CN108879746B - Centralized hybrid energy storage coordination control method based on multi-time scale demand response - Google Patents

Centralized hybrid energy storage coordination control method based on multi-time scale demand response Download PDF

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CN108879746B
CN108879746B CN201810682959.6A CN201810682959A CN108879746B CN 108879746 B CN108879746 B CN 108879746B CN 201810682959 A CN201810682959 A CN 201810682959A CN 108879746 B CN108879746 B CN 108879746B
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energy storage
load
charge
charging
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CN108879746A (en
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吕宏水
刘友波
杨冬梅
陈永华
何国鑫
廖秋萍
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Sichuan University
Nari Technology Co Ltd
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Nari Technology Co Ltd
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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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H02J3/382—
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

Abstract

The invention discloses a centralized hybrid energy storage coordination control method based on multi-time scale demand response, which comprises the following steps: s1, constructing a multi-type demand power consumption characteristic model of the user; s2, constructing a multi-time scale demand response model; s3, determining a coordination control strategy of the centralized hybrid energy storage system according to the multi-type demand power utilization characteristic model and the multi-time scale demand response model; the method provided by the invention is used for formulating the charging and discharging strategy of the centralized hybrid energy storage system based on the charging and discharging characteristics and the state of charge of the storage battery and the super capacitor and the characteristics of various requirements of users, so that the consumption of clean energy is promoted, the operation quality of the system is optimized and the operation economy of the centralized hybrid energy storage system is improved.

Description

Centralized hybrid energy storage coordination control method based on multi-time scale demand response
Technical Field
The invention belongs to the technical field of centralized hybrid energy storage coordination control, and particularly relates to a centralized hybrid energy storage coordination control method based on multi-time scale requirements.
Background
At present, clean energy is mostly merged into a user side in a small-capacity and distributed mode, randomness and fluctuation of output of various user power loads and distributed power supplies become great challenges of safe and economic operation of a system, and advantages of energy storage on optimization of operation of a power distribution network are mostly discussed on a single time scale from the aspects of load peak shifting, clean energy consumption and the like in the aspect of system adjustment and optimization by utilizing energy storage; however, the optimal operation of the hybrid energy storage system is connected to the grid at the user side with smaller capacity of clean energy, and the types of the electric loads are increased, so that the following problems are necessarily caused;
firstly, the rapid increase of controllable load and clean energy permeability at a user side causes large system operation fluctuation, and the single long-time scale demand prediction is difficult to reflect the current system operation situation;
focusing on user demand responses of different time scales, the corresponding demands of the users have obvious difference with system operation requirements, and optimizing the system operation quality while guaranteeing the economic efficiency of the users is a key contradiction of coordinated operation of the power grid;
and thirdly, the diversity of the energy storage device, and the energy type energy storage and the power type energy storage have advantages respectively.
Therefore, a scholars proposes that a hybrid energy storage system is used for solving the problem of system operation optimization, surplus clean energy is absorbed by using high energy density of energy type energy storage for power generation, system fluctuation is stabilized by using the rapid charging and discharging capacity of high-power energy storage, but the influence of short-term load prediction deviation on the operation control of a power distribution network is often ignored in the existing research, and the research on the influence of the regulation capacity of the power distribution network fluctuation and the system operation economy under a short time scale on the characteristics of large capacity of a storage battery, low response speed and rapid discharge of a super capacitor in the hybrid energy storage system is still delayed.
Disclosure of Invention
Aiming at the defects in the prior art, the centralized hybrid energy storage coordination control method based on the multi-time scale requirement solves the existing problems.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: a centralized hybrid energy storage coordination control method based on multi-time scale demand response comprises the following steps:
s1, constructing a multi-type demand power consumption characteristic model of the user, wherein the multi-type demand power consumption characteristic model comprises a user load power consumption characteristic model and a distributed power supply response characteristic model;
s2, constructing a multi-time scale demand response model, including a long-time scale user demand response optimization model and a short-time scale user demand prediction deviation stabilizing model;
and S3, determining a coordination control strategy of the centralized hybrid energy storage system according to the multi-type demand power utilization characteristic model and the multi-time scale demand response model.
The invention has the beneficial effects that: the centralized hybrid energy storage coordination control method based on multi-time scale demand response realizes the optimization coordination of multiple types of loads under multiple time scales by using the energy type and power type hybrid energy storage devices, fully utilizes the high energy density of the storage battery to promote the consumption of clean energy, realizes the peak clipping and valley filling of the loads, and fully utilizes the high power density of the super capacitor to realize the rapid stabilization of the fluctuation of the loads and the clean energy; in addition, the centralized hybrid energy storage coordination control method based on multi-time scale demand response, provided by the invention, is based on a multi-time scale user demand response target, considers the problems of fluctuation of clean energy and load in a long time scale and prediction deviation of a short time scale, and has high practicability.
Further, in the step S1:
the user load electricity utilization characteristic model comprises an uncontrollable load electricity utilization characteristic model, a controllable load model and a guidable load electricity utilization characteristic model;
the model of the electrical characteristics of the uncontrollable load is as follows:
Figure BDA0001711101360000021
in the formula (I), the compound is shown in the specification,
Figure BDA0001711101360000031
in order to control the electrical characteristics of the load,
Figure BDA0001711101360000032
prediction of the uncontrolled load at time tForce, degree of predicted deviation, and power consumption probability, TULThe power consumption duration of the uncontrollable load;
the controllable load electrical characteristic model is as follows:
Figure BDA0001711101360000033
in the formula (I), the compound is shown in the specification,
Figure BDA0001711101360000034
in order to control the electrical characteristics of the load,
Figure BDA0001711101360000035
respectively the predicted output, the predicted deviation degree, the power utilization probability and the user comfort requirement of the controllable load at the time t,
Figure BDA0001711101360000036
compensating prices for controllable load shedding at time T, TILThe power consumption time for the controllable load is prolonged;
the electric characteristic model capable of guiding the load is as follows:
Figure BDA0001711101360000037
in the formula (I), the compound is shown in the specification,
Figure BDA0001711101360000038
the power usage of the load may be directed for time t,
Figure BDA0001711101360000039
respectively a predicted output, a predicted deviation degree and a power utilization probability of a guidable load at the time t,
Figure BDA00017111013600000310
compensating prices for load shedding guidable at time T, TGLThe power utilization duration of the load can be guided;
the distributed power supply response characteristic model is as follows:
Figure BDA00017111013600000311
in the formula (I), the compound is shown in the specification,
Figure BDA00017111013600000312
for the response characteristics of the distributed power supply,
Figure BDA00017111013600000313
respectively the predicted output and the predicted deviation degree of the distributed power supply at the time t,
Figure BDA00017111013600000314
the power generation cost of the distributed power supply and the electricity selling price at the moment T, TDGIs the time period of power generation of the distributed power supply.
Further, it is characterized in that,
the user load electricity utilization characteristic model is an electricity utilization model of a load determined by load prediction under a long time scale, load deviation under a short time scale and load electricity utilization duration;
the load prediction model under the long-time scale is as follows:
Figure BDA00017111013600000315
wherein f (x) is a regression function of the load prediction,
Figure BDA00017111013600000316
is Lagrange multiplier, b is bias, K (x, x)i) Is a kernel function and meets the Mercer condition;
the kernel function expression is:
Figure BDA0001711101360000041
in the formula, K (x, x)i) As kernel function, x is a spatial sample，xiThe central position of the space sample x is shown, and sigma is a kernel function parameter;
the distributed power supply response characteristic model is a characteristic model constructed based on the output characteristics of wind and light distributed power supplies;
the predicted output model of the photovoltaic generator is as follows:
Figure BDA0001711101360000042
in the formula: f (P)PV) Outputting a probability function of power for the photovoltaic generator, wherein Gamma is a Gamma function, alpha and beta are shape parameters of beta distribution respectively, and P isPVIs the output power of the photovoltaic generator;
Figure BDA0001711101360000043
is the maximum output power of the photovoltaic array;
based on the probability function of the active power output of the photovoltaic generator, the expected value of the output power of the photovoltaic power generation system is as follows:
Figure BDA0001711101360000044
the wind speed vtThe probability density function of (a) is:
Figure BDA0001711101360000045
wherein f (v)t) Is the probability density function of the average wind speed, c, k are the scale parameter and the shape parameter of the Weibull distribution function respectively, vtInputting a random quantity of the wind speed at the time t;
based on wind speed vtThe relation function between the output power of the wind driven generator and the wind speed is as follows:
Figure BDA0001711101360000051
in the formula, PwtIs the output power v of the fanc，vf，vsCut-in wind speed, cut-out wind speed and rated wind speed, RwtThe rated capacity of the fan.
The beneficial effects of the above further scheme are: the simulation of different types of load power utilization characteristics and clean energy response characteristics of the user is realized, the influences of the power utilization comfort level, the power price, the prediction deviation and the like of the user on the power utilization characteristics of the user are fully considered, and the user demand response scene is refined.
Further, in the step S2:
the long-time scale user demand response model is as follows:
Figure BDA0001711101360000052
in the formula (I), the compound is shown in the specification,
Figure BDA0001711101360000053
for the minimum cost of the different demand responses of the users,
Figure BDA0001711101360000054
coordinated total cost for the ith integrated user, CUL，CIL，CGL，CDGRespectively are the uncontrollable load of a user, the interruptible load, the guidance load and the response cost of the household distributed power supply in the period, and respectively are as follows:
Figure BDA0001711101360000055
in the formula, ctThe time is the power grid electricity price at the time t, delta t is the long time scale response time interval, the time interval under the long time scale is 1 hour,
Figure BDA0001711101360000056
a price elastic coefficient for guiding a load;
the short-time scale user demand prediction deviation stabilizing model is as follows:
Figure BDA0001711101360000057
in the formula (I), the compound is shown in the specification,
Figure BDA0001711101360000058
to smooth out the cost of the deviation, TadFor periods of short timescales, Δ PDG，ΔPUL，ΔPIL，ΔPGLRespectively, the predicted deviation values of DG (distributed generator), uncontrollable load, interruptible load and directed load,
Figure BDA0001711101360000061
the discharge price and the charge price of the super capacitor at the moment t are respectively,
Figure BDA0001711101360000062
respectively representing the charging and discharging states of the super capacitor at the moment t;
Figure BDA0001711101360000063
in order to be in a charging state,
Figure BDA0001711101360000064
in the state of being discharged, the discharge electrode is,
Figure BDA0001711101360000065
Figure BDA0001711101360000066
the super capacitor does not act, Δ tadA response time interval of a short time scale.
