CN105811397B - A kind of microgrid dispatching method of providing multiple forms of energy to complement each other based on Multiple Time Scales - Google Patents
A kind of microgrid dispatching method of providing multiple forms of energy to complement each other based on Multiple Time Scales Download PDFInfo
- Publication number
- CN105811397B CN105811397B CN201610139613.2A CN201610139613A CN105811397B CN 105811397 B CN105811397 B CN 105811397B CN 201610139613 A CN201610139613 A CN 201610139613A CN 105811397 B CN105811397 B CN 105811397B
- Authority
- CN
- China
- Prior art keywords
- power output
- moment
- ice
- microgrid
- under
- 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
- 238000000034 method Methods 0.000 title claims abstract description 42
- 230000000295 complement effect Effects 0.000 title claims abstract description 26
- 238000004378 air conditioning Methods 0.000 claims abstract description 120
- 230000005611 electricity Effects 0.000 claims abstract description 92
- 239000002245 particle Substances 0.000 claims abstract description 42
- 238000005457 optimization Methods 0.000 claims abstract description 17
- 230000008030 elimination Effects 0.000 claims abstract description 11
- 238000003379 elimination reaction Methods 0.000 claims abstract description 11
- 238000005057 refrigeration Methods 0.000 claims description 23
- 239000007789 gas Substances 0.000 claims description 21
- 239000002131 composite material Substances 0.000 claims description 10
- 238000001816 cooling Methods 0.000 claims description 9
- 239000011159 matrix material Substances 0.000 claims description 6
- 238000005070 sampling Methods 0.000 claims description 6
- 238000010248 power generation Methods 0.000 claims description 4
- 230000000694 effects Effects 0.000 claims description 3
- 238000004146 energy storage Methods 0.000 claims description 3
- 239000000567 combustion gas Substances 0.000 claims description 2
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims 2
- 238000009825 accumulation Methods 0.000 claims 1
- 239000003345 natural gas Substances 0.000 claims 1
- 240000002853 Nelumbo nucifera Species 0.000 description 1
- 235000006508 Nelumbo nucifera Nutrition 0.000 description 1
- 235000006510 Nelumbo pentapetala Nutrition 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000012804 iterative process Methods 0.000 description 1
- 230000008092 positive effect Effects 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
-
- H02J3/005—
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
Abstract
The present invention relates to a kind of microgrid dispatching method of providing multiple forms of energy to complement each other based on Multiple Time Scales, technical characterstic are the following steps are included: step 1, setting micro-grid system scene and modeling respectively to cold, heat and electricity triple supply equipment, ice-storage air-conditioning and accumulator equipment in micro-grid system scene；Step 2, the uncertainty based on photovoltaic and the generation of wind power output scene and elimination technique analysis photovoltaic power output and wind power output, eliminate the fluctuation influence of photovoltaic and wind-powered electricity generation renewable energy power output；Step 3 establishes joint optimal operation model a few days ago；Step 4 establishes the real-time joint optimal operation model of microgrid；Step 5, based on improved Particle Swarm Optimization, to the microgrid, joint optimal operation model and the real-time joint optimal operation model of microgrid are solved a few days ago, obtain the microgrid operation reserve of providing multiple forms of energy to complement each other under Multiple Time Scales.The present invention can reduce the operating cost of microgrid, sufficiently improve the service efficiency of the energy, and play a significant role in terms of the peak load shifting of power grid.
Description
Technical field
The present invention relates to microgrid dispatching technique field, especially a kind of microgrid scheduling of providing multiple forms of energy to complement each other based on Multiple Time Scales
Method.
Background technique
Currently, important component of the microgrid as smart grid, is reducing energy consumption, is improving Power System Reliability and spirit
Activity etc. has great potential, and the microgrid management and running strategy study of providing multiple forms of energy to complement each other under Multiple Time Scales is smart grid
One of the focus on research direction in field.Existing microgrid scheduling strategy does not fully consider the renewable energy such as wind-powered electricity generation, photovoltaic
Fluctuation, or the influence of renewable energy fluctuation only is considered in scheduling a few days ago, and it is fluctuated in Real-Time Scheduling
Journal of Sex Research is insufficient；In addition, ice-storage air-conditioning operational mode is complicated, include the micro- of ice-storage air-conditioning in current research
Electric network model；Situation numerous for equipment in micro-grid system, constraint is complicated is found micro- under suitable multiple target Multiple Time Scales
Net Optimization scheduling algorithm is the outstanding problem put in face of microgrid builder.
Summary of the invention
What it is an object of the invention to overcome the deficiencies of the prior art and provide a kind of based on Multiple Time Scales provides multiple forms of energy to complement each other micro-
Net dispatching method establishes the microgrid model of providing multiple forms of energy to complement each other comprising ice-storage air-conditioning, solves the renewable energy such as wind-powered electricity generation, photovoltaic in day
Fluctuation problem under preceding scheduling and Real-Time Scheduling.
The present invention solves its technical problem and adopts the following technical solutions to achieve:
A kind of microgrid dispatching method of providing multiple forms of energy to complement each other based on Multiple Time Scales, comprising the following steps:
Micro-grid system scene is arranged and respectively to cold, heat and electricity triple supply equipment, ice storage sky in micro-grid system scene in step 1
The accumulator equipment that reconciles is modeled；
Step 2 analyzes photovoltaic power output and wind power output not based on photovoltaic and the generation of wind power output scene and elimination technique
Certainty, the fluctuation for eliminating photovoltaic and wind-powered electricity generation renewable energy power output influence；
Step 3 is established with the joint optimal operation model a few days ago of the minimum target of microgrid operating cost；
Step 4 is established under real-time exchange power and the Multiple Time Scales of the minimum target of scheduled net interchange difference
The real-time joint optimal operation model of microgrid；
Step 5, based on improved Particle Swarm Optimization, to the microgrid, joint optimal operation model and microgrid are real a few days ago
When joint optimal operation model solved, obtain the microgrid operation reserve of providing multiple forms of energy to complement each other under Multiple Time Scales.
Moreover, the specific steps of the step 1 are as follows: establish such as drag and about beam conditional equation: cold, heat and electricity three-way respectively
For the relational model and constraining equation between the electric power efficiency model of equipment, electricity power output and cold power output；Ice-storage air-conditioning
In air conditioning mode, ice-make mode, ice-melt mode and air-conditioning and ice-melt power output model in composite mode and constraining equation；It stores
Battery is dispatched and the operation constraining equation under Real-Time Scheduling a few days ago；
(1) relational model between the electric power efficiency model of the cold, heat and electricity triple supply equipment, electricity power output and cold power output
It is respectively as follows: with constraining equation
1. the electric power efficiency model of cold, heat and power triple supply system:
In formula, PCCHP(T) the electricity power output of T moment CCHP, P are indicatedmaxWith EmaxRespectively indicate CCHP under current operating conditions
Maximum electricity power output and maximum electrical efficiency, ISO represents standard condition, and f is the given nonlinear function of corresponding unit；ECCHP(T)
It is the electrical efficiency of gas turbine.
2. the relational model between the electricity power output of cold, heat and power triple supply system and cold power output:
A. microgrid is with the relational model under electric refrigeration mode between electricity power output and cold power output:
QCCHP(T)=g (PCCHP(T))
In formula, QCCHP(T) the cold power output of T moment CCHP, P are indicatedCCHP(T) the electricity power output of T moment CCHP is indicated；G function table
Show the functional relation between the cold power output of T moment gas turbine and electricity power output.
B. microgrid is with the relational model under cold power mode processed between electricity power output and cold power output:
PCCHP(T)=g-1(PCCHP(T))
In formula, PCCHP(T) the electricity power output of T moment CCHP is indicated；g-1The cold power output and electricity of function representation T moment gas turbine
Functional relation between power output.
