CN106056256A - Interdynamic microgrid scheduling method for balancing power supply and demand relation - Google Patents
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Abstract
The invention discloses an interdynamic microgrid scheduling method for balancing power supply and demand relation in the field of power system scheduling. The interdynamic microgrid scheduling method includes fitting the day-ahead load prediction curve, the day-ahead wind-power/photoelectric prediction curve, and the generating curve and making the day-ahead prediction electricity price based on the fitted curve; performing hour-ahead correction of the day-ahead curve; issuing the corrected electricity price based on the hour-ahead correction curve; acquiring and monitoring state parameters; inputting the acquired and monitored state parameters into the multi-target scheduling strategy to carry out the multi-target non-linear planning at the lowest cost; making artificial decisions; and verifying the supply and demand balance and trend distribution, and repeating the scheduling control of the next time period. The method considers multi-dimensional peak-shifting resources and multi-dimensional cost benefits, thereby solving the problem that the scheduled source for power supply and demand balance is single, and simultaneously optimizing the economic benefit and energy-saving and emission-reducing benefit.
Description
Technical field
The present invention relates to electric power system dispatching field, specifically refer to a kind of interactive micro-for balancing electric power relation between supply and demand
Net dispatching method.
Background technology
The peakload that power load growing, the especially sharp increase of peak phase in summer power consumption produce, sternly
Heavily destroy the relation of power supply and demand balance, even cause large-area power cuts to limit consumption, make the power quality of certain customers be subject to
Impact；On the other hand, electrical network is difficult to dissolve current a large amount of distributed news accessed, and causes serious resource wave
Take and economic loss.Additionally, the unbalanced supply-demand problem of power system is generally solved by scheduling variable load plant during peak load, adjust
The problems such as peak power plant off-capacity highlight day by day.Peak regulation factory can be divided into waterpower peak regulation factory and firepower peak regulation factory, wherein waterpower peak regulation
Factory is because the advantage such as fast response time, peak regulation efficiency is high and economic and environment-friendly is as the main force of peak regulation, but in dry season, and firepower peak regulation
Unit is main peak regulation resource, and peak regulation means are single.Therefore, need badly on the basis of tradition peak regulation resource, include in distributed
Generating (DG), user side demand response (DR) and energy storage (ES) resource carry out combined adjusting peak.
But, it is different from the flexible dispatching to conventional electric power generation resource, distributed power generation, user side demand response and energy storage money
Source is respectively provided with certain rigid nature, is i.e. only capable of the Spline smoothing by its running status and realizes equilibrium of supply and demand scheduling, therefore needs
The integration of above resource and integrated regulation and control is realized as a dispatching control center by virtual plant (VPP).
Above peak regulation resource is carried out the modeling analysis of various dimensions, especially energy-saving and emission-reduction and ES is acted on and adjust a few days ago
The overall process of degree is included in the generation of dispatching method, it is possible to realize the optimization of microgrid traffic control.Under unbalanced supply-demand relation,
ES has source lotus double grading, i.e. at electric power for carrying out partly dissolving to DG by ES more than taking, and by it during peak load
The electric power dissolved is sent to electrical network, thus can realize the compensation to the supply and demand vacancy caused because of factors such as uncertainties.ES is at microgrid
On the one hand the application of scheduling decreases and abandons wind, abandons light quantity, on the other hand decreases peak load reduction, on safe and reliable basis
On, it is achieved that energy-saving and emission-reduction and the purpose of economic optimization.