The beneficial effects of the above further scheme are: based on the demand difference that the system promotes the consumption of clean energy under a long time scale and the output fluctuation deviation stabilization of the load and the distributed power supply under a short time scale, demand response and deviation stabilization models with different time scales are constructed, the demand of the system under different time scales is solved in a targeted manner, the consumption of the clean energy in each time period can be effectively promoted, the influence of the output fluctuation of the load and the distributed power supply on operation can be reduced, and the economical efficiency of the operation of the system is improved to the maximum extent.
Further, the hybrid energy storage system comprises a storage battery and a super capacitor.
Further, the coordination control strategy of the centralized hybrid energy storage system aims at maximizing the profit of the hybrid energy storage system, and the objective function is as follows:
Figure BDA0001711101360000067
in the formula, maxCESSFor the maximum gain of the hybrid energy storage system, T is the total coordination period, Ty is the type of the time scale, N is the number of the comprehensive users, and delta TiIs the unit time length under the ith class time scale at the moment t,
Figure BDA0001711101360000068
Figure BDA0001711101360000069
the prices of discharging and charging of the storage battery are respectively used by the hybrid energy storage system under the requirement of the ith class of time scale at the time t,
Figure BDA00017111013600000610
the time t is the discharge and charge price of the hybrid energy storage system by using the super capacitor;
Figure BDA00017111013600000611
Figure BDA00017111013600000612
the electric quantity discharged and charged by the storage battery in the hybrid energy storage system is utilized according to the requirements of the jth user of the ith time scale at the moment t respectively,
Figure BDA00017111013600000613
demand utilization mix for jth user of ith class time scale at time tThe discharge and charge electric quantity of a super capacitor in the energy storage system;
factors influencing the profit maximization of the hybrid energy storage system comprise hybrid energy storage system state-of-charge constraint, hybrid energy storage system charge-discharge power constraint and system power balance constraint;
the state of charge of the centralized hybrid energy storage system is as follows:
Figure BDA0001711101360000071
in the formula (I), the compound is shown in the specification,
Figure BDA0001711101360000072
and
Figure BDA0001711101360000073
respectively the charge states of the storage battery and the super capacitor at the moment t;
Figure BDA0001711101360000074
Figure BDA0001711101360000075
respectively the charging efficiency and the discharging efficiency of the storage battery;
Figure BDA0001711101360000076
respectively the charging and discharging efficiency of the super capacitor;
Figure BDA0001711101360000077
Figure BDA0001711101360000078
respectively the charging and discharging power of the super capacitor at the time t;
Figure BDA0001711101360000079
respectively the charging and discharging power of the super capacitor at the time t;
Figure BDA00017111013600000710
storage battery respectively at time tThe charging and discharging states of the cell;
Figure BDA00017111013600000711
in order to be in a charging state,
Figure BDA00017111013600000712
in the state of being discharged, the discharge electrode is,
Figure BDA00017111013600000713
the battery does not operate; Δ t is the duration; rES，RECThe capacities of the storage battery and the super capacitor are respectively;
the state of charge constraint of the centralized hybrid energy storage system is as follows:
Figure BDA00017111013600000714
in the formula (I), the compound is shown in the specification,
Figure BDA00017111013600000715
and
Figure BDA00017111013600000716
respectively the upper and lower limits of the state of charge of the storage battery,
Figure BDA00017111013600000717
and
Figure BDA00017111013600000718
respectively representing the upper limit and the lower limit of the charge state of the super capacitor;
and the charge-discharge power constraint of the hybrid energy storage system is as follows:
Figure BDA00017111013600000719
in the formula (I), the compound is shown in the specification,
Figure BDA00017111013600000720
respectively the minimum charging power and the maximum charging power of the storage battery,
Figure BDA00017111013600000721
respectively the minimum and maximum discharge power of the storage battery;
Figure BDA00017111013600000722
respectively the minimum charging power and the maximum charging power of the super capacitor,
Figure BDA00017111013600000723
respectively the minimum and maximum discharge power of the super capacitor;
the system power balance constraint is as follows:
Figure BDA00017111013600000724
in the formula (I), the compound is shown in the specification,
Figure BDA00017111013600000725
the charge and discharge states of the storage battery at the time t are respectively,
Figure BDA00017111013600000726
respectively the charge and discharge states of the super capacitor at the time t,
Figure BDA00017111013600000727
the electric quantity discharged and charged by the storage battery at the time t respectively,
Figure BDA00017111013600000728
Figure BDA0001711101360000081
respectively the electric quantity discharged and charged by the stage capacitor at the time t,
Figure BDA0001711101360000082
respectively, the uncontrollable load, the controllable load, the guidable load and the predicted response quantity of the DG at the time t,
Figure BDA0001711101360000083
respectively, the uncontrollable load, the controllable load, the guidable load and the predicted deviation amount of the DG at the time t.
The beneficial effects of the above further scheme are: based on the response requirements of different time scales, the difference characteristics of energy type and power type energy storage in the centralized hybrid energy storage system are fully utilized, and the hybrid energy storage system can fully meet the requirements of users on different time scales and maximize the economic benefit of self operation.
Further, the step S3 is specifically:
s3-1, inputting user demand data and converting the user demand data into a corresponding data model;
the input user demand data comprises load prediction data of different types under multiple time scales, distributed power supply prediction output data and prediction deviation amount thereof;
s3-2, selecting the hybrid energy storage system according to the time scale:
if the input demand data is long-time scale prediction data, the step S3-3 is carried out;
if the input demand data is predicted deviation data of a short time scale, the step S3-5 is carried out;
s3-3, judging according to the discharge state of the hybrid energy storage system:
when the input distributed power supply predicted output data cannot be completely paid out, the step S3-41 is carried out;
when the user demand exceeds the set threshold (empirically set), go to step S3-42;
s3-41, judging according to the energy storage charge state in the hybrid energy storage system:
if the state of charge is less than the maximum allowable state of charge, calculating the charging capacity of the system based on the current price of electricity and the state of charge,
(1) if it is
Figure BDA0001711101360000084
And is
Figure BDA0001711101360000085
When it is in the valleyThe section price of electricity, the stored energy charge amount is
Figure BDA0001711101360000086
When the electricity price is in the usual time period, the energy storage charging quantity is
Figure BDA0001711101360000087
When the electricity price is in the peak time, the energy storage charging amount is
Figure BDA0001711101360000088
And proceeds to step S3-7;
(2) if it is
Figure BDA0001711101360000091
And is
Figure BDA0001711101360000092
When the electricity price is in the valley period, the energy storage charging amount is
Figure BDA0001711101360000093
When the time is the usual time, and
Figure BDA0001711101360000094
the charge amount of the stored energy is
Figure BDA0001711101360000095
Otherwise, the charging quantity is
Figure BDA0001711101360000096
When the peak time is the electricity price, cutting
Figure BDA0001711101360000097
The charge amount of the stored energy is
Figure BDA0001711101360000098
Otherwise, the charging quantity is
Figure BDA0001711101360000099
And proceeds to step S3-7;
(3) if it is
Figure BDA00017111013600000910
And is
Figure BDA00017111013600000911
When the electricity price is in the valley period, the energy storage charging amount is
Figure BDA00017111013600000912
When the time is the usual time, and
Figure BDA00017111013600000913
the charge amount of the stored energy is
Figure BDA00017111013600000914
Otherwise, the charging quantity is
Figure BDA00017111013600000915
When the peak time is the electricity price, and
Figure BDA00017111013600000916
the charge amount of the stored energy is
Figure BDA00017111013600000917
Otherwise, the charging quantity is
Figure BDA00017111013600000918
And proceeds to step S3-7;
(4) if it is
Figure BDA00017111013600000919
And is
Figure BDA00017111013600000920
When the electricity price is in the valley period, the energy storage charging amount is
Figure BDA00017111013600000921
When the time is the usual time, and
Figure BDA00017111013600000922
the charge amount of the stored energy is
Figure BDA00017111013600000923
Otherwise, the charging quantity is
Figure BDA00017111013600000924
When the peak time is the electricity price, cutting
Figure BDA00017111013600000925
The charge amount of the stored energy is
Figure BDA00017111013600000926
Otherwise, the charging quantity is
Figure BDA00017111013600000927
And proceeds to step S3-7;
otherwise, directly entering step S3-7;
s3-42, judging according to the energy storage charge state in the hybrid energy storage system:
if the state of charge is greater than the minimum allowable state of charge, calculating the discharge capacity of the system based on the current price of electricity and the state of charge,
(1) if it is
Figure BDA0001711101360000101
And is
Figure BDA0001711101360000102
When the electricity price is in the peak period, the energy storage and discharge amount is
Figure BDA0001711101360000103
When the electricity price is in the usual time period, the energy storage and discharge amount is
Figure BDA0001711101360000104
When the electricity price is in the off-peak period, the energy storage and discharge amount is
Figure BDA0001711101360000105
And proceeds to step S3-7；
(2) If it is
Figure BDA0001711101360000106
And is
Figure BDA0001711101360000107
When the electricity price is in the peak time period, the discharge quantity of the storage battery is
Figure BDA0001711101360000108
When the time is the usual time, and
Figure BDA0001711101360000109
the energy storage discharge capacity is
Figure BDA00017111013600001010
Otherwise, the energy storage and discharge capacity is
Figure BDA00017111013600001011
When the electricity price is in the off-peak period, and
Figure BDA00017111013600001012
the energy storage discharge capacity is
Figure BDA00017111013600001013
Otherwise, the energy storage and discharge capacity is
Figure BDA00017111013600001014
And proceeds to step S3-7;
(3) if it is
Figure BDA00017111013600001015
And is
Figure BDA00017111013600001016
When the electricity price is in the peak period, the energy storage and discharge amount is
Figure BDA00017111013600001017
When the time is the usual time, and
