CN108879746A - Centralized hybrid energy-storing control method for coordinating based on Multiple Time Scales demand response - Google Patents
Centralized hybrid energy-storing control method for coordinating based on Multiple Time Scales demand response Download PDFInfo
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Abstract
The invention discloses a kind of centralized hybrid energy-storing control method for coordinating based on Multiple Time Scales demand response, includes the following steps：S1, building user's polymorphic type demand electric characteristic models；S2, the demand response model for constructing Multiple Time Scales；S3, according to the demand response model of polymorphic type demand electric characteristic models and Multiple Time Scales, determine the coordination control strategy of centralization mixed energy storage system；The characteristics of charge-discharge characteristic of the method provided by the invention based on battery and super capacitor, state-of-charge and user's polymorphic type demand, formulates the charge and discharge strategy of centralized mixed energy storage system, to promote clean energy resource consumption, the quality that optimizes the system operation while the performance driving economy for improving centralized mixed energy storage system.
Description
Technical field
The invention belongs to centralized hybrid energy-storing Coordinated Control fields, and in particular to one kind is needed based on Multiple Time Scales
The centralized hybrid energy-storing control method for coordinating asked.
Background technique
Currently, the clean type energy is mostly incorporated to user side, user's polymorphic type power load and distribution with low capacity, distributing
The randomness and fluctuation of power supply power output become the huge challenge of system safe and economical operation, are carrying out system tune using energy storage
Section optimization aspect is inquired into energy storage from load shifting peak, clean energy resource consumption etc. mostly in single time scale and is matched to optimization
The advantage of operation of power networks；But the optimization of mixed energy storage system operation with clean energy resource more low capacity user side it is grid-connected, use
Electric load type increases the problem of also certainly existing the following aspects；
One, the rapid growth of user side controllable burden and clean energy resource permeability causes system operation fluctuation larger, single
The requirement forecasting of long time scale is difficult to reflect that system runs status；
Two, it is conceived to the user demand response of different time scales, corresponding demand and the system service requirement of user has
Apparent otherness, the quality that optimizes the system operation while ensureing user's economy are the key contradictions of electric network coordination operation；
Three, the diversity of energy storage device, energy type energy storage are each advantageous with power-type energy storage.
Therefore, there is scholar to propose to solve the problems, such as running Optimization using mixed energy storage system, utilize energy type energy storage
The superfluous clean energy resource power generation of high-energy density consumption, is fluctuated using the quick charge and discharge ability stabilizing system of high power energy storage, but
Influence of the Short Term Load deviation to power distribution network operation control is often had ignored in existing research, and is directed to mixed energy storage system
The tune that the characteristics of middle accumulator capacity is big but response speed is slow, super capacitor repid discharge fluctuates power distribution network under short-term time scale
Energy saving power and the research of the influence of system performance driving economy still stagnate.
Summary of the invention
For above-mentioned deficiency in the prior art, the centralization mixing storage provided by the invention based on Multiple Time Scales demand
Energy control method for coordinating solves the problems, such as above-mentioned.
In order to achieve the above object of the invention, the technical solution adopted by the present invention is：One kind is rung based on Multiple Time Scales demand
The centralized hybrid energy-storing control method for coordinating answered, includes the following steps：
S1, building user's polymorphic type demand electric characteristic models, including customer charge electric characteristic models and distributed electrical
Source response characteristic model；
S2, construct Multiple Time Scales demand response model, including long time scale user demand response optimization model with
Short-term time scale user demand prediction deviation stabilizes model；
S3, according to the demand response model of polymorphic type demand electric characteristic models and Multiple Time Scales, determine that centralization is mixed
Close the coordination control strategy of energy-storage system.
Beneficial effects of the present invention are：Centralized hybrid energy-storing provided by the invention based on Multiple Time Scales demand response
Control method for coordinating realizes the optimization to load polymorphic type under Multiple Time Scales using energy type and power-type hybrid accumulator
Coordinate, had both made full use of the high-energy density of battery to promote the consumption of clean energy resource, and realized the peak load shifting of load, and fill
Divide and load is quickly stabilized with what clean energy resource fluctuated using the high power density realization of super capacitor；In addition, the present invention is mentioned
Centralized hybrid energy-storing control method for coordinating based on Multiple Time Scales demand response is rung based on the user demand of Multiple Time Scales
Answer target, it is contemplated that clean energy resource and load are in the fluctuation of long time scale and the prediction deviation problem of short-term time scale, tool
There is very high practicability.
Further, in the step S1：
The customer charge electric characteristic models include uncontrollable load electric characteristic models, controllable burden model and can draw
That leads load uses electric characteristic models；
The uncontrollable load is with electric characteristic models：
In formula,For the use electrical characteristics of uncontrollable load,Respectively uncontrollable load is in t moment
Prediction power output, prediction deviation degree and electricity consumption probability, TULFor the electricity consumption duration of uncontrollable load；
The controllable burden is with electric characteristic models：
In formula,For the use electrical characteristics of controllable burden,Respectively controllable burden is in the pre- of t moment
Power, prediction deviation degree, electricity consumption probability and users'comfort requirement are measured,For t moment controllable burden cut down making up price,
TILFor controllable burden electricity consumption duration；
The bootable load is with electric characteristic models：
In formula,For the electricity consumption of the bootable load of t moment,Respectively bootable load is in t moment
Prediction power output, prediction deviation degree, electricity consumption probability,For the making up price that the bootable load of t moment is cut down, TGLIt is bootable
Load electricity consumption duration；
The distributed generation resource response characteristic model is：
In formula,For the response characteristic of distributed generation resource,Respectively prediction of the distributed generation resource in t moment
Power output and prediction deviation degree,The respectively sale of electricity electricity price of the cost of electricity-generating and t moment of distributed generation resource, TDGTo divide
The power generation duration of cloth power supply.
Further, which is characterized in that
The customer charge electric characteristic models be by load prediction under long time scale, short-term time scale load deviation with
And load electricity consumption duration determine load use electric model；
Load forecasting model is under the long time scale：
In formula, f (x) is the regression function of load prediction,For Lagrange multiplier, b is biasing, K (x, xi) it is core
Function, and meet Mercer condition；
The kernel function expression formula is：
In formula, K (x, xi) it is kernel function, x is space sample, xiFor the center of space sample x, σ is kernel function ginseng
Number；
The distributed generation resource response characteristic model is the characteristic mould constructed based on wind, light distribution formula power supply power producing characteristics
Type；
The photovoltaic generator prediction power output model be：
In formula：f(PPV) it is that photovoltaic generator exports active probability function, Г is Gamma function, and α, β are respectively beta
The form parameter of distribution, PPVFor the output power of photovoltaic generator；For the peak power output of photovoltaic array；
Based on the probability function of photovoltaic generator active power output, then the desired value of the photovoltaic power generation system output power
For：
The wind speed vtProbability density function be：
In formula, f (vt) be mean wind speed probability density function, c, k be respectively Weibull distribution function scale ginseng
Number, form parameter, vtThe random quantity of wind speed is inputted for t moment；
Based on wind speed vtProbability density function, the relation function between the output power and wind speed of the wind-driven generator
For：
In formula, PwtFor the output power v of blowerc, vf, vsRespectively cut wind speed, cut-out wind speed and rated wind speed, Rwt
For the rated capacity of blower.