3. the constraining equation that micro-grid system meets are as follows:
Wherein, ICCHP(T)∈(0,1)
In formula, ICCHP(T) switch state of T moment CCHP equipment is indicated；PCCHP(T) the electricity power output of T moment CCHP is indicated；
QCCHP(T) the cold power output of T moment CCHP is indicated；PmaxWith PminRespectively indicate CCHP under current operating conditions maximum electricity power output and
Minimum electricity power output；QmaxWith QminRespectively indicate the maximum cold power output and minimum cold power output of CCHP under current operating conditions；TvalleyTable
Show electric load paddy period.
(2) ice-storage air-conditioning is in composite mode in air conditioning mode, ice-make mode, ice-melt mode and air-conditioning and ice-melt
Power output model and constraining equation are respectively as follows:
1. the power output model of ice-storage air-conditioning in air-conditioning mode are as follows:
In formula, Pa(T) and Qa(T) T moment ice-storage air-conditioning power consumption in air-conditioning mode and refrigerating capacity are respectively indicated；
a1With a2For constant；
Its constraint condition are as follows:
In formula, Ia(T) switch state of T moment air conditioning mode is indicated；Qa(T) indicate T moment ice-storage air-conditioning in air-conditioning mould
Refrigerating capacity under formula；TvalleyIndicate electric load paddy period；Qa-maxWith Qa-minIt respectively indicates minimum and maximum under air conditioning mode
Refrigerating capacity；
2. power output model of the ice-storage air-conditioning under ice-make mode are as follows:
In formula, Pc(T) and Qc(T) T moment ice-storage air-conditioning power consumption in air-conditioning mode and refrigerating capacity are respectively indicated；
a1With a2For constant；
Its constraint condition are as follows:
In formula, Ic(T) and Ic(T-1) switch state at T moment and T-1 moment ice-make mode is respectively indicated；Qc(T) T is indicated
The refrigerating capacity of moment ice-storage air-conditioning in air-conditioning mode；TvalleyIndicate electric load paddy period；T0At the beginning of indicating the paddy period
It carves；Qa-maxIndicate the maximum cooling capacity under air conditioning mode.
3. constraint condition of the ice-storage air-conditioning under ice-melt mode are as follows:
In formula, Id(T) switch state of T moment ice-melt mode is indicated；Qd(T) and Qd-maxRespectively indicate T moment ice storage
Refrigerating capacity and maximum cooling capacity of the air-conditioning under ice-melt mode；TvalleyIndicate electric load paddy period；
4. ice-storage air-conditioning is in the power output model in composite mode of air conditioning mode and ice-melt mode are as follows:
IS (T)=(1- η1)IS(T-1)+η2Qc(T)-Qd(T)
In formula, IS (T) indicates the cold energy stored in T moment Ice Storage Tank；η1With η2Respectively indicate cold energy storage loss factor and
Refrigerating efficiency under air conditioning mode；Qc(T) refrigerating capacity of T moment ice-storage air-conditioning in air-conditioning mode is indicated；Qd(T) when indicating T
Carve refrigerating capacity of the ice-storage air-conditioning under ice-melt mode；
Its constraint condition are as follows:
In formula, ISmin(T) the minimum cold energy that storage is needed in Ice Storage Tank is indicated；It is stored in IS (T) expression T moment Ice Storage Tank
Cold energy；η1Indicate that cold energy stores loss factor；TvalleyIndicate electric load paddy period；
(3) battery is dispatched and the operation constraining equation under Real-Time Scheduling a few days ago；
1. battery dispatch a few days ago under operation constraining equation:
In formula, SOC indicates state-of-charge；SOCmaxWith SOCminRespectively indicate the bound of state-of-charge；ηb_cWith ηb_dPoint
It Biao Shi not charge and discharge electrostrictive coefficient；Pc-maxWith Pc-minRespectively indicate maximum charge power and minimum charge power；Pd-maxWith Pd-minRespectively
Indicate maximum discharge power and minimum discharge power；Ib_c(T) switch state of T moment battery charging, I are indicatedb_c(T)∈
(0,1)；Ib_d(T) switch state of T moment electric power storage tank discharge, I are indicatedb_d(T)∈(0,1)；Pb(T) T moment battery is indicated
Charge-discharge electric power；Δ t indicates interval of time；cbIndicate the capacity of battery.
2. the operation constraining equation under battery Real-Time Scheduling:
In formula, t+i | t indicates the dispatch value of i step forward；SOC (t+i+1 | t) and SOC (t+i | t) respectively indicate battery
I+1 step and the forward state-of-charge at i step moment forward；ηb_cWith ηb_dRespectively indicate charge and discharge electrostrictive coefficient；Pc-maxWith Pc-minTable respectively
Show maximum charge power and minimum charge power；Pd-maxWith Pd-maxRespectively indicate maximum discharge power and minimum discharge power；
Ib_c(t+i | t) indicate that i walks the switch state that moment battery charges forward；Ib_d(t+i | t) indicate that i walks moment battery forward
The switch state of electric discharge；Pb_r(t+i | t) indicate that i walks the charge-discharge electric power of moment battery forward under Real-Time Scheduling；Δ t is indicated
Interval of time；cbIndicate the capacity of battery.
Moreover, the specific steps of the step 2 include:
(1) it is based on wind power output and photovoltaic power generation output forecasting, establishes following wind-powered electricity generation and photovoltaic power output normal distribution probability model:
Wherein, σpv=0.1 μpv；σwind=0.1 μwind
In formula, μpvWith μwindIt is the predicted value of photovoltaic power output and wind power output respectively；σpvWith σwindIt is corresponding variance；Ppv
(T) and Pwind(T) be respectively T moment photovoltaic and wind-powered electricity generation practical power output；N indicates normal distribution；
(2) photovoltaic and wind power output scene for obeying above-mentioned probabilistic model are generated with latin hypercube sampling method,
Low probability scene therein is eliminated with scene elimination technique and merges the strong scene of correlation.
Moreover, step 2 (2) step generates comprising the concrete steps that for photovoltaic and wind power output scene:
1. by every dimension variable x after determining sample size HiDomain sectionIt is divided into H equal cells
Between, so thatAn original super cube is divided into HnA small cubes；
2. generating the matrix A of a H × n, then each column of matrix A are all one of ordered series of numbers { 1,2, H } random
Fully intermeshing；The corresponding selected small hypercube of every row of A a, if sample is randomly generated in each small hypercube
This, then select H sample, obeys wind-powered electricity generation described in step 2 (1) step and photovoltaic power output normal distribution probability model to generate
Photovoltaic and wind power output scene.
Moreover, the specific steps of the step 3 include:
(1) objective function of joint optimal operation model a few days ago is established with the minimum target of microgrid day total operating cost:
In formula, psIt is the probability that scene s occurs；It is that microgrid and major network exchange power under scene s；F (T) is day
Right gas consumption；cGrid(T) and cGasIt is electricity price and gas price respectively；T0At the beginning of indicating the paddy period；
(2) electric load of microgrid, the constraint condition of refrigeration duty joint optimal operation operation a few days ago are established
1. the constraint condition of the electric load of microgrid joint optimal operation operation a few days ago
In formula, Pload(T) be the microgrid T moment electric load；PCCHP(T) the electricity power output of T moment CCHP is indicated；Pa(T) it indicates
The power consumption of T moment ice-storage air-conditioning in air-conditioning mode；Pb(T) charge-discharge electric power of T moment battery is indicated；Pc(T) it indicates
The power consumption of T moment ice-storage air-conditioning in air-conditioning mode；Pd(T) power consumption at T moment under ice-melt mode is indicated；Table
Show the wind power output at T moment under s-th of scene；Indicate the photovoltaic power output at T moment under s-th of scene；It indicates
T moment microgrid exchanges power between major network under s-th of scene.
