CN107069807B - Containing uncertain budget adjust without balance nodes microgrid Robust Scheduling method - Google Patents
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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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
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Abstract
The present invention is constraint building microgrid robust Optimal Operation Model with system safety operation, is generated optimal solution set without balance nodes microgrid Robust Scheduling method containing what uncertain budget was adjusted with the minimum target of operating cost, Environmental costs and renewable energy fluctuation.Using grey entropy relation grade as the evaluation index of optimal solution set, the micro-capacitance sensor Robust Scheduling method with grey entropy relation grade for preferentially index is established.Currently and the state of history predicts the uncertain budget of current micro-capacitance sensor scheduling by mapping rule by energy-storage system, finally by the objective optimal solution of selecting of grey entropy relation grade as generator unit power output instruction.The method of the present invention can effectively enhance system to the resistance of uncertain factor, have extremely strong operability；Uncertain budget adjusts tactful control one, and the control characteristic of micro battery is adjusted while distribute micro battery generation schedule, make micro-capacitance sensor economy, it is environmentally friendly formulate generation schedule, scheduling process is with preferable robustness；Energy-storage system super-charge super-discharge can effectively be avoided.
Description
Technical field
The invention belongs to dispatching automation of electric power systems technical fields, in particular to put down containing the nothing that uncertain budget is adjusted
Weigh node microgrid Robust Scheduling method.
Background technique
In recent years, access renewable energy effectively expands power supply benefited surface on a large scale, improves capacity usage ratio, is promoted
Power supply reliability, promotes Power Market Development and reform, reduces carbon emission, reduce line loss, slow down load inflation
The pressure of growth.However, the uncertain factor in micro-capacitance sensor becomes one of the bottleneck of micro-capacitance sensor application, instantly about not true
The research of qualitative factor becomes the hot and difficult issue of related fields.
Micro-capacitance sensor be by distributed generation resource, load, energy-storage system and its control device organic combination together be transported to electricity
System.Its concept is derived from the development of distributed generation technology.Beauty, Europe, day etc., country was pushed away by exemplary engineering and relevant item
The micro-capacitance sensor classical architecture being adapted with itself is gone out.China adheres to the energy development strategy policy of " saving, cleaning, safety ",
It is intended to build clean and effective, safety, sustainable modern energy system；" energy development strategy action plan (2014- thus
The year two thousand twenty) " it proposes economization at first, base on our country, green low-carbon, the four major strategies for innovating driving；Establish national wind-light storage
Defeated demonstration project, the golden solar water light complementation micro-capacitance sensor power generating demonstration work of Zhoushan Of Zhejiang Province east Fushan island micro-capacitance sensor demonstration project and country
Multiple micro-capacitance sensor projects such as journey.
For the mixing micro-capacitance sensor containing wind light generation, micro-capacitance sensor is dispatched often using economy as target, to transport safely
Behavior restraint condition.With going deep into for environmental protection concept, the concepts such as " cleaning ", " green ", " sustainable " also become micro-capacitance sensor operation
Important requirement in the process, and produce a series of Environmental Technology indexs related with micro-capacitance sensor and target expectation.But in view of light
Electricity power output and load have fluctuation, intermittence and randomness in spatial and temporal distributions, when system power fluctuation is larger, system electricity
Pressure and frequency can exceed range of operation, so that the stability for influencing micro-grid system even causes power system accident.Therefore, such as
Where micro-capacitance sensor safe and environment-friendly, economic operation important in inhibiting is realized in uncertain environment.
Currently, electric system mainly passes through the spare capacity increased in scheduling process, and the adjusting dependent on true scheduling
Ability copes with the uncertain factor in micro-capacitance sensor.But spare increase can cause the increase of system annual calculating cost.And it is true
Scheduling mainly includes quick regulator generator group, and cutting load, abandonment electric light electricity etc. operate, but quickly regulator generator group can generate
Higher power generation expense, cutting load may cause overlay area power-off, and abandonment electric light electricity causes the waste of renewable energy.For
This, many experts and scholars propose the scheduling models such as robust Optimal Operation Model, probability constraints scheduling model, but robust optimization is deposited
The problem of conservative selects, and probability constraints need a large amount of historical data to support the problem of selecting with confidence level.Therefore, face
To the running uncertain factor of micro-capacitance sensor, scheduling strategy robustness how is improved, lowering micro-capacitance sensor operation risk becomes as electricity
Net operation there is an urgent need to.
Summary of the invention
For above-mentioned technical problem of the existing technology, what the present invention proposed to adjust containing uncertain budget saves without balance
Point microgrid Robust Scheduling method, realizes under uncertain environment, prevents energy storage while enhancing system call strategy robustness
System super-charge super-discharge.
To achieve the above object, the present invention adopts the following technical scheme:
Containing uncertain budget adjust without balance nodes microgrid Robust Scheduling method, comprising the following steps:
The first step determines parameter according to micro-capacitance sensor scheduling information, constructs micro-capacitance sensor Robust Scheduling model:
According to the general requirement that Uncertain environments generate electricity, enchancement factor is determined；Set description is not known not by boxlike
It determines the waving interval of parameter, minimum target is fluctuated with operating cost, Environmental costs and renewable energy, is dispatched with micro-capacitance sensor
Safe and sagging control service requirement is constraint building micro-capacitance sensor Robust Scheduling model；
Second step adjusts tactful uncertainty in traffic prediction by uncertain budget, and institute's predicted value is transmitted to micro-
In power grid Robust Scheduling model:
It is currently predicted, and will be predicted by mapping rule uncertainty in traffic with the state of history according to micro-capacitance sensor operation
Value is applied in current Robust Scheduling model；
Third step solves generation schedule and sagging control voltage magnitude using improvement differential evolution algorithm respectively, frequency, has
Function power and the rated value of reactive power and sagging coefficient；
The improvement differential evolution algorithm is based on differential evolution algorithm, when updating contemporary particle with grey entropy relation grade
For standard, outstanding particle is allowed to carry out local search using cloud model, updates outstanding particle so that part is more excellent, and will be updated excellent
Elegant particle is stored in external elite archive；Meanwhile it is outstanding to allow non-outstanding particle to pick out by chaos algorithm progress global search
Particle, and selected outstanding particle is stored in external elite archive；Next-generation searching process is subsequently entered until meeting eventually
Only until condition；It is that evaluation index selects optimal grain by grey entropy relation grade when each scheduling iteration process reaches maximum algebra
Son is used as generation schedule and sagging control plan, if dispatching continuation, passes through uncertain budget and adjusts tactful predict newly
Uncertainty prediction, repeats second step and third step, until finishing scheduling.
The uncertainty budget adjusts strategy are as follows:
Rule 1 predicts energy-storage system state offset without guiding principle amount and history uncertainty by the way of forgetting factor weighting
Linear weighted function is carried out, formula is as follows:
Rule 2 fluctuates historical power using weigthed sums approach and the prediction of history uncertainty is weighted, and formula is as follows:
Rule 3 is using the prediction of neural network uncertainty, load error prediction, photovoltaic power output error prediction, blower
Power output error prediction goes out with time, the prediction of history uncertainty, historical load error, history photovoltaic power output error, history blower
The mapping relations of power error, historical load error prediction, history photovoltaic power output error prediction, history blower power output error prediction,
Formula is as follows:
In three above formula, Γ (t) is the prediction of period t uncertainty；Γmin、ΓmaxRespectively uncertain prediction
Upper and lower limit；ω1、ω2、…、ωn... it is forgetting factor；Soct-1For the state-of-charge of t-1 moment energy-storage system； Soc
The respectively upper and lower bound of energy-storage system state-of-charge；Γ (0) is the initial value of uncertain budget；a1、a2、…、an...,
b1、b2、…、bn... respectively mapping rule coefficient；PL,t、PPV,t、PWT,tRespectively t moment load, photovoltaic power generation and wind-force hair
The power of electricity；ΔPL,tFor period t load error；ΔPPV,tFor period t photovoltaic power output error；ΔPWT,tFor period t blower power output
Error；For the prediction of period t load error；For period t photovoltaic power output error prediction；Go out for period t blower
Power error prediction；
Above-mentioned uncertainty budget adjusts the uncertain prediction that strategy is predicted and passes to Robust Scheduling model.
For the micro-capacitance sensor in isolated operation mode, micro-capacitance sensor is using reciprocity control strategy, that is, sagging control；Containing not true
Qualitative budget adjusts the Robust Scheduling model of strategy and coordinates to adjust while formulating generated output plan for the dispatching method of core
The control parameter for spending the sagging control of each inverter makes micro-capacitance sensor operation have robustness.
The present invention has the advantages that
System can effectively be enhanced to the resistance of uncertain factor by improving scheduling using robust optimum theory, and the method for the present invention is not
Need accurate probability distribution, it is only necessary to for the fluctuation range of uncertain parameter, there is extremely strong operability.
The uncertain budget of this method adjusts tactful control one, adjusts while distributing micro battery generation schedule micro-
The control characteristic of power supply, make micro-capacitance sensor economy, it is environmentally friendly formulate generation schedule, scheduling process has preferable robustness.
The feedback regulation strategy for introducing uncertain budget realizes the dynamic regulation of uncertain budget, effectively avoids energy storage
System super-charge super-discharge.
Detailed description of the invention
Fig. 1 is the mixing independent operating micro-grid system dispatch circuit figure of the embodiment of the present invention 1；
In figure:
1 battery
2 battery Boost/Buck charge-discharge circuits
3 battery Boost/Buck charge-discharge circuit filter capacitors
4 battery Boost/Buck charge-discharge circuit filter inductances
5 dc-link capacitances
6 unidirectional inverter circuits
7 unidirectional inverter circuit filter inductances
8 unidirectional inverter circuit filter capacitors
9 three-phase loads
10 miniature gas turbine transforming circuit LC filters
11 miniature gas turbine transforming circuit filter capacitors
12 miniature gas turbine transforming circuit filter inductances
13 miniature gas turbine inverters
14 miniature gas turbine Boost circuit filter capacitors
15 miniature gas turbine Boost circuits
16 miniature gas turbine Boost circuit inductance
17 miniature gas turbine Boost circuit capacitors
18 miniature gas turbine three-class power electronic transformers
19 miniature gas turbines
20 two-way DC/AC converters
21 two-way DC/AC converter LC filter inductances
22 two-way DC/AC converter LC filter capacitors
23 two-way DC/AC converter LC filters
24 diesel engine transforming circuit LC filters
25 diesel engine transforming circuit filter capacitors
26 diesel engine transforming circuit filter inductances
27 diesel engine inverters
28 diesel engine Boost circuit filter capacitors
29 diesel engine Boost circuits
30 diesel engine Boost circuit inductance
31 diesel engine Boost circuit capacitors
32 diesel engine three-class power electronic transformers
33 diesel engines
34 photovoltaic cells
35 photovoltaic cell Boost circuits
36 photovoltaic cell Boost circuit capacitors
37 photovoltaic cell Boost circuit inductance
38 wind-driven generator Boost circuits
39 wind-driven generator Boost circuit inductance
40 wind-driven generator Boost circuit capacitors
41 wind-driven generator uncontrollable rectifiers
42 wind-driven generators
43 fuel cell transforming circuit LC filters
44 fuel cell transforming circuit filter capacitors
45 fuel cell transforming circuit filter inductances
46 fuel cell inverters
47 fuel cell Boost circuit filter capacitors
48 fuel cell Boost circuits
49 fuel cell Boost circuit inductance
50 fuel cell Boost circuit capacitors
51 fuel cell second level electric power electric transformers
52 fuel cells
Fig. 2 is the flow chart for the micro-capacitance sensor Robust Scheduling method that the present invention adjusts strategy containing uncertain budget.
Fig. 3 is the mapping rule that the uncertain budget of the present invention adjusts strategy.
Specific embodiment
It elaborates, but does not constitute to the present invention to a specific embodiment of the invention below in conjunction in conjunction with specific attached drawing
The limitation of claims.
1, for purposes of illustration only, photovoltaic generation unit abbreviation PV, wind power generation unit abbreviation WT, miniature gas turbine power generation are single
First abbreviation MT, diesel power generation unit abbreviation DG, fuel-cell generation unit abbreviation FC, ac bus abbreviation Bus.
According to the difference of time scale use micro-capacitance sensor hierarchical control method: zero level control, level-one control, Two-stage control,
Three class control.Wherein, zero level control (Millisecond) are as follows: the control unit is located inside each generator unit and energy-storage units, maintains
The normal operation of each unit improves each unit controlling and economy, provides preparation for level-one control；Level-one control (second grade)
Are as follows: the sagging control of controllable micro battery itself is utilized, the power basic point and sagging coefficient of Energy Management System distribution are executed, is inhibited micro-
The instantaneous fluctuation within a narrow range of load in power grid；Two-stage control (minute grade) are as follows: utilize the sagging control of controllable micro battery itself, execute
Idling frequency and floating voltage in the droop characteristic of Energy Management System distribution, reply load are brought because of long-time fluctuation
Micro battery operation power basic point deviate serious, guarantee the safe operation of system frequency and voltage；Three class control (day grade/hour
Grade/real-time grade) are as follows: it relies on micro-capacitance sensor to dispatch a few days ago respectively in conjunction with power prediction and load prediction, in a few days dispatch and dispatch again and refer to
Determine base value, sagging coefficient, sagging idling frequency, sagging floating voltage and the operating status of each unit power.
As shown in Figure 1, micro-grid system is by energy-storage system Boost/Buck charge-discharge circuit 2, unidirectional inverter circuit 6, micro-
Type gas turbine three-class power electronic transformer 18, two-way AD/DC converter 20, diesel engine three-class power electronic transformer 32,
Photovoltaic Boost circuit 35, blower Boost circuit 38 and fuel cell inverter 51 form.
It mixes micro-capacitance sensor and is connected energy-storage system 1, wind-driven generator 42, photovoltaic cell 34 and load 6 by DC bus
Get up, MTTP (MPPT maximum power point tracking) controller controls wind-powered electricity generation unit and photovoltaic element with maximum power output, through photovoltaic electric
Pond Boost circuit 35 and wind-driven generator Boost circuit 38 stablize DC bus-bar voltage in 750V；Energy-storage system
The charge and discharge process of energy-storage system is controlled to adjust using Boost/Buck charge-discharge circuit 2；Ac bus is by miniature gas turbine
13, diesel-driven generator 33 and fuel cell 52 and AC load 9 connect, and three kinds of micro batteries pass through multi-stage power electronic transformer
Device includes miniature gas turbine three-class power electronic transformer 18, diesel engine three-class power electronic transformer 32 and fuel cell two
Grade electric power electric transformer 51 adjusts busbar voltage respectively, and inversion unit includes micro-gas-turbine in multi-stage power electronic transformer
The width of sagging control automatic adjustment alternating current is respectively adopted in machine inverter 13, diesel engine inverter 27 and fuel cell inverter 46
Value and frequency；Two-way AC/DC converter 20 carries out the two-way changing between direct current and exchange, and wherein inverter process equally uses
The sagging amplitude and frequency for controlling to adjust alternating current.Since system largely uses power electronic equipment, DC generation unit needs
By 5 stable DC busbar voltage of bus capacitor, each direct current component output end also needs capacitor burning voltage, such as battery
Boost/Buck charge-discharge circuit filter capacitor 3, unidirectional inverter circuit filter capacitor 8, photovoltaic cell Boost circuit capacitor
36 and wind-driven generator Boost circuit capacitor 40.And it exchanges side generator unit and harmonic wave is filtered out convenient for mixed using LC filter
The steady-state analysis for closing micro-capacitance sensor, such as miniature gas turbine transforming circuit LC filter 10, two-way DC/AC converter LC filter
23, diesel engine transforming circuit LC filter 24 and fuel cell transforming circuit LC filter 43.Therefore, transformer and converter
Key be how rationally to adjust the rated value of voltage magnitude in sagging control, frequency, active power and reactive power with it is sagging
Coefficient.
As shown in Fig. 2, firstly, since system is using reciprocity control strategy, the isolated operation side of integrated control strategy
Formula --- sagging control, system do not have balance nodes.Sagging control node generally uses the control mode of P-f/Q-V, sagging
Characteristic equation are as follows:
In formula (1),ωk,N、Vk,N, k ∈ { MT, FC, DG, Bus }, respectively inverter output voltage frequency
Reference value, amplitude reference value, frequency rated value and amplitude rated value；mi、niRespectively active and reactive power the sagging system of static state
Number；Pk,N、Pk、Qk,N、QkRespectively droop characteristic inverter rated active power, practical active power, rated reactive power and reality
Border reactive power.
In droop characteristic, need to meet:
Construct Robust Optimization Model:
Objective function
min Ft=min [C (Pt),B(Pt),F(Pt)] (3)
In formula (3) and formula (4): FtFor t session target function；C(Pt) be t period micro-capacitance sensor operating cost include miniature combustion
The operating cost of gas-turbine, fuel cell and diesel engine, i.e.,j∈SC={ MT, FC, DG }；B(Pt) it is t period micro- electricity
Net Environmental costs include the pollutant cost and punishment cost of miniature gas turbine, fuel cell and diesel engine, i.e.,j
∈SC={ MT, FC, DG }；F(Pt) it is t period micro-capacitance sensor renewable energy output pulsation degree；Point
Not Wei photovoltaic cell, wind-driven generator, energy-storage system Active Generation plan.
The constraint condition of traditional Robust Scheduling is respectively as follows:
-Ri·Δt≤Pi,t-Pi,t-1≤Ri·Δt (8)
-Di·Δt≤Qi,t-Qi,t-1≤Di·Δt (10)
Soct=Soct-1(1-η)+PESS,tηc/SESS,PESS,t> 0 (12)
Soct=Soct-1(1-η)+PESS,t/ηdSESS,PESS,t≤0 (13)
pq,z≥0 (18)
In formula (5)-formula (18), Pi,tIt is exported for micro battery i in the active power of period t, Qi,tIt is micro battery i in period t
Reactive power output, i ∈ SDG={ PV, WT, MT, FC, DG }, j ∈ SC={ MT, FC, DG }；PLoad,tFor the active negative of period t
Lotus；PESS,tIt is bright for the energy-storage system charge and discharge of period t；QLoad,tFor the load or burden without work of period t； Pi 、 Qi Respectively micro- electricity
Source i active and idle bound；Ri、DiRespectively active and idle climbing limit value；Δ t is scheduling step-length；SoctFor energy storage
State-of-charge when system period t；Soc、The respectively bound of energy-storage system state-of-charge；SESSFor energy-storage system
Capacity；η,ηc、ηdRespectively itself consumption efficiency, charge efficiency and the discharging efficiency of energy-storage system；PESS 、Respectively energy storage
The bound of system charge and discharge；L% is percentage reserve；z,pq, q ∈ SU={ PV, WT } is respectively auxiliary variable；Γ is uncertainty
Prediction；ρ is prediction max value of error；Respectively honourable period t predicts output power.
Sagging coefficient the relevant technologies constraint is respectively as follows:
In formula (19)-formula (22),The respectively expectation of micro battery k electric voltage frequency and amplitude；CPf 、 CQV 、Respectively coefficient bound.Remaining is defined as above.
Enhancing constraint is respectively as follows:
Wherein,Respectively photovoltaic
Battery, wind-driven generator, fuel cell, alternating current-direct current bus bi-directional inverter, miniature gas turbine and diesel-driven generator it is active
The reactive power plan of power planning and alternating current-direct current bus bi-directional inverter, miniature gas turbine and diesel-driven generator.Remaining is fixed
Justice is same as above.
As described above, the present invention introduces droop characteristic (such as on the basis of traditional robust optimizes (such as formula (3)-formula (18))
Formula (1)-formula (2)) and the relevant technologies constraint (such as formula (19)-formula (22)).Traditional power grid is to be set various power generations by transmission line
Standby to connect with load, for transmission line by alternating current from power generation node-node transmission to on electrical nodes, micro-capacitance sensor has ac bus
While there is DC bus again, the used two-way AC/DC inverter of two buses transmits energy, therefore, increases constraint (formula (23)-
Formula (30)) improve robust Optimal Operation Model.
2, it in a few days in scheduling process, introduces and improves Robust Optimization Model (formula (1)-formula (30)) and uncertain budget adjusting plan
Slightly carry out the dynamic dispatching of micro-capacitance sensor multiple target；Key is how to predict " uncertainty prediction ".The method of the present invention acquisition difference is gone through
History data configuration maps rule, and rule 1 is to acquire the historical data of the offset and uncertain prediction of energy-storage system current state,
The characteristic predicted by linear weighted function uncertainty in traffic；Rule 2 is to acquire the historical volatility feelings of wind-powered electricity generation, photoelectricity and load
Condition and history uncertainty prediction data pass through the current uncertain prediction of weigthed sums approach prediction；Rule 3 are as follows: if Fig. 3 is black
Chest strategy passes through neural network configuration " predicted value of the prediction error of load, wind-powered electricity generation and photovoltaic, current uncertain prediction "
With " time, history uncertainty prediction, the historical data and predicted value of the prediction error of load, wind-powered electricity generation and photovoltaic " model,
Mapping rule is completed by historical data training；The uncertain prediction of prediction will be transmitted to improved Shandong with numeric form
In stick scheduling model, hair solution generation schedule and sagging control voltage magnitude, frequency, active power are calculated to improve differential evolution
With the rated value and sagging coefficient of reactive power.
Uncertain budget adjusts strategy as shown in following formula (31) and (33):
Rule 1:
Rule 2:
Rule 3:
Neural network
For formula (31) into formula (33), Γ (t) is the prediction of period t uncertainty；Γmin、ΓmaxFor the upper of uncertainty prediction
Lower limit；ω1、ω2、…、ωn... it is forgetting factor；Soct-1For the state-of-charge of t-1 moment energy-storage system； SocFor storage
The bound of energy system state-of-charge；Γ (0) is the initial value of uncertain budget；a1、a2、…、an..., b1、b2、…、
bn... respectively mapping rule coefficient；PL,t PPV,t PWT,tThe respectively power of t moment load, photovoltaic power generation and wind-power electricity generation；
ΔPL,tFor period t load error；ΔPPV,tFor period t photovoltaic power output error；ΔPWT,tFor period t blower power output error；
For the prediction of period t load error；For period t photovoltaic power output error prediction；It is pre- for period t blower power output error
It surveys.
Rule 1: energy-storage system state is closer away from limit value, it was demonstrated that system prediction deviation is bigger, and micro-capacitance sensor scheduling should be more conservative；
Conversely, micro-capacitance sensor is more healthy and stronger.Data saturated phenomenon can be eliminated by introducing forgetting factor, reinforce subtracting while current data influences
The influence of small historical data.The algorithm has fast convergence rate, the strong feature of tracking ability.Rule 2: directly against load and wind
The fluctuation of electricity, photoelectricity power output, data use historical data, avoid the influence of the uncertain factors such as trend, network loss, meanwhile, it is right
Uncertain budget historical data weighted sum enhances the convergence rate of mapping process while guaranteeing tracking ability.Rule
3: use black box substrategy, directly the uncertain prediction of analysis and wind-powered electricity generation, photoelectricity and demand history error and predict error when
Between distribution on relationship.The nonlinear fitting ability of strategy enhancing mapping rule, learning rules are simple, real convenient for computer
It is existing, there is stronger robustness, memory capability, non-linear mapping capability and self-learning capability.
3, Differential Evolution Algorithm for Solving generation schedule and sagging control voltage magnitude, frequency, active power and idle are improved
The rated value of power and sagging coefficient.Chaos algorithm is introduced on the basis of differential evolution algorithm, is enhanced using chaos ergodic
The ability of searching optimum of algorithm；Cloud model is introduced, the part of the distribution character enhancing algorithm of " water dust " in multidimensional Normal Cloud is utilized
Search capability；It introduces grey entropy and grey relational grade evaluates each generation particle, distinguish particle close to desired degree, facilitate algorithm into one
Step processing.
Claims (2)
1. containing uncertain budget adjust without balance nodes microgrid Robust Scheduling method, characterized in that the following steps are included:
The first step determines parameter according to micro-capacitance sensor scheduling information, constructs micro-capacitance sensor Robust Scheduling model:
According to the general requirement that Uncertain environments generate electricity, enchancement factor is determined；It is uncertain that set description is not known by boxlike
The waving interval of parameter fluctuates minimum target with operating cost, Environmental costs and renewable energy, with micro-capacitance sensor Dispatch Safety
It is constraint building micro-capacitance sensor Robust Scheduling model with sagging control service requirement；
Second step adjusts tactful uncertainty in traffic prediction by uncertain budget, and institute's predicted value is transmitted to micro-capacitance sensor
In Robust Scheduling model:
It is currently predicted with the state of history by mapping rule uncertainty in traffic according to micro-capacitance sensor operation, and institute's predicted value is answered
For in current Robust Scheduling model；
Third step solves generation schedule and sagging control voltage magnitude, frequency, wattful power using differential evolution algorithm is improved respectively
The rated value of rate and reactive power and sagging coefficient；
The improvement differential evolution algorithm is based on differential evolution algorithm, when updating present age particle with grey entropy relation grade for mark
Standard allows outstanding particle to carry out local search using cloud model, updates outstanding particle so that part is more excellent, and by updated outstanding grain
Son is stored in external elite archive；Meanwhile allowing non-outstanding particle to carry out global search by chaos algorithm and picking out outstanding particle,
And selected outstanding particle is stored in external elite archive；Next-generation searching process is subsequently entered until meeting termination condition
Until；It is that evaluation index selects optimal particle conduct by grey entropy relation grade when each scheduling iteration process reaches maximum algebra
Generation schedule and sagging control plan are not known if scheduling continues by the way that uncertainty budget adjusting strategy prediction is new
Property prediction, second step and third step are repeated, until finishing scheduling；
The uncertainty budget adjusts strategy are as follows:
Rule 1 carries out energy-storage system state offset without guiding principle amount and the prediction of history uncertainty by the way of forgetting factor weighting
Linear weighted function, formula are as follows:
Rule 2 fluctuates historical power using weigthed sums approach and the prediction of history uncertainty is weighted, and formula is as follows:
Γ (t)=a1Γ(t-1)+a2Γ(t-2)+…+anΓ(t-n)+…+b1[PL, t-1-PPV, t-1-PWT, t-1]+b2[PL, t-2-
PPV, t-2-PWT, t-2]+…+bn[PL, t-n-PPV, t-n-PWT, t-n]+…
Rule 3 is using the prediction of neural network uncertainty, load error prediction, photovoltaic power output error prediction, blower power output
Error prediction and time, the prediction of history uncertainty, historical load error, history photovoltaic power output error, history blower power output are missed
The mapping relations of difference, historical load error prediction, history photovoltaic power output error prediction, history blower power output error prediction, formula
It is as follows:
In three above formula, Γ (t) is the prediction of period t uncertainty；Γmin、ΓmaxRespectively uncertain prediction it is upper,
Lower limit；ω1、ω2、…、ωn... it is forgetting factor；Soct-1For the state-of-charge of t-1 moment energy-storage system； SocRespectively
For the upper and lower bound of energy-storage system state-of-charge；Γ (0) is the initial value of uncertain budget；a1、a2、…、an..., b1、
b2、…、bn... respectively mapping rule coefficient；PL,t、PPV,t、PWT,tRespectively t moment load, photovoltaic power generation and wind-power electricity generation
Power；ΔPL,tFor period t load error；ΔPPV,tFor period t photovoltaic power output error；ΔPWT,tIt contributes and misses for period t blower
Difference；For the prediction of period t load error；For period t photovoltaic power output error prediction；For period t blower power output
Error prediction；
Above-mentioned uncertainty budget adjusts the uncertain prediction that strategy is predicted and passes to Robust Scheduling model.
2. as described in claim 1 containing uncertain budget adjust without balance nodes microgrid Robust Scheduling method, feature
It is that for the micro-capacitance sensor in isolated operation mode, micro-capacitance sensor is using reciprocity control strategy, that is, sagging control；Containing uncertain pre-
Calculate adjust strategy Robust Scheduling model be core dispatching method while formulating generated output plan coordinated scheduling it is each inverse
The control parameter for becoming the sagging control of device makes micro-capacitance sensor operation have robustness.
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