Above-mentioned further scheme has the beneficial effect that：Realize the different type load electrical characteristics and cleaning energy to user
The simulation of source response characteristic has fully considered user power utilization comfort level, electricity price and prediction deviation etc. to user power utilization characteristic
It influences, has refined user demand responding scene.
Further, in the step S2：
The long time scale user demand response model is：
In formula,For user's different demands response minimum cost,For the coordination assembly of i-th of synthetic user
This, CUL, CIL, CGL, CDGUser's uncontrollable load, interruptible load respectively in the period guide load and distributed generation resource of registering one's residence
Response cost, respectively：
In formula, ctFor t moment power grid electricity price, Δ t is long time scale response time interval, its time under long time scale
Between be divided into 1 hour,For the price elastic coefficient for guiding load；
The short-term time scale user demand prediction deviation stabilizes model and is：
In formula,Cost, T are stabilized for deviationadFor the period of short-term time scale, Δ PDG, Δ PUL, Δ PIL, Δ PGLRespectively
For DG (distributed generation resource), uncontrollable load, interruptible load and guidance load prediction deviation value,Respectively
Super capacitor t moment electric discharge, charging price,The respectively charging and discharging state of t moment super capacitor；For charged state,For discharge condition, Then super capacitor is motionless
Make, Δ tadFor the response time interval of short-term time scale.
Above-mentioned further scheme has the beneficial effect that：Promoted under long time scale based on system clean energy resource dissolve and
Load and distributed generation resource go out the demand difference that fluctuation deviation is stabilized under short-term time scale, construct the demand of different time scales
Response and deviation stabilize model, targetedly solve system in the demand of different time scales, it is clear can to effectively facilitate day part
The consumption of the clean energy, and the influence that load and distributed generation resource go out fluctuation to operation can be reduced, system is improved to the greatest extent
The economy of operation.
Further, the mixed energy storage system includes battery and super capacitor.
Further, the coordination control strategy of the centralized mixed energy storage system is with mixed energy storage system maximum revenue
For target, the objective function is：
In formula, maxCESSFor the maximum return of mixed energy storage system, T is to coordinate total period, and Ty is the type of time scale,
N is synthetic user number, Δ tiFor the unit time under t moment the i-th class time scale, Respectively the i-th class of t moment when
Between under scale demand mixed energy storage system using electric power storage tank discharge, the price of charging,T moment is hybrid energy-storing respectively
The price that system is discharged using super capacitor, charged； Respectively t moment is for j-th of the i-th class time scale use
Electric power storage tank discharge in the demand utilization mixed energy storage system at family, charging electricity,Respectively t moment is directed to i-th
The electricity of super capacitor electric discharge, charging in the demand utilization mixed energy storage system of j-th of user of class time scale；
Influencing the mixed energy storage system maximum revenue factor includes the constraint of mixed energy storage system state-of-charge, mixing storage
It can the constraint of system charge-discharge electric power and system power Constraints of Equilibrium；
It is described centralization mixed energy storage system state-of-charge be：
In formula,WithThe respectively state-of-charge of battery and super capacitor in t moment； Point
Not Wei battery charge and discharge efficiency；The respectively charge and discharge efficiency of super capacitor； When respectively t
Carve the charge and discharge power of super capacitor；The respectively charge and discharge power of t moment super capacitor；
The respectively charge and discharge state of t moment battery；For charged state,For discharge condition,Then battery is failure to actuate；Δ t is the duration；RES, RECThe respectively appearance of battery and super capacitor
Amount；
The centralization mixed energy storage system state-of-charge is constrained to：
In formula,WithThe respectively state-of-charge bound of battery,WithRespectively
For the state-of-charge bound of super capacitor；
The mixed energy storage system charge-discharge electric power constraint：
In formula,Respectively battery minimum, maximum charge power,Respectively store
Battery minimum, maximum discharge power；Respectively super capacitor minimum, maximum charge power,Respectively super capacitor minimum, maximum discharge power；
The system power Constraints of Equilibrium：
In formula,The respectively charging and discharging state of t moment battery,Respectively t moment is super
The charging and discharging state of capacitor,Respectively t moment electric power storage tank discharge, charging electricity, When respectively t
The electricity of the electric discharge of grade capacitor, charging is carved,Respectively uncontrollable load, controllable burden, bootable load,
DG t moment predicated response amount,Respectively uncontrollable load, controllable burden, can draw
Lead load, DG t moment prediction deviation amount.
Above-mentioned further scheme has the beneficial effect that：Response requirement based on different time scales, makes full use of centralization
The otherness feature of energy type and power-type energy storage in mixed energy storage system, mixed energy storage system can sufficiently realize user's difference
The demand of time scale, and maximize the economic benefit of self-operating.
Further, the step S3 is specially：
S3-1, input user demand data, are converted into corresponding data model；
The user demand data of input include different type load prediction data under Multiple Time Scales, distributed generation resource prediction
Force data and its prediction deviation amount out；
S3-2, mixed energy storage system is selected according to time scale：
If input demand data is long time scale prediction data, S3-3 is entered step；
If inputting the prediction deviation data that demand data is short-term time scale, S3-5 is entered step；
S3-3, judgement is entered according to the discharge condition of hybrid energy storage system：
When the distributed generation resource of input, which predicts force data, to be received and paid out completely, then S3-41 is entered step；
It (is empirically set) when user demand is more than the threshold value of setting, then enters step S3-42；
S3-41, determined according to energy storage charge state in mixed energy storage system：
If state-of-charge is less than maximum allowable state-of-charge, based on current electricity prices and state-of-charge computing system charging electricity
Amount,
(1) ifAndWhen, when for low ebb
Period electricity price, then energy storage charge volume bePeriod electricity price when to be flat, then energy storage charge volume beWhen for peak
Period electricity price, then energy storage charge volume beAnd enter step S3-7；
(2) ifAndWhen, when for low ebb
Period electricity price, then energy storage charge volume bePeriod electricity price when to be flat, andThen energy storage charge volume isOn the contrary then charge volume isWhen cutting for peak period electricity priceThen energy storage is charged
Amount isOn the contrary then charge volume isAnd enter step S3-7；
(3) ifAndWhen, when for low-valley interval
Electricity price, then energy storage charge volume bePeriod electricity price when to be flat, and
Then energy storage charge volume isOn the contrary then charge volume isWhen for peak period electricity price, andThen energy storage charge volume isOn the contrary then charge volume is
And enter step S3-7；
(4) ifAndWhen, when for low ebb
Period electricity price, then energy storage charge volume bePeriod electricity price when to be flat, andThen energy storage charge volume isOn the contrary then charge volume is
When cutting for peak period electricity priceThen energy storage charge volume isIt is on the contrary then
Charge volume isAnd enter step S3-7；
Otherwise it is directly entered step S3-7；
S3-42, determined according to energy storage charge state in mixed energy storage system：
If state-of-charge is greater than minimum allowable state-of-charge, based on current electricity prices and state-of-charge computing system electric discharge electricity
Amount,
(1) ifAndWhen, when for peak
Section electricity price, then energy storage discharge capacity bePeriod electricity price when to be flat, then energy storage discharge capacity beWhen for low-valley interval
Electricity price, then energy storage discharge capacity beAnd enter step S3-7；
(2) ifAndWhen, when for peak
Section electricity price, then battery discharge capacity bePeriod electricity price when to be flat, andThen energy storage discharge capacity isOn the contrary then energy storage discharge capacity isWhen for low-valley interval electricity price, andThen store up
Can discharge capacity beOn the contrary then energy storage discharge capacity isAnd enter step S3-7；
(3) ifAndWhen, when for peak
Section electricity price, then energy storage discharge capacity bePeriod electricity price when to be flat, and