Figure BDA00017111013600001018
the energy storage discharge capacity is
Figure BDA00017111013600001019
Otherwise, the discharge capacity is
Figure BDA00017111013600001020
When the electricity price is in the low valley period,
Figure BDA00017111013600001021
the energy storage discharge capacity is
Figure BDA00017111013600001022
Otherwise, the discharge capacity is
Figure BDA00017111013600001023
And proceeds to step S3-7;
(4) if it is
Figure BDA0001711101360000111
And is
Figure BDA0001711101360000112
When the electricity price is in the peak period, the energy storage and discharge amount is
Figure BDA0001711101360000113
When the time is the usual time, and
Figure BDA0001711101360000114
the energy storage discharge capacity is
Figure BDA0001711101360000115
Otherwise, the discharge capacity is
Figure BDA0001711101360000116
When the electricity price is in the valley period, the cutting machine
Figure BDA0001711101360000117
Discharge capacity of stored energyIs composed of
Figure BDA0001711101360000118
Otherwise, the discharge capacity is
Figure BDA0001711101360000119
And proceeds to step S3-7;
otherwise, directly entering step S3-7;
s3-5, judging according to the discharge state of the super capacitor:
when the predicted output of the distributed power supply is greater than 0 or the predicted load deviation is less than 0, the step S3-61 is carried out;
when the predicted output of the distributed power supply is smaller than 0 or the predicted load deviation is larger than 0, the step S3-62 is carried out;
s3-61, judging according to the charge state of the super capacitor:
if the state of charge is greater than the minimum allowable state of charge, calculating the discharge capacity of the system based on the current price of electricity and the state of charge,
(1) if it is
Figure BDA00017111013600001110
Or
Figure BDA00017111013600001111
And is
Figure BDA00017111013600001112
When, when
Figure BDA00017111013600001113
Then the discharge capacity of the super capacitor is
Figure BDA00017111013600001114
When in use
Figure BDA00017111013600001115
Then the discharge capacity of the super capacitor is
Figure BDA00017111013600001116
And proceeds to step S3-7;
(2) if it is
Figure BDA00017111013600001117
Or
Figure BDA00017111013600001118
And is
Figure BDA00017111013600001119
When, when
Figure BDA00017111013600001120
Then the discharge capacity of the super capacitor is
Figure BDA00017111013600001121
When in use
Figure BDA0001711101360000121
Then the discharge capacity of the super capacitor is
Figure BDA0001711101360000122
And proceeds to step S3-7;
otherwise, directly entering step S3-7;
s3-62, judging according to the charge state of the super capacitor;
if the state of charge is less than the maximum allowable state of charge, calculating the discharge capacity of the system based on the current price of electricity and the state of charge,
(1) if it is
Figure BDA0001711101360000123
Or
Figure BDA0001711101360000124
And is
Figure BDA0001711101360000125
When, when
Figure BDA0001711101360000126
Then the charging amount of the super capacitor is
Figure BDA0001711101360000127
When in use
Figure BDA0001711101360000128
Then the charging amount of the super capacitor is
Figure BDA0001711101360000129
And proceeds to step S3-7;
(2) if it is
Figure BDA00017111013600001210
Or
Figure BDA00017111013600001211
And is
Figure BDA00017111013600001212
When, when
Figure BDA00017111013600001213
Then the discharge capacity of the super capacitor is
Figure BDA00017111013600001214
When in use
Figure BDA00017111013600001215
Then the discharge capacity of the super capacitor is
Figure BDA00017111013600001216
And proceeds to step S3-7;
otherwise, directly entering step S3-7;
and S3-7, determining a coordination control strategy of the current centralized hybrid energy storage system at the current moment, and updating the energy storage state of charge.
The beneficial effects of the above further scheme are: and based on the user demand difference, a response strategy is formulated with the maximization of the operation income of the hybrid energy storage. The high-capacity storage of the storage battery is utilized to promote the consumption of clean energy and realize the energy transfer of the system; the system fluctuation problem caused by short-time scale prediction deviation is relieved by utilizing the quick response capability of the super capacitor, and based on the provided strategy, the user requirements of different time scales can be met, and the running economy of the hybrid energy storage system can be improved.
Drawings
Fig. 1 is a flowchart illustrating an implementation of a centralized hybrid energy storage coordination control method based on multi-time scale demand response according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating an implementation of a method for determining a coordination control strategy of a centralized hybrid energy storage system according to an embodiment of the present invention;
fig. 3 is a response scenario diagram of a centralized hybrid energy storage system according to an embodiment of the present invention;
fig. 4 is a configuration diagram of a centralized hybrid energy storage coordination control system in an embodiment provided by the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in fig. 1, the centralized hybrid energy storage coordination control method based on multi-time scale demand response includes the following steps:
s1, constructing a multi-type demand power consumption characteristic model of the user, wherein the multi-type demand power consumption characteristic model comprises a user load power consumption characteristic model and a distributed power supply response characteristic model;
in the step S1, the user load electricity consumption characteristic model is an electricity consumption model that determines the load based on the load prediction demand on the long-time scale, the load deviation on the short-time scale and the load electricity consumption duration according to the influence of different factors such as load electricity consumption demand and load electricity consumption probability of different types of loads, user electricity consumption comfort level, and electricity price; the user load electricity utilization characteristic model comprises an uncontrollable load electricity utilization characteristic model, a controllable load model and a guidable load electricity utilization characteristic model;
the load prediction model under the long-time scale is as follows:
Figure BDA0001711101360000131
wherein f (x) is a regression function of the load prediction,
Figure BDA0001711101360000141
is Lagrange multiplier, b is bias, K (x, x)i) Is a kernel function and meets the Mercer condition;
the kernel function expression is:
Figure BDA0001711101360000142
in the formula, K (x, x)i) Is a kernel function, x is a spatial sample, xiThe central position of the space sample x is shown, and sigma is a kernel function parameter;
the uncontrollable load, namely the traditional rigid load type, is generally not influenced by scheduling control and time duration electrovalence fluctuation and is mainly determined by the rigidity requirement of the load;
the model of the electrical characteristics of the uncontrollable load is
Figure BDA0001711101360000143
In the formula (I), the compound is shown in the specification,
Figure BDA0001711101360000144
in order to control the electrical characteristics of the load,
Figure BDA0001711101360000145
respectively, the predicted output, the predicted deviation degree and the power utilization probability of the uncontrollable load at the time T, TULThe power consumption duration of the uncontrollable load;
the controllable load is a load type strictly corresponding to scheduling management and control, and the system can reduce or interrupt the load according to the safe operation requirement under the load peak or the emergency and fault state of the system;
the controllable load electrical characteristic model is as follows:
Figure BDA0001711101360000146
in the formula (I), the compound is shown in the specification,
Figure BDA0001711101360000147
in order to control the electrical characteristics of the load,
Figure BDA0001711101360000148
respectively the predicted output, the predicted deviation degree, the power utilization probability and the user comfort requirement of the controllable load at the time t,
Figure BDA0001711101360000149
compensating prices for controllable load shedding at time T, TILThe power consumption time for the controllable load is prolonged;
the bootable load is a load which does not completely respond to dispatching management and control, but can be adjusted to a certain extent according to market electricity price fluctuation and has certain bootability;
the model of the electrical characteristics for the guidable load is as follows:
Figure BDA00017111013600001410
in the formula (I), the compound is shown in the specification,
Figure BDA00017111013600001411
the power usage of the load may be directed for time t,
Figure BDA00017111013600001412
respectively a predicted output, a predicted deviation degree and a power utilization probability of a guidable load at the time t,
Figure BDA0001711101360000151
complement for guiding load reduction at time tPrice compensation, TGLThe power utilization duration of the load can be guided;
the distributed power supply response characteristic model is constructed based on wind, light and other distributed power supply output characteristics, and is a response model determined based on output prediction under long-time scale requirements, output deviation under short-time scale and power generation duration according to the power generation cost and power selling price of the distributed power supply;
the distributed power supply response characteristic model is as follows:
Figure BDA0001711101360000152
in the formula (I), the compound is shown in the specification,
Figure BDA0001711101360000153
for the response characteristics of the distributed power supply,
Figure BDA0001711101360000154
respectively the predicted output and the predicted deviation degree of the distributed power supply at the time t,
Figure BDA0001711101360000155
the power generation cost of the distributed power supply and the electricity selling price at the moment T, TDGIs the time period of power generation of the distributed power supply.
The predicted output model of the photovoltaic generator is as follows:
Figure BDA0001711101360000156
in the formula: f (P)PV) Outputting a probability function of power for the photovoltaic generator, wherein Gamma is a Gamma function, alpha and beta are shape parameters of beta distribution respectively, and P isPVIs the output power of the photovoltaic generator;
Figure BDA0001711101360000157
is the maximum output power of the photovoltaic array;
based on the probability function of the active power output of the photovoltaic generator, the expected value of the output power of the photovoltaic power generation system is as follows:
Figure BDA0001711101360000158
the wind speed vtThe probability density function of (a) is:
Figure BDA0001711101360000159
wherein f (v)t) Is the probability density function of the average wind speed, c, k are the scale parameter and the shape parameter of the Weibull distribution function respectively, vtInputting a random quantity of the wind speed at the time t;
based on wind speed vtThe relation function between the output power of the wind driven generator and the wind speed is as follows:
Figure BDA0001711101360000161
in the formula, PwtIs the output power v of the fanc，vf，vsCut-in wind speed, cut-out wind speed and rated wind speed, RwtThe rated capacity of the fan.