2. the constraint condition of the refrigeration duty of microgrid joint optimal operation operation a few days ago
QCCHP(T)+Qa(T)+Qd(T)=Qload(T)
In formula, QCCHP(T) the cold power output of T moment CCHP is indicated；Qd(T) indicate T moment ice-storage air-conditioning under ice-melt mode
Refrigerating capacity；Qa(T) refrigerating capacity of T moment ice-storage air-conditioning in air-conditioning mode is respectively indicated；Qload(T) it indicates to predict a few days ago
The refrigeration duty demand of lower moment T；
Moreover, the specific steps of the step 4 include:
(1) reality of the microgrid under Multiple Time Scales is established with real-time exchange power and the minimum target of scheduled net interchange difference
When joint optimal operation model objective function:
In formula, PGrid_r(t+i | t) is the real-time exchange power of microgrid and power grid, and p is coefficient；Pb_r(t+i | t) indicate real
When scheduling under forward i walk moment battery charge-discharge electric power；PGrid(T) it indicates to exchange function between T moment microgrid and major network
Rate；Obj represents objective function.
(2) constraint condition of the real-time joint optimal operation operation of electric load of microgrid is established
Pwind_r(t+i|t)+Ppv_r(t+i|t)+PCCHP_r(T)+Pb_r(t+i|t)+PGrid_r(t+i|t)
=Pload_r(t+i|t)+Pa_r(T)+Pc(T)+Pd(T),t+i∈T
In formula, r represents Real-Time Scheduling；Pwind_r(t+i | t) indicate that i walks the wind power output at moment forward under Real-Time Scheduling；
Ppv_r(t+i | t) indicate that i walks the photovoltaic power output at moment forward under Real-Time Scheduling；PCCHP_r(T) indicate that the T moment fires under Real-Time Scheduling
The electricity power output of gas-turbine；Pb_r(t+i | t) indicate that i walks the charge-discharge electric power of moment battery forward under Real-Time Scheduling；PGrid_r(t+
I | t) indicate the real-time exchange power of microgrid and power grid；Pload_r(t+i | t) it indicates to i walks the electricity at moment forward under Real-Time Scheduling
Predicted load；Pa_r(T) power consumption at T moment under Real-Time Scheduling and air conditioning mode is indicated；Pc(T) indicate that T moment ice storage is empty
Adjust power consumption in air-conditioning mode；Pd(T) power consumption at T moment under ice-melt mode is indicated.
Moreover, the specific steps of the step 5 include:
(1) it chooses and is suitble to the variable of improved Particle Swarm Optimization as search particle, foundation is updated with particle position
The improved Particle Swarm Optimization model being characterized is updated with region of search and initializes population；
(2) optimize a few days ago and Real time optimal dispatch model with the improved Particle Swarm Optimization model solution, obtain
Microgrid operation reserve of providing multiple forms of energy to complement each other under Multiple Time Scales.
Moreover, step 5 (2) step method particularly includes:
The fitness Fit of particle individual in each population is evaluated, if Fit < pbestiThen pbest is replaced with Fiti；It is no
Then, then continue to judge pbestiWith the relationship of gbest, if pbesti< gbest, then use pbestiInstead of gbest；Otherwise, then more
New particle position and region of search position；Then judge whether that all particles have been calculated and whether meet termination condition, if meeting
Terminate to calculate；
Wherein, pbesti is the optimal solution individual extreme value that particle itself is found；Gbest is that entire population is found at present
Optimal solution global extremum.
The advantages and positive effects of the present invention are:
1, the present invention combines the actual features of microgrid operation, models to the exemplary apparatus in micro-grid system, especially examines
The influence for having considered ice-storage air-conditioning generates the fluctuation that the renewable energy such as wind-powered electricity generation, photovoltaic are solved with elimination technique with scene
Problem solves multiple target multiple constraint with the Modified particle swarm optimization algorithm characterized by particle position updates and region of search updates
Microgrid Optimal Scheduling under Multiple Time Scales.The present invention can reduce the operating cost of microgrid, sufficiently improve making for the energy
With efficiency, and play a significant role in terms of the peak load shifting of power grid.
2, the present invention first builds the equipment such as cold, heat and electricity triple supply equipment, ice-storage air-conditioning, battery in microgrid
Mould；Secondly based on the uncertainty of scene generation and elimination technique research photovoltaic and wind power output；It then sets up and is run with microgrid
The real-time joint optimal operation model of microgrid under the model of joint optimal operation a few days ago and Multiple Time Scales of the minimum target of cost；
The model dispatched a few days ago with Real-Time Scheduling is finally solved based on modified particle swarm optiziation.The present invention is established comprising ice storage sky
The microgrid model of providing multiple forms of energy to complement each other adjusted has fully considered that wind-powered electricity generation, photovoltaic are being dispatched a few days ago with the fluctuation problem under Real-Time Scheduling simultaneously
Advantage of the Modified particle swarm optimization algorithm in terms of solving the nonlinear optimal problem comprising stochastic variable is given full play to, is solved
Traditional microgrid dispatching method is unsatisfactory in the fluctuation for solve the problems, such as renewable energy and multiple target multiple constraint is more
The problem of model solution under time scale.
3, the equipment such as cold, heat and electricity triple supply equipment, ice-storage air-conditioning, battery of the invention in microgrid have carried out in detail
Modeling has fully considered photovoltaic and wind power output in scheduling a few days ago and the uncertainty under Real-Time Scheduling, with Latin hypercube
Body methods of sampling research scene generates and elimination technique, eliminates low probability scene and merges the strong scene of correlation, to disappear
In addition to the fluctuation of the renewable resources such as photovoltaic and wind-powered electricity generation influences.
4, the present invention is established respectively with the minimum target of microgrid operating cost and is exchanged with real-time exchange power with plan
The model of joint optimal operation a few days ago of the minimum target of power difference and real-time joint optimal operation model；It is calculated in conventional particle group
On the basis of method, the Modified particle swarm optimization algorithm model characterized by particle position updates and region of search updates is established,
Microgrid scheduling aspect of providing multiple forms of energy to complement each other under Multiple Time Scales achieves good effect.
Detailed description of the invention
Fig. 1 is dispatching method overview flow chart of the invention；
Fig. 2 is the real-time joint optimal operation flow chart of microgrid refrigeration duty of the invention；
Fig. 3 is to carry out microgrid joint optimal operation and the real-time joint optimal operation of microgrid a few days ago based on improvement particle swarm algorithm
Flow chart.
Specific embodiment
The embodiment of the present invention is described in further detail below in conjunction with attached drawing:
A kind of microgrid dispatching method of providing multiple forms of energy to complement each other based on Multiple Time Scales, as shown in Figure 1, comprising the following steps:
Micro-grid system scene is arranged and respectively to cold, heat and electricity triple supply equipment, ice storage sky in micro-grid system scene in step 1
The accumulator equipment that reconciles is modeled.
The specific steps of the step 1 are as follows: establish such as drag and about beam conditional equation: cold, heat and electricity triple supply equipment respectively
Electric power efficiency model, electricity power output cold power output between relational model and constraining equation；Ice-storage air-conditioning is in air-conditioning
Mode, ice-make mode, ice-melt mode and air-conditioning and ice-melt power output model in composite mode and constraining equation；Battery day
Preceding scheduling and the operation constraining equation under Real-Time Scheduling；
(1) relational model between the electric power efficiency model of the cold, heat and electricity triple supply equipment, electricity power output and cold power output
It is respectively as follows: with constraining equation
1. the electric power efficiency model of cold, heat and power triple supply system:
In formula, PCCHP(T) the electricity power output of T moment CCHP, P are indicatedmaxWith EmaxRespectively indicate CCHP under current operating conditions
Maximum electricity power output and maximum electrical efficiency, ISO represents standard condition, and f is the given nonlinear function of corresponding unit；ECCHP(T)
It is the electrical efficiency of gas turbine.