Then energy storage discharge capacity isOn the contrary then discharge capacity isWhen for low-valley interval electricity price,Then energy storage discharge capacity isOn the contrary then discharge capacity is
And enter step S3-7；
(4) ifAndWhen, when for peak
Section electricity price, then energy storage discharge capacity bePeriod electricity price when to be flat, andThen energy storage discharge capacity isOn the contrary then discharge capacity isWhen cutting for low-valley interval electricity priceStorage
Can discharge capacity beOn the contrary then discharge capacity isAnd enter step S3-7；
Otherwise it is directly entered step S3-7；
S3-5, determined according to the discharge condition of super capacitor：
When distributed generation resource prediction power output is greater than 0 or load prediction deviation is less than 0, then S3-61 is entered step；
When distributed generation resource prediction power output is less than 0 or load prediction deviation is greater than 0, then S3-62 is entered step；
S3-61, determined according to super capacitor state-of-charge：
If state-of-charge is greater than minimum allowable state-of-charge, based on current electricity prices and state-of-charge computing system electric discharge electricity
Amount,
(1) ifOrAndWhen, whenWhen, then super capacitor discharge capacity isWhenWhen, then super capacitor discharge capacity isIt goes forward side by side
Enter step S3-7；
(2) ifOrAndWhen, whenWhen, then super capacitor discharge capacity isWhenWhen, then super capacitor discharge capacity isAnd enter step S3-7；
Otherwise it is directly entered step S3-7；
S3-62, determined according to super capacitor state-of-charge；
If state-of-charge is less than maximum allowable state-of-charge, based on current electricity prices and state-of-charge computing system electric discharge electricity
Amount,
(1) ifOrAndWhen,
WhenWhen, then super capacitor charge volume isWhenWhen, then super capacitor charge volume isAnd
Enter step S3-7；
(2) ifOrAndWhen,
WhenWhen, then super capacitor discharge capacity isWhenWhen, then super capacitor discharge capacity isAnd enter step S3-7；
Otherwise it is directly entered step S3-7；
S3-7, the coordination control strategy for determining current centralized mixed energy storage system current time, update the charged shape of energy storage
State.
Above-mentioned further scheme has the beneficial effect that：Based on user demand difference, with the operation Income Maximum of hybrid energy-storing
Change and formulates response policy.Clean energy resource is promoted using the bulk storage of battery to dissolve, and realizes the energy transfer of system；
System fluctuation problem caused by short-term time scale prediction deviation is alleviated using the capability of fast response of super capacitor, is based on institute
Propose the economy that strategy had not only been able to satisfy different time scales user demand but also can improve mixed energy storage system operation.
Detailed description of the invention
Fig. 1 is that the centralized hybrid energy-storing in embodiment provided by the invention based on Multiple Time Scales demand response coordinates control
Method implementation flow chart processed；
Fig. 2 is to determine that the coordination control strategy method of centralized mixed energy storage system is realized in embodiment provided by the invention
Flow chart；
Fig. 3 is that the responding scene of centralized mixed energy storage system in embodiment provided by the invention constitutes figure；
Fig. 4 is the composition figure of centralized hybrid energy-storing coordinated control system in embodiment provided by the invention.
Specific embodiment
A specific embodiment of the invention is described below, in order to facilitate understanding by those skilled in the art this hair
It is bright, it should be apparent that the present invention is not limited to the ranges of specific embodiment, for those skilled in the art,
As long as various change is in the spirit and scope of the present invention that the attached claims limit and determine, these variations are aobvious and easy
See, all are using the innovation and creation of present inventive concept in the column of protection.
As shown in Figure 1, the centralized hybrid energy-storing control method for coordinating based on Multiple Time Scales demand response, including it is following
Step：
S1, building user's polymorphic type demand electric characteristic models, including customer charge electric characteristic models and distributed electrical
Source response characteristic model；
In above-mentioned steps S1, customer charge is according to different type load by load power demand and use with electric characteristic models
The difference factor influence such as electric probability, user power utilization comfort level, electricity price is based on load prediction demand under long time scale and in short-term
Between scale when load deviation and load electricity consumption duration determine load use electric model；Customer charge electric characteristic models include not
Controllable burden electric characteristic models, controllable burden model and bootable load use electric characteristic models；
Load forecasting model is under above-mentioned long time scale：
In formula, f (x) is the regression function of load prediction,For Lagrange multiplier, b is biasing, K (x, xi) it is core
Function, and meet Mercer condition；
Kernel function expression formula is：
In formula, K (x, xi) it is kernel function, x is space sample, xiFor the center of space sample x, σ is kernel function ginseng
Number；
Above-mentioned uncontrollable load, that is, conventional rigid load type, generally scheduled control is not influenced with duration Electricity price fluctuation,
Mainly determined by load rigid demand itself；
Uncontrollable load is with electric characteristic models
In formula,For the use electrical characteristics of uncontrollable load,Respectively uncontrollable load is in t moment
Prediction power output, prediction deviation degree and electricity consumption probability, TULFor the electricity consumption duration of uncontrollable load；
Above-mentioned controllable burden is the load type of stringent corresponding scheduling control, in load peak or system emergency, failure shape
Under state, system can require to cut down or interrupt the type load according to safe operation；
Controllable burden is with electric characteristic models：
In formula,For the use electrical characteristics of controllable burden,Respectively controllable burden is in the pre- of t moment
Power, prediction deviation degree, electricity consumption probability and users'comfort requirement are measured,The compensation valence cut down for t moment controllable burden
Lattice, TILFor controllable burden electricity consumption duration；
Bootable load, that is, incomplete response scheduling control load, but to a certain extent may be used according to market guidance fluctuation
It adjusts, there is certain bootable property；
Bootable load is with electric characteristic models：
In formula,For the electricity consumption of the bootable load of t moment,Respectively bootable load is in t moment
Prediction power output, prediction deviation degree, electricity consumption probability,For the making up price that the bootable load of t moment is cut down, TGLIt is bootable
Load electricity consumption duration；
Above-mentioned distributed generation resource response characteristic model is constructed based on wind, light distributed power supply power producing characteristics, be according to
According to distributed generation resource cost of electricity-generating, sale of electricity electricity price, based on power output is inclined when power output prediction, short-term time scale under long time scale demand
The response model that difference and power generation duration determine；
Above-mentioned distributed generation resource response characteristic model is：
In formula,For the response characteristic of distributed generation resource,Respectively prediction of the distributed generation resource in t moment
Power output and prediction deviation degree,The respectively sale of electricity electricity price of the cost of electricity-generating and t moment of distributed generation resource, TDGTo divide
The power generation duration of cloth power supply.
Photovoltaic generator prediction power output model be：
In formula：f(PPV) it is that photovoltaic generator exports active probability function, Г is Gamma function, and α, β are respectively beta
The form parameter of distribution, PPVFor the output power of photovoltaic generator；For the peak power output of photovoltaic array；
Based on the probability function of photovoltaic generator active power output, then the desired value of photovoltaic power generation system output power is：
The wind speed vtProbability density function be：
In formula, f (vt) be mean wind speed probability density function, c, k be respectively Weibull distribution function scale ginseng
Number, form parameter, vtThe random quantity of wind speed is inputted for t moment；
Based on wind speed vtProbability density function, the relation function between the output power and wind speed of wind-driven generator is：
In formula, PwtFor the output power v of blowerc, vf, vsRespectively cut wind speed, cut-out wind speed and rated wind speed, Rwt
For the rated capacity of blower.