S2, constructing a multi-time scale demand response model, including a long-time scale user demand response optimization model and a short-time scale user demand prediction deviation stabilizing model;
the multi-time scale demand response model in step S2 is an economy-oriented demand response model constructed according to the long-time scale clean energy consumption and short-time scale prediction deviation stabilizing model requirements, so as to promote clean energy consumption and optimize system operation quality;
the long-time-scale demand response optimization is based on time-of-use electricity price guidance, the uncontrollable load, the controllable load, the guidable load and the electricity consumption cost of a distributed power supply are calculated, the lowest electricity consumption cost of a user is taken as a target, and the multi-type demands of the user under the long-time scale are optimized to promote the consumption of clean energy;
the long-time scale demand response optimization model comprises the following steps:
Figure BDA0001711101360000162
in the formula (I), the compound is shown in the specification,
Figure BDA0001711101360000163
for the minimum cost of the different demand responses of the users,
Figure BDA0001711101360000164
coordinated total cost for the ith integrated user, CUL，CIL，CGL，CDGRespectively are the uncontrollable load of a user, the interruptible load, the guidance load and the response cost of the household distributed power supply in the period, and respectively are as follows:
Figure BDA0001711101360000171
in the formula, ctThe time is the power grid electricity price at the time t, delta t is the long time scale response time interval, the time interval under the long time scale is 1 hour,
Figure BDA0001711101360000172
a price elastic coefficient for guiding a load;
the prediction deviation stabilizing model of the short time scale is based on the charge and discharge capacity of the super capacitor, and the demand fluctuation is stabilized as much as possible so as to optimize the operation quality of the system;
the short-time scale user demand prediction deviation stabilizing model comprises the following steps:
Figure BDA0001711101360000173
in the formula (I), the compound is shown in the specification,
Figure BDA0001711101360000174
to smooth out the cost of the deviation, TadFor periods of short timescales, Δ PDG，ΔPUL，ΔPIL，ΔPGLRespectively DG, uncontrollable load, interruptible load and pilot load,
Figure BDA0001711101360000175
the discharge price and the charge price of the super capacitor at the moment t are respectively,
Figure BDA0001711101360000176
respectively representing the charging and discharging states of the super capacitor at the moment t;
Figure BDA0001711101360000177
in order to be in a charging state,
Figure BDA0001711101360000178
in the state of being discharged, the discharge electrode is,
Figure BDA0001711101360000179
the super capacitor does not act, Δ tadA response time interval of a short time scale.
And S3, determining a coordination control strategy of the centralized hybrid energy storage system according to the multi-type demand power utilization characteristic model and the multi-time scale demand response model.
The hybrid energy storage system comprises a storage battery and a super capacitor;
the hybrid energy storage system coordination control is realized by taking the hybrid energy storage system income maximization as a target, and a coordination control strategy of the hybrid energy storage system is formulated by considering the hybrid energy storage system state of charge constraint, the energy storage charging and discharging power constraint and the system power balance constraint;
the hybrid energy storage coordination strategy comprises the steps of promoting clean energy consumption by using the high energy density of the storage battery, and optimizing the user demand response in a long time scale; and rapidly stabilizing the user demand fluctuation of a short time scale by using the high power density of the super capacitor, and optimizing the operation quality of the system.
The objective function of the centralized hybrid energy storage coordination control strategy is as follows:
Figure BDA0001711101360000181
in the formula, maxCESSFor the maximum gain of the hybrid energy storage system, T is the total coordination period, Ty is the type of the time scale, N is the number of the comprehensive users, and delta TiIs the unit time length under the ith class time scale at the moment t,
Figure BDA0001711101360000182
Figure BDA0001711101360000183
the prices of discharging and charging of the storage battery are respectively used by the hybrid energy storage system under the requirement of the ith class of time scale at the time t,
Figure BDA0001711101360000184
the time t is the discharge and charge price of the hybrid energy storage system by using the super capacitor;
Figure BDA0001711101360000185
P
Figure BDA0001711101360000186
the electric quantity discharged and charged by the storage battery in the hybrid energy storage system is utilized according to the requirements of the jth user of the ith time scale at the moment t respectively,
Figure BDA0001711101360000187
the electric quantity discharged and charged by the super capacitor in the hybrid energy storage system is respectively used for the demands of the jth user in the ith time scale at the time t;
the state of charge of the centralized hybrid energy storage system is as follows:
Figure BDA0001711101360000188
in the formula (I), the compound is shown in the specification,
Figure BDA0001711101360000189
and
Figure BDA00017111013600001810
respectively the charge states of the storage battery and the super capacitor at the moment t;
Figure BDA00017111013600001811
Figure BDA00017111013600001812
respectively the charging efficiency and the discharging efficiency of the storage battery;
Figure BDA00017111013600001813
respectively the charging and discharging efficiency of the super capacitor;
Figure BDA00017111013600001814
Figure BDA00017111013600001815
respectively the charging and discharging power of the super capacitor at the time t;
Figure BDA00017111013600001816
respectively the charging and discharging power of the super capacitor at the time t;
Figure BDA00017111013600001817
the charging and discharging states of the storage battery at the time t are respectively;
Figure BDA00017111013600001818
in order to be in a charging state,
Figure BDA00017111013600001819
in the state of being discharged, the discharge electrode is,
Figure BDA00017111013600001820
the battery does not operate; Δ t is the duration; rES，RECThe capacities of the storage battery and the super capacitor are respectively;
the state of charge constraint of the centralized hybrid energy storage system is as follows:
Figure BDA00017111013600001821
in the formula (I), the compound is shown in the specification,
Figure BDA00017111013600001822
and
Figure BDA00017111013600001823
respectively the upper and lower limits of the state of charge of the storage battery,
Figure BDA00017111013600001824
and
Figure BDA00017111013600001825
respectively representing the upper limit and the lower limit of the charge state of the super capacitor;
and (3) charge and discharge power constraint of the hybrid energy storage system:
Figure BDA0001711101360000191
in the formula (I), the compound is shown in the specification,
Figure BDA0001711101360000192
respectively the minimum charging power and the maximum charging power of the storage battery,
Figure BDA0001711101360000193
respectively the minimum and maximum discharge power of the storage battery;
Figure BDA0001711101360000194
respectively the minimum charging power and the maximum charging power of the super capacitor,
Figure BDA0001711101360000195
respectively the minimum and maximum discharge power of the super capacitor;
system power balance constraint:
Figure BDA0001711101360000196
in the formula (I), the compound is shown in the specification,
Figure BDA0001711101360000197
the charge and discharge states of the storage battery at the time t are respectively,
Figure BDA0001711101360000198
respectively the charge and discharge states of the super capacitor at the time t,
Figure BDA0001711101360000199
the electric quantity discharged and charged by the storage battery at the time t respectively,
Figure BDA00017111013600001910
Figure BDA00017111013600001911
respectively the electric quantity discharged and charged by the stage capacitor at the time t,
Figure BDA00017111013600001912
respectively, the uncontrollable load, the controllable load, the guidable load and the predicted response quantity of the DG at the time t,
Figure BDA00017111013600001913
respectively, the uncontrollable load, the controllable load, the guidable load and the predicted deviation amount of the DG at the time t.
As shown in fig. 2, the step S3 specifically includes:
s3-1, inputting user demand data and converting the user demand data into a corresponding data model;
the input user demand data comprises load prediction data of different types under multiple time scales, distributed power supply prediction output data and prediction deviation amount thereof;
s3-2, selecting the hybrid energy storage system according to the time scale:
if the input demand data is long-time scale prediction data, the step S3-3 is carried out;
if the input demand data is predicted deviation data of a short time scale, the step S3-5 is carried out;
s3-3, judging according to the discharge state of the hybrid energy storage system:
when the input predicted output data of the distributed power supply cannot be completely paid out, namely PDG>PUL+PIL+PGLIf yes, the step S3-41 is entered;
when the user demand is too heavy (i.e. exceeds a preset threshold), the method goes to step S3-42;
the step S3-3 is to optimize the demand by taking a storage battery in the hybrid energy storage system as a main part and taking a super capacitor as an auxiliary part;
s3-41, judging according to the energy storage charge state in the hybrid energy storage system:
if the state of charge is less than the maximum allowable state of charge, i.e. when the state of charge is less than the maximum allowable state of charge
Figure BDA0001711101360000201
Calculating the charging capacity of the system based on the current price and the state of charge,
(1) if it is
Figure BDA0001711101360000202
And is
Figure BDA0001711101360000203
When the electricity price is in the valley period, the energy storage charging amount is
Figure BDA0001711101360000204
When the electricity price is in the usual time period, the energy storage charging quantity is
Figure BDA0001711101360000205
When the electricity price is in the peak time, the energy storage charging amount is
Figure BDA0001711101360000206
And proceeds to step S3-7;
(2)if it is
Figure BDA0001711101360000207
And is
Figure BDA0001711101360000208
When the electricity price is in the valley period, the energy storage charging amount is
Figure BDA0001711101360000209
When the time is the usual time, and
Figure BDA00017111013600002010
the charge amount of the stored energy is
Figure BDA00017111013600002011
Otherwise, the charging quantity is
Figure BDA00017111013600002012
When the peak time is the electricity price, cutting
Figure BDA00017111013600002013
The charge amount of the stored energy is
Figure BDA00017111013600002014
Otherwise, the charging quantity is
Figure BDA00017111013600002015
And proceeds to step S3-7;
(3) if it is
Figure BDA00017111013600002016
And is
Figure BDA00017111013600002017
When the electricity price is in the valley period, the energy storage charging amount is
Figure BDA00017111013600002018
When the time is the usual time, and
Figure BDA0001711101360000211
the charge amount of the stored energy is
Figure BDA0001711101360000212
Otherwise, the charging quantity is
Figure BDA0001711101360000213
When the peak time is the electricity price, and
Figure BDA0001711101360000214
the charge amount of the stored energy is
Figure BDA0001711101360000215
Otherwise, the charging quantity is
Figure BDA0001711101360000216
And proceeds to step S3-7;
(4) if it is
Figure BDA0001711101360000217
And is
Figure BDA0001711101360000218
When the electricity price is in the valley period, the energy storage charging amount is
Figure BDA0001711101360000219
When the time is the usual time, and
Figure BDA00017111013600002110
the charge amount of the stored energy is
Figure BDA00017111013600002111
Otherwise, the charging quantity is
Figure BDA00017111013600002112
When the peak time is the electricity price, cutting
Figure BDA00017111013600002113
The charge amount of the stored energy is
Figure BDA00017111013600002114
Otherwise, the charging quantity is
Figure BDA00017111013600002115
And proceeds to step S3-7;
otherwise, directly entering step S3-7;
s3-42, judging according to the energy storage charge state in the hybrid energy storage system:
if the state of charge is greater than the minimum allowable state of charge, i.e.
