CN110380405A - Consider that demand response cooperates with optimization micro-capacitance sensor operation method with energy storage - Google Patents
Consider that demand response cooperates with optimization micro-capacitance sensor operation method with energy storage Download PDFInfo
- Publication number
- CN110380405A CN110380405A CN201910598580.1A CN201910598580A CN110380405A CN 110380405 A CN110380405 A CN 110380405A CN 201910598580 A CN201910598580 A CN 201910598580A CN 110380405 A CN110380405 A CN 110380405A
- Authority
- CN
- China
- Prior art keywords
- micro
- capacitance sensor
- battery
- demand response
- formula
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- 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
-
- 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
-
- 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
-
- 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]
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention discloses a kind of consideration demand responses to cooperate with optimization micro-capacitance sensor operation method with energy storage, is related to micro-capacitance sensor optimization running technology field, the described method comprises the following steps: step 1 considers that demand response cooperates with Optimized model with energy storage to micro-capacitance sensor foundation；Step 2, the optimal value for calculating the collaboration Optimized model, obtain the operating scheme of micro-capacitance sensor.The present invention fully considers the response cost of user for demand response model, and is compensated according to the enthusiasm of user response, and excitation user participates in the enthusiasm of demand response.The problem larger for accumulator plant life consumption influences the factor of the life of storage battery by research, considers the influence for influencing maximum battery carrying capacity on economic load dispatching, constructs battery operating cost, achieve the effect that quantitative control.
Description
Technical field
The present invention relates to micro-capacitance sensor optimization running technology field more particularly to a kind of consideration demand response cooperate with energy storage it is excellent
Change micro-capacitance sensor operation method.
Background technique
Demand response (demand response, DR) refers to that user receives the electricity price signal or excitation of operator's sending
After signal, changes its conventional consumption habit, alleviate a kind of electricity consumption behavior of power supply shortage.DR can be divided into stimulable type DR with
Two kinds of price type DR.Demand response is introduced in micro-capacitance sensor will ensure that supply side and Demand-side all meet optimum operating condition, one
Determine the Flexible Power Grid that can be improved in degree, and alleviate the influence of the intermittent renewable energy, helps to establish one and more pass through
Ji, reliable micro-capacitance sensor.
Currently, having many research achievements about the research of demand response both at home and abroad.Demand response optimizes in micro-capacitance sensor to be transported
Application in row is an importance, and in this, Shao Jingke, Wang Buoyant, Tan Yanghong etc. write " the microgrid economy of meter and Demand Side Response
Optimized Operation " (Power System and its Automation journal, 2016,28 (10): 31-36.) with transferable load be main research pair
As in conjunction with tou power price, from the power demand of user and micro-capacitance sensor economic benefit and environmental benefit, building meter and demand are rung
The micro-capacitance sensor Optimal Operation Model answered.Sun Yujun, Wang Yan, Li Qiushuo etc. write " meter and the two stages rolling scheduling of user side interaction
Planning model " (south electric network technology, 2017,11 (6): 63-69.) be based on consumer psychology principle simulation user for electricity price
User satisfaction constraint is introduced in scheduling model by the response process of variation.Model is only built from the variation before and after average electricity price
Vertical satisfaction constraint does not consider that operator is supplied to the influence of user's compensation.Nan Sibo, Li Gengyin, week be bright etc. to write that " intelligence is small
Area can cut down flexible load real-time requirement response policy " (electric power system protection and control, 2019,47 (10): 42-50.) can
Reduction plans are divided into interruptible load and adjustable load, only utilize user's illumination and temperature pleasant degree for adjustable load
Set electric power grade.
Energy storage device can be effectively improved unbalanced supply-demand and abandonment in system and abandon optical issue, be that micro-capacitance sensor maintains to stablize fortune
Capable important equipment.It since the life consumption of energy storage device is larger, needs to frequently replace, it is therefore desirable to which feelings are run to energy storage device
Condition is controlled.Currently, considering micro-capacitance sensor economic load dispatching there are mainly two types of the research mode of service lifetime of accumulator.First is that
Accumulator cell charging and discharging conversion times in dispatching cycle are limited, such as Yang Xiu, Chen Jie, Zhu Lan write " the microgrid storage based on economic load dispatching
Can distribute rationally " (electric power system protection and control, 2013,41 (1): 53-60.).Second is that the carrying capacity of battery is limited to
In a certain range, as Mercier P, Cherkaoui R, Oudalov A write " Optimizing a Battery Energy
Storage System for Frequency Control Application in an Isolated Power System》
(IEEE Transactions on Power Systems,2009,24(3):1469-1477.)。
Existing demand response model does not consider that user participates in the satisfaction of demand response project, lacks for user's row
For modeling.And fixed making up price is thought of as the compensation for participating in demand response project user, is unable to fully swash
Encourage and guide user to participate in demand response.