2. the relational model between the electricity power output of cold, heat and power triple supply system and cold power output:
A. microgrid is with the relational model under electric refrigeration mode between electricity power output and cold power output:
QCCHP(T)=g (PCCHP(T))
In formula, QCCHP(T) the cold power output of T moment CCHP, P are indicatedCCHP(T) the electricity power output of T moment CCHP is indicated；G function table
Show the functional relation between the cold power output of T moment gas turbine and electricity power output.
B. microgrid is with the relational model under cold power mode processed between electricity power output and cold power output:
PCCHP(T)=g-1(PCCHP(T))
In formula, PCCHP(T) the electricity power output of T moment CCHP is indicated；g-1The cold power output and electricity of function representation T moment gas turbine
Functional relation between power output.
3. the constraining equation that micro-grid system meets are as follows:
Wherein, ICCHP(T)∈(0,1)
In formula, ICCHP(T) switch state of T moment CCHP equipment is indicated；PCCHP(T) the electricity power output of T moment CCHP is indicated；
QCCHP(T) the cold power output of T moment CCHP is indicated；PmaxWith PminRespectively indicate CCHP under current operating conditions maximum electricity power output and
Minimum electricity power output；QmaxWith QminRespectively indicate the maximum cold power output and minimum cold power output of CCHP under current operating conditions；TvalleyTable
Show electric load paddy period.
(2) ice-storage air-conditioning is in composite mode in air conditioning mode, ice-make mode, ice-melt mode and air-conditioning and ice-melt
Power output model and constraining equation are respectively as follows:
1. the power output model of ice-storage air-conditioning in air-conditioning mode are as follows:
In formula, Pa(T) and Qa(T) T moment ice-storage air-conditioning power consumption in air-conditioning mode and refrigerating capacity are respectively indicated；
a1With a2For constant；
Its constraint condition are as follows:
In formula, Ia(T) switch state of T moment air conditioning mode is indicated；Qa(T) indicate T moment ice-storage air-conditioning in air-conditioning mould
Refrigerating capacity under formula；TvalleyIndicate electric load paddy period；Qa-maxWith Qa-minIt respectively indicates minimum and maximum under air conditioning mode
Refrigerating capacity；
2. power output model of the ice-storage air-conditioning under ice-make mode are as follows:
In formula, Pc(T) and Qc(T) T moment ice-storage air-conditioning power consumption in air-conditioning mode and refrigerating capacity are respectively indicated；
a1With a2For constant；
Its constraint condition are as follows:
In formula, Ic(T) and Ic(T-1) switch state at T moment and T-1 moment ice-make mode is respectively indicated；Qc(T) T is indicated
The refrigerating capacity of moment ice-storage air-conditioning in air-conditioning mode；TvalleyIndicate electric load paddy period；T0At the beginning of indicating the paddy period
It carves；Qa-maxIndicate the maximum cooling capacity under air conditioning mode.
3. constraint condition of the ice-storage air-conditioning under ice-melt mode are as follows:
In formula, Id(T) switch state of T moment ice-melt mode is indicated；Qd(T) and Qd-maxRespectively indicate T moment ice storage
Refrigerating capacity and maximum cooling capacity of the air-conditioning under ice-melt mode；TvalleyIndicate electric load paddy period；
4. ice-storage air-conditioning is in the power output model in composite mode of air conditioning mode and ice-melt mode are as follows:
IS (T)=(1- η1)IS(T-1)+η2Qc(T)-Qd(T)
In formula, IS (T) indicates the cold energy stored in T moment Ice Storage Tank；η1With η2Respectively indicate cold energy storage loss factor and
Refrigerating efficiency under air conditioning mode；Qc(T) refrigerating capacity of T moment ice-storage air-conditioning in air-conditioning mode is indicated；Qd(T) when indicating T
Carve refrigerating capacity of the ice-storage air-conditioning under ice-melt mode；
Its constraint condition are as follows:
In formula, ISmin(T) the minimum cold energy that storage is needed in Ice Storage Tank is indicated；It is stored in IS (T) expression T moment Ice Storage Tank
Cold energy；η1Indicate that cold energy stores loss factor；TvalleyIndicate electric load paddy period；
(3) battery is dispatched and the operation constraining equation under Real-Time Scheduling a few days ago；
1. battery dispatch a few days ago under operation constraining equation:
In formula, SOC indicates state-of-charge；SOCmaxWith SOCminRespectively indicate the bound of state-of-charge；ηb_cWith ηb_dPoint
It Biao Shi not charge and discharge electrostrictive coefficient；Pc-maxWith Pc-minRespectively indicate maximum charge power and minimum charge power；Pd-maxWith Pd-minRespectively
Indicate maximum discharge power and minimum discharge power；Ib_c(T) switch state of T moment battery charging, I are indicatedb_c(T)∈
(0,1)；Ib_d(T) switch state of T moment electric power storage tank discharge, I are indicatedb_d(T)∈(0,1)；Pb(T) T moment battery is indicated
Charge-discharge electric power；Δ t indicates interval of time；cbIndicate the capacity of battery.
2. the operation constraining equation under battery Real-Time Scheduling:
In formula, t+i | t indicates the dispatch value of i step forward；SOC (t+i+1 | t) and SOC (t+i | t) respectively indicate battery
I+1 step and the forward state-of-charge at i step moment forward；ηb_cWith ηb_dRespectively indicate charge and discharge electrostrictive coefficient；Pc-maxWith Pc-minTable respectively
Show maximum charge power and minimum charge power；Pd-maxWith Pd-maxRespectively indicate maximum discharge power and minimum discharge power；
Ib_c(t+i | t) indicate that i walks the switch state that moment battery charges forward；Ib_d(t+i | t) indicate that i walks moment battery forward
The switch state of electric discharge；Pb_r(t+i | t) indicate that i walks the charge-discharge electric power of moment battery forward under Real-Time Scheduling；Δ t is indicated
Interval of time；cbIndicate the capacity of battery.
Step 2 analyzes photovoltaic power output and wind power output not based on photovoltaic and the generation of wind power output scene and elimination technique
Certainty, the fluctuation for eliminating photovoltaic and wind-powered electricity generation renewable energy power output influence.
The specific steps of the step 2 include:
(1) it is based on wind power output and photovoltaic power generation output forecasting, establishes following wind-powered electricity generation and photovoltaic power output normal distribution probability model:
Wherein, σpv=0.1 μpv；σwind=0.1 μwind
In formula, μpvWith μwindIt is the predicted value of photovoltaic power output and wind power output respectively；σpvWith σwindIt is corresponding variance；Ppv
(T) and Pwind(T) be respectively T moment photovoltaic and wind-powered electricity generation practical power output；N indicates normal distribution；
(2) photovoltaic and wind power output scene for obeying above-mentioned probabilistic model are generated with latin hypercube sampling method,
Low probability scene therein is eliminated with scene elimination technique and merges the strong scene of correlation.
Step 2 (2) step generates with latin hypercube sampling method and obeys wind described in step 2 (1) step
Electricity and the photovoltaic power output photovoltaic of normal distribution probability model and comprising the concrete steps that for wind power output scene:
1. by every dimension variable x after determining sample size HiDomain sectionIt is divided into H equal cells
Between, so thatAn original super cube is divided into HnA small cubes；
2. generating the matrix A of a H × n, then each column of matrix A are all one of ordered series of numbers { 1,2, H } random
Fully intermeshing；The corresponding selected small hypercube of every row of A a, if sample is randomly generated in each small hypercube
This, then select H sample, obeys wind-powered electricity generation described in step 2 (1) step and photovoltaic power output normal distribution probability model to generate
Photovoltaic and wind power output scene.