Figure BDA00017111013600002116
Calculating the system discharge capacity based on the current price and the state of charge,
(1) if it is
Figure BDA00017111013600002117
And is
Figure BDA00017111013600002118
When the electricity price is in the peak period, the energy storage and discharge amount is
Figure BDA00017111013600002119
When the electricity price is in the usual time period, the energy storage and discharge amount is
Figure BDA00017111013600002120
When the electricity price is in the off-peak period, the energy storage and discharge amount is
Figure BDA00017111013600002121
And proceeds to step S3-7;
(2) if it is
Figure BDA00017111013600002122
And is
Figure BDA00017111013600002123
When the electricity price is in the peak period, the energy storage and discharge amount is
Figure BDA0001711101360000221
When the time is the usual time, and
Figure BDA0001711101360000222
the energy storage discharge capacity is
Figure BDA0001711101360000223
Otherwise, the energy storage and discharge capacity is
Figure BDA0001711101360000224
When the electricity price is in the off-peak period, and
Figure BDA0001711101360000225
the energy storage discharge capacity is
Figure BDA0001711101360000226
Otherwise, the energy storage and discharge capacity is
Figure BDA0001711101360000227
And proceeds to step S3-7;
(3) if it is
Figure BDA0001711101360000228
And is
Figure BDA0001711101360000229
When the electricity price is in the peak period, the energy storage and discharge amount is
Figure BDA00017111013600002210
When the time is the usual time, and
Figure BDA00017111013600002211
the energy storage discharge capacity is
Figure BDA00017111013600002212
Otherwise, the discharge capacity is
Figure BDA00017111013600002213
When the electricity price is in the low valley period,
Figure BDA00017111013600002214
the energy storage discharge capacity is
Figure BDA00017111013600002215
Otherwise, the discharge capacity is
Figure BDA00017111013600002216
And proceeds to step S3-7;
(4) if it is
Figure BDA00017111013600002217
And is
Figure BDA00017111013600002218
When the electricity price is in the peak period, the energy storage and discharge amount is
Figure BDA00017111013600002219
When the time is the usual time, and
Figure BDA00017111013600002220
the energy storage discharge capacity is
Figure BDA00017111013600002221
Otherwise, the discharge capacity is
Figure BDA00017111013600002222
When the electricity price is in the valley period, the cutting machine
Figure BDA00017111013600002223
The energy storage discharge capacity is
Figure BDA00017111013600002224
Otherwise, the discharge capacity is
Figure BDA00017111013600002225
And proceeds to step S3-7;
otherwise, directly entering step S3-7;
in the above steps S3-41 and S3-42, the energy storage system makes a charging strategy according to the current state of charge and the charging price to consume the surplus power.
S3-5, judging according to the discharge state of the super capacitor:
when the predicted output of the distributed power supply is greater than 0 or the predicted load deviation is less than 0, namely delta PDG<0，ΔPUL+ΔPIL+ΔPGL<0, then go to step S3-61;
when the predicted output of the distributed power supply is less than 0 or the predicted load deviation is greater than 0, namely delta PDG>0，ΔPUL+ΔPIL+ΔPGL<0, then go to step S3-62;
step S3-5 is to stabilize the fluctuation of the super capacitor in the hybrid system;
s3-61, judging according to the charge state of the super capacitor:
if the state of charge is greater than the minimum allowable state of charge, i.e.
Figure BDA0001711101360000231
Calculating the system discharge capacity based on the current price and the state of charge,
(1) if it is
Figure BDA0001711101360000232
Or
Figure BDA0001711101360000233
And is
Figure BDA0001711101360000234
When, when
Figure BDA0001711101360000235
Then the discharge capacity of the super capacitor is
Figure BDA0001711101360000236
When in use
Figure BDA0001711101360000237
Then the discharge capacity of the super capacitor is
Figure BDA0001711101360000238
And proceeds to step S3-7;
(2) if it is
Figure BDA0001711101360000239
Or
Figure BDA00017111013600002310
And is
Figure BDA00017111013600002311
When, when
Figure BDA00017111013600002312
Then the discharge capacity of the super capacitor is
Figure BDA00017111013600002313
When in use
Figure BDA00017111013600002314
Then the discharge capacity of the super capacitor is
Figure BDA00017111013600002315
And proceeds to step S3-7;
otherwise, directly entering step S3-7;
in the step S3-61, the super capacitor makes a charging strategy according to the current state of charge and the charging price to eliminate the surplus power;
s3-62, judging according to the charge state of the super capacitor;
if the state of charge is less than the maximum allowable state of charge, i.e. when the state of charge is less than the maximum allowable state of charge
Figure BDA0001711101360000241
Calculating the system discharge capacity based on the current price and the state of charge,
(1) if it is
Figure BDA0001711101360000242
Or
Figure BDA0001711101360000243
And is
Figure BDA0001711101360000244
When, when
Figure BDA0001711101360000245
Then the charging amount of the super capacitor is
Figure BDA0001711101360000246
When in use
Figure BDA0001711101360000247
Then the charging amount of the super capacitor is
Figure BDA0001711101360000248
And proceeds to step S3-7;
(2) if it is
Figure BDA0001711101360000249
Or
Figure BDA00017111013600002410
And is
Figure BDA00017111013600002411
When, when
Figure BDA00017111013600002412
Then the discharge capacity of the super capacitor is
Figure BDA00017111013600002413
When in use
Figure BDA00017111013600002414
Then the discharge capacity of the super capacitor is
Figure BDA00017111013600002415
And proceeds to step S3-7;
otherwise, directly entering step S3-7;
in the above step S3-62, the super capacitor formulates a discharging strategy according to the current state of charge and the charging price to provide energy support for the system.
And S3-7, determining a coordination control strategy of the current centralized hybrid energy storage system at the current moment, and updating the energy storage state of charge.
In the step S3-7, security verification is further performed to perform security comparison on the charging and discharging strategy of the hybrid energy storage system at the current moment, including feasibility verification of the hybrid system and feasibility verification of system operation; the feasibility calculation of the hybrid system means that whether the charging and discharging amount of the stored energy has the risk of overcharge and overdischarge and whether the total times of the charging and discharging actions of the stored energy meets the maximum charging and discharging times constraint; and the feasibility calculation along with the operation of the system refers to calculating whether the active power in the system at the current moment meets the supply balance.
In an embodiment of the present invention, a response scenario configuration of the centralized hybrid energy storage system of the present invention is provided, as shown in fig. 3, which mainly includes a comprehensive user and a centralized hybrid energy storage system; the comprehensive users comprise users with conventional loads and novel loads such as electric vehicles or distributed power supplies such as wind power, light power and the like, wherein the conventional loads mainly comprise uncontrollable loads, controllable loads and guidable loads; the centralized hybrid energy storage system comprises a storage battery and a super capacitor. The comprehensive user demand target is mainly to realize the minimization of the demand cost of the user on the basis of the current electricity price according to the demand forecast of the user on a long time scale and the forecast deviation condition of the user on a short time scale, and simultaneously send a response request to the centralized hybrid energy storage system through an information interaction channel; the response target of the centralized hybrid energy storage system is based on the type and capacity of the system hybrid energy storage and the charging and discharging power thereof, based on the demand request instructions of different time scales, the charging and discharging strategy of the centralized hybrid energy storage system is formulated with the targets of maximizing the charging and discharging benefits of the centralized hybrid energy storage system and stabilizing the demand fluctuation of short time scales users, and the charging and discharging state of the energy storage is returned to the users through the information interaction channel.
In an embodiment of the present invention, a centralized hybrid energy storage coordination control system to which the present invention is applied is provided, as shown in fig. 4, mainly includes three layers:
firstly, a user multi-type demand prediction model: the method comprises the steps that a demand model considering uncertain factors is built according to different types of load characteristics and different distributed power supply response characteristic models in users, and a load demand response characteristic function of the demand model comprises uncertain influence factors such as multi-type load prediction, short-time scale prediction deviation degree, load electricity utilization probability, user comfort level requirements, market electricity price, compensation electricity price and electricity utilization duration; the distributed power supply response characteristic function comprises uncertain influence factors such as multi-type distributed power supply predicted output, short time scale predicted deviation, power generation cost, electricity receiving price, power generation duration and the like;
II, a multi-time scale demand response model: constructing a response model considering user response cost and system operation requirements under different time scales based on the demand response requirements of different time scales, wherein a long-time-scale demand response objective function of the response model aims at minimizing user economic cost and comprises uncontrollable load, interruptible load, guidable load and user distributed power supply response cost, and a short-time-scale demand response objective function of the response model aims at stabilizing system fluctuation and comprises uncontrollable load, interruptible load, guidable load and user distributed power supply predicted deviation cost under the short-time scale;
thirdly, hybrid energy storage economic coordination control: and a coordination control strategy aiming at maximizing the hybrid energy storage system income is formulated according to the demand response of different scales and types of loads, and the charge state, the energy storage charge-discharge power and the system power balance constraint of the hybrid energy storage system are considered at the same time, so that the economic operation of the centralized hybrid energy storage system and the user is realized.