Summary of the invention
It is an object of the invention to provide the interactive microgrid dispatching method of a kind of balancing electric power relation between supply and demand, it is possible to solve electricity
The problem that power equilibrium of supply and demand schedulable resource is single, optimizes its economic benefit and energy-saving and emission-reduction benefit simultaneously.Concrete steps include:
Step 1: matching prediction curve a few days ago also formulates forecasted electricity market price a few days ago
Statistics microgrid operation history data, formed a few days ago load prediction curve (P L,t), a few days ago wind-powered electricity generation prediction curve (P W,t) and day
Front photovoltaic generation prediction curve (P S,t), it is fitted to generate electricity curve and formulate forecasted electricity market price according to thisr；
Step 2: curve a few days ago is modified
Multiple period will be divided into, according to load condition on the same day, weather condition and microgrid ruuning situation, to step by prediction curve a few days ago
The prediction curve a few days ago of 1 (P X,t) when carrying out before revise, the curve that is fitted to generate electricity formed the t period fair curve (P X+ɛ,t), and
According to this forecasted electricity market price is modified tor’, issue modified pricer’；
Step 3: the monitoring of quantity of state and collection
Carry out monitoring and the collection of quantity of state, including microgrid actual motion parameter, peak regulation nargin and capacity thereof, energy storage nargin and
Capacity, meteorological condition etc.；
Step 4: input Multiobjective Scheduling strategy solves
The service data that input Multiobjective Scheduling strategy, prediction data based on step 3 and step 4 gather, runs about at microgrid
Bundle, virtual plant are cut down and are exerted oneself, turn to object function with cost minimization under climbing rate and capacity-constrained thereof and carry out multiple target non-thread
Property programming evaluation, formed cost minimization scheduling scheme；
Step 5: manual decision
The scheme of previous step virtual plant aid decision is submitted to control centre, and dispatcher manually determines according to practical situation
Plan, if scheme optimum then carries out next step, otherwise returns step 3 modified price again until optimum；
Step 6: the verification equilibrium of supply and demand and trend distribution
Whether the verification equilibrium of supply and demand and trend distribution meet standard-required, if being unsatisfactory for, then need to be entered by artificial regulatory behavior
Row sum-equal matrix is until conformance with standard requirement；
Step 7: repeat subsequent period scheduling controlling
On the basis of period t conformance with standard requires, start the scheduling controlling of subsequent period from step 2.
The useful achievement of the present invention is, it is proposed that the interactive microgrid dispatching method of a kind of balancing electric power relation between supply and demand, with
Existing microgrid dispatching method compares, and main advantage and improvement are as follows:
By virtual plant as dispatching control center, on the basis of tradition equilibrium of supply and demand resource, include distributed power generation, use in
Side, family demand response and energy storage resource form supply and demand combined regulating, and by a few days ago, time before the prediction side coordinated of Multiple Time Scales
Method, improves precision of prediction；Additionally, tou power price is predicted with linearization technique, reduce tou power price prediction
Difficulty, the most effectively.
Accompanying drawing explanation
Fig. 1 is the Organization Chart of equilibrium of supply and demand scheduling.
Fig. 2 is the overall flow figure of the present invention.
Fig. 3 is the step explanatory diagram of the present invention.