Step 3 is established with the joint optimal operation model a few days ago of the minimum target of microgrid operating cost.
The specific steps of the step 3 include:
(1) objective function of joint optimal operation model a few days ago is established with the minimum target of microgrid day total operating cost:
In formula, psIt is the probability that scene s occurs；It is that microgrid and major network exchange power under scene s；F (T) is day
Right gas consumption；cGrid(T) and cGasIt is electricity price and gas price respectively；T0At the beginning of indicating the paddy period；
(2) electric load of microgrid, the constraint condition of refrigeration duty joint optimal operation operation a few days ago are established
1. the constraint condition of the electric load of microgrid joint optimal operation operation a few days ago
In formula, Pload(T) be the microgrid T moment electric load；PCCHP(T) the electricity power output of T moment CCHP is indicated；Pa(T) it indicates
The power consumption of T moment ice-storage air-conditioning in air-conditioning mode；Pb(T) charge-discharge electric power of T moment battery is indicated；Pc(T) it indicates
The power consumption of T moment ice-storage air-conditioning in air-conditioning mode；Pd(T) power consumption at T moment under ice-melt mode is indicated；Table
Show the wind power output at T moment under s-th of scene；Indicate the photovoltaic power output at T moment under s-th of scene；It indicates
T moment microgrid exchanges power between major network under s-th of scene.
2. the constraint condition of the refrigeration duty of microgrid joint optimal operation operation a few days ago
QCCHP(T)+Qa(T)+Qd(T)=Qload(T)
In formula, QCCHP(T) the cold power output of T moment CCHP is indicated；Qd(T) indicate T moment ice-storage air-conditioning under ice-melt mode
Refrigerating capacity；Qa(T) refrigerating capacity of T moment ice-storage air-conditioning in air-conditioning mode is respectively indicated；Qload(T) it indicates to predict a few days ago
The refrigeration duty demand of lower moment T；
Step 4 is established under real-time exchange power and the Multiple Time Scales of the minimum target of scheduled net interchange difference
The real-time joint optimal operation model of microgrid.
The specific steps of the step 4 include:
(1) reality of the microgrid under Multiple Time Scales is established with real-time exchange power and the minimum target of scheduled net interchange difference
When joint optimal operation model objective function:
In formula, PGrid_r(t+i | t) is the real-time exchange power of microgrid and power grid, and p is coefficient；Pb_r(t+i | t) indicate real
When scheduling under forward i walk moment battery charge-discharge electric power；PGrid(T) it indicates to exchange function between T moment microgrid and major network
Rate；Obj represents objective function.
(2) constraint condition of the real-time joint optimal operation operation of electric load of microgrid is established
Pwind_r(t+i|t)+Ppv_r(t+i|t)+PCCHP_r(T)+Pb_r(t+i|t)+PGrid_r(t+i|t)
=Pload_r(t+i|t)+Pa_r(T)+Pc(T)+Pd(T),t+i∈T
In formula, r represents Real-Time Scheduling；Pwind_r(t+i | t) indicate that i walks the wind power output at moment forward under Real-Time Scheduling；
Ppv_r(t+i | t) indicate that i walks the photovoltaic power output at moment forward under Real-Time Scheduling；PCCHP_r(T) indicate that the T moment fires under Real-Time Scheduling
The electricity power output of gas-turbine；Pb_r(t+i | t) indicate that i walks the charge-discharge electric power of moment battery forward under Real-Time Scheduling；PGrid_r(t+
I | t) indicate the real-time exchange power of microgrid and power grid；Pload_r(t+i | t) it indicates to i walks the electricity at moment forward under Real-Time Scheduling
Predicted load；Pa_r(T) power consumption at T moment under Real-Time Scheduling and air conditioning mode is indicated；Pc(T) indicate that T moment ice storage is empty
Adjust power consumption in air-conditioning mode；Pd(T) power consumption at T moment under ice-melt mode is indicated.
Wherein, the real-time joint optimal operation of microgrid refrigeration duty process as shown in Fig. 2, the specific steps are that: calculate first
The variable quantity of the real-time refrigeration duty of microgrid simultaneously judges the variable quantity whether less than 0, if the variable quantity is continued to determine whether less than 0
There is electric energy to flow to major network from CCHP, it is preferential to reduce Q if there is electric energy to flow to major network from CCHPCCHP(T), if without electric energy from CCHP
Major network is flowed to, then preferentially reduces Qa(T)；If the variable quantity of the real-time refrigeration duty of microgrid is greater than zero, electric energy has been continued to determine whether
Major network is flowed to from CCHP, it is preferential to reduce Q if there is electric energy to flow to major network from CCHPa(T), if flowing to major network from CCHP without electric energy,
Then preferentially reduce QCCHP(T), real-time refrigeration duty power output Q is finally acquired according to refrigeration duty balancea_r(T) and QCCHP-r(T)。
As shown in Figure 2, the working principle of the real-time joint optimal operation of microgrid refrigeration duty are as follows: the fluctuation of refrigeration duty is stored by ice
The variation of cold air-conditioning and gas turbine electricity power output balances, and it is finally all negative by electricity in real time that refrigeration duty scheduling and new energy go out fluctuation
The balance of lotus is realized.When real-time cooling is load unbalanced, according to the case where the lacking with gas turbine electric load that be full of of refrigeration duty, certainly
The adjustment sequence of the fixed moment gas turbine and the cold power output of ice-storage air-conditioning.For example, if real-time cooling underload and combustion gas
Turbine has electric power more than needed to flow to major network, then preferentially adjusts the cold power output of gas turbine, otherwise preferentially adjusts ice-storage air-conditioning in air-conditioning
Refrigerating capacity under mode.
Step 5, based on improved Particle Swarm Optimization, to the microgrid, joint optimal operation model and microgrid are real a few days ago
When joint optimal operation model solved, obtain the microgrid operation reserve of providing multiple forms of energy to complement each other under Multiple Time Scales.
The specific steps of the step 5, as shown in Figure 3:
(1) it chooses and is suitble to the variable of improved Particle Swarm Optimization as search particle, foundation is updated with particle position
The improved Particle Swarm Optimization model being characterized is updated with region of search and initializes population；
(2) optimize a few days ago and Real time optimal dispatch model with the improved Particle Swarm Optimization model solution, obtain
Microgrid operation reserve of providing multiple forms of energy to complement each other under Multiple Time Scales.
Particle position amendment is in order to solve the problems, such as coupling multiple constraint, in an iterative process, forcibly more new particle
Position, to ensure that particle is always in feasible zone；The update of region of search is i.e. in the initial stage of search, and each particle is with larger
The region of search scan for, with quickly determine target approximate location, with iterations going on, constantly reduce search sky
Between, to fast implement the accurate positioning of target, region of search radius is as follows with the calculation formula that iteration changes:
D=d0/(1+exp((i-0.7imax)/5))
In formula, d0For constant, d is generally taken0=50；I is current iteration number；imaxIt is maximum number of iterations, generally takes imax
=200；Exp indicates exponential function.
Its method particularly includes: the fitness Fit of particle individual in each population is evaluated first, if Fit < pbestiThen
Pbest is replaced with Fiti；Otherwise, then continue to judge pbestiWith the relationship of gbest, if pbesti< gbest, then use pbesti
Instead of gbest；Otherwise, then particle position and region of search position are updated；Then judge whether that all particles have been calculated and whether expire
Sufficient termination condition terminates to calculate if meeting.
Wherein, pbesti is the optimal solution individual extreme value that particle itself is found；Gbest is that entire population is found at present
Optimal solution global extremum.
It is emphasized that embodiment of the present invention be it is illustrative, without being restrictive, therefore packet of the present invention
Include and be not limited to embodiment described in specific embodiment, it is all by those skilled in the art according to the technique and scheme of the present invention
The other embodiments obtained, also belong to the scope of protection of the invention.
Claims (8)
1. a kind of microgrid dispatching method of providing multiple forms of energy to complement each other based on Multiple Time Scales, it is characterised in that the following steps are included:
Step 1, setting micro-grid system scene and respectively to cold, heat and electricity triple supply equipment in micro-grid system scene, ice-storage air-conditioning and
Accumulator equipment is modeled；Such as drag and about beam conditional equation: the electricity power output effect of cold, heat and electricity triple supply equipment are established respectively
Relational model and constraining equation between rate model, electricity power output and cold power output；Ice-storage air-conditioning is in air conditioning mode, ice making mould
Formula, ice-melt mode and air-conditioning and ice-melt power output model in composite mode and constraining equation；Battery is dispatched and reality a few days ago
When scheduling under operation constraining equation；
Step 2, generated based on photovoltaic and wind power output scene and elimination technique analysis photovoltaic power output and wind power output it is uncertain
Property, the fluctuation for eliminating photovoltaic and wind-powered electricity generation renewable energy power output influences；Based on wind power output and photovoltaic power generation output forecasting, establish such as
Lower wind-powered electricity generation and photovoltaic power output normal distribution probability model；It is generated with latin hypercube sampling method and obeys above-mentioned probabilistic model
Photovoltaic and wind power output scene, eliminate low probability scene therein with scene elimination technique and close the strong scene of correlation
And；
Step 3 is established with the joint optimal operation model a few days ago of the minimum target of microgrid operating cost；
Step 4 is established with the microgrid under real-time exchange power and the Multiple Time Scales of the minimum target of scheduled net interchange difference
Real-time joint optimal operation model；Multiple Time Scales are established with real-time exchange power and the minimum target of scheduled net interchange difference
Under the real-time joint optimal operation model of microgrid objective function；Establish the real-time joint optimal operation operation of electric load of microgrid
Constraint condition；
Step 5, the model of joint optimal operation a few days ago is combined in real time with microgrid based on improved Particle Swarm Optimization it is excellent
Change scheduling model to be solved, obtains the microgrid operation reserve of providing multiple forms of energy to complement each other under Multiple Time Scales.
2. a kind of microgrid dispatching method of providing multiple forms of energy to complement each other based on Multiple Time Scales according to claim 1, it is characterised in that:
The specific steps of the step 1 are as follows: establish such as drag and about beam conditional equation: the electricity power output of cold, heat and electricity triple supply equipment respectively
Relational model and constraining equation between efficiency Model, electricity power output and cold power output；Ice-storage air-conditioning is in air conditioning mode, ice making
Mode, ice-melt mode and air-conditioning and ice-melt power output model in composite mode and constraining equation；Battery dispatch a few days ago with
Operation constraining equation under Real-Time Scheduling；
(1) the relational model peace treaty between the electric power efficiency model of the cold, heat and electricity triple supply equipment, electricity power output and cold power output
Beam conditional equation is respectively as follows:
1. the electric power efficiency model of cold, heat and power triple supply system:
In formula, PCCHP(T) the electricity power output of T moment CCHP, P are indicatedmaxWith EmaxRespectively indicate the maximum of CCHP under current operating conditions
Electricity power output and maximum electrical efficiency, ISO represent standard condition, and f is the given nonlinear function of corresponding unit；ECCHPIt (T) is combustion gas
The electrical efficiency of turbine；fISOIt indicates to correspond to the given nonlinear function of unit under standard condition；PISO-maxIt indicates under standard condition
The maximum electricity power output of CCHP cold, heat and electricity triple supply equipment；
2. the relational model between the electricity power output of cold, heat and power triple supply system and cold power output:
A. microgrid is with the relational model under electric refrigeration mode between electricity power output and cold power output:
QCCHP(T)=g (PCCHP(T))
In formula, QCCHP(T) the cold power output of T moment CCHP, P are indicatedCCHP(T) the electricity power output of T moment CCHP is indicated；G function representation T
Functional relation between the cold power output and electricity power output of moment gas turbine；
B. microgrid is with the relational model under cold power mode processed between electricity power output and cold power output:
PCCHP(T)=g-1(PCCHP(T))
In formula, PCCHP(T) the electricity power output of T moment CCHP is indicated；g-1Electricity power output and the cold power output of function representation T moment gas turbine
Between functional relation；
3. the constraining equation that micro-grid system meets are as follows:
Wherein, ICCHP(T)∈[0,1]；
In formula, ICCHP(T) switch state of T moment CCHP equipment is indicated；PCCHP(T) the electricity power output of T moment CCHP is indicated；QCCHP
(T) the cold power output of T moment CCHP is indicated；PmaxWith PminRespectively indicate the maximum electricity power output and minimum of CCHP under current operating conditions
Electricity power output；QmaxWith QminRespectively indicate the maximum cold power output and minimum cold power output of CCHP under current operating conditions；TvalleyElectric load
Paddy period；
(2) ice-storage air-conditioning is in air conditioning mode, ice-make mode, ice-melt mode and air-conditioning and ice-melt power output in composite mode
Model and constraining equation are respectively as follows:
1. the power output model of ice-storage air-conditioning in air-conditioning mode are as follows:
In formula, Pa(T) and Qa(T) T moment ice-storage air-conditioning power consumption in air-conditioning mode and refrigerating capacity are respectively indicated；a1With a2
For constant；
Its constraint condition are as follows:
In formula, Ia(T) switch state of T moment air conditioning mode is indicated；Qa(T) T moment ice-storage air-conditioning is indicated in air-conditioning mode
Refrigerating capacity；TvalleyIndicate electric load paddy period；Qa-maxWith Qa-minRespectively indicate the minimum and maximum refrigeration under air conditioning mode
Amount；
2. power output model of the ice-storage air-conditioning under ice-make mode are as follows:
In formula, Pc(T) and Qc(T) power consumption and refrigerating capacity of the T moment ice-storage air-conditioning under ice-make mode are respectively indicated；a1With a2
For constant；
Its constraint condition are as follows:
In formula, Ic(T) and Ic(T-1) switch state at T moment and T-1 moment ice-make mode is respectively indicated；Qc(T) the T moment is indicated
Refrigerating capacity of the ice-storage air-conditioning under ice-make mode；TvalleyIndicate electric load paddy period；T0At the beginning of indicating the paddy period；
Qa-maxIndicate the maximum cooling capacity under air conditioning mode；
3. constraint condition of the ice-storage air-conditioning under ice-melt mode are as follows:
In formula, Id(T) switch state of T moment ice-melt mode is indicated；Qd(T) and Qd-maxT moment ice-storage air-conditioning is respectively indicated to exist
Refrigerating capacity and maximum cooling capacity under ice-melt mode；TvalleyIndicate electric load paddy period；
4. ice-storage air-conditioning is in the power output model in composite mode of ice-make mode and ice-melt mode are as follows:
IS (T)=(1- η1)IS(T-1)+η2Qc(T)-Qd(T)
In formula, IS (T) indicates the cold energy stored in T moment Ice Storage Tank；η1With η2Respectively indicate cold energy storage loss factor and air-conditioning
Refrigerating efficiency under mode；Qc(T) refrigerating capacity of the T moment ice-storage air-conditioning under ice-make mode is indicated；Qd(T) T moment ice is indicated
Refrigerating capacity of the cold accumulation air-conditioner under ice-melt mode；
Its constraint condition are as follows:
In formula, ISmin(T) the minimum cold energy that storage is needed in Ice Storage Tank is indicated；IS (T) indicates to store in T moment Ice Storage Tank cold
Energy；η1Indicate that cold energy stores loss factor；TvalleyIndicate electric load paddy period；
(3) battery is dispatched and the operation constraining equation under Real-Time Scheduling a few days ago；
1. battery dispatch a few days ago under operation constraining equation:
In formula, SOC indicates state-of-charge；SOCmaxWith SOCminRespectively indicate the bound of state-of-charge；ηb_cWith ηb_dTable respectively
Show charge and discharge electrostrictive coefficient；Pc-maxWith Pc-minRespectively indicate maximum charge power and minimum charge power；Pd-maxWith Pd-minIt respectively indicates
Maximum discharge power and minimum discharge power；Ib_c(T) switch state of T moment battery charging, I are indicatedb_c(T)∈[0,1]；
Ib_d(T) switch state of T moment electric power storage tank discharge, I are indicatedb_d(T)∈[0,1]；Pb(T) charge and discharge of T moment battery is indicated
Electrical power；Δ t indicates interval of time；cbIndicate the capacity of battery；
2. the operation constraining equation under battery Real-Time Scheduling:
In formula, t+i | t indicates the dispatch value of i step forward；SOC (t+i+1 | t) and SOC (t+i | t) respectively indicate battery i forward
+ 1 step and forward i walk the state-of-charge at moment；ηb_cWith ηb_dRespectively indicate charge and discharge electrostrictive coefficient；Pc-maxWith Pc-minIt respectively indicates most
Big charge power and minimum charge power；Pd-maxWith Pd-maxRespectively indicate maximum discharge power and minimum discharge power；Ib_c(t+
I | t) indicate that i walks the switch state that moment battery charges forward；Ib_d(t+i | t) indicate that i walks moment electric power storage tank discharge forward
Switch state；Pb_r(t+i | t) indicate that i walks the charge-discharge electric power of moment battery forward under Real-Time Scheduling；When Δ t indicates one section
Between be spaced；cbIndicate the capacity of battery.