In one embodiment of the present invention, the functional principle of the steps in the present invention is provided:
in the step S1, according to the power consumption characteristics of different types of loads, the load of the comprehensive user is divided into three categories, namely an uncontrollable load, a controllable load and a guidable load, and the distributed power supply is divided into two categories, namely photovoltaic power generation and wind power generation;
on the basis of the multi-type demand model, a vector machine regression combination model is used for predicting real-time load demands, random influence factors reflecting the power consumption characteristics of the load are introduced aiming at different types, so that the power consumption characteristics of the load are reflected, for example, when the load is an uncontrollable load, the power consumption is influenced by market factors, only three uncertain factors of a model prediction deviation influence factor, load power consumption probability and power consumption duration are considered, when the load is a controllable load, the power consumption is uniformly scheduled and managed by a system, so that the influence of uncertain factors such as user power consumption comfort level and system reduction compensation price and the like are considered in an incremental mode, when the load is a guidable load, the power consumption has certain marketability, and the fluctuation of the market price has obvious influence on the load; the distributed power supply response model is the supply demand of a comprehensive user, and in order to promote the consumption and ensure the benefit of an owner, the power generation cost and the electricity selling price are important factors influencing the response; the multi-type demand model has the advantages that the response characteristics of various loads and distributed power generation are fully highlighted, the accuracy of prediction simulation is improved, and the fluctuation of user demands along with the influence of uncertain factors is reflected.
In the step S2, different demand response models are constructed according to different time scales;
the long-time-scale demand response model is based on the day-ahead demand forecast, the aim of minimizing the response cost of various types of loads and distributed power supplies is taken as the target, and the consumption of clean energy is promoted. The decision of the long-time-scale demand response is to make a user demand response request based on the response cost of multiple types of loads of the user, the request aims to realize the minimum electricity consumption cost of the user based on the demand prediction result under the long-time scale, for example, when the electricity price is higher or the clean energy output is insufficient, the controllable load is properly reduced, and when the electricity price is lower or the clean energy output is excessive, the controllable load can be guided to be influenced by the electricity price to properly improve the electricity consumption of the load, so that the utilization rate of the clean energy is improved, and the electricity consumption cost of the user is reduced;
the short-time scale prediction deviation response model is a short-time scale prediction deviation amount based on user requirements, and aims to minimize the cost of the energy storage system for stabilizing the demand prediction deviation, so that the influence of demand fluctuation on system operation is reduced. The decision of the demand response of the short time scale is to formulate a user demand response request based on the fluctuation stabilizing cost of the hybrid energy storage system, the request aims to realize the fluctuation stabilizing of the system by utilizing the super capacitor based on the short-time predicted deviation amount of various types of loads of users so as to optimize the operation quality of the system, for example, when the output prediction of the distributed power supply is excessive or the load prediction is insufficient, the super capacitor is charged to absorb the excessive electric quantity in the system, and when the output prediction of the distributed power supply is insufficient or the load prediction is excessive, the super capacitor is discharged to provide electric quantity support for the excessive demand response in the system.
In the step S3, the centralized hybrid energy storage system makes an energy storage response strategy based on the user demands at different time scales;
the demand prediction result of the long time scale is set as day-ahead prediction data performed at intervals of 1 hour, and the demand prediction deviation of the short time scale is set as hour-ahead prediction deviation data performed at intervals of 10 minutes.
When the input request is the user requirement of a long time scale, the centralized hybrid energy storage system makes a coordination response strategy taking the running economy of the hybrid energy storage system as a target in a response mode taking a storage battery as a main super capacitor as an auxiliary;
when the input request is the user requirement of a short time scale, the centralized hybrid energy storage system mainly adopts a super capacitor response means, and makes an energy storage coordination response strategy by taking the user requirement prediction deviation under the short time scale as a target so as to realize the optimization of the centralized hybrid energy storage on the multi-time scale requirement of the user.
Assuming that the capacity of a storage battery in the centralized hybrid energy storage system is 600kW, the capacities of 2 super capacitors are 300kW, the constraint of the state of charge of the stored energy is between 20% and 85%, the maximum allowable action times of the storage battery and the super capacitors in one day are 3 times and 20 times respectively, and the time-of-use price is shown in table 1:
TABLE 1 time of use price
Figure BDA0001711101360000281
As shown in fig. 2, the centralized energy storage system provided by the present invention is adopted to perform coordination control on the user demand response;
the coordination control method can be summarized as follows: considering the influences of various loads in users, the electricity utilization characteristics of distributed power supplies and various uncertainty factors, simulating the predicted output of each user demand under a long time scale by taking 1 hour as a time interval, and simultaneously acquiring the deviation of the user demand in the next 1 hour and a long-time prediction result by taking 10 minutes as a time interval in the system operation process to provide a data basis for subsequently utilizing a centralized hybrid energy storage system to perform economic optimization; in order to meet the requirements of promoting clean energy consumption on a long time scale and the targets of stabilizing demand fluctuation on a short time scale, a multi-time-scale response model is constructed on the basis of user demand prediction quantity and demand deviation quantity; the difference of energy type and power type energy storage response in the centralized hybrid energy storage system is fully utilized, an energy storage response strategy is formulated according to the requirements of users at different time scales, the energy storage capacity of the storage battery and the rapid charging and discharging capacity of the super capacitor are fully adjusted, and the running economy of the hybrid energy storage system is improved while the multi-time scale response requirements of the users are ensured.
In one embodiment of the invention, the main processes of the realization of the method comprise the steps of constructing a prediction model of multi-type load requirements and distributed power supply response characteristics, constructing a multi-time scale requirement response model and providing a coordination response strategy of the centralized hybrid energy storage system.
When a system model is built, considering diversity of user side loads and difference of distributed power supplies, and building power utilization models of multi-type load power utilization characteristics and output models of the distributed power supplies; aiming at the response requirements of the multi-type demands under different time scales, a multi-time scale demand response model is constructed in order to meet the goals of clean energy consumption under a long time scale and fluctuation stabilization under a short time scale; response capabilities of different types of energy storage devices in the centralized hybrid energy storage system are utilized, and a response strategy for energy storage is reasonably formulated based on a time scale and a user demand state, so that user-side clean energy consumption is promoted, system fluctuation is reduced, and the running economy of the hybrid energy storage system is improved.
Based on the coordination control aspect of a centralized hybrid energy storage system, the traditional load prediction model is difficult to reflect the difference of power consumption of different types of loads, the controllability of a flexible load cannot be highlighted, in addition, the load and a distributed power supply are greatly influenced by environmental change factors, the prediction accuracy is low, the operation optimization result is excessively large in deviation from the actual result, and the economy of the energy storage system is difficult to guarantee; in addition, the requirement of multiple time scales and multiple types of user requirements is difficult to meet by a single type of energy storage. Therefore, the invention fully considers the characteristics of different types of load electricity consumption and distributed power output in users, adopts the response strategy of the centralized hybrid energy storage system based on different response targets of user requirements under multiple time scales, promotes the consumption of clean energy under a long time scale, realizes the effective stabilization of the requirement fluctuation under a short time scale, and simultaneously ensures the operation economy of the hybrid energy storage system, and the specific meanings are as follows:
the multi-type power utilization model comprises: the load is predicted by adopting a support vector regression combination model, wind-solar output is simulated by respectively utilizing Weiull distribution and beta distribution, and power utilization response models with different types of requirements are constructed based on factors such as uncertainty of user requirements, controllable power of the requirements, requirements on power utilization comfort level of users, power price and the like, so that comprehensive description of power utilization characteristics of various types of loads in the users is realized.
Demand response on multiple timescales: aiming at the problems of demand fluctuation caused by the influence of environmental factors on various demands of users and the consumption of grid-connected clean energy in the users, a demand response model aiming at promoting the consumption of the clean energy in a long time scale and a demand adjustment model aiming at stabilizing the demand fluctuation in a short time scale are constructed from the system operation requirements in different time scales, so that the requirements of the system in various aspects of operation are met, and the system economy is improved.
Selection of a centralized hybrid energy storage system response strategy: based on the diversity of the hybrid energy storage system, the clean energy consumption is taken as a main target under a long-time scale, and the storage battery is selected as a main target and the super capacitor is selected as an auxiliary target to realize the mass transfer of energy in time; the method mainly aims at stabilizing the fluctuation of the demand in a short time scale, and the super capacitor is selected as a main response mode, so that the economy of the centralized hybrid energy storage system is improved while the demands of different time scales are met.
The invention has the beneficial effects that: the method comprises the steps of constructing an optimization control model of the centralized hybrid energy storage system with the aim of economy; considering the consumption and stabilization of distributed power supply output and multi-type load power utilization characteristics under multiple time scales and uncertainty influence thereof by using a centralized hybrid energy storage system, the method constructs an uncertain multi-type load power utilization characteristic model and a distributed power supply output model, and realizes the demand response description of the multi-type load and the distributed power supply; aiming at the operation demand difference between a long time scale and a short time scale, demand optimization and deviation response models of different time scales are constructed, a hybrid energy storage system coordination control strategy based on multiple time scales is provided, the operation economy of the hybrid energy storage system is guaranteed, clean energy consumption is promoted, and demand fluctuation is stabilized. The method makes a charging and discharging strategy of the centralized hybrid energy storage system based on the charging and discharging characteristics and the state of charge of the storage battery and the super capacitor and the characteristics of various requirements of users, promotes the consumption of clean energy, optimizes the operation quality of the system and improves the operation economy of the centralized hybrid energy storage system.

Claims (6)

1. The centralized hybrid energy storage coordination control method based on multi-time scale demand response is characterized by comprising the following steps of:
s1, constructing a multi-type demand power consumption characteristic model of the user, wherein the multi-type demand power consumption characteristic model comprises a user load power consumption characteristic model and a distributed power supply response characteristic model;
s2, constructing a multi-time scale demand response model, including a long-time scale user demand response optimization model and a short-time scale user demand prediction deviation stabilizing model, wherein the long-time scale user demand response optimization model is as follows:
Figure FDA0003312037950000011
in the formula (I), the compound is shown in the specification,
Figure FDA0003312037950000012
for the minimum cost of the different demand responses of the users,
Figure FDA0003312037950000013
coordinated total cost for the ith integrated user, CUL，CIL，CGL，CDGRespectively are the uncontrollable load of a user, the interruptible load, the guidance load and the response cost of the household distributed power supply in the period, and respectively are as follows:
Figure FDA0003312037950000014
wherein T is the total period of coordination, ctThe time is the power grid electricity price at the time t, delta t is the long time scale response time interval, the time interval under the long time scale is 1 hour,
Figure FDA0003312037950000015
in order to guide the price elastic coefficient of the load,
Figure FDA0003312037950000016
the amount of power used for the uncontrollable load at time t,
Figure FDA0003312037950000017
the amount of electricity used for the controllable load at time t,
Figure FDA0003312037950000018
for the predicted contribution of the controllable load at time t,
Figure FDA0003312037950000019
for the compensation price of the controllable load shedding at time t,
Figure FDA00033120379500000110
the compensation price of the load shedding can be guided for the time t,
Figure FDA00033120379500000111
to guide the predicted contribution of the load at time t,
Figure FDA00033120379500000112
the power consumption of the load guidable for time t, cDGIn order to account for the cost of the power generation of the distributed power supply,
Figure FDA00033120379500000113
for the predicted contribution of the distributed power supply at time t,
Figure FDA00033120379500000114
response characteristics of the distributed power supply;
the short-time scale user demand prediction deviation stabilizing model is as follows:
Figure FDA0003312037950000021
in the formula (I), the compound is shown in the specification,
Figure FDA0003312037950000022
to smooth out the cost of the deviation, TadFor periods of short timescales, Δ PDG，ΔPUL，ΔPIL，ΔPGLRespectively DG, uncontrollable load, interruptible load and pilot load,
Figure FDA0003312037950000023
the discharge price and the charge price of the super capacitor at the moment t are respectively,
Figure FDA0003312037950000024
respectively representing the charging and discharging states of the super capacitor at the moment t;
Figure FDA0003312037950000025
in order to be in a charging state,
Figure FDA0003312037950000026
in the state of being discharged, the discharge electrode is,
Figure FDA0003312037950000027
the super capacitor does not act, Δ tadA response time interval that is a short timescale;
and S3, determining a coordination control strategy of the centralized hybrid energy storage system according to the multi-type demand power utilization characteristic model and the multi-time scale demand response model.
2. The centralized hybrid energy storage coordination control method based on multi-time scale demand response according to claim 1, wherein in said step S1:
the user load electricity utilization characteristic model comprises an uncontrollable load electricity utilization characteristic model, a controllable load model and a guidable load electricity utilization characteristic model;
the model of the electrical characteristics of the uncontrollable load is as follows:
Figure FDA0003312037950000028
in the formula (I), the compound is shown in the specification,
Figure FDA0003312037950000029
the amount of power used for the uncontrollable load at time t,
Figure FDA00033120379500000210
respectively, the predicted output, the predicted deviation degree and the power utilization probability of the uncontrollable load at the time T, TULThe power consumption duration of the uncontrollable load;
the controllable load electrical characteristic model is as follows:
Figure FDA00033120379500000211
in the formula (I), the compound is shown in the specification,
Figure FDA00033120379500000212
the amount of electricity used for the controllable load at time t,
Figure FDA00033120379500000213
respectively the predicted output, the predicted deviation degree, the power utilization probability and the user comfort requirement of the controllable load at the time t,
Figure FDA00033120379500000214
compensating prices for controllable load shedding at time T, TILThe power consumption time for the controllable load is prolonged;
the electric characteristic model capable of guiding the load is as follows:
Figure FDA00033120379500000215
in the formula (I), the compound is shown in the specification,
Figure FDA0003312037950000031
the power usage of the load may be directed for time t,
Figure FDA0003312037950000032
respectively a predicted output, a predicted deviation degree and a power utilization probability of a guidable load at the time t,
Figure FDA0003312037950000033
compensating prices for load shedding guidable at time T, TGLThe power utilization duration of the load can be guided;
the distributed power supply response characteristic model is as follows:
Figure FDA0003312037950000034
in the formula (I), the compound is shown in the specification,
Figure FDA0003312037950000035
for the response characteristics of the distributed power supply,
Figure FDA0003312037950000036
respectively the predicted output and the predicted deviation degree of the distributed power supply at the time t,
Figure FDA0003312037950000037
the power generation cost of the distributed power supply and the electricity selling price at the moment T, TDGIs the time period of power generation of the distributed power supply.
3. The centralized hybrid energy storage coordination control method based on multi-time scale demand response of claim 2,
the user load electricity utilization characteristic model is an electricity utilization model of a load determined by load prediction under a long time scale, load deviation under a short time scale and load electricity utilization duration;
the load prediction model under the long-time scale is as follows:
Figure FDA0003312037950000038
wherein f (x) is a regression function of the load prediction, mui，
Figure FDA0003312037950000039
Is Lagrange multiplier, b is bias, K (x, x)i) Is a kernel function and meets the Mercer condition;
the kernel function expression is:
Figure FDA00033120379500000310
in the formula, K (x, x)i) Is a kernel function, x is a spatial sample, xiThe central position of the space sample x is shown, and sigma is a kernel function parameter;
the distributed power supply response characteristic model is a characteristic model constructed based on the output characteristics of wind and light distributed power supplies;
the predicted output model of the photovoltaic generator is as follows:
Figure FDA0003312037950000041
in the formula: f (P)PV) Outputting a probability function of power for the photovoltaic generator, wherein Gamma is a Gamma function, alpha and beta are shape parameters of beta distribution respectively, and P isPVIs the output power of the photovoltaic generator;
Figure FDA0003312037950000042
is the maximum output power of the photovoltaic array;
based on the probability function of the active power output of the photovoltaic generator, the expected value of the output power of the photovoltaic power generation system is as follows:
Figure FDA0003312037950000043
wind speed vtThe probability density function of (a) is:
Figure FDA0003312037950000044
wherein f (v)t) Is the probability density function of the average wind speed, c, k are the scale parameter and the shape parameter of the Weibull distribution function respectively, vtInputting a random quantity of the wind speed at the time t;
based on wind speed vtThe relation function between the output power of the wind driven generator and the wind speed is as follows:
Figure FDA0003312037950000045
in the formula, PwtIs the output power v of the fanc，vf，vsCut-in wind speed, cut-out wind speed and rated wind speed, RwtThe rated capacity of the fan.
4. The centralized hybrid energy storage coordination control method based on multi-time scale demand response of claim 1, characterized in that the hybrid energy storage system comprises a storage battery and a super capacitor.
5. The centralized hybrid energy storage coordination control method based on multi-time scale demand response of claim 4, wherein the coordination control strategy of the centralized hybrid energy storage system aims at maximizing the hybrid energy storage system profit, and the objective function is as follows:
Figure FDA0003312037950000051
in the formula, maxCESSFor the maximum gain of the hybrid energy storage system, T is the total coordination period, Ty is the type of the time scale, N is the number of the comprehensive users, and delta TiIs the unit time length under the ith class time scale at the moment t,
Figure FDA0003312037950000052
Figure FDA0003312037950000053
the prices of discharging and charging of the storage battery are respectively used by the hybrid energy storage system under the requirement of the ith class of time scale at the time t,
Figure FDA0003312037950000054
price for discharging and charging hybrid energy storage system by using super capacitor at time t respectively；
Figure FDA0003312037950000055
Figure FDA0003312037950000056
The electric quantity discharged and charged by the storage battery in the hybrid energy storage system is utilized according to the requirements of the jth user of the ith time scale at the moment t respectively,
Figure FDA0003312037950000057
the electric quantity discharged and charged by the super capacitor in the hybrid energy storage system is respectively used for the demands of the jth user in the ith time scale at the time t;
factors influencing the profit maximization of the hybrid energy storage system comprise hybrid energy storage system state-of-charge constraint, hybrid energy storage system charge-discharge power constraint and system power balance constraint;
the state of charge of the centralized hybrid energy storage system is as follows:
Figure FDA0003312037950000058
in the formula (I), the compound is shown in the specification,
Figure FDA0003312037950000059
and
Figure FDA00033120379500000510
respectively the charge states of the storage battery and the super capacitor at the moment t;
Figure FDA00033120379500000511
Figure FDA00033120379500000512
respectively the charging efficiency and the discharging efficiency of the storage battery;
Figure FDA00033120379500000513
respectively the charging and discharging efficiency of the super capacitor;
Figure FDA00033120379500000514
Figure FDA00033120379500000515
respectively the charging and discharging power of the super capacitor at the time t;
Figure FDA00033120379500000516
respectively the charging and discharging power of the super capacitor at the time t;
Figure FDA00033120379500000517
the charging and discharging states of the storage battery at the time t are respectively;
Figure FDA00033120379500000518
in order to be in a charging state,
Figure FDA00033120379500000519
in the state of being discharged, the discharge electrode is,
Figure FDA00033120379500000520
the battery does not operate; Δ t is the duration;
Figure FDA00033120379500000522
RECthe capacities of the storage battery and the super capacitor are respectively;
the state of charge constraint of the centralized hybrid energy storage system is as follows:
Figure FDA00033120379500000523
in the formula (I), the compound is shown in the specification,
Figure FDA0003312037950000061
and
Figure FDA0003312037950000062
respectively the upper and lower limits of the state of charge of the storage battery,
Figure FDA0003312037950000063
and
Figure FDA0003312037950000064
respectively representing the upper limit and the lower limit of the charge state of the super capacitor;
and the charge-discharge power constraint of the hybrid energy storage system is as follows:
Figure FDA0003312037950000065
in the formula (I), the compound is shown in the specification,
Figure FDA0003312037950000066
respectively the minimum charging power and the maximum charging power of the storage battery,
Figure FDA0003312037950000067
respectively the minimum and maximum discharge power of the storage battery;
Figure FDA0003312037950000068
respectively the minimum charging power and the maximum charging power of the super capacitor,
Figure FDA0003312037950000069
respectively the minimum and maximum discharge power of the super capacitor;
the system power balance constraint is as follows:
Figure FDA00033120379500000610
in the formula (I), the compound is shown in the specification,
Figure FDA00033120379500000611
the charge and discharge states of the storage battery at the time t are respectively,
Figure FDA00033120379500000612
respectively the charge and discharge states of the super capacitor at the time t,
Figure FDA00033120379500000613
the electric quantity discharged and charged by the storage battery at the time t respectively,
Figure FDA00033120379500000614
Figure FDA00033120379500000615
respectively the electric quantity discharged and charged by the stage capacitor at the time t,
Figure FDA00033120379500000616
respectively, the uncontrollable load, the controllable load, the guidable load and the predicted response quantity of the DG at the time t,
Figure FDA00033120379500000617
respectively, the uncontrollable load, the controllable load, the guidable load and the predicted deviation amount of the DG at the time t.
6. The centralized hybrid energy storage coordination control method based on multi-time scale demand response according to claim 5, wherein said step S3 specifically comprises:
s3-1, inputting user demand data and converting the user demand data into a corresponding data model;
the input user demand data comprises load prediction data of different types under multiple time scales, distributed power supply prediction output data and prediction deviation amount thereof;
s3-2, selecting the hybrid energy storage system according to the time scale:
if the input demand data is long-time scale prediction data, the step S3-3 is carried out;
if the input demand data is predicted deviation data of a short time scale, the step S3-5 is carried out;
s3-3, judging according to the discharge state of the hybrid energy storage system:
when the input distributed power supply predicted output data cannot be completely paid out, the step S3-41 is carried out;
when the user demand exceeds the set threshold, the step S3-42 is carried out;
s3-41, judging according to the energy storage charge state in the hybrid energy storage system:
if the state of charge is less than the maximum allowable state of charge, calculating the charging capacity of the system based on the current price of electricity and the state of charge,
(1) if it is
Figure FDA0003312037950000071
And is
Figure FDA0003312037950000072
When the electricity price is in the valley period, the energy storage charging amount is
Figure FDA0003312037950000073
When the electricity price is in the usual time period, the energy storage charging quantity is
Figure FDA0003312037950000074
When the electricity price is in the peak time, the energy storage charging amount is
Figure FDA0003312037950000075
And proceeds to step S3-7;
(2) if it is
Figure FDA0003312037950000076
And is
Figure FDA0003312037950000077
When the electricity price is in the valley period, the energy storage charging amount is
Figure FDA0003312037950000078
When the time is the usual time, and
Figure FDA0003312037950000079
the charge amount of the stored energy is
Figure FDA00033120379500000710
Otherwise, the charging quantity is
Figure FDA00033120379500000711
When the peak time is the electricity price, cutting
Figure FDA00033120379500000712
The charge amount of the stored energy is
Figure FDA00033120379500000713
Otherwise, the charging quantity is
Figure FDA00033120379500000714
And proceeds to step S3-7;
(3) if it is
Figure FDA00033120379500000715
And is
Figure FDA00033120379500000716
When the electricity price is in the valley period, the energy storage charging amount is
Figure FDA00033120379500000717
When the time is the usual time, and
Figure FDA00033120379500000718
the charge amount of the stored energy is
Figure FDA00033120379500000719
Otherwise, the charging quantity is
Figure FDA00033120379500000720
When the peak time is the electricity price, and
Figure FDA00033120379500000721
the charge amount of the stored energy is
Figure FDA0003312037950000081
Otherwise, the charging quantity is
Figure FDA0003312037950000082
And proceeds to step S3-7;
(4) if it is
Figure FDA0003312037950000083
And is
Figure FDA0003312037950000084
When the electricity price is in the valley period, the energy storage charging amount is
Figure FDA0003312037950000085
When the time is the usual time, and
Figure FDA0003312037950000086
the charge amount of the stored energy is
Figure FDA0003312037950000087
Otherwise, the charging quantity is
Figure FDA0003312037950000088
When the peak time is the electricity price, cutting
Figure FDA0003312037950000089
The charge amount of the stored energy is
Figure FDA00033120379500000810
Otherwise, the charging quantity is
Figure FDA00033120379500000811
And proceeds to step S3-7;
otherwise, directly entering step S3-7;
s3-42, judging according to the energy storage charge state in the hybrid energy storage system:
if the state of charge is greater than the minimum allowable state of charge, calculating the discharge capacity of the system based on the current price of electricity and the state of charge,
(1) if it is
Figure FDA00033120379500000812
And is
Figure FDA00033120379500000813
When the electricity price is in the peak period, the energy storage and discharge amount is
Figure FDA00033120379500000814
When the electricity price is in the usual time period, the energy storage and discharge amount is
Figure FDA00033120379500000815
When the electricity price is in the off-peak period, the energy storage and discharge amount is
Figure FDA00033120379500000816
And proceeds to step S3-7;
(2) if it is
Figure FDA00033120379500000817
And is
Figure FDA00033120379500000818
When the electricity price is in the peak period, the energy storage and discharge amount is
Figure FDA00033120379500000819
When the time is the usual time, and
Figure FDA00033120379500000820
the energy storage discharge capacity is
Figure FDA00033120379500000821
Otherwise, the energy storage and discharge capacity is
Figure FDA00033120379500000822
When the electricity price is in the off-peak period, and
Figure FDA00033120379500000823
the energy storage discharge capacity is
Figure FDA00033120379500000824
Otherwise, the energy storage and discharge capacity is
Figure FDA00033120379500000825
And proceeds to step S3-7;
(3) if it is
Figure FDA0003312037950000091
And is
Figure FDA0003312037950000092
When the electricity price is in the peak period, the energy storage and discharge amount is
Figure FDA0003312037950000093
When the time is the usual time, and
Figure FDA0003312037950000094
the energy storage discharge capacity is
Figure FDA0003312037950000095
Otherwise, the discharge capacity is
Figure FDA0003312037950000096
When it is a valleyThe electricity price in the time period is,
Figure FDA0003312037950000097
the energy storage discharge capacity is
Figure FDA0003312037950000098
Otherwise, the discharge capacity is
Figure FDA0003312037950000099
And proceeds to step S3-7;
(4) if it is
Figure FDA00033120379500000910
And is
Figure FDA00033120379500000911
When the electricity price is in the peak period, the energy storage and discharge amount is
Figure FDA00033120379500000912
When the time is the usual time, and
Figure FDA00033120379500000913
the energy storage discharge capacity is
Figure FDA00033120379500000914
Otherwise, the discharge capacity is
Figure FDA00033120379500000915
When the electricity price is in the valley period, the cutting machine
Figure FDA00033120379500000916
The energy storage discharge capacity is
Figure FDA00033120379500000917
Otherwise, the discharge capacity is
Figure FDA00033120379500000918
And proceeds to step S3-7;
otherwise, directly entering step S3-7;
s3-5, judging according to the discharge state of the super capacitor:
when the predicted output of the distributed power supply is greater than 0 or the predicted load deviation is less than 0, the step S3-61 is carried out;
when the predicted output of the distributed power supply is smaller than 0 or the predicted load deviation is larger than 0, the step S3-62 is carried out;
s3-61, judging according to the charge state of the super capacitor:
if the state of charge is greater than the minimum allowable state of charge, calculating the discharge capacity of the system based on the current price of electricity and the state of charge,
(1) if it is
Figure FDA00033120379500000919
Or
Figure FDA00033120379500000920
And is
Figure FDA00033120379500000921
When, when
Figure FDA00033120379500000922
Then the discharge capacity of the super capacitor is
Figure FDA00033120379500000923
When in use
Figure FDA00033120379500000924
Then the discharge capacity of the super capacitor is
Figure FDA00033120379500000925
And proceeds to step S3-7;
(2) if it is
Figure FDA0003312037950000101
Or
Figure FDA0003312037950000102
And is
Figure FDA0003312037950000103
When, when
Figure FDA0003312037950000104
Then the discharge capacity of the super capacitor is
Figure FDA0003312037950000105
When in use
Figure FDA0003312037950000106
Then the discharge capacity of the super capacitor is
Figure FDA0003312037950000107
And proceeds to step S3-7;
otherwise, directly entering step S3-7;
s3-62, judging according to the charge state of the super capacitor;
if the state of charge is less than the maximum allowable state of charge, calculating the discharge capacity of the system based on the current price of electricity and the state of charge,
(1) if it is
Figure FDA0003312037950000108
Or
Figure FDA0003312037950000109
And is
Figure FDA00033120379500001010
When, when
Figure FDA00033120379500001011
Then the charging amount of the super capacitor is
Figure FDA00033120379500001012
When in use
Figure FDA00033120379500001013
Then the charging amount of the super capacitor is
Figure FDA00033120379500001014
And proceeds to step S3-7;
(2) if it is
Figure FDA00033120379500001015
Or
Figure FDA00033120379500001016
And is
Figure FDA00033120379500001017
When, when
Figure FDA00033120379500001018
Then the discharge capacity of the super capacitor is
Figure FDA00033120379500001019
When in use
Figure FDA00033120379500001020
Then the discharge capacity of the super capacitor is
Figure FDA00033120379500001021
And proceeds to step S3-7;
otherwise, directly entering step S3-7;
and S3-7, determining a coordination control strategy of the current centralized hybrid energy storage system at the current moment, and updating the energy storage state of charge.
CN201810682959.6A 2018-06-28 2018-06-28 Centralized hybrid energy storage coordination control method based on multi-time scale demand response Active CN108879746B (en)

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