.
In order to achieve the above purpose, the technical solutions provided by the embodiments of the present invention are as follows:
a photovoltaic power stable output control method based on an energy storage operation state comprises the following steps:
s1, selecting the length y of the time section window of the research object and the running data P (t);
s2, determining the expected output target value P based on the optimal power output modelGAnd setting an initial SOC value;
s3, setting particle swarm dimension D and maximum iteration number MmaxConvergence accuracy CσSimultaneously initializing a particle swarm position x and a velocity v;
s4, calculating the fitness value of each particle according to the charge and discharge strategyAnd extremizing its own particle piAnd global example extremum pgComparing, if the fitness value is smaller, updating piAnd pgIf not, the particle velocity V is updatedidAnd position Xid；
S5, calculating delta sigma2And judging whether the convergence condition is satisfiedIf yes, obtaining the optimal energy storage capacity WO(ii) a If not, the example is released again to create a new population, and step S4 is repeated, wherein Δ σ2Is the amount of variation of the population or global fitness variance of the population of particles, CσIs constant and close to zero.
As a further improvement of the present invention, in the optimal power output model in step S1:
photovoltaic power output power P at time tP(t) and grid-connected target power Pref(t) the difference Δ P (t) is Δ P (t) ═ PP(t)-Pref(t)；
When the energy storage system is in a charging state: for charging and discharging power of the energy storage system at time t, ηCCharging efficiency of the energy storage system;
when the energy storage system is in a discharge state: for charging and discharging power of the energy storage system at time t, ηDIs the discharge efficiency of the energy storage system.
As a further improvement of the present invention, in the optimal power output model in step S1:
when the energy storage system is in a charging state: pESS(t)＝i(t)ΔP(t)ηC，For charging and discharging power of the energy storage system at time t, ηCIn order to provide the charging efficiency of the energy storage system,i(t) is a charging and discharging power correction coefficient at the time t;
when the energy storage system is in a discharge state: pESS(t)＝i(t)ΔP(t)/ηD，For charging and discharging power of the energy storage system at time t, ηDIn order to achieve the discharge efficiency of the energy storage system,iand (t) is a charge and discharge power correction coefficient at time t.
As a further improvement of the present invention, the classification of the energy storage system according to the limitation of the SOC at runtime includes: pre-overdischarge area [ Q ]SOClow-L2，QSOClow-L1]Normal region [ Q ]SOClow-L1，QSOChigh-L1]Pre-overcharge region [ Q ]SOChigh-L1，QSOChigh-L2]。
As a further improvement of the present invention, the charge and discharge at the time t is performedElectric power correction factoriThe (t) is specifically:
in the pre-overcharge region, in the charged stateIn the discharge statei(t) is 1;
in the normal region, in the charged state and in the discharged statei(t) are all 1;
in the pre-overdischarge region, in the charging statei(t) is 1, in the discharge state
As a further improvement of the present invention, in the charging and discharging strategy in step S4, the objective of energy storage capacity optimization is:
minC＝KLρLLLOST+KSρSLSHORT+KEρELESS+CC；
where ρ isL、ρS、ρERespectively discarding light loss energy, smooth power shortage loss energy and corresponding unit price of converted energy of the energy storage system running off line for the photovoltaic power station; rhoLLLOSTThe cost of light energy is abandoned for the photovoltaic power station; rhoSLSHORTEnergy costs are lost for smoothing power shortages of the photovoltaic power station; rhoELESSThe reduced energy loss cost for the offline operation of the energy storage system; kL、KSAnd KEA penalty factor for operating cost; cCThe input cost of the energy storage system.
As a further improvement of the present invention, in the charging and discharging strategy in step S4, the light loss energy discarded by the photovoltaic power station, the smooth power shortage loss energy and the converted energy of the energy storage system running off-line are respectively:
in the formula, NyTime year for the subject; g. h is NyThe charge and discharge process is continuous in the yeariAdjusting the total times of the operation interval less than 1; p and q are respectively the initial time and the end time of the g interval; u and v are respectively the initial time and the end time of the h interval; k is NyThe total number of times the operating state of the energy storage system is above the maximum state of charge in the year; l is NyThe total number of times the energy storage system operating state is below the minimum state of charge in the year; x and y are respectively the initial time and the end time of the k interval; z, a are the initial and end times of the l interval, respectively.
As a further improvement of the present invention, in the charge and discharge strategy in step S4, the constraint conditions include:
charge and discharge power constraint:
-PDηD≤PW(t)-Pref(t)≤PC，PDand PCRespectively the limit charge and discharge power of the energy storage system;
and (3) restricting the fluctuation level of the output power of the photovoltaic power station:
P{|ΔPd(t)|≤ΔPdmax}≥Λ，ΔPd(t)ΔPd(t) is the fluctuation value of the output power of the photovoltaic power station after being stabilized by the energy storage system, delta PdmaxThe maximum allowable range upper limit of the fluctuation value is Λ, which is the corresponding credibility level;
and (3) charge quantity constraint:
andrespectively the minimum and maximum charge capacity of the energy storage unit.
The invention has the following beneficial effects:
the invention considers that the photovoltaic power has stronger fluctuation due to the influence of natural conditions, and realizes the stable output control of the photovoltaic power by utilizing the stored energy. The effective adjustment of the energy storage charge state is realized through the charge and discharge power correction coefficient, so that excessive charge and discharge are avoided, the photovoltaic output fluctuation is fully stabilized, the energy storage service life is effectively prolonged, and the system operation cost is reduced.
Drawings
Fig. 1 is a schematic flow chart of a photovoltaic power stable output control method based on an energy storage operation state according to the present invention.
FIG. 2 is a graph of a selected time cross-section expected output in an embodiment of the present invention.
FIG. 3 is a schematic illustration of cross-sectional smoothing effect at selected times in an embodiment of the present invention.
FIG. 4 is a schematic diagram of an SOC curve according to an embodiment of the invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the prior art, the charge state of the energy storage system is not considered in the related energy storage operation strategies for stabilizing the photovoltaic power fluctuation, obviously, the service life of the energy storage system is greatly reduced due to the frequent overcharge and overdischarge phenomena of the energy storage system or the abnormal working charge state for a long time, the cost of the energy storage system is greatly increased, and the economic consideration is not facilitated; secondly, the overcharge and the overdischarge of the energy storage system make the charge and discharge power difficult to control, which can cause the power injected into the power grid to fluctuate violently and affect the stability of the power grid. State of charge refers to the ratio of its remaining capacity to its fully charged capacity. When the SOC is 1, it indicates that the battery is fully charged, and when the SOC is 0, it indicates that the battery is fully discharged. In an energy storage power station, in a normal condition, the maximum value of the state of charge in each battery pack is taken as the state of charge value of the whole energy storage system during charging; and taking the minimum value of the charge state in each battery pack as the charge state value of the whole energy storage system during discharging. Thus, the overcharge and overdischarge phenomena of the single battery can be effectively prevented.
According to the invention, the energy storage device is adjusted to work in a normal working range all the time by adjusting the energy storage charging and discharging system, and the energy storage charge state and the photovoltaic power output stability are considered, so that grid-connected power fluctuation can be effectively stabilized and the charge state of the energy storage system can be accurately adjusted.
Referring to fig. 1, a method for controlling stable output of photovoltaic power based on an energy storage operating state according to the present invention includes:
s1, selecting the length y of the time section window of the research object and the running data P (t);
s2, determining the expected output target value P based on the optimal power output modelGAnd setting an initial SOC value;
s3, setting particle swarm dimension D and maximum iteration number MmaxConvergence accuracy CσSimultaneously initializing a particle swarm position x and a velocity v;
S4、calculating the fitness value of each particle according to the charge-discharge strategyAnd extremizing its own particle piAnd global example extremum pgComparing, if the fitness value is smaller, updating piAnd pgIf not, the particle velocity V is updatedidAnd position Xid；
S5, calculating delta sigma2And judging whether the convergence condition is satisfiedIf yes, obtaining the optimal energy storage capacity WO(ii) a If not, the example is released again to create a new population, and step S4 is repeated, wherein Δ σ2Is the amount of variation of the population or global fitness variance of the population of particles, CσIs constant and close to zero.
According to the method, the energy storage device is adjusted to work in a normal working range all the time by adjusting the energy storage charging and discharging system, the energy storage charge state and the photovoltaic power output stability are considered at the same time, and grid-connected power fluctuation can be effectively stabilized and the energy storage system charge state can be accurately adjusted.
The energy storage strategy of the energy storage system of the photovoltaic power station is as follows: when the photovoltaic power output power is larger than the grid-connected power reference value, the energy storage system is charged to stabilize the fluctuation of the output power; when the photovoltaic power output power is smaller than the grid-connected power reference value, the energy storage system discharges to make up for the deficiency of the output power, so that the output power of the photovoltaic power is smoothed, and the stability of the photovoltaic power grid-connected power is realized.
Photovoltaic power output power P at time tP(t) and grid-connected target power Pref(t) the difference Δ p (t) is:
ΔP(t)＝PP(t)-Pref(t) (1)
the charging and discharging power of the energy storage system is shown in the formulas (2) and (3).
When the energy storage system is in a charging state:
when the energy storage system is in a discharging state:
in the formula:charging and discharging power for the energy storage system at the moment t; when in useWhen the energy storage system is charged,when the energy storage system is discharged ηCFor the charging efficiency of the energy storage system, 0.65-0.85, η is generally selectedDFor the discharge efficiency of the energy storage system, it is generally about 0.95.
The charging and discharging power instruction of the energy storage power station should consider the current SOC level and the power instruction size at the current moment, namely when the SOC is in the normal working range, the charging and discharging power of the energy storage power station is kept unchanged; when the SOC is beyond the line to the abnormal working range, the charging and discharging power needs to be adjusted in time to prevent the phenomenon of overcharge and overdischarge.
And setting the limit classification of the SOC when the energy storage system operates. Wherein Q isSOCmaxAnd QSOCminUpper and lower limits, [ Q ] of the energy storage system state of charge, respectivelySOCmin，QSOClow-L2]Is an overdischarge region, [ Q ]SOClow-L2，QSOClow-L1]Is a pre-overdischarge region, [ Q ]SOClow-L1，QSOChigh-L1]Is a normal region, [ Q ]SOChigh-L1，QSOChigh-L2]For the pre-overcharge region, [ Q ]SOChigh-L2，QSOCmax]The operating interval of the energy storage system in different charge states is an overcharge region, wherein QSOChigh-L2And QSOClow-L2And respectively overcharging and overdischarging warning lines.
The change of the charge state operation interval of the energy storage system causes the corresponding adjustment of the power correction coefficient, and the charge and discharge power of the energy storage system is changed through the power correction coefficient so as to achieve the purpose of controlling the operation of the energy storage system in advance and avoid the energy storage system from achieving the state of overcharge and overdischarge. The specific control strategy is shown in table 1.
TABLE 1 Power correction factor control rule
When the state of charge of the energy storage system is higher, namely in a pre-overcharge region, the energy storage tends to be saturated. If in the charging stateNeed to be aligned withPerforming pre-control, adjusting power correction coefficient by formula (4), and correctingThe charging speed is reduced to relieve the rising speed of the state of charge of the energy storage system and prevent the energy storage system from generating an overcharged state; if in the discharge stateThe original value is maintained. Vice versa, when the state of charge of the energy storage system is low, namely in the pre-overdischarge region, if the energy storage system is in the discharge stateAdjusting the power correction coefficient by equation (5) to correctThe charge state of the energy storage system is reduced to slow down the reduction speed of the charge state of the energy storage system, so that the energy storage system is prevented from generating a deep discharge state. If in the charging stateThe original value is maintained. And when the charge state of the energy storage system is in a normal region, maintaining the correction coefficient unchanged so as to ensure that the energy storage system is charged and discharged normally. Wherein,
in the formula,i(t) a charging and discharging power correction coefficient at the moment t, and the value is 1 when the energy storage system is positioned in a normal area; qSOCAnd (t) is the state of charge of the energy storage system at time t. The invention adopts a logarithmic barrier function, and when the charge state is close to QSOCmaxOr QSOClow-L2The convergence of the logarithmic function is strong, so that the time can be reduced more quicklyiAnd (t) the function of controlling the charge and discharge power in advance is better played, and the state of charge of the energy storage system is effectively prevented from reaching the over-charge or over-discharge state.
It should be noted that the power correction coefficient control method provided by the present invention achieves Q in the state of charge of the energy storage systemSOChigh-L2When the temperature of the water is higher than the set temperature,i(t) the minimum value is not 0, which aims to ensure the full utilization of the energy storage capacity and still continue charging; and the state of charge reaches QSOClow-L2When it has already beeniAnd (t) the correction is zero, so that the lowest capacity of the energy storage system can be strictly controlled, the energy storage system is thoroughly prevented from operating in an over-discharge area, and the service life loss of the energy storage system is reduced.
Therefore, the adjusted charging and discharging power of the energy storage system can be obtained.
When the energy storage system is in a charging state:
PESS(t)＝i(t)ΔP(t)ηC(6)
when the energy storage system is in a discharging state:
PESS(t)＝i(t)ΔP(t)/ηD(7)
in formulas (6) and (7): pESS(t) the charging and discharging power of the energy storage system is adjusted by the power correction coefficient at the moment t, and when P isESSWhen (t) > 0, the energy storage system is charged, PESSAnd (t) < 0, discharging the energy storage system.
The optimization of the energy storage capacity of the photovoltaic power station aims at adjusting the mutual restriction relation between the input cost and the operation cost on the premise of ensuring the reduction of the fluctuation of the output power of the photovoltaic power station, and realizes the optimization of the operation benefit of the energy storage system of the photovoltaic power station by using the input cost and the operation cost of the lowest energy storage on the premise of ensuring the smooth output power.
The photovoltaic power station is configured with different energy storage capacities to obtain different power fluctuation stabilizing effects, and the comprehensive benefit of energy storage is optimized by aiming at the restriction relation between the input cost of the energy storage capacity and the operation cost on the premise of ensuring that the fluctuation requirement of the output power of photovoltaic power is met. Wherein the input cost C of the energy storage systemCMaintenance costs C including energy storage systemMReplacement cost (considered only when the service life of the energy storage unit is less than the engineering year) of each energy storage unit of the energy storage system CRAnd the capital investment cost C of the energy storage systemB。
CC＝CM+CR+CB(8)
CM＝YNbessρ (9)
CB＝Nbessρ1WO+Nbessρ2WOm (10)
In the formula: y is working time; n is a radical ofbessFor accumulators in energy storage systemsThe number of the particles; rho is the maintenance price of the unit capacity of the energy storage capacity; rho1Installing price for unit capacity of energy storage capacity; wOA rated value for an optimal energy storage capacity of the photovoltaic power station; rho2Is the energy storage capacity per capacity price; m is a depreciation coefficient defined as:in the formula: r is depreciation rate; l ismThe engineering age is the engineering age.
The operation cost comprises the photovoltaic power station light loss cost caused by the adjustment of the power correction coefficient, the smooth power shortage loss cost and the conversion loss cost of the energy storage system running in an offline mode, and the three cost are changed due to the change of the energy storage capacity.
Because the output power of the photovoltaic power station has annual periodicity, the annual output power of the photovoltaic power station is taken as a research object for optimizing the energy storage capacity, and the photovoltaic power station abandons light loss energy, the smooth power shortage loss energy and the converted energy of the energy storage system running off line are respectively shown as a formula (11), a formula (12) and a formula (13):
in the formula: n is a radical ofyTime year for the subject; g. h is NyThe charge and discharge process is continuous in the yeariAdjusting the total times of the operation interval less than 1; p and q are respectively the initial time and the end time of the g interval; u and v are respectively the initial time and the end time of the h interval; k is NyThe total number of times the operating state of the energy storage system is above the maximum state of charge in the year; l is NyAnnual energy storage system operationTotal number of times the state is below the minimum state of charge; x and y are respectively the initial time and the end time of the k interval; z, a are the initial and end times of the l interval, respectively.
The energy storage capacity optimization target of the photovoltaic power station is as follows:
minC＝KLρLLLOST+KSρSLSHORT+KEρELESS+CC(14)
in the formula: rhoL、ρS、ρERespectively discarding light loss energy, smooth power shortage loss energy and corresponding unit price of converted energy of the energy storage system running off line for the photovoltaic power station; rhoLLLOSTThe cost of light energy is abandoned for the photovoltaic power station; rhoSLSHORTEnergy costs are lost for smoothing power shortages of the photovoltaic power station; rhoELESSThe reduced energy loss cost for the offline operation of the energy storage system; kL、KSAnd KEA penalty factor for operating cost; cCThe input cost of the energy storage system.
In equation (13), the reduced loss cost for the energy storage system to run off-line comprises 2 parts: firstly, when the energy storage system operates in an over-high charge state, the energy storage system is not in a reasonable operation state, and the reduction cost of the service life of the energy storage system is influenced; and secondly, when the charge state of the energy storage system is too low, the energy storage system is not in a reasonable running state, and the reduction cost of the service life of the energy storage system is influenced.
The constraint conditions in the charge and discharge strategy of the invention comprise:
charge and discharge power constraint:
-PDηD≤PW(t)-Pref(t)≤PC(15)
in the formula: pDAnd PCThe discharge is regarded as a negative charging process, and the magnitude of the discharge is based on the absolute value of the discharge.
The constraint conditions comprise photovoltaic power station output power fluctuation level constraint:
P{|ΔPd(t)|≤ΔPdmax}≥Λ (16)
in the formula: delta Pd(t)ΔPd(t) is a fluctuation value of the output power of the photovoltaic power station after being stabilized by the energy storage system; delta PdmaxThe maximum allowable upper range limit of the fluctuation value, and Λ the corresponding credibility level.
And (3) charge quantity constraint:
in the formula:andrespectively the minimum and maximum charge capacity of the energy storage unit.
In order to verify the effectiveness of the method, the optimal energy storage capacity is calculated based on actual operation data of a certain photovoltaic power station in Qinghai. In this embodiment, a PSO algorithm is considered to solve a random optimization problem that the present invention includes a dynamic boundary condition and includes a plurality of random variables, and a specific model solving step is as follows:
step 1: selecting the length y of a time section window of a research object and operation data P (t) thereof;
step 2: determining an expected output target value PG based on the optimal power output model, and giving an initial SOC equivalent value;
and step 3: setting a particle swarm dimension D and a maximum iteration number MmaxConvergence accuracy CσSimultaneously initializing a particle swarm position x and a velocity v;
and 4, step 4: according to the charging and discharging strategy of the invention, setting c1、c2、ω、Vmin、VmaxAnd the like, the parameters of the system,calculation of fitness value of each particle by combining equations (11-17)And extremizing its own particle piAnd global example extremum pgComparing, if the fitness value is smaller, updating piAnd pgIf not, the particle velocity V is updatedidAnd position Xid；
And 5: calculating Delta sigma2Judging whether a convergence condition is met, wherein the search convergence condition is as follows:
in the formula, delta sigma2Is the amount of variation of the population or global fitness variance of the population of particles, CσIs constant and close to zero. If yes, obtaining the optimal energy storage capacity WO(ii) a If not, the example is released again to establish a new population, and the step (4) is repeated.
The invention starts from the optimal capacity WOIndex parameters such as the stabilizing power offset x, the SOC extreme value out-of-limit times N, SOC process curve and the like measure the effectiveness of the method. The installed capacity of the photovoltaic power station is 9MW, the acquisition frequency is 5min, and the stabilizing target value is shown in figure 2.
According to the charging and discharging power adjustment strategy and the energy storage capacity optimization calculation model, the obtained stabilizing fluctuation output curve is shown in fig. 3, and the calculation result of the method is shown in table 2.
TABLE 2 calculation results
By analyzing the embodiment, the method effectively realizes the optimization of the energy storage capacity in the aspect of capacity planning; in the aspect of stabilizing the power offset, the method is similar to the conventional method and is slightly increased, because the power correction coefficient adjustment strategy improves the probability of abandoning light or stabilizing insufficient energy; the invention greatly reduces the value of N in the aspect of operation beyond the limit value, the reduction amplitude reaches 96.2 percent, and the effect is obvious. And (5) observing the change condition of the SOC in the process of acquiring the optimal capacity of the energy storage power station, as shown in FIG. 4. It can be seen that the SOC does not run beyond the limit value in the section in the method of the present invention, which effectively guarantees the service life of the ESS.
In conclusion, the capacity optimization calculation model comprehensively considers the overall economy of the energy storage power station in the configuration and operation processes, and is beneficial to the effective combination with the site. The theoretical research provides theoretical premises and guarantees for the optimization of the energy storage capacity. Meanwhile, practical data example analysis verifies the conclusion.
According to the technical scheme, the invention has the following beneficial effects:
the invention considers that the photovoltaic power has stronger fluctuation due to the influence of natural conditions, and realizes the stable output control of the photovoltaic power by utilizing the stored energy. The effective adjustment of the energy storage charge state is realized through the charge and discharge power correction coefficient, so that excessive charge and discharge are avoided, the photovoltaic output fluctuation is fully stabilized, the energy storage service life is effectively prolonged, and the system operation cost is reduced. The problem of optimizing the capacity of the photovoltaic power station configuration energy storage is further discussed in the control strategy technology, and important theoretical support is provided for the optimized operation of the photovoltaic power station configuration energy storage system. The actual operation data of the photovoltaic power station in the Qinghai region are used for calculation, the result shows that the stable output of the photovoltaic power can be realized, the fluctuation range of the energy storage charge state can be controlled, excessive charging and discharging are effectively avoided, and the method has high feasibility and applicability.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (8)
1. A photovoltaic power stable output control method based on an energy storage operation state is characterized by comprising the following steps:
s1, selecting the length y of the time section window of the research object and the running data P (t);
s2, determining the expected output target value P based on the optimal power output modelGAnd setting an initial SOC value;
s3, setting particle swarm dimension D and maximum iteration number MmaxConvergence accuracy CσSimultaneously initializing a particle swarm position x and a velocity v;
s4, calculating the fitness value px of each particle according to the charge and discharge strategyidAnd extremizing its own particle piAnd global example extremum pgComparing, if the fitness value is smaller, updating piAnd pgIf not, the particle velocity V is updatedidAnd position Xid；
S5, calculating delta sigma2And judging whether the convergence condition is satisfiedIf yes, obtaining the optimal energy storage capacity WO(ii) a If not, the example is released again to create a new population, and step S4 is repeated, wherein Δ σ2Is the amount of variation of the population or global fitness variance of the population of particles, CσIs constant and close to zero.
2. The method according to claim 1, wherein in the optimal power output model in step S1:
photovoltaic power output power P at time tP(t) and grid-connected target powerIs Δ P (t) ═ PP(t)-Pref(t)；
When the energy storage system is in a charging state: for charging and discharging power of the energy storage system at time t, ηCCharging efficiency of the energy storage system;
when the energy storage system is in a discharge state: for charging and discharging power of the energy storage system at time t, ηDIs the discharge efficiency of the energy storage system.
3. The method according to claim 2, wherein in the optimal power output model in step S1:
when the energy storage system is in a charging state: pESS(t)＝i(t)ΔP(t)ηC，For the charging and discharging power of the energy storage system at time t, ηCIn order to provide the charging efficiency of the energy storage system,i(t) is a charging and discharging power correction coefficient at the time t;
when the energy storage system is in a discharge state: pESS(t)＝i(t)ΔP(t)/ηD，For charging and discharging power of the energy storage system at time t, ηDIn order to achieve the discharge efficiency of the energy storage system,iand (t) is a charge and discharge power correction coefficient at time t.
4. The photovoltaic power stable output control method based on the energy storage operation state as claimed in claim 3, wherein the classification of the energy storage system according to the limitation of the SOC at operation comprises: pre-overdischarge area [ Q ]SOClow-L2，QSOClow-L1]Normal region [ Q ]SOClow-L1，QSOChigh-L1]Pre-overcharge region [ Q ]SOChigh-L1，QSOChigh-L2]。
5. The photovoltaic power stable output control method based on the energy storage operation state as claimed in claim 4, wherein the charging and discharging power correction coefficient at the time tiThe (t) is specifically:
in the pre-overcharge region, in the charged stateIn the discharge statei(t) is 1;
in the normal region, in the charged state and in the discharged statei(t) are all 1;
in the pre-overdischarge region, in the charging statei(t) is 1, in the discharge state
6. The method according to claim 1, wherein in the charging and discharging strategy in step S4, the objective of energy storage capacity optimization is:
minC＝KLρLLLOST+KSρSLSHORT+KEρELESS+CC；
where ρ isL、ρS、ρERespectively discarding light loss energy, smooth power shortage loss energy and corresponding unit price of converted energy of the energy storage system running off line for the photovoltaic power station; rhoLLLOSTThe cost of light energy is abandoned for the photovoltaic power station; rhoSLSHORTEnergy costs are lost for smoothing power shortages of the photovoltaic power station; rhoELESSThe reduced energy loss cost for the offline operation of the energy storage system; kL、KSAnd KEA penalty factor for operating cost; cCThe input cost of the energy storage system.
7. The method according to claim 6, wherein in the charging and discharging strategy in step S4, the light loss energy discarded by the photovoltaic power station, the power loss energy smoothed by the shortage, and the converted energy of the energy storage system running off line are respectively:
in the formula, NyTime year for the subject; g. h is NyThe charge and discharge process is continuous in the yeariAdjusting the total times of the operation interval less than 1; p and q are respectively the initial time and the end time of the g interval; u and v are respectively the initial time and the end time of the h interval; k is NyThe total number of times the operating state of the energy storage system is above the maximum state of charge in the year; l is NyThe total number of times the energy storage system operating state is below the minimum state of charge in the year; x and y are respectively the initial time and the end time of the k interval; z, a are the initial and end times of the l interval, respectively.
8. The method according to claim 1, wherein in the charge-discharge strategy in step S4, the constraint conditions include:
charge and discharge power constraint:
-PDηD≤PW(t)-Pref(t)≤PC，PDand PCRespectively the limit charge and discharge power of the energy storage system;
and (3) restricting the fluctuation level of the output power of the photovoltaic power station:
P{|ΔPd(t)|≤ΔPdmax}≥Λ，ΔPd(t)ΔPd(t) is the fluctuation value of the output power of the photovoltaic power station after being stabilized by the energy storage system, delta PdmaxThe maximum allowable range upper limit of the fluctuation value is Λ, which is the corresponding credibility level;
and (3) charge quantity constraint:
andrespectively the minimum and maximum charge capacity of the energy storage unit.
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| CN115149556A (en) * | 2022-06-06 | 2022-10-04 | 国网湖北省电力有限公司电力科学研究院 | Energy storage power station group power grid AGC coordination control method considering SOC |
| CN115149556B (en) * | 2022-06-06 | 2024-08-23 | 国网湖北省电力有限公司电力科学研究院 | AGC coordinated control method for energy storage power station group power grid in consideration of SOC |
| CN115395547A (en) * | 2022-08-31 | 2022-11-25 | 国网河南省电力公司南阳供电公司 | Flexible energy storage system optimal configuration method based on whole county photovoltaic propulsion |
| CN115395547B (en) * | 2022-08-31 | 2024-05-07 | 国网河南省电力公司南阳供电公司 | Flexible energy storage system optimal configuration method based on whole county photovoltaic propulsion |
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