CN108039736B - A kind of large capacity heat accumulation storage coordinated scheduling method for improving wind-powered electricity generation and receiving ability - Google Patents
A kind of large capacity heat accumulation storage coordinated scheduling method for improving wind-powered electricity generation and receiving ability Download PDFInfo
- Publication number
- CN108039736B CN108039736B CN201711121854.5A CN201711121854A CN108039736B CN 108039736 B CN108039736 B CN 108039736B CN 201711121854 A CN201711121854 A CN 201711121854A CN 108039736 B CN108039736 B CN 108039736B
- Authority
- CN
- China
- Prior art keywords
- heat
- storage
- wind
- electricity
- power
- 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.)
- Active
Links
- 230000005611 electricity Effects 0.000 title claims abstract description 76
- 238000009825 accumulation Methods 0.000 title claims abstract description 29
- 238000003860 storage Methods 0.000 title claims abstract description 27
- 238000000034 method Methods 0.000 title claims abstract description 17
- 238000013439 planning Methods 0.000 claims abstract description 68
- 238000005096 rolling process Methods 0.000 claims abstract description 40
- 238000004146 energy storage Methods 0.000 claims abstract description 21
- 238000010438 heat treatment Methods 0.000 claims abstract description 15
- 239000002245 particle Substances 0.000 claims description 25
- 238000005338 heat storage Methods 0.000 claims description 24
- 238000005457 optimization Methods 0.000 claims description 15
- 238000012983 electrochemical energy storage Methods 0.000 claims description 14
- 238000010248 power generation Methods 0.000 claims description 4
- 238000010348 incorporation Methods 0.000 claims description 2
- 239000000126 substance Substances 0.000 abstract description 3
- 241000256844 Apis mellifera Species 0.000 description 31
- 230000005619 thermoelectricity Effects 0.000 description 12
- 238000005516 engineering process Methods 0.000 description 6
- 230000008569 process Effects 0.000 description 5
- 239000003245 coal Substances 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000011156 evaluation Methods 0.000 description 4
- 238000000605 extraction Methods 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 230000009194 climbing Effects 0.000 description 3
- 230000000875 corresponding effect Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000005485 electric heating Methods 0.000 description 2
- 238000009472 formulation Methods 0.000 description 2
- 238000009434 installation Methods 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 230000001172 regenerating effect Effects 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 230000009897 systematic effect Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 238000005520 cutting process Methods 0.000 description 1
- 230000029087 digestion Effects 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000012804 iterative process Methods 0.000 description 1
- 230000002045 lasting effect Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- ZYYBBMCBAFICKK-UHFFFAOYSA-N perfluoro-N-cyclohexylpyrrolidine Chemical compound FC1(F)C(F)(F)C(F)(F)C(F)(F)N1C1(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C1(F)F ZYYBBMCBAFICKK-UHFFFAOYSA-N 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
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
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
-
- 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
-
- 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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E70/00—Other energy conversion or management systems reducing GHG emissions
- Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Battery Electrode And Active Subsutance (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
A kind of large capacity heat accumulation storage coordinated scheduling method for improving wind-powered electricity generation and receiving ability of the present invention, belongs to electric system and heating system coordinated operation field.The present invention proposes on the basis of the multi-source system that fired power generating unit, wind power plant and cogeneration units are constituted is planned a few days ago, increase chemical energy storage and the rolling planning of heat accumulation electric boiler and on-line planning, in a few days remaining heat accumulation-storage generation schedule is rollably corrected by rolling planning, it is continuously adjusted again by on-line planning, the basic operating point of heat accumulation-storage is formulated, final online plan and the minor swing actually planned are undertaken by Automatic Generation Control heat accumulation-power storage system.On the one hand, reduce conventional thermoelectric unit thermic load peak value, " electricity determining by heat " must generated output, increase network load valley using heat accumulation during night dip electricity price and storage, to reserve greater room for wind-powered electricity generation online, promote wind-powered electricity generation and receive ability.
Description
Technical field
The present invention relates to electric system and heating system coordinated operation field, especially a kind of wind-powered electricity generation that improves receives ability
Large capacity heat accumulation storage coordinated scheduling method
Technical background
The intrinsic energy density of wind-powered electricity generation is low, randomness, intermittent feature, causes its scale is grid-connected to jeopardize operation of power networks
Safety and stability brings very big challenge to power grid frequency modulation and spare capacity planning, to cause current major wind power plant wind-abandoning phenomenon
Seriously, economic benefit is seriously affected.If wind-powered electricity generation installation account for installation total amount ratio it is lesser when, by traditional power grid technology
And increase the means such as water power, Gas Generator Set, it can guarantee power grid security substantially；But if proportion reaches 10% even more
Height, the peak modulation capacity of power grid and safe operation will face huge challenge.
Large-scale energy storage system has the characteristics that dynamic response characteristic is good, the service life is long, high reliablity with it, is improving power grid
It is of interest both at home and abroad that wind-powered electricity generation, which receives capability realm,.Meanwhile in recent years high capacity cell energy storage technology be developed rapidly and
Using corresponding technology path is gradually clear, and some typical energy-storage battery technologies tentatively have applied to electric system
All various aspects such as frequency modulation, peak regulation." identity " transfer characteristic of energy-storage system fast and flexible during operation of power networks, goes out with wind-powered electricity generation
The time-varying characteristics of power form complementation, and wind storage, which combines, makes wind power output become opposite " controllable, adjustable ", improve power grid and receive wind-powered electricity generation
Ability.But electrochemical energy storage system is at high cost, to the three Norths containing high accounting wind-powered electricity generation " network system adaptability is strong, it is only capable of making single
Pure peak-load regulating supplement improves wind-powered electricity generation and receives ability effect limited to power grid peak-valley difference is reduced.
In general, network load peak is on daytime, and night is at a low ebb, the peak valley with heat supply network load, wind power output
Characteristic is exactly the opposite, i.e. the variation tendency of heat supply network load, wind power output and network load is at reverse characteristic, and heat supply network load and wind
The variation tendency of electricity power output is then substantially at characteristic in the same direction.Since wind-abandoning phenomenon occurs mainly in the big hair of night wind-powered electricity generation, thermic load height
During peak, electric load low ebb, if wind-powered electricity generation can be converted into heat supply network peak adjusting heat source at this time, can be promoted wind electricity digestion rate and
Can reduce thermoelectricity unit for thermal peak and " electricity determining by heat " must generated output, improve peak load regulation network ability.Wind-power electricity generation itself
Have the characteristics that demodulate peak, and the user that heats needs lasting thermal energy to heat, therefore only mentions by wind-powered electricity generation heating electric boiler
It is that cannot be fully solved the scheme for stablizing heating for heating, in order to guarantee to provide stable heat source, wind power heating to heating user
It must be added to hold over system in system.In recent years, hold over system progress is very fast, and technology reaches its maturity, hold over system efficiency
Up to 2500MJ/m3 or more, the thermal efficiency is up to 90%-97%.At full capacity after accumulation of heat, individually can continually and steadily it heat 24 hours
More than, peak value heats 8 hours or more, fully meet wind power heating technical requirements.But current technology majority does not consider in electric heat supply
Install regenerative apparatus in system additional, and its adjustment speed can not participate in electric system primary frequency modulation.
Summary of the invention
The present invention proposes on the basis that the multi-source system that fired power generating unit, wind power plant and cogeneration units are constituted is planned a few days ago
On, increase chemical energy storage and the rolling planning of heat accumulation electric boiler and on-line planning, is rollably corrected by rolling planning in a few days remaining
Heat accumulation-storage generation schedule, then continuously adjusted by on-line planning, formulate the basic operating point of heat accumulation-storage, final online meter
The minor swing drawn and actually planned is undertaken by Automatic Generation Control heat accumulation-power storage system.
To achieve the above object, the present invention has following technical solution, the specific steps are as follows:
Step 1 establishes planned dispatching model a few days ago, and planned dispatching program formulates base according to short term predicted data a few days ago a few days ago
This plan a few days ago, basic scheduling scheme is sent in rolling planning scheduler program, the dispatching cycle planned a few days ago be for 24 hours,
15min is 1 scheduling slot, is updated 1 time every for 24 hours, and objective function is planned a few days ago should be using economy as target, and safety is constraint,
For encouragement wind-powered electricity generation online, cost of wind power generation is disregarded in the scheduling model, with the minimum objective function of system total consumption of coal amount:
In formula: F is system total consumption of coal amount, t；For n-th, the steam power plant thermoelectricity unit coal consumption amount of t period i-th, t/h；For t period jth platform fired power generating unit coal consumption amount, t/h；T be 1 dispatching cycle it is total when number of segment；When Δ t is a scheduling
The time interval of section, h；R is steam power plant's sum；NiFor i-th steam power plant's thermoelectricity unit quantity；S is fired power generating unit sum；
Fired power generating unit consumption characteristic equation:
FCON=a0+a1P+a2P2 (2)
In formula: P is generated output, MW；a0a1a2For fitting coefficient
Thermoelectricity unit consumption characteristic equation:
FCHP=e0+e1P+e2D+e3P2+e4PD+e5D2 (3)
In formula: D is heat supply steam extraction amount, t/h；e0~e5For fitting coefficient.
Step 1.1, constraint condition, constraint condition include climbing for electric equilibrium, for thermal balance, unit output constraint, unit
Constraint, heat accumulation electric boiler units limits.
Step 1.1.1, for electric equilibrium
In formula:For the electrical power of i-th steam power plant of t period, n-th thermoelectricity unit, MW；For t period jth platform
Thermoelectricity electrical power, MW；It always surfs the Internet power for t period wind power plant, MW；For first of band of the institute, steam power plant of t period i-th
The electrical power of heat exchange station peak regulation heat accumulation electric boiler consumption, MW；LiBy i-th steam power plant band heat exchange station sum.
Step 1.1.2 is for thermal balance
If disregarding the heat exchange loss between heating system level-one net and second level net, heating system is put down respectively by for thermal region
Weighing apparatus:
In formula: Qt,i,jFor i-th institute, steam power plant first of heat exchange station of band (secondary heat networks) thermic load of t period, GJ/h；
For i-th institute, steam power plant first of heat exchange station of band (secondary heat networks) peak regulation heat accumulation electric boiler thermal power of t period, GJ/h；For t
N-th, the steam power plant thermoelectricity unit heating power of period i-th, GJ/h, expression formula are
In formula: Dt,j,nFor the steam extraction amount of n-th, the steam power plant thermal power plant unit of t period i-th, t/h；Δ H is steam enthalpy drop,
kJ/kg。
Step 1.1.3, unit output constrains
Dmin≤D≤Dmax (8)
0≤PCW≤PWF (10)
In formula:Respectively fired power generating unit power output bound, MW；Dmin、DmaxRespectively thermoelectricity unit heat supply
Steam extraction rate bound, t/h；When respectively thermoelectricity unit heat supply steam extraction rate is D in its electricity power output
Lower limit, MW；PWFFor wind power plant prediction power, MW.
Step 1.1.4, unit ramp loss
In formula:The respectively climbing of fired power generating unit, landslide rate, MW/h； Respectively thermoelectricity
The climbing of unit, landslide rate, MW/h.
Step 1.1.5, heat accumulation electric boiler units limits
In formula:Respectively heat accumulation electric boiler electrical power bound, MW.
Step 2 establishes rolling planning scheduling model, and rolling planning scheduler program is according to Extended short-term prediction data and unit
Real-time running data is rollably corrected to be planned a few days ago, and revised basic scheduling scheme is sent to on-line planning scheduler program
In；On the basis of plan a few days ago, rolling planning updates 1 time every 4h, is responsible for rolling the scheduling meter for updating the remaining period in 1d
It draws；This patent studies wind-powered electricity generation accumulation of heat using the association system of heat storage electric boiler joint energy storage operation Income Maximum as objective function
Formula electric boiler-electrochemical energy storage system Optimized Operation operation problem, under non-direct-furnish mode, using Income Maximum as target
Objective function is
In formula:The abandonment electricity dissolved for t period wind power plant using heat storage electric boiler and electrochemical energy storage；For t
The heat that period provides to heat supply company；It is the t period to the purchase of electricity of grid company；c1For wind-powered electricity generation rate for incorporation into the power network, member/
(kw·h)；c2For heat price, member/kJ； c3For preferential power purchase price, member/(kwh)；It can be seen that always from formula (14)
Revenue function is made of 3 parts, and respectively wind power plant increases sale of electricity income, the regenerative electrochemical that consumption abandonment generates electricity using operating condition
Boiler sells the cost of hot income and grill pan furnace system to grid company power purchase to heat supply company.
The heat that step 2.1, t period provide to heat supply companyIt can be further represented as
In formula: α is that electricity turns hot coefficient, GJ (MWh)；For in t period power purchase electricity be used to heat electric boiler directly to
The part of pipe network heat supply；It is t period heat-accumulator tank to the heat of pipe network heat supply；For the electricity of t period electrochemical energy storage electric discharge
Amount.From formula (15) as can be seen that being made of to the heat that heat supply company provides 3 parts, including electric boiler directly feeds heat supply public affairs
The heat of department, after electric boiler supplies heat and the electrochemical energy storage supply electric boiler conversion of heat supply company after heat-accumulator tank accumulation of heat
Supply the heat of heat supply company.
Step 2.2, t period system can be further represented as to the electricity of power grid power purchase
In formula:To be used to heat the part that electric boiler is heat-accumulator tank heat accumulation heat supply in t period power purchase electricity；For t
The electricity of period electrochemical energy storage storage charging.From formula (16) as can be seen that 3 can also be divided into from grid company power purchase electricity
Divide, respectively electrochemical energy storage storage electricity, directly feed the electric boiler power consumption of heat supply company and is heated for heat-accumulator tank
Electric boiler use power consumption.
Step 2.3 constraint condition: constraint condition includes dissolving abandonment constraint, operating condition constraint, energy storage heat-storing device energy about
Beam, electric boiler power constraint, heat-accumulator tank accumulation of heat power constraint.
Step 2.3.1, abandonment constrains
The optimal control method of heat storage electric boiler that this patent is studied fusion energy storage be premised on dissolving abandonment,
So the abandonment generated energy that t moment wind power plant is sold to grid company should be not more than the total abandonment electricity of wind power plantI.e.
Step 2.3.2, operating condition constrains
Under non-straight powering mode, wind power plant has different operating conditions with grid company and heat supply company respectively.No
Different constraint condition is shown as to the coordination optimization control problem studied with operating condition.
Purchase sale of electricity between wind power plant and grid company considers 2 kinds of situations.The first situation is that wind power plant allows daily
The fixed electricity of abandonment period additional issue, this electricity is also the electricity that daily heat storage electric boiler is dissolved in the load valley period,
It is as defined in operating condition, i.e.,
There are also a kind of operating conditions, only require that heat storage electric boiler is not less than wind-powered electricity generation in the electricity of load valley period power purchase
Field can be expressed as using the electricity of abandonment additional issue
Heat storage electric boiler and the operating condition of heat supply company define lower limit of each moment to the heat supply of heat supply company, i.e.,
When optimizing, different operating conditions can be selected to constrain according to different operating conditions.
Step 2.3.3, power-balance constraint
Power-balance constraint includes that the heating power balance constraint of heat storage electric boiler and the electrical power of electrochemical energy storage balance
Constraint, is respectively as follows:
Step 2.3.4, thermal storage and energy accumulation energy constraint
Within the scope of the quantity of heat storage of heat-accumulator tank each period should be at reasonably, i.e.,
In formula: QmaxFor heat-accumulator tank maximum quantity of heat storage；QminFor heat-accumulator tank minimum quantity of heat storage；
Similarly, the reserve of electricity of energy-storage battery should within the scope of suitable state-of-charge (stateofcharge, SOC),
I.e.
In formula:For t period energy-storage battery reserve of electricity；WSOCmax、WSOCminRespectively indicate the maximum value of state-of-charge with most
Small value generally takes " 0.2 ", " 0.8 ".
Step 2.3.5, electric boiler power constraint
In formula:General power is run for t period electric boiler, should be less than the maximum power P of electric boilerhmax。
Step 2.3.6, heat-accumulator tank accumulation of heat power constraint
It is limited by electric boiler power constraint, heat-accumulator tank is in the increased amount of stored heat of t moment
Step 3 establishes on-line planning scheduling model, on-line planning scheduler program according to ultra-short term prediction data to amendment after
Operation plan adjusted in real time, to make and the higher operation plan of load matching degree.On-line planning is in terms of rolling
It divides basic scheduling scheme into, is updated 1 time every 15 min, be responsible for arranging upcoming subsequent period Real-Time Scheduling plan,
Line plan is with the minimum objective function of energy storage power storage system adjustment cost, it is contemplated that the factor for encouraging wind-powered electricity generation online, in target letter
Abandonment penalty term is added in number, shown in the objective function of integration such as formula (27):
In formula:For i-th n-th, heat accumulation power station heat storage can adjustment cost,For the adjusting of jth platform energy-storage battery
Cost, γ are abandonment penalty coefficient, t/MWh；PWAFor total abandonment amount, MWh.
Wherein, the adjustment cost of the adjustment cost of heat storage can and heat storage can be indicated by formula (28) and formula (29) respectively:
In formula: CHSTFor heat storage can adjustment cost,For on-line planning heat storage can cost of electricity-generating,For rolling planning
Heat storage can cost of electricity-generating.
In formula: CBESSFor energy-storage battery adjustment cost,For the hot cost of electricity-generating of on-line planning energy-storage battery,For
Rolling planning energy-storage battery heat supply cost of electricity-generating.
Step 3.1, the constraint condition of on-line planning include electric equilibrium constraint, thermal balance constraint, unit a few days ago in the works
Units limits, unit ramp loss, the constraint of heat accumulation electric boiler, the constraint of chemical energy storage battery, wind power plant constraint.Due to on-line planning
It is the static optimization of single period, therefore the Climing constant in on-line planning need to only consider mutually to hold in the mouth with the practical power output of upper period unit
It connects.On this basis, on-line planning should also have energy storage power storage system power output on the basic operating point that rolling planning is formulated
Deviation constraint, as shown in formula (30):
In formula:For each energy storage storage electrical power of t moment on-line planning, MW；β is constraint multiplier.
Step 4, introducing particle swarm optimization algorithm, which improve artificial bee colony algorithm, solves Multiple Time Scales rolling scheduling
Model widens optimizing of the field as particle swarm optimization algorithm to the individual for falling into local extremum on its existing position
Range re-starts search, accelerates algorithm and jumps out local restriction to search optimal solution.
Step 4.1, the position and speed for setting i-th of particle are respectively Xi=(xi1,xi2,...,xiD) and Vi=(vi1,
vi2,...,viD), fitness is determined by an optimised functional value, the history that particle is lived through according to itself
Desired positions Pbest=(pi1,pi2,...,piD) and the desired positions G that is lived through of entire populationbest=(gi1,gi2,...,giD)
The speed of itself and position at present are updated；
For particle after kth time iteration, the more new formula of speed and position is as follows:
Vi,d(k+1)=wvI, d(k)+c1·r1(PBest, d-xI, d(k))
+c2·r2(GBest, d-xi,d(k)) (23)
xi,d(k+1)=xi,d(k)+vi,d(k+1) (24)
In formula, j ∈ (1,2 ..., N), N are population scale, and d ∈ (1,2 ... D), D is search space, xi,dIt (k) is grain
The d of the sub- position i ties up component, vi,d(k) component, P are tieed up for the d of particle i speedbest,dFor the d dimension point of particle i desired positions
Amount, Gbest,dComponent, r are tieed up for the d of desired positions in particle group1And r2For the random number between [0,1], c1And c2For part
Accelerated factor and global accelerated factor, w are inertia weight coefficient.
Step 4.2 solves process
By combining PSO algorithm, current optimal situation was not only considered, but also have global exploration, so that new search bee
More detailed optimizing has been done in a certain range, so that search bee has excellent performance, and then ensure that the rapidity of global optimization.
Steps are as follows for the Optimized Operation physical planning of step 4.3, a few days ago plan, rolling planning and on-line planning:
Step 4.3.1, read in primary data: including Load flow calculation data, control variable description and various equatioies and
Inequality constraints condition；Input the dimension and upper limit value and lower limit value and day part thermoelectricity load of each unit allocation variable；Be arranged bee colony and
Particle swarm optimization algorithm parameter；
Step 4.3.2, initialize: artificial bee colony algorithm and the number of iterations of PSO are set as 0.In the value of control variable
In range, population x is randomly generated, the position for employing bee is initialized, and the quantity of bee is employed to be equal to the number for following bee
Amount；
Step 4.3.3, to every employ food source corresponding to bee carry out the evaluation of income degree, and to the position of food source into
Row updates.Bee is followed to scan for generating new food source in the neighborhood of selected food source, and according to the selecting party of roulette
Formula carries out the update of position to food source.
Step 4.3.4, judge that some food source after reaching upper limit limit, employs whether bee is updated, if do not had still
Have, then this employs bee to switch to search bee, updates position using particle swarm algorithm；
Step 4.3.5, the evaluation of income degree is carried out to updated food source.Judge whether to meet termination condition, if discontented
Sufficient termination condition, then turn to step 2；Otherwise, circulation, output optimal scheduling instruction are jumped out.
Step 5, on the basis of plan a few days ago, increase rolling planning and on-line planning, rollably repaired by rolling planning
Just in a few days remaining generation schedule, then continuously adjusted by on-line planning, formulates the basic operating point of unit, final online plan with
The minor swing actually planned is undertaken by automatic-generation-control unit.
The utility model has the advantages that
This patent proposes a kind of Novel electric-heat integration system coordination dispatching method, jumps out conventional electric power system scope, utilizes
Electric power and therrmodynamic system complementary relationship improve most optimum distribution of resources ability, meanwhile, solve its coupled thermomechanics relationship and new energy simultaneously
Contradiction between net.It is specific consider wind power prediction on the basis of, on the basis of plan a few days ago, increase rolling planning and
Line plan is rollably corrected in a few days remaining generation schedule by rolling planning, then is continuously adjusted by on-line planning, and unit is formulated
Basic operating point, final online plan undertakes with the minor swing actually planned by automatic-generation-control unit.On the one hand, reduce
Conventional thermoelectric unit thermic load peak value, " electricity determining by heat " must generated output, utilize heat accumulation and storage during night dip electricity price
Increase network load valley, to reserve greater room for wind-powered electricity generation online, promotes wind-powered electricity generation and receive ability.
Detailed description of the invention:
Fig. 1 is multi-source heat and power supply structure chart provided by the invention；
Fig. 2 is Multiple Time Scales operation plan schematic diagram provided by the invention；
Fig. 3 is that multi-source Multiple Time Scales provided by the invention dispatch systematic schematic diagram；
Fig. 4 is the optimization algorithm process provided by the invention that ABC is improved based on population；
Specific implementation method:
A specific embodiment of the invention is described with reference to the accompanying drawing, so that those skilled in the art is better
Understand the present invention.
Fig. 1 is multi-source heat and power supply structure chart.
Currently, most of heat storage can is by wind power plant investment construction because far from wind power plant for hot-zone, due to wind power plant
Farther out with thermal storage and energy accumulation device distance, new route is larger to boiler progress direct power supply investment by wind field, therefore adopts mostly at present
With non-straight powering mode, i.e. power grid is all sent into wind power plant power generation, and heat storage can is again from power grid power purchase heat supply, abandonment power generation and grill pan
The electricity price operating condition of furnace power purchase is negotiated to determine by wind power plant and grid company.Fired power generating unit, wind power plant, cogeneration units, chemistry
The powering mode of energy storage and heat storage can is as shown in Figure 1.
Fig. 2 is Multiple Time Scales operation plan schematic diagram.
Electric power, heating power production link are unified to be optimized, and realizes that electric system and heating system coordinated operation are conducive to consumption and abandon
Wind.But the prediction of wind power output, electric load, thermic load has error, and error is positively correlated with predicted time, causes single
Operation plan and actual load deviation are larger a few days ago, and execution degree is not high, the electric heating joint after can not adapting to wind-powered electricity generation large-scale grid connection
Scheduling.Therefore, this patent proposes to increase rolling planning and on-line planning on the basis of plan a few days ago, roll by rolling planning
In a few days remaining generation schedule is corrected dynamicly, then is continuously adjusted by on-line planning, and the basic operating point of unit, final online are formulated
Plan is undertaken with the minor swing actually planned by automatic-generation-control unit.
Planned dispatching program formulates basic plan a few days ago according to short term predicted data a few days ago a few days ago, by basic scheduling scheme
It is sent in rolling planning scheduler program, rolling planning scheduler program is according to Extended short-term prediction data and unit real time execution number
Plan a few days ago according to rollably correcting, and revised basic scheduling scheme is sent in on-line planning scheduler program, it is online to count
It draws scheduler program to adjust revised operation plan in real time according to ultra-short term prediction data, to make and load
With the higher operation plan of degree.
As shown, the dispatching cycle planned is that for 24 hours, 15min is 1 scheduling slot a few days ago, updated 1 time every for 24 hours；?
Plan a few days ago on the basis of, rolling planning updates 1 time every 4h, is responsible for rolling the operation plan for updating the remaining period in 1d；And
On-line planning, for basic scheduling scheme, is updated 1 time with rolling planning every 15min, is responsible for arranging upcoming subsequent period
Real-Time Scheduling plan, belongs to static optimization.
Fig. 3 is that multi-source Multiple Time Scales dispatch systematic schematic diagram
Particle swarm algorithm is improved by self-regulation, writes the Optimized Operation journey of plan, rolling planning and on-line planning a few days ago
Sequence, each Optimized Operation program execute Multiple Time Scales rolling scheduling to electric heating association system by following process:
1) short term predicted data is inputted, planning optimization program a few days ago is run, is planned the basic operating point of unit a few days ago, deposited
Unit output data are planned in storage a few days ago.
2) input expanding short term predicted data, operation rolling planning optimize program, obtain through the revised machine of rolling planning
The basic operating point of group, stores revised rolling planning unit output data.
3) ultra-short term prediction data is inputted, constantly runs online planning optimization program successively to period machine single in following 4h
Group power output optimizes, until all formulation finishes on-line planning in 4h.
2) and 3) 4) execute repeatedly, until in a few days, all on-line plannings formulations are finished.
By this scheme, the operation plan of each time scale can be finally obtained.
Fig. 4 is the optimization algorithm process that ABC is improved based on population.
Multiple Time Scales rolling scheduling model is multidimensional, a nonlinear optimal problem, and basic artificial bee colony algorithm is being located
Convergence rate is slower when managing such problem, and is easily trapped into locally optimal solution.This patent on the basis of basic particle group algorithm,
Particle swarm algorithm is added and improves ability of searching optimum and quick convergence rate.
Artificial bee colony algorithm is a kind of Stochastic Optimization Algorithms, by simulating the local optimal searching behavior of each honeybee individual, finally
It bursts global optimum in group and obtains optimal solution, it is not necessary to grasp any apriority information, have good robustness
And wide applicability.
Leading in ant colony algorithm and follows the food source location update formula of cutting edge of a knife or a sword at bee are as follows:
Vij=Sij+rij(Sij+Skj) (21)
In formula, and k ∈ (1,2 ... L), L is the quantity for employing bee, j ∈ (1,2 ..., N), k ≠ j, rijFor section [- 1,1]
Between random number, and i ≠ j, it controls the size in neighborhood search space, with gradually close, the search of Optimum Solution
Neighborhood space it is also smaller and smaller.SijFor the current position of food source, SkjFor the neighborhood individual food source position selected at random.
Observation bee selects food source selection, probability P according to the information for employing bee to share in the way of rouletteiAre as follows:
In formula, f (δi) be i-th of food source income angle value.
Based on particle group optimizing ABC improved method
Traditional ABC algorithm in an iterative process, if the searched number updated in some nectar source has reached preparatory setting value,
In order to avoid algorithm falls into local extremum, ABC algorithm must reinitialize population and re-search for, but it is to local extremum
Using the information for ignoring individual extreme value, the fine or not degree in new nectar source, therefore algorithm are unable to satisfy by the way of generating at random
Meaningless iterative calculation can be brought, convergence speed of the algorithm is reduced, and then affects the ability of the global optimizing of algorithm.For
This disadvantage this patent introduces particle swarm optimization algorithm and makes improvements.Particle swarm optimization algorithm has very strong global search
Ability and quick convergence rate widen a field as particle on its existing position to the individual for falling into local extremum
The Search Range of colony optimization algorithm re-starts search, can accelerate algorithm and jump out local restriction to search optimal solution.
If the position and speed of i-th of particle is respectively Xi=(xi1,xi2,...,xiD) and Vi=(vi1,vi2,...,
viD), fitness is determined by an optimised functional value, the history desired positions that particle is lived through according to itself
Pbest=(pi1,pi2,...,piD) and the desired positions G that is lived through of entire populationbest=(gi1,gi2,...,giD) at present from
The speed of body and position are updated.
For particle after kth time iteration, the more new formula of speed and position is as follows:
Vi,d(k+1)=wvI, d(k)+c1·r1(PBest, d-xI, d(k))
+c2·r2(GBest, d-xi,d(k)) (23)
xi,d(k+1)=xi,d(k)+vi,d(k+1) (24)
In formula, j ∈ (1,2 ..., N), N are population scale, and d ∈ (1,2 ... D), D is search space, xi,dIt (k) is grain
The d of the sub- position i ties up component, vi,d(k) component, P are tieed up for the d of particle i speedbest,dFor the d dimension point of particle i desired positions
Amount, Gbest,dComponent, r are tieed up for the d of desired positions in particle group1And r2For the random number between [0,1], c1And c2For part
Accelerated factor and global accelerated factor, w are inertia weight coefficient.
Solve process
By combining PSO algorithm, current optimal situation was not only considered, but also have global exploration, so that new search bee
More detailed optimizing has been done in a certain range, so that search bee has excellent performance, and then ensure that the rapidity of global optimization.
Steps are as follows for the Optimized Operation physical planning of plan, rolling planning and on-line planning a few days ago:
1: reading in primary data: about including Load flow calculation data, the description of control variable and various equatioies and inequality
Beam condition；Input the dimension and upper limit value and lower limit value and day part thermoelectricity load of each unit allocation variable；Bee colony is set and population is excellent
Change algorithm parameter；
2: initialization: the number of iterations of ABC and PSO is set as 0.In the value range of control variable, kind is randomly generated
Group x, initializes the position for employing bee, and the quantity of bee is employed to be equal to the quantity for following bee；
3: employing food source corresponding to bee to carry out the evaluation of income degree to every, and the position of food source is updated.
Follow bee according to scanning for generating new food source in the neighborhood of selected food source, and according to the selection mode pair of roulette
The update of food source progress position.
4: judging that some food source after reaching upper limit limit, employs whether bee is updated, if still without should
It employs bee to switch to search bee, updates position using particle swarm algorithm；
5: the evaluation of income degree is carried out to updated food source.Judge whether to meet termination condition, if being unsatisfactory for terminating item
Part then turns to step 2；Otherwise, circulation, output optimal scheduling instruction are jumped out.
Claims (3)
1. a kind of large capacity heat accumulation storage coordinated scheduling method for improving wind-powered electricity generation and receiving ability, it is characterised in that including following step
Rapid: step 1 establishes planned dispatching model a few days ago, and basic scheduling scheme is sent in rolling planning scheduler program；Step 2 is built
Vertical rolling planning scheduling model, revised basic scheduling scheme is sent in on-line planning scheduler program；Step 3 is established
On-line planning scheduling model is made and the higher operation plan of load matching degree；Step 4 introduces particle swarm optimization algorithm pair
Artificial bee colony algorithm, which improves, solves Multiple Time Scales rolling scheduling model；Step 5, a few days ago plan on the basis of, increase
Rolling planning and on-line planning rollably correct in a few days remaining generation schedule by rolling planning, then constantly by on-line planning
Adjustment, formulates the basic operating point of unit, final online plan is held with the minor swing actually planned by automatic-generation-control unit
Load；
Scheduling model described in step 2 is on the basis of plan a few days ago, and rolling planning updates 1 time every 4h, is responsible for rolling
Update the operation plan of remaining period in 1d；Using the association system of heat storage electric boiler joint energy storage operation Income Maximum as target
Function studies wind-powered electricity generation heat storage electric boiler-electrochemical energy storage system Optimized Operation operation problem, under non-direct-furnish mode, with
Income Maximum is that the objective function of target is
In formula:The abandonment electricity dissolved for t period wind power plant using heat storage electric boiler and electrochemical energy storage；For the t period
The heat provided to heat supply company；It is the t period to the purchase of electricity of grid company；c1For wind-powered electricity generation rate for incorporation into the power network, member/(kw
h)；c2For heat price, member/kJ；c3For preferential power purchase price, member/(kwh), from formula (14) as can be seen that total income letter
Number is made of 3 parts, respectively wind power plant increase consumption abandonment power generation using operating condition sale of electricity income, heat storage electric boiler is to confession
Hot company sells the cost of hot income and grill pan furnace system to grid company power purchase.
2. described according to claim 1, a kind of large capacity heat accumulation storage coordinated scheduling method for improving wind-powered electricity generation and receiving ability,
It is characterized in that, scheduling model described in step 2 further indicates are as follows:
The heat that step 2.1, t period provide to heat supply companyIt can be further represented as
In formula: α is that electricity turns hot coefficient GJ/ (MWh)；To be used to heat electric boiler directly to pipe network in t period power purchase electricity
The part of heat supply；It is t period heat-accumulator tank to the heat of pipe network heat supply；For t period electrochemical energy storage electric discharge electricity,
From formula (15) as can be seen that being made of to the heat that heat supply company provides 3 parts, heat supply company is directly fed including electric boiler
Heat, electric boiler supplies the heat of heat supply company after heat-accumulator tank accumulation of heat and electrochemical energy storage is supplied after electric boiler is converted and supplied
The heat of heat supply company；
Step 2.2, t period system can be further represented as to the electricity of power grid power purchase
In formula:To be used to heat the part that electric boiler is heat-accumulator tank heat accumulation heat supply in t period power purchase electricity；For the t period
The electricity of electrochemical energy storage storage charging, from formula (16) as can be seen that 3 parts can also be divided into from grid company power purchase electricity,
Respectively electrochemical energy storage storage electricity directly feeds the electric boiler power consumption of heat supply company and the electricity for heat-accumulator tank heating
Boiler uses power consumption.
3. described according to claim 1, a kind of large capacity heat accumulation storage coordinated scheduling method for improving wind-powered electricity generation and receiving ability,
Be characterized in that, dispatching method described in step 5 on the one hand, reduce conventional thermoelectric unit thermic load peak value, electricity determining by heat must
Generated output increases network load valley using heat accumulation during night dip electricity price and storage, to reserve more for wind-powered electricity generation online
Large space promotes wind-powered electricity generation and receives ability.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711121854.5A CN108039736B (en) | 2017-11-14 | 2017-11-14 | A kind of large capacity heat accumulation storage coordinated scheduling method for improving wind-powered electricity generation and receiving ability |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711121854.5A CN108039736B (en) | 2017-11-14 | 2017-11-14 | A kind of large capacity heat accumulation storage coordinated scheduling method for improving wind-powered electricity generation and receiving ability |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108039736A CN108039736A (en) | 2018-05-15 |
| CN108039736B true CN108039736B (en) | 2019-05-17 |
Family
ID=62093317
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711121854.5A Active CN108039736B (en) | 2017-11-14 | 2017-11-14 | A kind of large capacity heat accumulation storage coordinated scheduling method for improving wind-powered electricity generation and receiving ability |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108039736B (en) |
Families Citing this family (26)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108808739B (en) * | 2018-06-15 | 2020-09-29 | 南方电网科学研究院有限责任公司 | Method and device for dispatching power of power grid with energy storage system and dispatching equipment |
| CN108628176B (en) * | 2018-06-29 | 2021-03-19 | 国网山东省电力公司电力科学研究院 | Thermoelectric combined optimization scheduling method considering heat supply network flexibility |
| CN108984875B (en) * | 2018-07-02 | 2022-11-08 | 中国电力工程顾问集团西北电力设计院有限公司 | Method for calculating capacity of photo-thermal unit participating in power balance |
| CN109270839B (en) * | 2018-09-26 | 2021-09-14 | 沈阳工业大学 | Series control method for objects without self-balancing capability |
| CN109256802B (en) * | 2018-10-08 | 2020-11-10 | 南京千智电气科技有限公司 | Peak-shaving power supply control method and device |
| CN109245175B (en) * | 2018-11-21 | 2022-04-01 | 郑州大学 | Large-scale wind power plant energy storage capacity optimization method considering auxiliary service compensation |
| CN109473998B (en) * | 2018-12-21 | 2021-07-02 | 华中科技大学 | Energy storage configuration method applied to wind power plant output smoothing |
| CN109659958B (en) * | 2019-01-11 | 2022-05-06 | 国网辽宁省电力有限公司沈阳供电公司 | Power system and peak-load and frequency modulation method thereof |
| CN109727158B (en) * | 2019-01-25 | 2020-12-18 | 燕山大学 | Electric heating comprehensive energy system scheduling method based on improved weak robust optimization |
| CN109687532B (en) * | 2019-03-08 | 2021-02-05 | 燕山大学 | Combined heat and power scheduling method for improving wind power consumption based on cooperative game |
| CN110119850B (en) * | 2019-05-22 | 2023-04-07 | 长沙理工大学 | Heat storage amount two-stage optimization scheduling method based on photo-thermal power generation adjustment |
| CN110318877B (en) * | 2019-07-05 | 2022-02-11 | 东北大学 | Wind power consumption method of cogeneration system based on bimodal decoupling |
| CN110957765B (en) * | 2019-11-04 | 2024-02-09 | 深圳供电局有限公司 | Day-ahead energy consumption optimal scheduling method for coal mine industrial users |
| CN110956314B (en) * | 2019-11-18 | 2024-01-23 | 西安热工研究院有限公司 | Improved particle swarm optimization-based capacity planning method for hybrid energy storage system |
| CN112993973B (en) * | 2019-12-16 | 2023-09-26 | 国网辽宁省电力有限公司 | Heat storage device and configuration method and device of power storage device |
| CN112989279B (en) * | 2019-12-16 | 2023-11-17 | 国网辽宁省电力有限公司 | Scheduling method and device of electric heating combined system containing wind power |
| CN111191353B (en) * | 2019-12-18 | 2023-09-05 | 国网辽宁省电力有限公司电力科学研究院 | Coordinated operation method of electric-thermal comprehensive energy system considering thermal inertia |
| CN111106615B (en) * | 2020-01-03 | 2023-03-31 | 国网内蒙古东部电力有限公司 | Method for reducing peak-valley difference of power grid based on battery energy storage device and electric heat storage device |
| EP3848865A1 (en) * | 2020-01-13 | 2021-07-14 | Siemens Gamesa Renewable Energy Innovation & Technology, S.L. | Power management of a hybrid power plant |
| CN111244989B (en) * | 2020-01-15 | 2023-03-24 | 国网内蒙古东部电力有限公司 | Coordinated operation method of wind power generating set and thermal power generating set based on battery energy storage |
| CN111769602B (en) * | 2020-07-12 | 2022-06-21 | 国网山西省电力公司电力科学研究院 | Optimized scheduling method for multi-time-scale wind storage combined system |
| CN112101622A (en) * | 2020-08-13 | 2020-12-18 | 国网辽宁省电力有限公司电力科学研究院 | Power distribution network flexibility potential mining method based on mixed integer linear programming |
| CN112700077A (en) * | 2020-08-20 | 2021-04-23 | 国家电网公司西北分部 | Multi-energy collaborative planning optimization compilation system with feedback function |
| CN113251459B (en) * | 2021-06-08 | 2022-07-08 | 国网山东省电力公司电力科学研究院 | Method and system for deep peak shaving of thermoelectric unit |
| CN113765123B (en) * | 2021-09-03 | 2023-09-19 | 广东电网有限责任公司电力调度控制中心 | New energy source automatic control strategy analysis method and system |
| CN113656988A (en) * | 2021-10-20 | 2021-11-16 | 中国能源建设集团湖南省电力设计院有限公司 | Optimization method for improving wind power consumption energy storage power |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102170170B (en) * | 2011-04-02 | 2013-02-13 | 清华大学 | Wind-power adsorption connected large-power-grid scheduling rolling planning method |
| CN104933516A (en) * | 2015-05-27 | 2015-09-23 | 华南理工大学 | Multi-time-scale power system robustness scheduling system design method |
| CN106992556B (en) * | 2017-05-24 | 2020-01-03 | 南方电网科学研究院有限责任公司 | Optimal scheduling method based on hybrid power supply multi-time scale complementation |
| CN107248017B (en) * | 2017-07-26 | 2020-08-04 | 广东电网有限责任公司电力调度控制中心 | Real-time power generation plan optimization method considering cogeneration |
| CN107332286B (en) * | 2017-07-27 | 2019-09-20 | 清华大学 | A kind of cogeneration of heat and power containing heat accumulation and wind-powered electricity generation coordinated scheduling method |
-
2017
- 2017-11-14 CN CN201711121854.5A patent/CN108039736B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN108039736A (en) | 2018-05-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108039736B (en) | A kind of large capacity heat accumulation storage coordinated scheduling method for improving wind-powered electricity generation and receiving ability | |
| CN110311421A (en) | Micro-capacitance sensor Multiple Time Scales energy management method based on Demand Side Response | |
| CN103346562B (en) | Multi-time scale microgrid energy control method considering demand response | |
| CN108183500B (en) | Multi-energy complementary rural micro-energy network capacity optimization configuration method and device | |
| CN107958300A (en) | A kind of more microgrid interconnected operation coordinated scheduling optimization methods for considering interactive response | |
| CN107332286B (en) | A kind of cogeneration of heat and power containing heat accumulation and wind-powered electricity generation coordinated scheduling method | |
| CN108229025A (en) | A kind of more microgrid active distribution system economic optimization dispatching methods of supply of cooling, heating and electrical powers type | |
| CN108009693A (en) | Grid-connected micro-capacitance sensor dual blank-holder based on two-stage demand response | |
| CN111008739B (en) | Optimal regulation and control and income distribution method and system for cogeneration virtual power plant | |
| CN109242350A (en) | The cooling heating and power generation system capacity configuration optimizing method of meter and translatable load | |
| CN105337303A (en) | Capacity optimization configuration method for combined heat and power generation micro grid containing heat pump | |
| CN108964128A (en) | The low-carbon economy for coordinating heat supply based on electric boiler and heat-storing device dispatches method for solving | |
| CN104135025A (en) | Microgrid economic operation optimization method based on fuzzy particle swarm algorithm and energy saving system | |
| CN109634119A (en) | A kind of energy internet optimal control method based in a few days rolling optimization | |
| CN105225022A (en) | A kind of economy optimizing operation method of cogeneration of heat and power type micro-capacitance sensor | |
| CN112290533A (en) | Method for scheduling comprehensive energy micro-grid for hydrogen energy-natural gas mixed energy storage | |
| CN103903066A (en) | Virtual power plant stratified random optimized dispatching method | |
| CN109412158A (en) | A kind of sending end power grid Unit Combination progress control method for considering to abandon energy cost constraint | |
| CN108808737A (en) | Promote the active distribution network Optimization Scheduling of renewable distributed generation resource consumption | |
| Zhang et al. | Energy optimal management of microgrid with high photovoltaic penetration | |
| CN107565880B (en) | Optimization-type wind light mutual complementing hybrid power system | |
| CN108667077A (en) | A kind of wind storage association system Optimization Scheduling | |
| CN110222867A (en) | A kind of cogeneration type microgrid economic operation optimization method | |
| CN114648250A (en) | Park comprehensive energy system planning method considering comprehensive demand response and carbon emission | |
| CN112883630B (en) | Multi-microgrid system day-ahead optimization economic dispatching method for wind power consumption |
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 |