S2, construct Multiple Time Scales demand response model, including long time scale user demand response optimization model with
Short-term time scale user demand prediction deviation stabilizes model；
The demand response model of Multiple Time Scales in above-mentioned steps S2 be according to long time scale clean energy resource consumption with
Short-term time scale prediction deviation stabilizes model needs, building using economy as the demand response model of target, to promote to clean energy
Source consumption, optimize the system operation quality；
Long time scale for demand response optimization is guided based on tou power price, calculates uncontrollable load, controllable negative
The electric cost of lotus, bootable load and distributed generation resource, with the minimum target of user power utilization cost, to long-time in user
Polymorphic type demand optimizes under scale, to promote clean energy resource to dissolve；
Above-mentioned long time scale demand response Optimized model is：
In formula,For user's different demands response minimum cost,For the coordination assembly of i-th of synthetic user
This, CUL, CIL, CGL, CDGUser's uncontrollable load, interruptible load respectively in the period guide load and distributed generation resource of registering one's residence
Response cost, respectively：
In formula, ctFor t moment power grid electricity price, Δ t is long time scale response time interval, its time under long time scale
Between be divided into 1 hour,For the price elastic coefficient for guiding load；
It is to stabilize demand wave as far as possible based on super capacitor charging and discharging capabilities that the prediction deviation of short-term time scale, which stabilizes model,
It is dynamic, with the quality that optimizes the system operation；
Above-mentioned short-term time scale user demand prediction deviation stabilizes model and is：
In formula,Cost, T are stabilized for deviationadFor the period of short-term time scale, Δ PDG, Δ PUL, Δ PIL, Δ PGLRespectively
For DG, uncontrollable load, interruptible load and guidance load prediction deviation value,Respectively super capacitor is in t
The electric discharge at quarter, charging price,The respectively charging and discharging state of t moment super capacitor；For the shape that charges
State,For discharge condition,Then super capacitor is failure to actuate, Δ tadFor short time ruler
The response time interval of degree.
S3, according to the demand response model of polymorphic type demand electric characteristic models and Multiple Time Scales, determine that centralization is mixed
Close the coordination control strategy of energy-storage system.
Above-mentioned mixed energy storage system includes battery and super capacitor；
It realizes that mixed energy storage system coordinated control turns to target with mixed energy storage system Income Maximum, considers hybrid energy-storing system
State-of-charge of uniting constrains, energy storage charge-discharge electric power constrains and system power Constraints of Equilibrium, formulates the coordination of mixed energy storage system
Control strategy；
Hybrid energy-storing coordination strategy includes promoting clean energy resource consumption with the high-energy density of battery, optimizes long-time ruler
The user demand of degree responds；It is fluctuated with the user demand for quickly stabilizing short-term time scale with the high power density of super capacitor, it is excellent
Change system running quality.
The objective function of above-mentioned centralization hybrid energy-storing coordination control strategy is：
In formula, maxCESSFor the maximum return of mixed energy storage system, T is to coordinate total period, and Ty is the type of time scale,
N is synthetic user number, Δ tiFor the unit time under t moment the i-th class time scale, Respectively the i-th class of t moment when
Between under scale demand mixed energy storage system using electric power storage tank discharge, the price of charging,T moment is mixing storage respectively
The price that energy system is discharged using super capacitor, charged；PRespectively t moment is directed to j-th of the i-th class time scale
Electric power storage tank discharge in the demand utilization mixed energy storage system of user, charging electricity,Respectively t moment is directed to
The electricity of super capacitor electric discharge, charging in the demand utilization mixed energy storage system of i-th j-th of user of class time scale；
It is above-mentioned centralization mixed energy storage system state-of-charge be：
In formula,WithThe respectively state-of-charge of battery and super capacitor in t moment； Point
Not Wei battery charge and discharge efficiency；The respectively charge and discharge efficiency of super capacitor； When respectively t
Carve the charge and discharge power of super capacitor；The respectively charge and discharge power of t moment super capacitor；
The respectively charge and discharge state of t moment battery；For charged state,For discharge condition,Then battery is failure to actuate；Δ t is the duration；RES, RECThe respectively appearance of battery and super capacitor
Amount；
Centralized mixed energy storage system state-of-charge is constrained to：
In formula,WithThe respectively state-of-charge bound of battery,WithRespectively
For the state-of-charge bound of super capacitor；
The constraint of mixed energy storage system charge-discharge electric power：
In formula,Respectively battery minimum, maximum charge power,Respectively store
Battery minimum, maximum discharge power；Respectively super capacitor minimum, maximum charge power,Respectively super capacitor minimum, maximum discharge power；
System power Constraints of Equilibrium：
In formula,The respectively charging and discharging state of t moment battery,Respectively t moment is super
The charging and discharging state of capacitor,Respectively t moment electric power storage tank discharge, charging electricity, Respectively t
The electricity of moment grade capacitor electric discharge, charging,Respectively uncontrollable load, controllable burden, bootable negative
Lotus, DG t moment predicated response amount,Respectively uncontrollable load, controllable burden, can
Guide load, DG t moment prediction deviation amount.
As shown in Fig. 2, above-mentioned steps S3 is specially：
S3-1, input user demand data, are converted into corresponding data model；
The user demand data of input include different type load prediction data under Multiple Time Scales, distributed generation resource prediction
Force data and its prediction deviation amount out；
S3-2, mixed energy storage system is selected according to time scale：
If input demand data is long time scale prediction data, S3-3 is entered step；
If inputting the prediction deviation data that demand data is short-term time scale, S3-5 is entered step；
S3-3, judgement is entered according to the discharge condition of hybrid energy storage system：
When the distributed generation resource of input, which predicts force data, to be received and paid out completely, i.e. PDG>PUL+PIL+PGLWhen, then into
Enter step S3-41；
When user demand is overweight (when being more than preset threshold value), then S3-42 is entered step；
Above-mentioned steps S3-3 is based on battery in mixed energy storage system, progress demand optimization supplemented by super capacitor；
S3-41, determined according to energy storage charge state in mixed energy storage system：
If state-of-charge is less than maximum allowable state-of-charge, i.e.,Based on current electricity prices and lotus
Electricity condition computing system charge capacity,
(1) ifAndWhen, when for low ebb
Period electricity price, then energy storage charge volume bePeriod electricity price when to be flat, then energy storage charge volume beWhen for peak
Period electricity price, then energy storage charge volume beAnd enter step S3-7；
(2) ifAndWhen, when for low ebb
Period electricity price, then energy storage charge volume bePeriod electricity price when to be flat, andThen energy storage charge volume isOn the contrary then charge volume isWhen cutting for peak period electricity priceThen energy storage is filled
Electricity isOn the contrary then charge volume isAnd enter step S3-7；
(3) ifAndWhen, when for low-valley interval
Electricity price, then energy storage charge volume bePeriod electricity price when to be flat, and
Then energy storage charge volume isOn the contrary then charge volume isWhen for peak period electricity price, andThen energy storage charge volume isOn the contrary then charge volume isAnd enter step S3-7；
(4) ifAndWhen, when for low ebb
Period electricity price, then energy storage charge volume bePeriod electricity price when to be flat, andThen energy storage charge volume isOn the contrary then charge volume isWhen cutting for peak period electricity priceThen energy storage is charged
Amount isOn the contrary then charge volume isAnd enter step S3-7；
Otherwise it is directly entered step S3-7；
S3-42, determined according to energy storage charge state in mixed energy storage system：
If state-of-charge is greater than minimum allowable state-of-charge, i.e.,Based on current electricity prices with it is charged
State computation system discharge electricity,
(1) ifAndWhen, when for peak
Section electricity price, then energy storage discharge capacity bePeriod electricity price when to be flat, then energy storage discharge capacity beWhen for low-valley interval
Electricity price, then energy storage discharge capacity beAnd enter step S3-7；
(2) ifAndWhen, when for peak
Period electricity price, then energy storage discharge capacity bePeriod electricity price when to be flat, andThen energy storage discharge capacity isOn the contrary then energy storage discharge capacity isWhen for low-valley interval electricity price, andThen store up
Can discharge capacity beOn the contrary then energy storage discharge capacity isAnd enter step S3-7；
(3) ifAndWhen, when for peak period
Electricity price, then energy storage discharge capacity bePeriod electricity price when to be flat, andThen
Energy storage discharge capacity isOn the contrary then discharge capacity isWhen for low-valley interval electricity price,Then energy storage discharge capacity isOn the contrary then discharge capacity is
And enter step S3-7；
(4) ifAndWhen, when for peak
Period electricity price, then energy storage discharge capacity bePeriod electricity price when to be flat, andThen energy storage discharge capacity isOn the contrary then discharge capacity isWhen cutting for low-valley interval electricity priceThen
Energy storage discharge capacity isOn the contrary then discharge capacity isAnd enter step S3-7；
Otherwise it is directly entered step S3-7；
In above-mentioned steps S3-41 and S3-42, energy-storage system is according to current state-of-charge and charging electricity pricing charging strategy
To dissolve dump power.
S3-5, determined according to the discharge condition of super capacitor：
When distributed generation resource prediction power output is greater than 0 or load prediction deviation is less than 0, i.e. Δ PDG<0, Δ PUL+ΔPIL+Δ
PGL<0, then enter step S3-61；
When distributed generation resource prediction power output is less than 0 or load prediction deviation is greater than 0, i.e. Δ PDG>0, Δ PUL+ΔPIL+Δ
PGL<0, then enter step S3-62；
Above-mentioned steps S3-5 carries out fluctuation with super capacitor in hybrid system and stabilizes；
S3-61, determined according to super capacitor state-of-charge：
If state-of-charge is greater than minimum allowable state-of-charge, i.e.,Based on current electricity prices and lotus
Electricity condition computing system discharge electricity amount,
(1) ifOrAndWhen, whenWhen, then super capacitor discharge capacity isWhenWhen, then super capacitor discharge capacity isIt goes forward side by side
Enter step S3-7；
(2) ifOrAndWhen, whenWhen, then super capacitor discharge capacity isWhenWhen, then super capacitor discharge capacity isAnd enter step S3-7；
Otherwise it is directly entered step S3-7；
Super capacitor is surplus to eliminate according to current state-of-charge and charging electricity pricing charging strategy in above-mentioned steps S3-61
Remaining electric power；
S3-62, determined according to super capacitor state-of-charge；
If state-of-charge is less than maximum allowable state-of-charge, i.e.,Based on current electricity prices and lotus
Electricity condition computing system discharge electricity amount,
(1) ifOrAndWhen,
WhenWhen, then super capacitor charge volume isWhenWhen, then super capacitor charge volume isAnd
Enter step S3-7；
(2) ifOrAndWhen,
WhenWhen, then super capacitor discharge capacity isWhen
When, then super capacitor discharge capacity isAnd enter step S3-7；
Otherwise it is directly entered step S3-7；
Super capacitor is mentioned according to current state-of-charge with charging electricity pricing electric discharge strategy for system in above-mentioned steps S3-62
It is supported for energy.
S3-7, the coordination control strategy for determining current centralized mixed energy storage system current time, update the charged shape of energy storage
State.
In above-mentioned steps S3-7, also needs to carry out safety check, current time mixed energy storage system charge and discharge strategy is carried out
Safety is relatively tested, and is calculated including the feasibility to hybrid system and is calculated to system operational feasibility；To the feasibility of hybrid system
It calculates and refers to whether the discharge and recharge for calculating energy storage is full with the presence or absence of super-charge super-discharge risk and energy storage charge and discharge movement total degree
Foot maximum charge and discharge count constraint；Refer to whether active power is full in accounting current time system with the accounting of system operational feasibility
Foot is for balance.
In one embodiment of the invention, the responding scene for providing centralized mixed energy storage system of the invention is constituted,
As shown in figure 3, mainly including comprehensive user and centralized mixed energy storage system；Comprehensive user include conventional load with it is electronic
The user of the novel loads such as automobile or wind, light distributed power supply, conventional load mainly include uncontrollable load, controllable burden with
Bootable load；Centralized mixed energy storage system includes battery and super capacitor.Comprehensive user demand target mainly according to
It is realized according to the requirement forecasting under its own long time scale and the prediction deviation situation under short-term time scale based on current electricity prices
Self-demand cost minimization, while respond request is issued to centralized mixed energy storage system by information interaction passage；It concentrates
The response target of formula mixed energy storage system is based on based on the type of system hybrid energy-storing, capacity and its charge-discharge electric power
The requirement request of different time scales instructs, and with the charge and discharge maximum revenue of centralized mixed energy storage system, stabilizes the short time
Scale user demand fluctuation is the charge and discharge strategy of target making centralization hybrid energy-storing, and passes through information interaction passage to user
Return to the charging and discharging state of energy storage.
In one embodiment of the invention, the centralized hybrid energy-storing coordinated control system that the present invention is applied to is provided
The composition of system, as shown in figure 4, mainly including three levels：
One, user's polymorphic type Demand Forecast Model：It is rung for different type part throttle characteristics, different distributions formula power supply in user
Characteristic model building is answered to consider that the demand model of uncertain factor, workload demand response characteristic function contain polymorphic type load
Prediction, short-term time scale prediction deviation degree, load electricity consumption probability, users'comfort requirement, market guidance, compensation electricity price and use
Probabilistic influence factor such as electric duration；Distributed generation resource response characteristic function contains polymorphic type distributed generation resource and predicts
Power, cost of electricity-generating, receives probabilistic influence factor such as electric electricity price and power generation duration at short-term time scale prediction deviation；
Two, Multiple Time Scales demand response model：It is different that demand response based on different time scales requires building to consider
The response model of user response cost, system service requirement under time scale, the demand response objective function of long time scale
Target is minimized with user's economic cost, contains uncontrollable load, interruptible load, bootable load and user distribution formula
Power supply response cost, short-term time scale demand response objective function contain short time ruler using stabilizing system fluctuation as target
The lower consumption uncontrollable load of degree, interruptible load, guidance load and user distribution formula power supply prediction deviation amount cost；
Three, hybrid energy-storing harmonious economy controls：It formulates for the demand response of different scale polymorphic type load to mix storage
Energy system benefit maximum turns to the coordination control strategy of target, while considering mixed energy storage system state-of-charge, energy storage charge and discharge function
Rate and system power Constraints of Equilibrium, to realize the economical operation of centralized mixed energy storage system and user.
In one embodiment of the invention, the principle of work and power of each step in the present invention is provided：
In above-mentioned steps S1, according to different type load electrical characteristics, the load of synthetic user is divided into uncontrollable negative
Lotus, controllable burden and bootable load three categories, distributed generation resource are divided into two class of photovoltaic power generation and wind-power electricity generation；
On the basis of above-mentioned polymorphic type demand model, using vector machine regression combination model prediction load real-time requirement,
Introducing reaction for different type, it uses the Random Effect factor of electrical characteristics, thus reflect the use electrical characteristics of the type load, such as
When load be uncontrollable load when, electricity consumption is influenced by the market factor, thus only consider introduce the model prediction deviation effects factor,
Load electricity consumption probability and three kinds of uncertain factors of electricity consumption duration, when load is controllable burden, electricity consumption is by the unified tune of system
Degree management, therefore increment considers that user power utilization comfort level and system are cut down the uncertain factors such as making up price and influenced, and works as load
When for bootable load, electricity consumption has certain marketability, and the fluctuation of the market price has apparent influence on it；Above-mentioned point
Cloth power supply response model is the supply class demand of synthetic user, guarantees the income of the owner, hair while to promote its consumption
Electric cost and sale of electricity electricity price are then an important factor for influencing its response；The advantage of this polymorphic type demand model is sufficiently to highlight
Each type load and the response characteristic of distributed generation resource power generation, improve the accuracy of prognosis modelling, reflect user demand with
The fluctuation that uncertain factor influences.
In above-mentioned steps S2, the difference according to time scale constructs different demand response models；
The demand response model of long time scale is based on requirement forecasting amount a few days ago, with polymorphic type load and distributed generation resource
Response cost is minimised as target, promotes clean energy resource consumption.The decision of the demand response of long time scale is more based on user
The response cost of type load formulates user demand respond request, which is intended to based on requirement forecasting result under long time scale
It realizes user power utilization cost minimization, such as when electricity price is higher or clean energy resource undercapacity, suitably reduction controllable burden, works as electricity
When valence is lower or clean energy resource power output is superfluous, bootable load is influenced that its electricity consumption can be properly increased by electricity price, to improve cleaning
Energy utilization rate reduces user power utilization cost；
The prediction deviation response model of short-term time scale is then the short-term time scale prediction deviation amount based on user demand, with
Energy-storage system stabilizes requirement forecasting deviation cost minimization and turns to target, reduces the influence that demand fluctuation runs system.Short time
The decision of the demand response of scale is that the cost formulation user demand respond request of fluctuation is stabilized based on mixed energy storage system, this is asked
It asks and is intended to the short-term prediction departure based on user's polymorphic type load, system fluctuation is stabilized using super capacitor realization, with
Optimize the system operation quality, such as when distributed generation resource contributes prediction superfluous or load prediction deficiency, super capacitor charging with
Superfluous electricity in consumption system, when distributed generation resource contributes insufficient prediction or load prediction surplus, super capacitor electric discharge is
Superfluous demand response provides electricity support in system.
In above-mentioned steps S3, centralized mixed energy storage system formulates the sound of energy storage based on different time scales user demand
Answer strategy；
Be arranged long time scale requirement forecasting result be to be within 1 hour prediction data a few days ago that time interval carries out, it is short
The requirement forecasting deviation of time scale is to be within 10 minutes prediction deviation data before hour that time interval carries out.
When input request is the user demand of long time scale, centralized mixed energy storage system is super based on battery
Response mode supplemented by capacitor is formulated using mixed energy storage system performance driving economy as the coordinate responses strategy of target；
When input request is the user demand of short-term time scale, centralized mixed energy storage system responds hand with super capacitor
Duan Weizhu, to stabilize, user demand prediction deviation is the coordinate responses strategy of target making energy storage under short-term time scale, to realize
Optimization of the centralized hybrid energy-storing to user's Multiple Time Scales demand.
Assuming that accumulator capacity is 600kW in centralized mixed energy storage system, the capacity of 2 super capacitors is 300kW,
The state-of-charge of energy storage constrains the maximum allowable action frequency of battery and super capacitor in one day between 20% to 85%
Respectively 3 times and 20 times, tou power price is as shown in table 1：
1 tou power price of table
Coordinated control is carried out as shown in Fig. 2, responding using centralized energy-storage system provided by the invention to user demand；
Its control method for coordinating can be summarized as：Consider user in use the electrical characteristics of polymorphic type load and distributed generation resource with it is more
The influence of class uncertain factor simulated prediction power output of each user demand under long time scale with 1 hour for time interval,
Simultaneously in system operation, with 10 minutes for time interval, obtains user demand and predicted in 1 hour future with long-time
As a result departure carries out economy optimization for later use centralization mixed energy storage system and provides data basis；To meet length
The target for stabilizing demand fluctuation under time scale under the requirement of promotion clean energy resource consumption, short-term time scale, is based on user demand
The response model of premeasuring and demand disruption amount building Multiple Time Scales；Make full use of energy type in centralized mixed energy storage system
With the otherness of power-type energy storage response, energy storage response policy is formulated for the demand of user's different time scales, is given full play to
The energy storage of battery and the quick charge and discharge ability of super capacitor, while guaranteeing the response demand of user's Multiple Time Scales
Improve the economy of mixed energy storage system operation.
In one embodiment of the invention, the main process that the method for the present invention is realized includes building polymorphic type workload demand
With the prediction model, building Multiple Time Scales demand response model, the centralized hybrid energy-storing of proposition of distributed generation resource response characteristic
System coordination response policy.
The present invention considers the diversity of user side load and the otherness of distributed generation resource, structure when building system model
Build the power output model with electric model and distributed generation resource of polymorphic type load electrical characteristics；For polymorphic type demand in different time
Response requirement under scale fluctuates the target stabilized, structure under clean energy resource consumption, short-term time scale under long time scale to meet
Build the demand response model of Multiple Time Scales；Utilize the response energy of different type energy storage device in centralized mixed energy storage system
Power, the response policy based on time scale Yu the rational energy storage of user demand state, to promote user side clean energy resource to dissolve,
Reduce the economy that system fluctuation improves mixed energy storage system operation simultaneously.
In terms of coordinated control based on centralized mixed energy storage system, traditional load forecasting model is difficult to react inhomogeneity
The otherness of type load electricity consumption, can not highlight the controllability of flexible load, in addition, load and distributed generation resource by environmental change because
Element is affected, and prediction accuracy is low, causes running optimizatin result and actual deviation excessive, so that the economy of energy-storage system
It is difficult to be protected；Furthermore the energy storage of single type is difficult to meet the requirement of Multiple Time Scales, multiple types of users demand.Therefore,
The present invention fully considers the electricity consumption of different type load and distributed generation resource power producing characteristics in user, based on user under Multiple Time Scales
The different response targets of demand had both been promoted and had been cleaned under long time scale using the response policy of centralized mixed energy storage system
Energy consumption, and realize and the effective of demand fluctuation under short-term time scale is stabilized, while having ensured the fortune of mixed energy storage system
Row economy, concrete meaning are as follows：
Polymorphic type electric model：Load is predicted using support vector regression built-up pattern, is utilized respectively simultaneously
Weiull distribution and beta distribution simulation scene power output, uncertainty, demand controllable force, user power utilization based on user demand are relaxed
The factors such as appropriateness requirement, electricity price, construct the electricity consumption response model of different type demand, realize to polymorphic type load electricity consumption in user
Comprehensive description of characteristic.
The demand response of Multiple Time Scales：For user's polymorphic type demand demand fluctuation caused by by such environmental effects with
And in user grid-connected clean energy resource consumption problem, the system service requirement under different time scales, construct long-time ruler
Demand response model under degree to promote clean energy resource to dissolve for target, to stabilize demand fluctuation as target under short-term time scale
Demand adjusts model, meets system operation multiple requirements, improves system economy.
The selection of centralized mixed energy storage system response policy：Diversity based on mixed energy storage system, long time scale
Under using clean energy resource consumption as main target, selection based on battery, energy in time big is realized supplemented by super capacitor
Amount transfer；To stabilize demand fluctuation as main target under short-term time scale, select with super capacitor as main response mode, full
The economy of centralized mixed energy storage system is improved while sufficient different time scales demand.
Beneficial effects of the present invention are：Present invention building optimizes control by the centralized mixed energy storage system of target of economy
Simulation；Consider using the consumption of centralized mixed energy storage system, stabilize distributed generation resource power output and polymorphic type under Multiple Time Scales
Load electrical characteristics and uncertainty influence, and this method is constructed based on uncertain polymorphic type load electric characteristic models
With distributed generation resource power output model, the demand response description to polymorphic type load and distributed generation resource is realized；For for a long time
The operation demand difference of scale and short-term time scale constructs demand optimization and the deviation response model of different time scales, proposes
Mixed energy storage system coordination control strategy based on Multiple Time Scales promotees while guaranteeing mixed energy storage system performance driving economy
Into clean energy resource consumption, demand fluctuation is stabilized.It is charge-discharge characteristic of the mentioned method based on battery and super capacitor, charged
The characteristics of state and user's polymorphic type demand, formulates the charge and discharge strategy of centralized mixed energy storage system, and clean energy resource is promoted to disappear
It receives, the quality that optimizes the system operation while the performance driving economy for improving centralized mixed energy storage system.
Claims (7)
1. the centralized hybrid energy-storing control method for coordinating based on Multiple Time Scales demand response, which is characterized in that including following
Step：
S1, building user's polymorphic type demand electric characteristic models, including customer charge electric characteristic models and distributed generation resource are rung
Answer characteristic model；
S2, construct Multiple Time Scales demand response model, including long time scale user demand response optimization model in short-term
Between scale user demand prediction deviation stabilize model；
S3, according to the demand response model of polymorphic type demand electric characteristic models and Multiple Time Scales, determine centralization mixing storage
It can system coordination control strategy.
2. the centralized hybrid energy-storing control method for coordinating according to claim 1 based on Multiple Time Scales demand response,
It is characterized in that, in the step S1：
The customer charge electric characteristic models include uncontrollable load electric characteristic models, controllable burden model and may be guided negative
Lotus uses electric characteristic models；
The uncontrollable load is with electric characteristic models：
In formula,For the electricity consumption of t moment uncontrollable load,Respectively uncontrollable load is in the pre- of t moment
Measure power, prediction deviation degree and electricity consumption probability, TULFor the electricity consumption duration of uncontrollable load；
The controllable burden is with electric characteristic models：
In formula,For the electricity consumption of t moment controllable burden,Respectively controllable burden is in the pre- of t moment
Power, prediction deviation degree, electricity consumption probability and users'comfort requirement are measured,For t moment controllable burden cut down making up price,
TILFor controllable burden electricity consumption duration；
The bootable load is with electric characteristic models：
In formula,For the electricity consumption of the bootable load of t moment,Respectively bootable load is in the pre- of t moment
Power, prediction deviation degree, electricity consumption probability are measured,For the making up price that the bootable load of t moment is cut down, TGLFor bootable load
Electricity consumption duration；
The distributed generation resource response characteristic model is：
In formula,For the response characteristic of distributed generation resource,Respectively distributed generation resource is contributed in the prediction of t moment
With prediction deviation degree, cDG,The respectively sale of electricity electricity price of the cost of electricity-generating and t moment of distributed generation resource, TDGFor distributed electrical
The power generation duration in source.
3. the centralized hybrid energy-storing control method for coordinating according to claim 2 based on more time demand responses, special
Sign is,
The customer charge electric characteristic models are by load prediction, short-term time scale load deviation under long time scale and to bear
What lotus electricity consumption duration determined load uses electric model；
Load forecasting model is under the long time scale：
In formula, f (x) is the regression function of load prediction, μi,For Lagrange multiplier, b is biasing, K (x, xi) it is kernel function,
And meet Mercer condition；
The kernel function expression formula is：
In formula, K (x, xi) it is kernel function, x is space sample, xiFor the center of space sample x, σ is kernel functional parameter；
The distributed generation resource response characteristic model is the characteristic model constructed based on wind, light distribution formula power supply power producing characteristics；
The photovoltaic generator prediction power output model be：
In formula：f(PPV) it is that photovoltaic generator exports active probability function, Г is Gamma function, and α, β are respectively beta distribution
Form parameter, PPVFor the output power of photovoltaic generator；For the peak power output of photovoltaic array；
Based on the probability function of photovoltaic generator active power output, then the desired value of the photovoltaic power generation system output power is：
The wind speed vtProbability density function be：
In formula, f (vt) be mean wind speed probability density function, c, k are respectively the scale parameter of Weibull distribution function, shape
Parameter, vtThe random quantity of wind speed is inputted for t moment；
Based on wind speed vtProbability density function, the relation function between the output power and wind speed of the wind-driven generator is：
In formula, PwtFor the output power v of blowerc, vf, vsRespectively cut wind speed, cut-out wind speed and rated wind speed, RwtFor wind
The rated capacity of machine.
4. the centralized hybrid energy-storing coordinated control side according to claim 2 based on Multiple Time Scales demand response model
Method, which is characterized in that in the step S2：
The long time scale user demand response model is：
In formula,For user's different demands response minimum cost,For the coordination totle drilling cost of i-th of synthetic user,
CUL, CIL, CGL, CDGUser's uncontrollable load, interruptible load respectively in the period guide load and distributed generation resource of registering one's residence is rung
Cost is answered, respectively：
In formula, T is to coordinate total period, ctFor t moment power grid electricity price, Δ t is long time scale response time interval, long-time ruler
Spending its lower time interval is 1 hour,For the price elastic coefficient for guiding load；
The short-term time scale user demand prediction deviation stabilizes model and is：
In formula,Cost, T are stabilized for deviationadFor the period of short-term time scale, Δ PDG, Δ PUL, Δ PIL, Δ PGLRespectively DG,
The prediction deviation value of uncontrollable load, interruptible load and guidance load,Respectively super capacitor putting in t moment
Electricity, charging price,The respectively charging and discharging state of t moment super capacitor；For charged state,For discharge condition,Then super capacitor is failure to actuate, Δ tadFor short-term time scale
Response time interval.
5. the centralized hybrid energy-storing control method for coordinating according to claim 1 based on multiple dimensioned demand response, special
Sign is that the mixed energy storage system includes battery and super capacitor.
6. the centralized hybrid energy-storing control method for coordinating according to claim 5 based on multiple dimensioned demand response, special
Sign is that the coordination control strategy of the centralization mixed energy storage system turns to target with mixed energy storage system Income Maximum, institute
Stating objective function is：
In formula, maxCESSFor the maximum return of mixed energy storage system, T is to coordinate total period, and Ty is the type of time scale, and N is
Synthetic user number, Δ tiFor the unit time under t moment the i-th class time scale, Respectively the i-th class of t moment time ruler
Mixed energy storage system utilizes the price of electric power storage tank discharge, charging under degree demand,T moment is mixed energy storage system respectively
Utilize the price of super capacitor electric discharge, charging； Respectively t moment is for i-th j-th of user's of class time scale
Electric power storage tank discharge in demand utilization mixed energy storage system, charging electricity,When respectively t moment is directed to the i-th class
Between j-th of user of scale demand utilization mixed energy storage system in super capacitor electric discharge, charging electricity；
Influencing the mixed energy storage system maximum revenue factor includes the constraint of mixed energy storage system state-of-charge, hybrid energy-storing system
Charge-discharge electric power of uniting constrains and system power Constraints of Equilibrium；
It is described centralization mixed energy storage system state-of-charge be：
In formula,WithThe respectively state-of-charge of battery and super capacitor in t moment； Respectively
The charge and discharge efficiency of battery；The respectively charge and discharge efficiency of super capacitor； Respectively t moment
The charge and discharge power of super capacitor；The respectively charge and discharge power of t moment super capacitor；Point
Not Wei t moment battery charge and discharge state；For charged state,For discharge condition,Then battery is failure to actuate；Δ t is the duration；RES, RECThe respectively appearance of battery and super capacitor
Amount；
The centralization mixed energy storage system state-of-charge is constrained to：
In formula,WithThe respectively state-of-charge bound of battery,WithIt is respectively super
The state-of-charge bound of grade capacitor；
The mixed energy storage system charge-discharge electric power constraint：
In formula,Respectively battery minimum, maximum charge power,Respectively battery
Minimum, maximum discharge power；Respectively super capacitor minimum, maximum charge power,
Respectively super capacitor minimum, maximum discharge power；
The system power Constraints of Equilibrium：
In formula,The respectively charging and discharging state of t moment battery,Respectively t moment super capacitor
Charging and discharging state,Respectively t moment electric power storage tank discharge, charging electricity, Respectively t moment grade
The electricity of capacitor electric discharge, charging,Respectively uncontrollable load, controllable burden, bootable load, DG exist
The predicated response amount of t moment,Respectively uncontrollable load, controllable burden, bootable negative
Lotus, DG t moment prediction deviation amount.
7. the centralized hybrid energy-storing control method for coordinating according to claim 6 based on Multiple Time Scales demand response,
It is characterized in that, the step S3 is specially：
S3-1, input user demand data, are converted into corresponding data model；
The user demand data of input include different type load prediction data under Multiple Time Scales, distributed generation resource prediction power output
Data and its prediction deviation amount；
S3-2, mixed energy storage system is selected according to time scale：
If input demand data is long time scale prediction data, S3-3 is entered step；
If inputting the prediction deviation data that demand data is short-term time scale, S3-5 is entered step；
S3-3, judgement is entered according to the discharge condition of hybrid energy storage system：
When the distributed generation resource of input, which predicts force data, to be received and paid out completely, then S3-41 is entered step；
When user demand is more than the threshold value of setting, then S3-42 is entered step；
S3-41, determined according to energy storage charge state in mixed energy storage system：
If state-of-charge is less than maximum allowable state-of-charge, it is based on current electricity prices and state-of-charge computing system charge capacity,
(1) ifAndWhen, when for low-valley interval
Electricity price, then energy storage charge volume bePeriod electricity price when to be flat, then energy storage charge volume beWhen for peak period
Electricity price, then energy storage charge volume beAnd enter step S3-7；
(2) ifAndWhen, when for low-valley interval electricity
Valence, then energy storage charge volume bePeriod electricity price when to be flat, and
Then energy storage charge volume isOn the contrary then charge volume isWhen cutting for peak period electricity priceThen energy storage charge volume isOn the contrary then charge volume is
And enter step S3-7；
(3) ifAndWhen, when for low-valley interval electricity
Valence, then energy storage charge volume bePeriod electricity price when to be flat, and
Then energy storage charge volume isOn the contrary then charge volume isWhen for peak period electricity price, andThen energy storage charge volume isOn the contrary then charge volume is
And enter step S3-7；
(4) ifAndWhen, when for low-valley interval electricity
Valence, then energy storage charge volume bePeriod electricity price when to be flat, and
Then energy storage charge volume isOn the contrary then charge volume isWhen cutting for peak period electricity priceThen energy storage charge volume isOn the contrary then charge volume isAnd enter step S3-7；
Otherwise it is directly entered step S3-7；
S3-42, determined according to energy storage charge state in mixed energy storage system：
If state-of-charge is greater than minimum allowable state-of-charge, it is based on current electricity prices and state-of-charge computing system discharge electricity amount,
(1) ifAndWhen, when for peak period electricity
Valence, then energy storage discharge capacity bePeriod electricity price when to be flat, then energy storage discharge capacity beWhen for low-valley interval electricity
Valence, then energy storage discharge capacity beAnd enter step S3-7；
(2) ifAndWhen, when for peak period electricity
Valence, then energy storage discharge capacity bePeriod electricity price when to be flat, andThen energy storage discharge capacity isOn the contrary then energy storage discharge capacity isWhen for low-valley interval electricity price, andThen store up
Can discharge capacity beOn the contrary then energy storage discharge capacity isAnd enter step S3-7；
(3) ifAndWhen, when for peak period electricity
Valence, then energy storage discharge capacity bePeriod electricity price when to be flat, and
Then energy storage discharge capacity isOn the contrary then discharge capacity isWhen for low-valley interval electricity price,Then energy storage discharge capacity isOn the contrary then discharge capacity is
And enter step S3-7；
(4) ifAndWhen, when for peak period electricity
Valence, then energy storage discharge capacity bePeriod electricity price when to be flat, andThen energy storage discharge capacity isOn the contrary then discharge capacity isWhen cutting for low-valley interval electricity priceThen store up
Can discharge capacity beOn the contrary then discharge capacity isAnd enter step S3-7；
Otherwise it is directly entered step S3-7；
S3-5, determined according to the discharge condition of super capacitor：
When distributed generation resource prediction power output is greater than 0 or load prediction deviation is less than 0, then S3-61 is entered step；
When distributed generation resource prediction power output is less than 0 or load prediction deviation is greater than 0, then S3-62 is entered step；
S3-61, determined according to super capacitor state-of-charge：
If state-of-charge is greater than minimum allowable state-of-charge, it is based on current electricity prices and state-of-charge computing system discharge electricity amount,
(1) ifOrAndWhen, whenWhen, then super capacitor discharge capacity isWhenWhen, then super capacitor discharge capacity isIt goes forward side by side
Enter step S3-7；
(2) ifOrAndWhen, whenWhen, then super capacitor discharge capacity isWhenWhen, then super capacitor discharge capacity isAnd enter step S3-7；
Otherwise it is directly entered step S3-7；
S3-62, determined according to super capacitor state-of-charge；
If state-of-charge is less than maximum allowable state-of-charge, it is based on current electricity prices and state-of-charge computing system discharge electricity amount,
(1) ifOrAndWhen, whenWhen, then super capacitor charge volume isWhenWhen, then super capacitor charge volume isIt goes forward side by side
Enter step S3-7；
(2) ifOrAndWhen, whenWhen, then super capacitor discharge capacity isWhenWhen, then super capacitor discharge capacity isAnd enter step S3-7；
Otherwise it is directly entered step S3-7；
S3-7, the coordination control strategy for determining current centralized mixed energy storage system current time, update energy storage charge state.
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