Consider micro-capacitance sensor economic load dispatching there are mainly two types of the research mode of service lifetime of accumulator.First is that limiting scheduling
Accumulator cell charging and discharging conversion times in period, however the number needs specifically limited are set according to actual conditions, occurrence is not
It is good to determine.Furthermore, it is possible to which the excessively harsh battery of number of dimensions limitation occur is unable to give full play the effect for alleviating load change, warp
Ji property is poor；Or certain discharge power is excessive, depth of discharge is larger, the lost of life.Therefore, accumulator cell charging and discharging is pursued simply
It is not quite reasonable that conversion times, which are reduced with prolonging service life of battery,.Second is that the carrying capacity of battery is limited to a certain range
It is interior, but this method Consideration is not comprehensive, and meter and carrying capacity do not influence specifically, only qualitative contrlol.
Therefore, those skilled in the art is dedicated to developing a kind of consideration demand response and cooperates with optimization micro-capacitance sensor fortune with energy storage
Row method fully considers the response cost of user for demand response model, and is mended according to the enthusiasm of user response
It repays, excitation user participates in the enthusiasm of demand response.The problem larger for accumulator plant life consumption is influenced by research
The factor of the life of storage battery considers to influence economic load dispatching the influence of maximum battery carrying capacity, the operation of building battery at
This, achievees the effect that quantitative control.
Summary of the invention
In view of the above drawbacks of the prior art, the technical problem to be solved by the present invention is to when micro-capacitance sensor optimizes and runs
The response cost for fully considering user is compensated according to the enthusiasm of user response, and excitation user participates in the product of demand response
Polarity, and the influence of meter and battery carrying capacity, construct battery operating cost, achieve the effect that quantitative control.
To achieve the above object, the present invention provides a kind of consideration demand responses cooperates with optimization micro-capacitance sensor operation side with energy storage
Method, which is characterized in that the described method comprises the following steps:
Step 1 considers that demand response cooperates with Optimized model with energy storage to micro-capacitance sensor foundation；
Step 2, the optimal value for calculating the collaboration Optimized model, obtain the operating scheme of micro-capacitance sensor.
Further, it includes optimization aim letter that consideration demand response described in the step 1, which cooperates with Optimized model with energy storage,
Several and corresponding constraint condition two parts, the optimization aim are that micro-capacitance sensor operator cost is minimum, the micro-capacitance sensor operator
Cost includes power supply cost F1With demand response cost F2Two parts, the constraint condition include power-balance constraint in micro-capacitance sensor,
Each micro- source power output restriction, diesel-driven generator Climing constant, interconnection constraint, demand response constraint and battery in micro-capacitance sensor
Carrying capacity constraint.
Further, the power supply cost F1Including fuel cost Cf(t), micro-capacitance sensor operator is in power exchange
Real-time deal cost Cr(t) and battery operating cost Cop(t), it is expressed as follows:
In formula: T is to take 24 dispatching cycle；
The demand response cost F2It is expressed as follows:
In formula: x is that user cuts down power；Y is that the compensation for participating in interruptible load project user is given by micro-capacitance sensor operator；
J is the total number of users that interruptible load project is participated in micro-capacitance sensor；λ is deploying node.
Further, the optimization aim F is by the power supply cost F1With demand response cost F2As the optimization mould
Two sub-goals of type handle this multi-objective optimization question using weigthed sums approach, introduce weight coefficient w1And w2, table
Show as follows:
F=w1F1+w2F2
In formula: w1+w2=1.
Further, the constraint condition specifically includes:
Power-balance constraint is expressed as follows in the micro-capacitance sensor:
In formula: PW(t)、PS(t)、Pr(t)、PBAT(t)、PLIt (t) is respectively time t wind driven generator output power, photovoltaic
The initial workload demand of system output power, tie-line power transmission, battery transimission power, micro-capacitance sensor；
Each micro- source power output restriction is expressed as follows in the micro-capacitance sensor:
Pi,max≤Pi(t)≤Pi,min
0≤PW(t)≤Wt
0≤PS(t)≤St
In formula: Pi,max、Pi,minRespectively diesel-driven generator i peak power output, minimum output power；Wt、StRespectively
Time t predicts wind-force maximum output, prediction photovoltaic maximum output；
The diesel-driven generator Climing constant is expressed as follows:
-ΔPi,down≤Pi(t+1)-Pi(t)≤ΔPi,up
In formula: Δ Pi,up、ΔPi,downIt is maximum upward, the maximum rate of climbing downwards of diesel-driven generator respectively；
The interconnection constraint representation is as follows:
-Prmax≤Pr(t)≤Prmax
In formula: PrmaxIt is the tie-line power transmission upper limit；
The demand response constraint representation is as follows:
yj(t)-(K1,jxj(t)2+K2,jxj(t)-K2,jxj(t)θj)≥0
J=1 ..., J
In formula: K1And K2It is the constant greater than 0 for cost coefficient；θ is user type, is joined according to different types of user
The wish degree of interruptible load project is added to classify, value range is 0≤θ≤1；B is that micro-capacitance sensor operator implements daily
Demand response the estimate for a project；CM is to participate in the user of interruptible load project in advance by maximum electricity that it can be interrupted daily；
The battery carrying capacity constraint representation is as follows:
SOCmin≤SOC≤SOCmax
In formula: SOCmax、SOCminThe respectively bound of battery carrying capacity,
According to battery maximum discharge current Idisch-max, obtain the battery carrying capacity variation upper limit:
In formula: ηdischFor battery discharging efficiency,
According to battery maximum charging current Ich-max, obtain battery carrying capacity variation lower limit:
In formula: ηchEfficiency to charge the battery,
At the first and last moment of dispatching cycle, the carrying capacity of battery should be consistent:
SOC (1)=SOC (T+1).
Further, the fuel cost Cf(t) it is expressed as follows:
In formula: Cf,i(PiIt (t)) is fuel cost of i-th of diesel-driven generator in time t；PiIt (t) is i-th of diesel generation
Power of the machine in time t；I is diesel-driven generator number in micro-capacitance sensor；
The real-time deal cost is expressed as follows:
Cr(Pr (t))=γt·Pr(t)
In formula: γtIt is the real-time deal electricity price of time t；Pr (t) is time t micro-capacitance sensor in spot market transaction power, if
Buy and take just, sell take it is negative.
Further, the battery operating cost Cop(t) it is expressed as follows:
In formula: CSBFor the initial outlay cost of accumulator plant；R (t-1) is time t-1 life of storage battery loss factor；
AopIt (t) is handling capacity of the battery within t-1~t time；AtotalFor the effective throughput of battery.
Further, the effective throughput A of the batterytotalIt is expressed as follows:
In formula: n is total test number；QNFor the rated capacity of battery；hiDepth of discharge when being tested for i-th；NiFor
Cycle-index when i-th is tested.
Further, handling capacity A of the battery within t-1~t timeop(t) it is expressed as follows:
Aop(t)=| Δ SOC (t) | QN
In formula: | Δ SOC (t) | it is battery carrying capacity change absolute value.
Further, the time t-1 life of storage battery loss factor r (t-1) is expressed as follows:
R (t-1)=mSOC (t-1)+d
In formula: SOC (t-1) is time t-1 battery carrying capacity；M and d is fitting constant.
Beneficial effects of the present invention:
1, power consumer independently chooses whether to participate in demand response project according to itself wish, but currently most of demands
Response model participates in considering not comprehensively for demand response satisfaction for user.Also, the compensation for giving power consumer is all
Fixed making up price is unable to fully that user is encouraged to participate in demand response project.Interruptible load project have fast response time,
It is the important tool of dispatching of power netwoks mechanism peak regulation and processing emergency when being not carried out the advantages of zero cost.Therefore consider user
Participate in interruptible load project.Initially set up user participate in interruptible load project cost, obtain user participate in can interrupt it is negative
The income of lotus project determines the satisfaction of user's participation demand response project with this.Wherein, the given use of micro-capacitance sensor operator
The compensation at family is not fixed making up price, according to user type, participates in the wish degree of demand response project and cuts down electricity
Measure comprehensive descision.
2, consider the factor for influencing service lifetime of accumulator in micro-capacitance sensor Economic Dispatch Problem, introduce and relate in optimization aim
And battery operating cost at this item, achieve the effect that quantitative control.
Detailed description of the invention
Fig. 1 is relational graph between the battery carrying capacity and life consumption coefficient of a preferred embodiment of the invention；
Fig. 2 is the flow chart of the micro-capacitance sensor operation method of a preferred embodiment of the invention；
Fig. 3 is that the system prediction value of a preferred embodiment of the invention and deploying node change over time figure；
Fig. 4 is that the micro-grid load power of a preferred embodiment of the invention changes over time figure；
Fig. 5 is that user's interrupt power of a preferred embodiment of the invention changes over time figure；
Fig. 6 is that the obtained compensation of user of a preferred embodiment of the invention changes over time figure；
Fig. 7 is that the battery carrying capacity of a preferred embodiment of the invention changes over time figure；
Fig. 8 is that the accumulator cell charging and discharging power of a preferred embodiment of the invention changes over time figure.
Specific embodiment
The preferred embodiment of the present invention is introduced below with reference to Figure of description, keeps its technology contents more clear and convenient for reason
Solution.The present invention can be emerged from by many various forms of embodiments, and protection scope of the present invention is not limited only to text
In the embodiment mentioned.
The present invention relates to implement demand response project in micro-capacitance sensor and consider that operating cost energy storage device optimizes operating scheme.
For demand response model, the response cost of user being fully considered, and being compensated according to the enthusiasm of user response, excitation is used
The enthusiasm of family participation demand response.The problem larger for accumulator plant life consumption influences the battery longevity by research
The factor of life considers the influence for influencing maximum battery carrying capacity on economic load dispatching, constructs battery operating cost, reaches fixed
Measure the effect of control.Make specific introduce below:
One, the foundation of demand response and battery operating cost model
1, demand response model
1.1 users participate in interruptible load project cost
The cost that user participates in interruptible load project is that user loses when cutting down electricity consumption to itself bring.User
The cost for participating in interruptible load project can be represented by the following formula:
C (x, θ)=K1x2+K2x-K2xθ (1)
In formula: K1And K2It is the constant greater than 0 for cost coefficient, interruptible load project can be participated according to accumulation user
The historical data of rate selection carries out parameter using Taylor series second outspread formula to formula (1) by economic analysis method and estimates
Meter obtains；θ is user type, is classified according to the wish degree that different types of user participates in interruptible load project, is taken
Value range is 0≤θ≤1；X is that user cuts down power.
1.2 users participate in interruptible load project yield
The income that user participates in interruptible load project can be represented by the formula:
U (θ, x, y)=y-c (θ, x) (2)
In formula: y is that the compensation for participating in interruptible load project user is given by micro-capacitance sensor operator.In real life, only
There is U >=0, user can just participate in interruptible load project.
1.3 micro-capacitance sensor operators implement interruptible load project cost
When pressure micro-capacitance sensor operator, which occurs, in power supply powers to a certain specific position, power supply cost can be sufficiently expensive.
Therefore, the cost needs that micro-capacitance sensor operator implements interruptible load project consider the cost under not saving to customer power supply,
It can be represented by the formula
B (θ, λ)=y- λ x (3)
In formula: λ is deploying node, calculates and obtains using optimal load flow.
A user participates in interruptible load project more than 1.4
Its maximum electricity CM that can be interrupted daily is reported to micro-capacitance sensor fortune in advance by the user for participating in interruptible load project
Seek quotient.Since CM can embody the wish degree that user participates in interruptible load project, micro-capacitance sensor operator is according to the CM of user
It determines its θ, according to the principle that θ value is successively decreased user is ranked up.
User can just be ready to participate in Demand-side when the income that each user participates in interruptible load project is greater than or equal to zero
Response.Therefore, for user j, there are reasonability constraints:
In formula: J is the total number of users that interruptible load project is participated in micro-capacitance sensor.
Sufficiently to excite user to participate in the enthusiasm of interruptible load project, micro-capacitance sensor operator needs according to user response
Enthusiasm compensate, more be ready response user's interests obtained should be more.Accordingly, there exist consistency constraints:
2, battery operating cost
2.1 service lifetime of accumulator
The service life of battery can be by cycle-index table of the battery that production firm provides under different depth of discharges
Show, battery circulation sum such as following formula:
In formula: N is depth of discharge hNUnder battery cycle-index；a1~a5The correlation provided for storage battery production manufacturer
Coefficient.
But since battery is not can guarantee each depth of discharge in actual work identical, generally using effectively gulping down
The amount of spitting carries out service lifetime of accumulator prediction.Effective throughput is that battery discharge and recharge in whole life cycle is total
With.The effective throughput of battery can be estimated using following formula:
In formula: AtotalFor the effective throughput of battery, n is total test number；QNFor the rated capacity of battery；hiFor
Depth of discharge when i-th is tested；NiCycle-index when being tested for i-th.
2.2 battery operating costs
Handling capacity A of the battery within t-1~t timeop(t) it can indicate are as follows:
Aop(t)=| Δ SOC (t) | QN (8)
In formula: | Δ SOC (t) | it is the carrying capacity SOC change absolute value of battery
Life consumption COEFFICIENT K (t) of the battery within t-1~t time can indicate are as follows:
Most important influence factor is the carrying capacity of battery in micro-capacitance sensor economic load dispatching, so mainly considering battery
The influence of carrying capacity.
When the carrying capacity of battery is 0.5, battery is often handled up 1Ah electricity, for battery actual life
1.3Ah effective throughput can be reduced；When the carrying capacity of battery is 1, battery is often handled up 1Ah electricity, for electric power storage
0.55Ah effective throughput can be reduced for the actual life of pond.It is hereby achieved that life of storage battery loss factor r and electric power storage
Relationship between the carrying capacity SOC of pond, as shown in Figure 1.
Sectional linear fitting is carried out to experimental data, available following formula:
R (t)=mSOC (t)+d (10)
In formula: m and d is fitting constant.
The operating cost C of available batteryop(t):
In formula: CSBFor the initial outlay cost of accumulator plant.
Two, optimize micro-capacitance sensor operation method and solve
1, objective function
With the minimum optimization aim of micro-capacitance sensor operator cost, micro-capacitance sensor operator cost F is made of two parts: power supply at
This F1With demand response cost F2。
Power supply cost F1Including fuel cost Cf(t), micro-capacitance sensor operator is in power exchange real-time deal cost Cr
(t), battery operating cost Cop(t)。
In formula: T is to take 24 dispatching cycle.
Fuel cost, transaction cost in formula:
Cr(Pr(t))=γt·Pr(t)
In formula: Cf,i(PiIt (t)) is fuel cost of i-th of diesel-driven generator in time t；I is diesel generation in micro-capacitance sensor
Machine number；γtIt is the real-time deal electricity price of time t；Pr (t) is time t micro-capacitance sensor in spot market transaction power, is taken just if buying
Sell take it is negative.
Cf,i(Pi(t))=aiPi(t)2+biPi(t)
In formula: aiAnd biIt is diesel generating set fuel cost coefficient.
Demand response cost F2It is as follows:
In formula: J is number of users in micro-capacitance sensor.
Since the demand response project implementation has certain complexity, micro-capacitance sensor operator and user's communication are needed,
User is encouraged actively to participate in demand response project.And it needs the electricity consumption situation to user to be investigated, determines that user participates in use
The cost and enthusiasm at family.The operation difficulty of micro-capacitance sensor operator is increased to a certain extent.Therefore, not by F1And F2Directly
Connect addition, but by F1And F2Two sub-goals as Optimized model.For this multi-objective optimization question, added using linear
The processing of power method, introduces weight coefficient w1And w2
F=w1F1+w2F2
In formula: w1+w2=1.
2, constraint condition
Power-balance in micro-capacitance sensor:
In formula: PW(t)、PS(t)、PL(t) be respectively time t wind driven generator output power, photovoltaic system output power,
The initial workload demand of micro-capacitance sensor.
Each micro- source goes out power limit in micro-capacitance sensor:
Pi,max≤Pi(t)≤Pi,min
0≤PW(t)≤Wt
0≤PS(t)≤St
In formula: Pi,max、Pi,minRespectively diesel-driven generator i peak power output and minimum output power.
Climing constant:
-ΔPi,down≤Pi(t+1)-Pi(t)≤ΔPi,up
In formula: Δ Pi,up、ΔPi,downBe respectively diesel-driven generator maximum upwards with the maximum rate of climbing downwards.
Interconnection constraint:
-Prmax≤Pr(t)≤Prmax
In formula: PrmaxIt is the tie-line power transmission upper limit.
Demand response constraint:
yj(t)-(K1,jxj(t)2+K2,jxj(t)-K2,jxj(t)θj)≥0
J=1 ..., J
In formula: B is that micro-capacitance sensor operator implements demand response the estimate for a project daily.
There is the requirement of certain bound to the carrying capacity of battery in the process of running:
SOCmin≤SOC≤SOCmax
In formula: SOCmax、SOCminThe respectively bound of battery carrying capacity.
According to battery maximum discharge current Idisch-max, the available battery carrying capacity variation upper limit:
According to battery maximum charging current Ich-max, available battery carrying capacity variation lower limit:
At the first and last moment of dispatching cycle, the carrying capacity of battery should be consistent:
SOC (1)=SOC (T+1)
Three, embodiment
As shown in Fig. 2, micro-capacitance sensor operation method according to an embodiment of the present invention, comprising the following steps:
S1, demand response, which cooperates with Optimized model with energy storage, to be considered to micro-capacitance sensor foundation；
S2, the optimal value for calculating collaboration Optimized model, obtain the operating scheme of micro-capacitance sensor.
Example chooses the validity of the micro-capacitance sensor verifying Optimized model of a small-scale.Micro-capacitance sensor is by diesel-driven generator, wind
Power generator, photovoltaic cell and battery are constituted.Diesel-driven generator parameter is as shown in table 1.Customer parameter such as 2 institute of table in micro-capacitance sensor
Show.Battery basic parameter is as shown in table 3.Wind power photovoltaic prediction, micro-capacitance sensor initial load, deploying node data such as Fig. 3
It is shown.
1 diesel-driven generator parameter of table
2 customer parameter of table
3 accumulator parameter of table
Fig. 4 gives the variation for considering demand response front and back micro-grid load power.It can be seen that when electricity consumption in micro-capacitance sensor
Peak period (photovoltaic system is not contributed), when deploying node is higher, user voluntarily cuts down power.Therefore, demand response is added
Power shortage problem in micro-capacitance sensor can be effectively relieved later.
The case where user's interrupt power and user's obtained compensation is set forth in Fig. 5 and Fig. 6.In micro-capacitance sensor peak of power consumption
Phase, user voluntarily cut down power, alleviate shortage of electric power.Always interruption electricity is 30kWh to user 1, total compensation obtained is
124.32$；Always interruption electricity is 35kWh to user 2, total compensation obtained is 140 $；It is 40kWh, institute that user 3, which always interrupts electricity,
The total compensation obtained is 158.26 $.The maximum electricity CM that can be interrupted daily reported according to three users to micro-capacitance sensor operator,
User 3 is most to be ready to participate in demand response project, and user 2 takes second place, and 1 enthusiasm of user is minimum.Numerical results embody in model
Consistency constraint, give most compensation for the higher user of response enthusiasm.In entire dispatching cycle, micro-capacitance sensor fortune
Seeking the compensation of quotient's total payoff is 422.57 $.
Observation Fig. 7 and Fig. 8 can be seen that after meter and battery operating cost, and the SOC of battery is in more time section
It is interior all in a higher level；Battery Δ SOC reduces, and the variation of battery SOC slows down, and illustrates that charge and discharge process is more flat
It is slow.Therefore, it is added after battery operating cost in a model, battery can be made to operate in the higher state of SOC, charge and discharge
Cheng Bianhuan extends its service life.
The preferred embodiment of the present invention has been described in detail above.It should be appreciated that the ordinary skill of this field is without wound
The property made labour, which according to the present invention can conceive, makes many modifications and variations.Therefore, all technician in the art
Pass through the available technology of logical analysis, reasoning, or a limited experiment on the basis of existing technology under this invention's idea
Scheme, all should be within the scope of protection determined by the claims.
Claims (10)
1. a kind of consideration demand response cooperateed with energy storage optimization micro-capacitance sensor operation method, which is characterized in that the method includes with
Lower step:
Step 1 considers that demand response cooperates with Optimized model with energy storage to micro-capacitance sensor foundation；
Step 2, the optimal value for calculating the collaboration Optimized model, obtain the operating scheme of micro-capacitance sensor.
2. considering that demand response cooperates with optimization micro-capacitance sensor operation method with energy storage as described in claim 1, which is characterized in that institute
It includes optimization object function and corresponding constraint condition two that consideration demand response described in step 1, which is stated, with energy storage collaboration Optimized model
Part, the optimization aim are that micro-capacitance sensor operator cost is minimum, and micro-capacitance sensor operator cost includes power supply cost F1With
Demand response cost F2Two parts, the constraint condition include power-balance constraint in micro-capacitance sensor, each micro- source power output in micro-capacitance sensor
Restriction, diesel-driven generator Climing constant, interconnection constraint, demand response constraint and the constraint of battery carrying capacity.
3. considering that demand response cooperates with optimization micro-capacitance sensor operation method with energy storage as claimed in claim 2, which is characterized in that institute
State power supply cost F1Including fuel cost Cf(t), real-time deal cost C of the micro-capacitance sensor operator in power exchanger(t) and
Battery operating cost Cop(t), it is expressed as follows:
In formula: T is to take 24 dispatching cycle；
The demand response cost F2It is expressed as follows:
In formula: x is that user cuts down power；Y is that the compensation for participating in interruptible load project user is given by micro-capacitance sensor operator；J is
The total number of users of interruptible load project is participated in micro-capacitance sensor；λ is deploying node.
4. considering that demand response cooperates with optimization micro-capacitance sensor operation method with energy storage as claimed in claim 2, which is characterized in that institute
Stating optimization aim F is by the power supply cost F1With demand response cost F2As two sub-goals of the Optimized model, for
This multi-objective optimization question, is handled using weigthed sums approach, introduces weight coefficient w1And w2, it is expressed as follows:
F=w1F1+w2F2
In formula: w1+w2=1.
5. considering that demand response cooperates with optimization micro-capacitance sensor operation method with energy storage as claimed in claim 4, which is characterized in that institute
Constraint condition is stated to specifically include:
Power-balance constraint is expressed as follows in the micro-capacitance sensor:
In formula: PW(t)、PS(t)、Pr(t)、PBAT(t)、PLIt (t) is respectively time t wind driven generator output power, photovoltaic system
The initial workload demand of output power, tie-line power transmission, battery transimission power, micro-capacitance sensor；
Each micro- source power output restriction is expressed as follows in the micro-capacitance sensor:
Pi,max≤Pi(t)≤Pi,min
0≤PW(t)≤Wt
0≤PS(t)≤St
In formula: Pi,max、Pi,minRespectively diesel-driven generator i peak power output, minimum output power；Wt、StRespectively time t
Predict wind-force maximum output, prediction photovoltaic maximum output；
The diesel-driven generator Climing constant is expressed as follows:
-ΔPi,down≤Pi(t+1)-Pi(t)≤ΔPi,up
In formula: Δ Pi,up、ΔPi,downIt is maximum upward, the maximum rate of climbing downwards of diesel-driven generator respectively；
The interconnection constraint representation is as follows:
-Prmax≤Pr(t)≤Prmax
In formula: PrmaxIt is the tie-line power transmission upper limit；
The demand response constraint representation is as follows:
yj(t)-(K1,jxj(t)2+K2,jxj(t)-K2,jxj(t)θj)≥0
J=1 ..., J
In formula: K1And K2It is the constant greater than 0 for cost coefficient；θ is user type, and being participated according to different types of user can
The wish degree of interruptible load project is classified, and value range is 0≤θ≤1；B is that micro-capacitance sensor operator implements demand daily
Respond the estimate for a project；CM is to participate in the user of interruptible load project in advance by maximum electricity that it can be interrupted daily；
The battery carrying capacity constraint representation is as follows:
SOCmin≤SOC≤SOCmax
In formula: SOCmax、SOCminThe respectively bound of battery carrying capacity,
According to battery maximum discharge current Idisch-max, obtain the battery carrying capacity variation upper limit:
In formula: ηdischFor battery discharging efficiency,
According to battery maximum charging current Ich-max, obtain battery carrying capacity variation lower limit:
In formula: ηchEfficiency to charge the battery,
At the first and last moment of dispatching cycle, the carrying capacity of battery should be consistent:
SOC (1)=SOC (T+1).
6. considering that demand response cooperates with optimization micro-capacitance sensor operation method with energy storage as claimed in claim 3, which is characterized in that institute
State fuel cost Cf(t) it is expressed as follows:
In formula: Cf,i(PiIt (t)) is fuel cost of i-th of diesel-driven generator in time t；Pi(t) exist for i-th of diesel-driven generator
The power of time t；I is diesel-driven generator number in micro-capacitance sensor；
The real-time deal cost is expressed as follows:
Cr(Pr (t))=γt·Pr(t)
In formula: γtIt is the real-time deal electricity price of time t；Pr (t) is time t micro-capacitance sensor in spot market transaction power, if buying
Take just, sell take it is negative.
7. considering that demand response cooperates with optimization micro-capacitance sensor operation method with energy storage as claimed in claim 3, which is characterized in that institute
State battery operating cost Cop(t) it is expressed as follows:
In formula: CSBFor the initial outlay cost of accumulator plant；R (t-1) is time t-1 life of storage battery loss factor；Aop(t)
For handling capacity of the battery within t-1~t time；AtotalFor the effective throughput of battery.
8. considering that demand response cooperates with optimization micro-capacitance sensor operation method with energy storage as claimed in claim 7, which is characterized in that institute
State the effective throughput A of batterytotalIt is expressed as follows:
In formula: n is total test number；QNFor the rated capacity of battery；hiDepth of discharge when being tested for i-th；NiIt is i-th
Cycle-index when secondary test.
9. considering that demand response cooperates with optimization micro-capacitance sensor operation method with energy storage as claimed in claim 7, which is characterized in that institute
State handling capacity A of the battery within t-1~t timeop(t) it is expressed as follows:
Aop(t)=| Δ SOC (t) | QN
In formula: | Δ SOC (t) | it is battery carrying capacity change absolute value.
10. considering that demand response cooperates with optimization micro-capacitance sensor operation method with energy storage as claimed in claim 7, which is characterized in that
The time t-1 life of storage battery loss factor r (t-1) is expressed as follows:
R (t-1)=mSOC (t-1)+d
In formula: SOC (t-1) is time t-1 battery carrying capacity；M and d is fitting constant.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910598580.1A CN110380405B (en) | 2019-07-04 | 2019-07-04 | Micro-grid operation method based on cooperative optimization of demand response and energy storage |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910598580.1A CN110380405B (en) | 2019-07-04 | 2019-07-04 | Micro-grid operation method based on cooperative optimization of demand response and energy storage |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110380405A true CN110380405A (en) | 2019-10-25 |
| CN110380405B CN110380405B (en) | 2023-04-25 |
Family
ID=68251903
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910598580.1A Active CN110380405B (en) | 2019-07-04 | 2019-07-04 | Micro-grid operation method based on cooperative optimization of demand response and energy storage |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110380405B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113139268A (en) * | 2020-01-19 | 2021-07-20 | 荣盛盟固利新能源科技有限公司 | Method for evaluating cycle life of lithium battery based on response surface method |
| CN113991719A (en) * | 2021-12-03 | 2022-01-28 | 华北电力大学 | Island group energy utilization optimization scheduling method and system with participation of electric ship |
| CN114301081A (en) * | 2022-01-11 | 2022-04-08 | 大连理工大学 | Micro-grid optimization method considering energy storage life loss and demand response of storage battery |
| CN114944698A (en) * | 2022-04-29 | 2022-08-26 | 南通电博士自动化设备有限公司 | Intelligent control method and system for diesel generator group |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105977991A (en) * | 2016-05-10 | 2016-09-28 | 浙江工业大学 | Independent micro grid optimization configuration method considering price-type demand response |
| CN107194516A (en) * | 2017-06-07 | 2017-09-22 | 华北电力大学 | Multi-energy complementary micro-grid distributed optimization dispatching method containing multiagent |
| CN107403256A (en) * | 2017-07-01 | 2017-11-28 | 华中科技大学 | One kind considers the probabilistic photovoltaic microgrid battery energy storage collocation method of demand response |
| CN108429288A (en) * | 2018-04-12 | 2018-08-21 | 荆州市荆力工程设计咨询有限责任公司 | A kind of off-network type micro-capacitance sensor energy storage Optimal Configuration Method considering demand response |
| CN108539739A (en) * | 2018-05-10 | 2018-09-14 | 安徽理工大学 | Micro-capacitance sensor runs energy management optimization method |
| US20180357577A1 (en) * | 2017-06-07 | 2018-12-13 | Johnson Controls Technology Company | Building energy optimization system with economic load demand response (eldr) optimization |
| CN109658012A (en) * | 2019-01-22 | 2019-04-19 | 武汉理工大学 | It is a kind of meter and Demand Side Response micro-capacitance sensor multiple target economic load dispatching method and device |
| CN109740846A (en) * | 2018-11-30 | 2019-05-10 | 国网江苏省电力有限公司电力科学研究院 | Intelligent residential district demand response dispatching method and system |
-
2019
- 2019-07-04 CN CN201910598580.1A patent/CN110380405B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105977991A (en) * | 2016-05-10 | 2016-09-28 | 浙江工业大学 | Independent micro grid optimization configuration method considering price-type demand response |
| CN107194516A (en) * | 2017-06-07 | 2017-09-22 | 华北电力大学 | Multi-energy complementary micro-grid distributed optimization dispatching method containing multiagent |
| US20180357577A1 (en) * | 2017-06-07 | 2018-12-13 | Johnson Controls Technology Company | Building energy optimization system with economic load demand response (eldr) optimization |
| CN107403256A (en) * | 2017-07-01 | 2017-11-28 | 华中科技大学 | One kind considers the probabilistic photovoltaic microgrid battery energy storage collocation method of demand response |
| CN108429288A (en) * | 2018-04-12 | 2018-08-21 | 荆州市荆力工程设计咨询有限责任公司 | A kind of off-network type micro-capacitance sensor energy storage Optimal Configuration Method considering demand response |
| CN108539739A (en) * | 2018-05-10 | 2018-09-14 | 安徽理工大学 | Micro-capacitance sensor runs energy management optimization method |
| CN109740846A (en) * | 2018-11-30 | 2019-05-10 | 国网江苏省电力有限公司电力科学研究院 | Intelligent residential district demand response dispatching method and system |
| CN109658012A (en) * | 2019-01-22 | 2019-04-19 | 武汉理工大学 | It is a kind of meter and Demand Side Response micro-capacitance sensor multiple target economic load dispatching method and device |
Non-Patent Citations (3)
| Title |
|---|
| 李姚旺等: "考虑需求响应不确定性的光伏微电网储能系统优化配置", 《电力系统保护与控制》 * |
| 李盛伟: ""计及储能和用户需求响应的并网型微网优化调度模型"", 《电工电能新技术》 * |
| 田德等: "基于需求侧响应与成本模型的风电中的储能系统运行优化", 《农业工程学报》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113139268A (en) * | 2020-01-19 | 2021-07-20 | 荣盛盟固利新能源科技有限公司 | Method for evaluating cycle life of lithium battery based on response surface method |
| CN113991719A (en) * | 2021-12-03 | 2022-01-28 | 华北电力大学 | Island group energy utilization optimization scheduling method and system with participation of electric ship |
| CN113991719B (en) * | 2021-12-03 | 2023-11-24 | 华北电力大学 | Energy consumption optimization scheduling method and system for island group participated in by electric ship |
| CN114301081A (en) * | 2022-01-11 | 2022-04-08 | 大连理工大学 | Micro-grid optimization method considering energy storage life loss and demand response of storage battery |
| CN114944698A (en) * | 2022-04-29 | 2022-08-26 | 南通电博士自动化设备有限公司 | Intelligent control method and system for diesel generator group |
| CN114944698B (en) * | 2022-04-29 | 2024-05-03 | 湛江伟力机电设备有限公司 | Intelligent diesel generator group control method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110380405B (en) | 2023-04-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110380405A (en) | Consider that demand response cooperates with optimization micro-capacitance sensor operation method with energy storage | |
| CN106651026B (en) | Multi-time scale microgrid energy management optimization scheduling method | |
| CN102289566B (en) | Multiple-time-scale optimized energy dispatching method for micro power grid under independent operation mode | |
| CN102104251B (en) | Microgrid real-time energy optimizing and scheduling method in parallel running mode | |
| CN107565607B (en) | Micro-grid multi-time scale energy scheduling method based on real-time electricity price mechanism | |
| CN109740827A (en) | A kind of regional complex energy system planning optimization method based on dual-layer optimization | |
| CN103346562A (en) | Multi-time scale microgrid energy control method considering demand response | |
| CN103151797A (en) | Multi-objective dispatching model-based microgrid energy control method under grid-connected operation mode | |
| US20230070151A1 (en) | Hierarchical energy management for community microgrids with integration of second-life battery energy storage systems and photovoltaic solar energy | |
| CN105140941B (en) | Consider that lithium battery can be changed the micro-capacitance sensor Optimization Scheduling of depreciable cost and practical charge and discharge strategy | |
| CN111786422B (en) | Real-time optimization scheduling method for participating in upper-layer power grid by micro-power grid based on BP neural network | |
| CN104158203A (en) | Micro-grid power supply capacity optimization configuration method | |
| CN109149651A (en) | It is a kind of meter and pressure regulation ancillary service income light-preserved system optimizing operation method | |
| CN106447131A (en) | Independent microgrid photovoltaic output power prediction method and energy regulation method | |
| CN112508325B (en) | Household micro-grid multi-time scale electric energy scheduling method | |
| CN113780670B (en) | Two-stage-based regional power grid electric automobile peak shaving optimization scheduling method | |
| CN110245794A (en) | Consider that the multiple-energy-source of flexibility collects central flame storage capacitance double layer optimization method | |
| CN104578145B (en) | A kind of continuous duty type load and energy control method towards intelligent power | |
| CN112366684A (en) | Island micro-grid system | |
| CN115133548A (en) | Storage-contained power system optimal scheduling method considering operation flexibility | |
| CN112329260A (en) | Multi-energy complementary micro-grid multi-element multi-target optimization configuration and optimization operation method | |
| Ma et al. | Energy storage capacity optimization for improving the autonomy of grid-connected microgrid | |
| CN113690875A (en) | Micro-grid real-time interaction equivalent model establishing method | |
| CN116961008A (en) | Micro-grid capacity double-layer optimization method considering power spring and load demand response | |
| CN114498769B (en) | High-proportion wind-solar island micro-grid group energy scheduling method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |