CN110011307A - One provenance-lotus curve adjustment Optimized model and method - Google Patents
One provenance-lotus curve adjustment Optimized model and method Download PDFInfo
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- CN110011307A CN110011307A CN201910411260.0A CN201910411260A CN110011307A CN 110011307 A CN110011307 A CN 110011307A CN 201910411260 A CN201910411260 A CN 201910411260A CN 110011307 A CN110011307 A CN 110011307A
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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
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
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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
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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Abstract
The present invention discloses one provenance-lotus curve adjustment Optimized model and method, and step includes: that S1. determines original and desired new energy power output, load curve；S2. the expectation curve of new energy power curve and load curve and the sequence of differences of primitive curve are sought, and high frequency power sequence and low frequency power sequence are broken down into using variation Mode Decomposition；S3. it using high frequency series and low frequency sequence as the charge-discharge electric power constraint of power-type, energy-type cells in cloud energy storage, establishes source-lotus optimization of profile and adjusts model.The present invention using propose now cloud energy storage, Spot Price as background, adjust model realization by establishing source-lotus optimization of profile and source-lotus curve adjusted in real time with least cost, to realize source-lotus coordinated operation and maximize and dissolve new energy.
Description
Technical field
The present invention relates to New-energy power system technical field more particularly to one provenance-lotus curve adjustment Optimized model with
Method.
Background technique
Currently, source-net-lotus-storage coordinated operation is the important means of renewable energy consumption, realizes source-net-lotus-storage association
Tune then needs to adjust source lotus curve accordingly using energy storage, Demand-side controllable resources etc..With ubiquitous electric power Internet of Things
The it is proposed of concept and the appearance of 5G technology, the information interconnection of electric system and On-line Control will be pushed further into.Cloud energy storage conduct
A kind of novel shared energy storage business model, by by the distributed energy storage of user side and newly-built centralized energy storage information
It is aggregated to cloud, signs Tenancy Agreement with user, realizes the shared of energy storage facility, is expected to that traditional energy storage is replaced to realize as following
The important means of source lotus curve adjustment.
For power type battery existing for cloud energy-storage system and energy-type cells value of leass difference and demand response cost
The price variance problem occurred with depth is adjusted, needs to establish one provenance-lotus curve adjustment Optimized model and method, rationally to divide
With power type battery, energy-type cells and demand response to the regulated quantity of source lotus curve, the lowest cost is realized.
Summary of the invention
The technical problem to be solved in the present invention is that: for source lotus curve adjustment amount between cloud energy storage and demand response
Assignment problem, the present invention provide one provenance-lotus curve adjustment Optimized model, realize source-lotus curve adjustment amount in cloud energy storage and need
Ask the optimum allocation between response.
In order to solve the above technical problems, technical solution proposed by the present invention are as follows:
One provenance-lotus curve adjustment Optimized model and method, step include:
S1. original and desired new energy power output, load curve are determined；
S2. the expectation curve of new energy power curve and load curve and the sequence of differences of primitive curve are sought, and is used
Variation Mode Decomposition is broken down into high frequency power sequence and low frequency power sequence；
S3. using high frequency series and low frequency sequence as the charge-discharge electric power of power-type, energy-type cells in cloud energy storage
Constraint establishes source-lotus optimization of profile and adjusts model.
As a further improvement of the present invention, new energy curve sequence of differences and load curve difference sequence in the step S2
Column are acquired with primitive curve as difference with specific reference to the expectation curve in the step S1.
It as a further improvement of the present invention, will also be to difference after obtaining new energy sequence of differences and load sequence of differences
Sequence carries out variation mode decomposition and reconstruct, and sequence of differences is decomposed into high frequency section and low frequency part, the specific steps are as follows:
S21. sequence of differences is resolved into the intrinsic mode function u of K centre frequency from low to highk(t)
S22. the Mutual information entropy between each adjacent submodule state is calculated, comentropy expression formula is as follows:
MI(uk,uk+1)=H (uk)+H(uk+1)-H(uk,uk+1)
P (u in formulak) be k-th of submodule state energy value.
S23. Mutual information entropy is normalized:
S24. separation of the minimum point as high fdrequency component and low frequency component for choosing Mutual information entropy, to high fdrequency component with
Low frequency component is reconstructed, and obtains low frequency power curve PLemaxWith high frequency power curve PLpmax。
As a further improvement of the present invention, specifically by the low frequency power curve P in the step S2LemaxWith high frequency function
Rate curve PLpmaxCharge and discharge constraint as energy-type cells and power type battery in cloud energy-storage battery.In addition, target power system
System also needs to meet battery charging and discharging constraint, demand response electricity tariff constraint, cloud energy storage charge-discharge electric power Constraints of Equilibrium, demand elasticity square
Battle array equality constraint.
As a further improvement of the present invention, the source-lotus optimization of profile adjusts specific steps are as follows: in advance with cloud energy storage and
Demand response the lowest cost is target, adjusts model based on above-mentioned constraint building source-lotus optimization of profile, is based on the source-lotus
Optimization of profile adjusts the energy storage of model reasonable distribution cloud and the demand response proportion in new energy-load curve is adjusted.
Compared with the prior art, the advantages of the present invention are as follows:
1) present invention improves over original source lotus curve adjustment means, it is contemplated that uses under the following ubiquitous electric power Internet of Things background
Cloud energy storage is realized with the mode that price type combines and is adjusted synchronously to source lotus curve.
2) this hair be make to fluctuate in power curve faster part as far as possible using power type battery in cloud energy-storage system into
Row charge and discharge use energy-type cells in cloud energy-storage system to carry out charge and discharge the relatively slow part of fluctuation as far as possible, use and become
Mode Decomposition is divided to carry out decomposition and reconstruction to power difference sequence, to be high frequency section by total power difference curve separating
With low frequency part, thus reduce cloud energy storage expense and extend battery life.
3) assignment problem of the face source lotus curve adjustment amount of the present invention between cloud energy storage and demand response establishes corresponding
Optimized model, realize and original source, lotus curve be adjusted to by expectation curve with minimum adjustment cost.
4) source-lotus curve adjustment Optimized model that the present invention establishes can be combined with existing source lotus Coordination Model, be led to
It crosses using resource adjustments sources, lotus curve such as cloud energy storage, demand responses, realizes that net lotus in source stores up coordinated operation, promote new energy consumption.
Detailed description of the invention
Fig. 1 is the implementation process schematic diagram of the present embodiment source-lotus curve adjustment Optimized model and method.
Fig. 2 be specific embodiment it is original contribute with it is expected new energy, load curve.
Fig. 3 is specific embodiment new energy, load sequence of differences figure.
Fig. 4 is the present embodiment variation mode decomposition flow chart.
Fig. 5 is specific embodiment load sequence of differences variation mode decomposition result figure.
Fig. 6 is specific embodiment new energy sequence of differences variation mode decomposition result figure.
Fig. 7 is distribution diagram of the load sequence of differences between cloud energy storage is corresponding to demand in the specific embodiment of the invention.
Specific embodiment
Below in conjunction with Figure of description and specific preferred embodiment, the invention will be further described, but not therefore and
It limits the scope of the invention.
As shown in Figure 1, the present embodiment source-lotus curve adjustment Optimized model and method, step include:
S1. original and desired new energy power output, load curve are determined；
S2. the expectation curve of new energy power curve and load curve and the sequence of differences of primitive curve are sought, and is used
Variation Mode Decomposition is broken down into high frequency power sequence and low frequency power sequence；
S3. using high frequency series and low frequency sequence as the charge-discharge electric power of power-type, energy-type cells in cloud energy storage
Constraint establishes source-lotus optimization of profile and adjusts model.
The present embodiment looks to the future under energy storage containing cloud and Spot Price background, and new energy-load curve regulating measure will
It is converted into stimulable type demand response through cloud energy storage and price type demand response and is adjusted by traditional energy storage.And cloud energy storage system
Containing the faster higher energy-type cells of power type battery and energy density are responded in system, due to its manufacturing cost and use
The difference in service life, hiring cost also can be different.In addition, price type demand response is with the increasing for adjusting depth, user price
Certain variation can also occur for sensitivity coefficient, it is meant that its adjustment cost can increase with the increase for adjusting depth, and demand
Response is limited to the regulated quantity of load curve, needs to combine with cloud energy storage.The total power curve of electric system is by many items
The different small-power curve combination of vibration frequency forms.By original new energy in such a way that cloud energy storage is in conjunction with demand response
It is adjusted to during expectation curve with load curve, it is desirable to which the faster part of power difference sequence fluctuation uses power-type as far as possible
Battery carries out charge and discharge, to reduce adjustment cost and increase battery life.Therefore it is considered as variation mode decomposition and reconstructing method
Sequence of differences is decomposed into high frequency section and low frequency part, and establishes source lotus curve adjustment Optimized model, is realized with least cost
Primitive curve is adjusted to expectation curve.
It is original as shown in Figure 2 with desired new energy power output, load curve described in step S1 in the present embodiment.
In the present embodiment, load curve sequence of differences is with specific reference to the expected load curve in the step S1 in step S2
It is acquired with original loads curve as difference；Step S2 new energy sequence of differences is specifically real by new energy capacity and original new energy
Border power output sequence of differences and the practical power output of original new energy and the sequence of differences sum of the two of desired new energy power output form, specifically
As shown in Figure 3.
In the present embodiment, after obtaining new energy sequence of differences and load sequence of differences, also variation is carried out to sequence of differences
Mode decomposition and reconstruct, are decomposed into high frequency section and low frequency part for sequence of differences, the specific steps are as follows:
S21. sequence of differences is resolved into the intrinsic mode function u of K centre frequency from low to highk(t), flow chart is such as
Shown in Fig. 4, decomposition result is as shown in Figure 5,6.
S22. the Mutual information entropy between each adjacent submodule state is calculated, comentropy expression formula is as follows:
MI(uk,uk+1)=H (uk)+H(uk+1)-H(uk,uk+1) (1)
P (u in formulak) be k-th of submodule state energy value.
S23. Mutual information entropy is normalized:
S24. separation of the minimum point as high fdrequency component and low frequency component for choosing Mutual information entropy, to high fdrequency component with
Low frequency component is reconstructed, and obtains low frequency power curve PLemaxWith high frequency power curve PLpmax。
In the present embodiment, specifically by the low frequency power curve P in the step S2LemaxWith high frequency power curve PLpmax
Charge and discharge constraint as energy-type cells and power type battery in cloud energy-storage battery.In addition, target power system also needs to meet
Following constraint: battery charging and discharging constraint, demand response electricity tariff constraint, cloud energy storage charge-discharge electric power Constraints of Equilibrium, demand elasticity square
Battle array equality constraint.
1. battery charging and discharging constrains
|PLe(t)|≤|PLemax(t)| (4)
|PLp(t)|≤|PLpmax(t)| (5)
P in formulaLemax、PLpmaxThe low frequency power curve and high frequency power that are load difference curve after variation mode decomposition
Curve.
2. demand response electricity tariff constraint
ρmin≤ρ(t)≤ρmax (6)
ρ in formulamin、ρmaxThe respectively minimum value and maximum value of demand response electricity price.
3. cloud energy storage charge-discharge electric power Constraints of Equilibrium
4. demand elasticity matrix equality constrains
Δ P and Δ ρ respectively indicates the incrementss of electrical demand and electricity price, P in formula0And ρ0Respectively indicating original electricity needs
Summation electricity price；εii、εijRespectively self-elasticity coefficient and coefficient of cross elasticity, i, j are respectively the i-th period and j-th of period.
In the present embodiment, source-lotus optimization of profile adjusts specific steps are as follows: in advance most with cloud energy storage and demand response totle drilling cost
Low is target, adjusts model based on above-mentioned constraint building source-lotus optimization of profile, adjusts model based on the source-lotus optimization of profile
The energy storage of reasonable distribution cloud and the demand response proportion in new energy-load curve is adjusted.
In the present embodiment, source-lotus optimization of profile adjusts model objectives function are as follows:
The smallest objective function of total adjustment cost
In above formula, PLe(t)、PLpIt (t) is respectively the t period using the power of energy-type cells charge and discharge and using power-type
The power of the charge and discharge of battery；The respectively unit energy appearance of cloud energy-storage system energy-type cells, power type battery
Measure lease expenses；For the income for carrying out tou power price front and back power grid, the difference of the two is then demand response cost；PL0
(t)、PL1It (t) is the load curve before and after implementation tou power price；rpTo carry out the fixation electricity price before tou power price；ρ (t) is real-time
Electricity price；R is that Utilities Electric Co. is that user is encouraged to participate in demand response to the electricity price discount of user.
To verify effectiveness of the invention, certain industrial park containing honourable fiery storing cogeneration system is chosen as analysis pair
As wherein including 3 conventional power units, total installation of generating capacity 800MW.Installed capacity of wind-driven power is 150MW, and photovoltaic installed capacity is
100MW.Cloud energy-storage system energy-type cells, power type battery unit energy capacity value of leass be respectively 300 yuan/MWh,
200 yuan/MWh.Original electricity price rp=0.6 yuan/KWh, the fluctuation range of electricity price is 0.4~1.4 yuan/KWh, demand response user's
Electricity price discount takes 0.8.
For new energy sequence of differences, high frequency power curve is broken down into using variation mode decomposition and low frequency power is bent
Line, is respectively adopted in cloud energy-storage system power type battery and energy-type cells to adjust, cloud energy storage adjustment cost and using single
Battery comparison it is as shown in table 1.
New energy sequence of differences adjustment cost under 1 different modes of table
New energy curve ratio is adjusted by purchase cloud energy storage service as seen from the above table to adjust using the single battery of tradition
Cost is lower, and can reduce abandonment in large quantities and abandon light.
The regulative mode of load sequence of differences is divided into three kinds herein, mode 1 is adjusted only with cloud energy storage；Mode 2, only
Using demand response；Mode 3, cloud energy storage and demand response optimization distribute.The Cost comparisons of three kinds of modes are as shown in table 2 below.
Load sequence of differences adjustment cost under 2 different modes of table
By upper Biao Ke get, although mode 1 can completely eliminate load sequence of differences, needed since the cost of cloud energy storage is higher than
The cost of response is sought, therefore its totle drilling cost is higher than the totle drilling cost of mode 3；In mode 2, since Electricity price fluctuation needs to control certain
In range, therefore the variation of electrical demand amount is limited, so mode 2 cannot completely eliminate load deviation sequence, it will lead to portion
Load loss and a large amount of abandonment is divided to abandon light；Mode 3 is optimized by second stage and realizes load sequence of differences in cloud energy storage and need
The reasonable distribution between response is sought, load sequence of differences can not only be completely eliminated, and it is compared with mode 1 and mode 2, it is total to adjust
It is minimum to save cost.
This example is adjusted load sequence of differences by mode 3, load sequence of differences cloud energy storage and demand response it
Between distribution it is as shown in Figure 7.Under the premise of known to the following cloud energy storage service price, reasonably distributed by adjusting Spot Price
Ratio of the load sequence of differences in demand response and cloud energy storage are adjusted, realizes that total adjustment cost is minimum.For example, by being born in Fig. 5
Lotus sequence of differences figure it is found that the 0-6 period need to guide user power utilization to increase load, it is electric as seen from Figure 7 therefore in the 0-6 period
Valence controls always in 0.4 yuan/KWh of lower limit, but since in the load valley period, Demand Elasticity Coefficient is smaller, therefore electrical demand
Rising only by a small margin.Since load power curve most of in the 0-6 period belongs to energy type power curve, so when use
More energy-type cells are adjusted, since cost is relatively low for the charge and discharge of power type battery, therefore power type battery charge and discharge about
It can be adjusted as much as possible using power type battery within the scope of beam.
Can be obtained by above-mentioned test result, the present embodiment by using variation mode decomposition method by new energy power output, load
Sequence of differences carries out decomposition and reconstruction into high frequency section and low frequency part, and is introduced into source lotus optimization of profile and adjusts model, leads to
The regulated quantity for crossing the energy storage of reasonable distribution cloud, demand response, can efficiently reduce total adjustment cost.This method can be applied with model
In following demand side management.
Above-mentioned only presently preferred embodiments of the present invention, is not intended to limit the present invention in any form.Although of the invention
It has been disclosed in a preferred embodiment above, however, it is not intended to limit the invention.Therefore, all without departing from technical solution of the present invention
Content, technical spirit any simple modifications, equivalents, and modifications made to the above embodiment, should all fall according to the present invention
In the range of technical solution of the present invention protection.
Claims (5)
1. one provenance-lotus curve adjustment Optimized model and method, which is characterized in that step includes:
S1. original and desired new energy power output, load curve are determined；
S2. the expectation curve of new energy power curve and load curve and the sequence of differences of primitive curve are sought, and uses variation
Mode Decomposition is broken down into high frequency power sequence and low frequency power sequence；
S3. it is constrained high frequency series and low frequency sequence as the charge-discharge electric power of power-type in cloud energy storage, energy-type cells,
It establishes source-lotus optimization of profile and adjusts model.
2. source according to claim 1-lotus curve adjustment Optimized model and method, which is characterized in that in the step S2
Load curve sequence of differences is acquired with original loads curve as difference with specific reference to the expectation expectation curve in the step S1；It is described
Step S2 new energy sequence of differences specifically by new energy capacity and the practical power output sequence of differences of original new energy and it is original newly
The practical power output of the energy and the sequence of differences sum of the two of desired new energy power output form.
3. source according to claim 2-lotus curve adjustment Optimized model and method, which is characterized in that it is poor to obtain new energy
After value sequence and load sequence of differences, also variation mode decomposition and reconstruct are carried out to sequence of differences, sequence of differences is decomposed into
High frequency section and low frequency part, the specific steps are as follows:
S21. sequence of differences is resolved into the intrinsic mode function u of K centre frequency from low to highk(t)
S22. the Mutual information entropy between each adjacent submodule state is calculated, comentropy expression formula is as follows:
MI(uk,uk+1)=H (uk)+H(uk+1)-H(uk,uk+1)
P (u in formulak) be k-th of submodule state energy value.
S23. Mutual information entropy is normalized:
S24. separation of the minimum point of Mutual information entropy as high fdrequency component and low frequency component is chosen, to high fdrequency component and low frequency
Component is reconstructed, and obtains low frequency power curve PLemaxWith high frequency power curve PLpmax。
4. source according to claim 3-lotus curve adjustment Optimized model and method, which is characterized in that in the step S2
Specifically by the low frequency power curve PLemaxWith high frequency power curve PLpmaxAs energy-type cells and power in cloud energy-storage battery
The charge and discharge of type battery constrain.In addition, target power system also need to meet battery charging and discharging constraint, demand response electricity tariff constraint,
Cloud energy storage charge-discharge electric power Constraints of Equilibrium, the constraint of demand elasticity matrix equality.
5. source according to claim 4-lotus curve adjustment Optimized model and method, which is characterized in that the source-lotus curve
Optimizing regulation specific steps are as follows: in advance using cloud energy storage and demand response the lowest cost as target, source-is constructed based on above-mentioned constraint
Lotus optimization of profile adjusts model, adjusts the energy storage of model reasonable distribution cloud and demand response new based on the source-lotus optimization of profile
Proportion in the energy-load curve adjusting.
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| CN109190882A (en) * | 2018-07-25 | 2019-01-11 | 南京邮电大学 | Microgrid economic optimization method of commerce under Power Market based on cloud energy storage |
| CN109473972A (en) * | 2018-08-31 | 2019-03-15 | 长沙理工大学 | Whole source lotus is assisted to store up optimal control method based on more power curve |
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Application publication date: 20190712 |