Detailed description of the invention
The present invention is the interactive microgrid dispatching method of a kind of balancing electric power relation between supply and demand, is embodied as step as follows:
Step 1: matching prediction curve a few days ago also formulates forecasted electricity market price a few days ago
Statistics microgrid operation history data, formed a few days ago load prediction curve (P L,t), a few days ago wind-powered electricity generation prediction curve (P W,t) and day
Front photovoltaic generation prediction curve (P S,t), it is fitted to generate electricity curve and formulate forecasted electricity market price according to thisr；
DefinitionδFor coefficient of association, and calculated by following formula:
δ =(ΔP m,Ʃt-1/Δr t-1)·( r B /P B)
Wherein: ΔP m,Ʃt-1For the variable quantity of load, Δ in t-1 period matched curveP m,Ʃt-1=P m,Ʃt-1-ΔP B,t-1；r BFor intending
Close the zero potential energy under base lotus in curve；Δr t-1For the variable quantity of electricity price, Δ in t-1 period matched curver t-1=r t-1-Δr B；P BFor the baseline load in matched curve
A prediction curve good according to matching formulates forecasted electricity market price a few days ago, following formula calculate:
r= r B + r B · ΔP m,Ʃt/(P B·δ )
Wherein: ΔP m,ƩtFor the variable quantity of load, Δ in t period matched curveP m,Ʃt=P m,Ʃt-ΔP B,Ʃt
Step 2: curve a few days ago is modified
Multiple period will be divided into, according to load condition on the same day, weather condition and microgrid ruuning situation, to step by prediction curve a few days ago
The prediction curve a few days ago of 1 (P X,t) when carrying out before revise, the curve that is fitted to generate electricity formed the t period fair curve (P X+ɛ,t), and
According to this forecasted electricity market price is modified tor’, issue modified pricer’, wherein foundationP X+Modified pricer’Method with reference to step 1
Step 3: the monitoring of quantity of state and collection
Carry out monitoring and the collection of quantity of state, including microgrid actual motion parameter, peak regulation nargin and capacity thereof, energy storage nargin and
Capacity, meteorological condition etc.
Step 4: input Multiobjective Scheduling strategy solves
The service data that input Multiobjective Scheduling strategy, prediction data based on step 3 and step 4 gather, runs about at microgrid
Bundle, virtual plant are cut down and are exerted oneself, turn to object function with cost minimization under climbing rate and capacity-constrained thereof and carry out multiple target non-thread
Property programming evaluation, formed cost minimization scheduling scheme
Certain period cost function of t firepower regulating units is calculated by following formula:
f 1=Ʃ[C G,i,t+ C UG,i ·(1- U G,i,t-1)+λ i·exp(λ i P i t)] ·U G,i,t;(i=1···NG)
Wherein:C G,i,tFor the operating cost of t period fired power generating unit i,C G,i,t=α P t,i 2+β P t,i+γ；C UG,iFor t period fire
The start-up cost of group of motors i；U G,i,t-1For t-1 period fired power generating unit i running status (with two-dimensional discrete numeric representation, wherein 0
For shutting down, 1 for running)；U G,i,tFor the running status of t period fired power generating unit i, (with two-dimensional discrete numeric representation, wherein 0 for stopping
Machine, 1 for running)；λ i·exp(λ i P i t) it is the blowdown cost of t period fired power generating unit i
Additionally, under certain period t by ES realize a certain degree of cut load compensation, abandon wind compensate and abandon light compensate, it may be assumed that
f 2= P’ LS,t ·r LS + P’ WS,t ·r WS + P’ SS,t ·r SS
Wherein:P’ LS,tLoading is cut for revising,P’ LS,t = P LS,t - P CLS,t ,P LS,tFor cutting loading,P CLS,tFor cutting load
Compensation dosage, is determined by ES energy storage situation when supply-less-than-demand；P’ WS,tAir quantity is abandoned for revising,P’ WS,t = P WS,t - P CWS,t ,P WS,tFor abandoning air quantity,P CWS,tFor abandoning wind compensation dosage, determine by ES energy storage situation when supply exceed demand；P’ SS,tLight quantity is abandoned for revising,P’ SS,t = P SS,t - P CSS,t ,P SS,tFor abandoning light quantity,P CSS,tFor abandoning light compensation dosage, when supply exceed demand by ES energy storage situation certainly
Fixed;r LS、r WS、r SSRespectively cut load cost, abandon eolian and abandon light cost
Demand response is implemented in user side, and the cost function under certain period t is as follows:
f 3=ƩC I,n,t+ ƩC P,m,t;(n=1···NI, m=1···NP)
Wherein:C I,n,tFor the demand response cost based on excitation of user n under period t, with reference to excitation to user under electricity market
Strategy calculates；C P,m,tFor demand response based on the price cost of user m under period t, with reference under electricity market to user's
Incentives strategy calculates
Final object function is multiobjective non linear programming:
min{ f 1, f 2, f 3}
Constraints is as follows:
1) power-balance constraint:
ƩP G,i,t+P W,t+P S,t+P ’ LS,t+ΔP ES,t = P L,t + P ’ WS,t +P ’ SS,t;(i=1···NG)
Wherein: ΔP ES,t For energy storage surplus
2) wind-powered electricity generation, photoelectricity units limits:
0≤P W,t ≤P Wmax,t
0≤P S,t ≤P Smax,t
3) compensation dosage constraint:
A, abandon wind/abandon light and dissolve compensation:
0≤P CWS,t+ P CSS,t ≤P ES
0≤P CWS,t ≤P ES
0≤P CSS,t ≤P ES
B, cut load compensation:
0≤P CLS,t ≤P ES
Wherein:P ESFor energy storage device capacity
4) electromotor constraint:
Including bound constraint of exerting oneself, minimum start-off time constraints, spare capacity constraint and Climing constant
The data of all collections are transferred to control centre, above-mentioned solution procedure can be realized by the calculation procedure of existing maturation
Step 5: manual decision
The scheme of previous step virtual plant aid decision is submitted to control centre, and dispatcher manually determines according to practical situation
Plan, if scheme optimum then carries out next step, otherwise returns step 3 modified price again until optimum；
Step 6: the verification equilibrium of supply and demand and trend distribution
Whether the verification equilibrium of supply and demand and trend distribution meet standard-required, if being unsatisfactory for, then need to be entered by artificial regulatory behavior
Row sum-equal matrix is until conformance with standard requirement；
Step 7: repeat subsequent period scheduling controlling
On the basis of period t conformance with standard requires, start the scheduling controlling of subsequent period from step 2.
The present invention provides the interactive microgrid dispatching method of a kind of balancing electric power relation between supply and demand, based on virtual plant by microgrid
The problem of economic load dispatching is modeled, and is solved by the method for Non-Linear Programming, on the one hand considers various dimensions peak regulation
The coordination of resource is mutual, improves peak modulation capacity and economic benefit；On the other hand various dimensions economic benefit is considered, it is achieved energy-conservation
Reduce discharging.The above, the only specific implementation method of the present invention, but protection scope of the present invention is not limited thereto, and any ripe
Know those skilled in the art in the technical scope that the invention discloses, the change that can readily occur in or replacement, all should contain
Cover within protection scope of the present invention, therefore.Protection scope of the present invention should be as the criterion with the protection domain of claims.
Claims (3)
1. the interactive microgrid dispatching method of a balancing electric power relation between supply and demand, it is characterised in that comprise the following steps:
Step 1: matching prediction curve a few days ago also formulates forecasted electricity market price a few days ago
Statistics microgrid operation history data, formed a few days ago load prediction curve (P L,t), a few days ago wind-powered electricity generation prediction curve (P W,t) and day
Front photovoltaic generation prediction curve (P S,t), it is fitted to generate electricity curve and formulate forecasted electricity market price according to thisr；
Step 2: curve a few days ago is modified
Multiple period will be divided into, according to load condition on the same day, weather condition and microgrid ruuning situation, to step by prediction curve a few days ago
The prediction curve a few days ago of 1 (P X,t) when carrying out before revise, the curve that is fitted to generate electricity formed the t period fair curve (P X+ɛ,t), and
According to this forecasted electricity market price is modified tor’, issue modified pricer’；
Step 3: the monitoring of quantity of state and collection
Carry out monitoring and the collection of quantity of state, including microgrid actual motion parameter, peak regulation nargin and capacity thereof, energy storage nargin and
Capacity, meteorological condition etc.；
Step 4: input Multiobjective Scheduling strategy solves
The service data that input Multiobjective Scheduling strategy, prediction data based on step 3 and step 4 gather, runs about at microgrid
Bundle, virtual plant are cut down and are exerted oneself, turn to object function with cost minimization under climbing rate and capacity-constrained thereof and carry out multiple target non-thread
Property programming evaluation, formed cost minimization scheduling scheme；
Step 5: manual decision
The scheme of previous step virtual plant aid decision is submitted to control centre, and dispatcher manually determines according to practical situation
Plan, if scheme optimum then carries out next step, otherwise returns step 3 modified price again until optimum；
Step 6: the verification equilibrium of supply and demand and trend distribution
Whether the verification equilibrium of supply and demand and trend distribution meet standard-required, if being unsatisfactory for, then need to be entered by artificial regulatory behavior
Row sum-equal matrix is until conformance with standard requirement；
Step 7: repeat subsequent period scheduling controlling
On the basis of period t conformance with standard requires, start the scheduling controlling of subsequent period from step 2.
2. according to described in claims 1, multiple target equilibrium of supply and demand scheduling strategy, it is characterised in that described step 3, step 4 are adjusted
The modeling process of degree strategy is:
f 1=Ʃ[C G,i,t+ C UG,i ·(1- U G,i,t-1)+λ i·exp(λ i P i t)] ·U G,i,t;(i=1···NG)
Wherein:C G,i,tFor the operating cost of t period fired power generating unit i,C G,i,t=α P t,i 2+β P t,i +γ；C UG,iFor t period thermoelectricity
The start-up cost of unit i；U G,i,t-1For the running status of t-1 period fired power generating unit i, (with two-dimensional discrete numeric representation, wherein 0 is
Shutting down, 1 for running)；U G,i,tFor t period fired power generating unit i running status (with two-dimensional discrete numeric representation, wherein 0 for shut down,
1 for running)；λ i·exp(λ i P i t) it is the blowdown cost of t period fired power generating unit i
Additionally, under certain period t by ES realize a certain degree of cut load compensation, abandon wind compensate and abandon light compensate, it may be assumed that
f 2= P’ LS,t ·r LS + P’ WS,t ·r WS + P’ SS,t ·r SS
Wherein:P’ LS,tLoading is cut for revising,P’ LS,t = P LS,t - P CLS,t ,P LS,tFor cutting loading,P CLS,tFor cutting load
Compensation dosage, is determined by ES energy storage situation when supply-less-than-demand；P’ WS,tAir quantity is abandoned for revising,P’ WS,t = P WS,t - P CWS,t ,P WS,tFor abandoning air quantity,P CWS,tFor abandoning wind compensation dosage, determine by ES energy storage situation when supply exceed demand；P’ SS,tLight quantity is abandoned for revising,P’ SS,t = P SS,t - P CSS,t ,P SS,tFor abandoning light quantity,P CSS,tFor abandoning light compensation dosage, when supply exceed demand by ES energy storage situation certainly
Fixed;r LS、r WS、r SSRespectively cut load cost, abandon eolian and abandon light cost
Demand response is implemented in user side, and the cost function under certain period t is as follows:
f 3=ƩC I,n,t+ ƩC P,m,t;(n=1···NI, m=1···NP)
Wherein:C I,n,tFor the demand response cost based on excitation of user n under period t, with reference to excitation to user under electricity market
Strategy calculates；C P,m,tFor demand response based on the price cost of user m under period t, with reference under electricity market to user's
Incentives strategy calculates
Final object function is multiobjective non linear programming:
min{ f 1, f 2, f 3}。
3. according to the manual decision described in claims 1 and regulation and control method, it is characterised in that described step 5, step 6 include:
The scheme of step 4 virtual plant aid decision is submitted to control centre, and dispatcher manually determines according to practical situation
Plan, if scheme optimum then carries out next step, otherwise return step 3 modified price again until the optimum verification equilibrium of supply and demand and
Whether trend distribution meets standard-required, if being unsatisfactory for, then needs to be adjusted by artificial regulatory behavior until conformance with standard is wanted
Ask.
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