3. a kind of microgrid dispatching method of providing multiple forms of energy to complement each other based on Multiple Time Scales according to claim 1 or 2, feature exist
In: the specific steps of the step 2 include:
(1) it is based on wind power output and photovoltaic power generation output forecasting, establishes following wind-powered electricity generation and photovoltaic power output normal distribution probability model:
Wherein, σpv=0.1 μpv；σwind=0.1 μwind
In formula, μpvWith μwindIt is the predicted value of photovoltaic power output and wind power output respectively；σpvWith σwindIt is corresponding variance；Ppv(T)
With Pwind(T) be respectively T moment photovoltaic and wind-powered electricity generation practical power output；N indicates normal distribution；
(2) photovoltaic and wind power output scene for obeying above-mentioned probabilistic model are generated with latin hypercube sampling method, are used
Scene elimination technique eliminates low probability scene therein and merges the strong scene of correlation.
4. a kind of microgrid dispatching method of providing multiple forms of energy to complement each other based on Multiple Time Scales according to claim 3, it is characterised in that:
Step 2 (2) step generates comprising the concrete steps that for photovoltaic and wind power output scene:
1. by every dimension variable x after determining sample size HiDomain sectionH equal minizones are divided into, so thatAn original super cube is divided into HnA small cubes；
2. generating the matrix A of a H × n, then each column of matrix A are all a random full rows of ordered series of numbers { 1,2, H }
Column；The corresponding selected small hypercube of every row of A, if a sample is randomly generated in each small hypercube,
H sample is selected, to generate the light for obeying wind-powered electricity generation described in step 2 (1) step and photovoltaic power output normal distribution probability model
Volt and wind power output scene.
5. a kind of microgrid dispatching method of providing multiple forms of energy to complement each other based on Multiple Time Scales according to claim 1 or 2, feature exist
In: the specific steps of the step 3 include:
(1) objective function of joint optimal operation model a few days ago is established with the minimum target of microgrid day total operating cost:
In formula, psIt is the probability that scene s occurs；It is that microgrid and major network exchange power under scene s；F (T) is natural gas
Consumption；cGrid(T) and cGasIt is electricity price and gas price respectively；T0At the beginning of indicating the paddy period；T0+ 23 be from T0Start, warp
At the time of after spending 23 hours；
(2) electric load of microgrid, the constraint condition of refrigeration duty joint optimal operation operation a few days ago are established
1. the constraint condition of the electric load of microgrid joint optimal operation operation a few days ago
In formula, Pload(T) be the microgrid T moment electric load；PCCHP(T) the electricity power output of T moment CCHP is indicated；Pa(T) when indicating T
Carve the power consumption of ice-storage air-conditioning in air-conditioning mode；Pb(T) charge-discharge electric power of T moment battery is indicated；Pc(T) when indicating T
Carve power consumption of the ice-storage air-conditioning under ice-make mode；Pd(T) power consumption at T moment under ice-melt mode is indicated；It indicates
The wind power output at T moment under s-th of scene；Indicate the photovoltaic power output at T moment under s-th of scene；Indicate s
T moment microgrid exchanges power between major network under a scene；
2. the constraint condition of the refrigeration duty of microgrid joint optimal operation operation a few days ago
QCCHP(T)+Qa(T)+Qd(T)=Qload(T)
In formula, QCCHP(T) the cold power output of T moment CCHP is indicated；Qd(T) system of the T moment ice-storage air-conditioning under ice-melt mode is indicated
Cooling capacity；Qa(T) refrigerating capacity of T moment ice-storage air-conditioning in air-conditioning mode is respectively indicated；Qload(T) when indicating predict a few days ago under
Carve the refrigeration duty demand of T.
6. a kind of microgrid dispatching method of providing multiple forms of energy to complement each other based on Multiple Time Scales according to claim 1 or 2, feature exist
In: the specific steps of the step 4 include:
(1) joined in real time with the microgrid that real-time exchange power is established under Multiple Time Scales with the minimum target of scheduled net interchange difference
Close the objective function of Optimal Operation Model:
In formula, PGrid_r(t+i | t) is the real-time exchange power of microgrid and power grid, and p is coefficient；Pb_r(t+i | t) it indicates to adjust in real time
I walks the charge-discharge electric power of moment battery forward under degree；PGrid(T) it indicates to exchange power between T moment microgrid and major network；
Obj represents objective function；
(2) constraint condition of the real-time joint optimal operation operation of electric load of microgrid is established
Pwind_r(t+i|t)+Ppv_r(t+i|t)+PCCHP_r(T)+Pb_r(t+i|t)+PGrid_r(t+i|t)
=Pload_r(t+i|t)+Pa_r(T)+Pc(T)+Pd(T),t+i∈T
In formula, r represents Real-Time Scheduling；Pwind_r(t+i | t) indicate that i walks the wind power output at moment forward under Real-Time Scheduling；Ppv_r(t+
I | t) indicate that i walks the photovoltaic power output at moment forward under Real-Time Scheduling；PCCHP_r(T) T moment gas turbine under Real-Time Scheduling is indicated
Electricity power output；Pb_r(t+i | t) indicate that i walks the charge-discharge electric power of moment battery forward under Real-Time Scheduling；PGrid_r(t+i | t) it indicates
The real-time exchange power of microgrid and power grid；Pload_r(t+i | t) it indicates to predict the electric load that i walks the moment forward under Real-Time Scheduling
Value；Pa_r(T) power consumption at T moment under Real-Time Scheduling and air conditioning mode is indicated；Pc(T) indicate that T moment ice-storage air-conditioning is making ice
Power consumption under mode；Pd(T) power consumption at T moment under ice-melt mode is indicated.
7. a kind of microgrid dispatching method of providing multiple forms of energy to complement each other based on Multiple Time Scales according to claim 5, it is characterised in that:
The specific steps of the step 5 include:
(1) it chooses and is suitble to the variable of improved Particle Swarm Optimization as search particle, establish and update and search with particle position
Improved Particle Swarm Optimization model that rope area update is characterized simultaneously initializes population；
(2) optimize a few days ago and Real time optimal dispatch model with the improved Particle Swarm Optimization model solution, when obtaining more
Between microgrid operation reserve of providing multiple forms of energy to complement each other under scale.
8. a kind of microgrid dispatching method of providing multiple forms of energy to complement each other based on Multiple Time Scales according to claim 6, it is characterised in that:
Step 5 (2) step method particularly includes:
The fitness Fit of particle individual in each population is evaluated, if Fit < pbestiThen pbest is replaced with Fiti；Otherwise, then
Continue to judge pbestiWith the relationship of gbest, if pbesti< gbest, then use pbestiInstead of gbest；Otherwise, then grain is updated
Sub- position and region of search position；Then judge whether that all particles have been calculated and whether meet termination condition, terminate if meeting
It calculates；
Wherein, pbesti is the optimal solution individual extreme value that particle itself is found；It is optimal that gbest is that entire population is found at present
Solve global extremum.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610139613.2A CN105811397B (en) | 2016-03-11 | 2016-03-11 | A kind of microgrid dispatching method of providing multiple forms of energy to complement each other based on Multiple Time Scales |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610139613.2A CN105811397B (en) | 2016-03-11 | 2016-03-11 | A kind of microgrid dispatching method of providing multiple forms of energy to complement each other based on Multiple Time Scales |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105811397A CN105811397A (en) | 2016-07-27 |
| CN105811397B true CN105811397B (en) | 2019-02-22 |
Family
ID=56468186
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610139613.2A Active CN105811397B (en) | 2016-03-11 | 2016-03-11 | A kind of microgrid dispatching method of providing multiple forms of energy to complement each other based on Multiple Time Scales |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105811397B (en) |
Families Citing this family (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106451553B (en) * | 2016-11-22 | 2019-01-04 | 安徽工程大学 | A kind of photovoltaic microgrid range optimization dispatching method of Multiple Time Scales |
| CN106709610B (en) * | 2017-01-12 | 2020-04-21 | 浙江大学 | Micro-grid electricity energy storage and ice storage combined optimization scheduling method |
| CN106849190B (en) * | 2017-03-21 | 2019-05-17 | 国网天津市电力公司 | A kind of microgrid real-time scheduling method of providing multiple forms of energy to complement each other based on Rollout algorithm |
| CN107273673B (en) * | 2017-06-01 | 2020-03-17 | 国网江苏省电力公司经济技术研究院 | Air conditioner water chiller group control method considering stabilizing wind power fluctuation |
| CN107895971B (en) * | 2017-11-28 | 2021-05-07 | 国网山东省电力公司德州供电公司 | Regional energy Internet scheduling method based on stochastic programming and model predictive control |
| CN109375588B (en) * | 2018-02-12 | 2021-01-01 | 浙江大学 | Factory multi-energy complementary optimization scheduling method considering generalized demand response |
| CN110739710A (en) * | 2018-07-20 | 2020-01-31 | 中国农业大学 | Method and device for coordinated scheduling of multiple energy types based on optimization algorithm |
| CN109004686B (en) * | 2018-08-29 | 2021-04-30 | 三峡大学 | Cold, heat and power combined supply type micro-grid system considering ice storage air conditioner multi-mode |
| WO2020081003A1 (en) * | 2018-10-17 | 2020-04-23 | Agency For Science, Technology And Research | Cooling plant system and method of operating said system |
| CN109409595B (en) * | 2018-10-19 | 2020-10-16 | 南京南瑞继保电气有限公司 | Garden multi-energy complementary system day-ahead scheduling method |
| CN109711080B (en) * | 2019-01-03 | 2021-10-29 | 山东大学 | Multi-time scale optimization operation method for combined cooling heating and power system |
| CN111463836B (en) * | 2020-05-13 | 2023-05-26 | 陕西燃气集团新能源发展股份有限公司 | Comprehensive energy system optimal scheduling method |
| CN112186801B (en) * | 2020-09-25 | 2022-05-17 | 大连理工大学 | Method for improving distributed photovoltaic grid-connected capacity of rural distribution network |
| CN114301072A (en) * | 2021-11-22 | 2022-04-08 | 深圳供电局有限公司 | Micro-grid scheduling method |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013082698A1 (en) * | 2011-12-05 | 2013-06-13 | Hatch Ltd. | System, method and controller for managing and controlling a micro-grid |
| CN103778485A (en) * | 2014-02-24 | 2014-05-07 | 南方电网科学研究院有限责任公司 | Distributed generation energy supply system and optimization method thereof |
-
2016
- 2016-03-11 CN CN201610139613.2A patent/CN105811397B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013082698A1 (en) * | 2011-12-05 | 2013-06-13 | Hatch Ltd. | System, method and controller for managing and controlling a micro-grid |
| CN103778485A (en) * | 2014-02-24 | 2014-05-07 | 南方电网科学研究院有限责任公司 | Distributed generation energy supply system and optimization method thereof |
Non-Patent Citations (1)
| Title |
|---|
| 分布式冷热电三联供系统节能协调优化调度;周任军等;《电网技术》;20120630;第6卷(第36期);第8-14页 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105811397A (en) | 2016-07-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105811397B (en) | A kind of microgrid dispatching method of providing multiple forms of energy to complement each other based on Multiple Time Scales | |
| Maleki et al. | Optimal sizing of autonomous hybrid photovoltaic/wind/battery power system with LPSP technology by using evolutionary algorithms | |
| CN102694391B (en) | Day-ahead optimal scheduling method for wind-solar storage integrated power generation system | |
| CN105990838B (en) | A kind of island microgrid Optimization Scheduling of meter and schedulable and translatable load | |
| CN103606969B (en) | Containing the island microgrid Optimization Scheduling of new forms of energy and desalinization load | |
| CN108009693A (en) | Grid-connected micro-capacitance sensor dual blank-holder based on two-stage demand response | |
| CN111463836A (en) | Optimized scheduling method for comprehensive energy system | |
| CN105391092B (en) | The optimising and adjustment method that virtual plant multiple target based on Dependent-chance Programming is bidded | |
| CN109523065A (en) | A kind of micro- energy net Optimization Scheduling based on improvement quanta particle swarm optimization | |
| CN110188915A (en) | Energy-storage system Optimal Configuration Method and system in virtual plant based on scene collection | |
| CN109103929A (en) | Based on the power distribution network economic optimization dispatching method for improving dynamic gram Li Sijin model | |
| CN111445107A (en) | Multi-objective optimization configuration method for cold-heat-power combined supply type micro-grid | |
| CN107273968A (en) | A kind of Multiobjective Scheduling method and device based on dynamic fuzzy Chaos-Particle Swarm Optimization | |
| CN110796279A (en) | Multi-energy complementary optimization operation method and system | |
| CN112836849A (en) | Virtual power plant scheduling method considering wind power uncertainty | |
| CN114595961A (en) | Scheduling method and device for biomass energy multi-energy utilization system | |
| CN112269966B (en) | Communication base station virtual power plant power generation capacity measurement method considering standby demand | |
| CN110992206B (en) | Optimal scheduling method and system for multi-source electric field | |
| CN114091825A (en) | Bidding method for new-power storage station participating in electric energy-frequency modulation auxiliary service market | |
| CN112883630A (en) | Day-ahead optimized economic dispatching method for multi-microgrid system for wind power consumption | |
| Ren et al. | Life-cycle-based multi-objective optimal design and analysis of distributed multi-energy systems for data centers | |
| CN110059853B (en) | Configuration method of equipment in combined cooling heating and power system | |
| CN110504684B (en) | Day-ahead optimal scheduling method for regional multi-microgrid system | |
| CN110135094A (en) | A kind of virtual plant Optimization Scheduling based on shrink space harmony algorithm | |
| CN109840621A (en) | Consider the grid type micro-capacitance sensor Multipurpose Optimal Method a few days ago that energy-storage system influences |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |