CN106786793A - A kind of supply of cooling, heating and electrical powers type microgrid operation method based on robust optimization - Google Patents
A kind of supply of cooling, heating and electrical powers type microgrid operation method based on robust optimization Download PDFInfo
- Publication number
- CN106786793A CN106786793A CN201611149137.9A CN201611149137A CN106786793A CN 106786793 A CN106786793 A CN 106786793A CN 201611149137 A CN201611149137 A CN 201611149137A CN 106786793 A CN106786793 A CN 106786793A
- Authority
- CN
- China
- Prior art keywords
- represent
- unit
- power
- periods
- formula
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- 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
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
-
- 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
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P80/00—Climate change mitigation technologies for sector-wide applications
- Y02P80/10—Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier
- Y02P80/14—District level solutions, i.e. local energy networks
Abstract
The invention discloses the supply of cooling, heating and electrical powers type microgrid operation method optimized based on robust, the operation method is comprised the following steps：Step 10）Set up the object function of supply of cooling, heating and electrical powers type microgrid economical operation；Step 20）Set up qualitative constraint really in the operation constraint of supply of cooling, heating and electrical powers type microgrid；Step 30）The uncertain constraint set up in the operation constraint of supply of cooling, heating and electrical powers type microgrid；Step 40）Solving-optimizing model, obtains system operation controlled quentity controlled variable, and send command adapted thereto to each equipment in system；The Optimized model includes step 10）The object function of foundation, step 20）Set up qualitative constraint and step 30 really）The uncertain constraint of foundation.The operation method eliminates the adverse effect that regenerative resource and negative rules bring.
Description
Technical field
The invention belongs to cold, heat and power triple supply system field, it particularly relates to a kind of cool and thermal power based on robust optimization
Alliance type microgrid operation method.
Background technology
With the increasingly depleted of the non-renewable energy resources such as oil, coal, how fully to develop regenerative resource, while
Further improving energy utilization rate turns into extremely urgent problem.Cooling heating and power generation system using natural gas energy resource carry out it is hot and cold,
Electricity coproduction, on the one hand substantially increase efficiency of energy utilization, on the other hand to make full use of wind-powered electricity generation, photovoltaic distributed can be again
Give birth to the energy and provide possibility, therefore as the important directions of future source of energy System Development.
Current supply of cooling, heating and electrical powers type microgrid exerts oneself predicted value according to regenerative resource in operation and predicted load is carried out
Scheduling, being exerted oneself with customer charge due to regenerative resource has certain stochastic behaviour, therefore has in real time execution and greatly may be used very much
Can deviate predicted value, cause supply of cooling, heating and electrical powers microgrid actually to be run with optimal economy, or even to microgrid
The safety of system constitutes huge threat.
The content of the invention
Technical problem：The technical problems to be solved by the invention are：A kind of supply of cooling, heating and electrical powers based on robust optimization is provided
Type microgrid operation method, by setting up the uncertain constraint that regenerative resource is exerted oneself with load power, makes system in renewable energy
Source is actual exert oneself or load actual value and predicted value between there is relatively large deviation in the case of, remain to safe operation, eliminating can be again
The adverse effect that the raw energy and negative rules bring.
Technical scheme：In order to solve the above technical problems, embodiment of the present invention proposition is a kind of based on the cold and hot of robust optimization
CCHP type microgrid operation method, the operation method is comprised the following steps：
Step 10) set up the object function of supply of cooling, heating and electrical powers type microgrid economical operation；
Step 20) set up qualitative constraint really in the operation constraint of supply of cooling, heating and electrical powers type microgrid；
Step 30) set up the uncertainty constraint that supply of cooling, heating and electrical powers type microgrid is run in constraint；
Step 40) solving-optimizing model, system operation controlled quentity controlled variable is obtained, and accordingly finger is sent to each equipment in system
Order；The Optimized model includes step 10) object function, the step 20 set up) set up qualitative constraint and step 30 really) set up
Uncertain constraint.
As preference, described step 10) in, the supply of cooling, heating and electrical powers type microgrid economical operation object function such as formula of foundation
(1) shown in：
In formula, C represents system operation cost；T represents current time；T represents control time domain；Represent that the t periods are cold and hot
The cost that CCHP type microgrid is interacted with power network,Shown in expression formula such as formula (2)；The fuel of expression system t periods into
This,Shown in expression formula such as formula (3)；The aging cost of battery of t periods is represented,Shown in expression formula such as formula (4)；
The operation expense of expression system t periods,Shown in expression formula such as formula (5)；
In formula,Represent t periods system to power network purchase electricity price, unit：Unit/kWh；Represent t period systems
To power network power purchase power, unit：kW；Represent t periods system to power network sale of electricity electricity price, unit：Unit/kWh；Represent
T periods system is to power network sale of electricity power, unit：kW；Δ t represents time interval；
In formula,Represent t period systems buying Gas Prices, unit：Unit/m3；Represent the miniature combustion of t periods
Gas-turbine consumes fuel power, unit：kW；Represent that t period gas fired-boilers consume fuel power, unit：kW；Hng
Represent heating value of natural gas, unit：kWh/m3；
In formula, RbtRepresent the unit interval aging cost of battery, unit：Unit/h；Represent that t period batteries are put
Electricity condition；Represent t period battery states of charge；
In formula,Represent the electrical power of miniature gas turbine t periods, unit：kW；Rmt,rmRepresent miniature gas turbine
Operation and maintenance cost, unit：Unit/kWh；Represent the power of gas fired-boiler t periods, unit：kW；Rb,rmRepresent gas fired-boiler
Operation and maintenance cost, unit：Unit/kWh；Represent the thermic load power of t period systems, unit：kW；ηheRepresent heat exchange
Device efficiency；Rhe,rmRepresent heat exchanger operation and maintenance cost, unit：Unit/kWh；Represent the adsorbent refrigerator t periods
Input power, unit：kW；Rac,rmRepresent adsorbent refrigerator operation and maintenance cost, unit：Unit/kWh；Represent electric refrigerating machine
The input power of t periods, unit：kW；Rec,rmRepresent electric refrigerating machine operation and maintenance cost, unit：Unit/kWh；Represent light
Lie prostrate the predicted value of t periods；Rpv,rmRepresent photovoltaic cell maintenance cost unit：Unit/kWh；Represent the battery t periods
Charge power, unit：kW；Represent battery t period discharge powers, unit：kW；Rbt,rmRepresent battery operation maintenance
Cost coefficient, unit：Unit/kWh；Represent the accumulation of heat power of heat storage tank t periods, unit：kWh；Represent accumulation of heat
The heat release power of groove t periods, unit：kW；Rtst,rmRepresent heat storage tank operation and maintenance cost coefficient, unit：Unit/kWh.
As preference, described step 20) specifically include：
Step 201) set up miniature gas turbine thermoelectric (al) power constraint and Climing constant：
The thermoelectric (al) power constraint of miniature gas turbine is determined, as shown in formula (6)：
In formula,Miniature gas turbine t period running status variables are represented,Represent miniature gas turbine fortune
OK,Represent that miniature gas turbine is shut down；Represent the 1st section after the linearisation of miniature gas turbine thermoelectrical efficiency curve segmentation
Corresponding electrical power lower limit, unit：kW；LmtRepresent the thermoelectrical efficiency curve index set of miniature gas turbine piece-wise linearization；Represent the amount that the electrical power that t periods miniature gas turbine is produced falls in the segmentation of thermoelectrical efficiency curve kth, unit：kW；Represent the kth section binary coding variable of micro-gas-turbine thermoelectrical efficiency curve segmentation linearisation；Represent micro-gas-turbine
The jth section binary coding variable of piece-wise linearization thermoelectrical efficiency curve；Represent miniature gas turbine thermoelectrical efficiency curve point
The corresponding electrical power upper limit of+1 section of kth, unit after section linearisation：kW；Represent the thermoelectricity of miniature gas turbine piece-wise linearization
The corresponding electrical power lower limit of efficiency curve kth section, unit：kW；Represent that miniature gas turbine runs what is produced in the t periods
Thermal power, unit：kW；After the curve segmentation linearisation of expression miniature gas turbine thermoelectrical efficiency under the 1st section of corresponding thermal power
Limit；The slope of kth section after the curve segmentation linearisation of expression miniature gas turbine thermoelectrical efficiency；
Miniature gas turbine start and stop Climing constant and continuous operation Climing constant are determined, as shown in formula (7)：
In formula,Represent the lower limit that miniature gas turbine is exerted oneself, unit：kW；Represent what miniature gas turbine was exerted oneself
The upper limit, unit：kW；Represent the electrical power of miniature gas turbine t periods, unit：kW；Represent micro-gas-turbine unit
Maximum drop power in continuous running status, unit：kW；The electrical power of miniature gas turbine t-1 periods is represented, it is single
Position：kW；The power that most increases when representing that micro-gas-turbine unit starts, unit：kW；
Step 202) set up supply of cooling, heating and electrical powers type microgrid and interact power constraint with power network, as shown in formula (8)：
In formula,Represent t period cool and thermal power microgrids from power network power purchase power, unit：kW；Represent that the t periods are cold
Thermoelectricity microgrid from power network power purchase state,The t periods from power network power purchase are represented,Represent the t periods not from power network
Power purchase；The upper limit that expression system is interacted with major network power, unit kW；Represent that t period cool and thermal power microgrids are sold to power network
Electrical power, unit：kW；T period cool and thermal power microgrids are represented to power network sale of electricity state,Represent the t periods to
Power network sale of electricity,Represent the t periods not from power network sale of electricity；
Step 203) constraints that battery runs is set up, as shown in formula (9)：
In formula,The charged state of battery t periods is represented,Represent that battery charges；Table
Show that battery does not charge；Represent the charge power minimum value of battery, unit：kW；Represent t period batteries
Charge power, unit：kW；Represent the charge power maximum of battery, unit：kW；Represent the battery t periods
Discharge condition,Represent battery discharging；Represent that battery does not discharge；Represent the electric discharge of battery
Power minimum, unit：kW；Represent the discharge power of t period batteries, unit：kW；Represent putting for battery
Electrical power maximum, unit：kW；Represent the energy of t periods in battery, unit：kWh；Represent the in battery
The energy of t-1 periods, unit：kWh；σbtRepresent the self-energy proportion of goods damageds of battery；Represent the charge efficiency of battery；Represent battery discharging efficiency；Represent that battery stores the lower limit of energy, unit：kWh；Represent battery storage
The upper limit of energy, unit：kWh；Δ t represents time interval；
Step 204) constraints that heat storage tank runs is set up, as shown in formula (10)：
In formula,The heat release state of heat storage tank t periods is represented,Heat storage tank heat release is represented,Table
Show heat storage tank not heat release；Represent the accumulation of heat lower limit of the power of heat storage tank, unit：kW；Represent the heat storage tank t periods
Heat release power, unit：kW；Represent the accumulation of heat upper limit of the power of heat storage tank, unit：kW；Represent the heat storage tank t periods
Heat storage state,Heat storage tank accumulation of heat is represented,Represent heat storage tank not accumulation of heat；Represent putting for heat storage tank
Thermal power lower limit, unit：kW；Represent the accumulation of heat power of heat storage tank t periods, unit：kWh；Represent heat storage tank
The heat release upper limit of the power, unit：kW；Represent the energy of t periods in heat storage tank, unit：kWh；Represent in heat storage tank
The energy of t-1 periods, unit：kWh；σtstRepresent the self-energy proportion of goods damageds of heat storage tank；Represent the accumulation of heat effect of heat storage tank
Rate；Represent that heat storage tank discharges the efficiency of heat；Represent that heat storage tank stores the upper limit of energy, unit：kWh；Represent
Heat storage tank stores the lower limit of energy, unit：kWh；
Step 205) set up auxiliary equipment operation constraint, as shown in formula (11) to formula (14)：
Set up the gas fired-boiler operation constraints as shown in formula (11)：
In formula,Represent that gas fired-boiler goes out the lower limit of activity of force, unit：kW；Represent the work(of gas fired-boiler t periods
Rate, unit：kW；Represent that gas fired-boiler goes out the upper limit of activity of force, unit：kW；
Set up the electric refrigerating plant operation constraints as shown in formula (12)：
In formula,Represent electric refrigerating plant input power lower limit, unit：kW；Represent the electric refrigerating plant t periods
Input electric power, unit：kW；Represent the electric refrigerating plant input power upper limit, unit：kW；
Set up the absorption refrigerating equipment operation constraints as shown in formula (13)：
In formula,Represent absorption refrigerating equipment input power lower limit, unit：kW；Represent absorption refrigerating equipment
The input electric power of t periods, unit：kW；Represent the absorption refrigerating equipment input power upper limit, unit：kW；
Set up the heat-exchanger rig operation constraints as shown in formula (14)：
In formula,Represent heat-exchanger rig input power lower limit, unit：kW；Represent the defeated of heat-exchanger rig t periods
Enter electrical power, unit：kW；Represent the heat-exchanger rig input power upper limit, unit：kW.
As preference, described step 30) specifically include：
Step 301) set up as shown in formula (15) cold power-balance uncertain constraint：
In formula, COPacRepresent absorption refrigerating equipment Energy Efficiency Ratio；Represent that absorption refrigerating equipment is input into the t periods
Thermal power, unit：kW；COPecRepresent electric refrigeration plant Energy Efficiency Ratio；Represent the electricity that electric refrigeration plant is input into the t periods
Power, unit：kW；Represent probable value of the refrigeration duty in the t periods, unit：kW；Represent that t period refrigeration duty power is pre-
Measured value, unit：kW；Represent the lower limit deviation ratio of cooling load prediction value；The lower limit deviation of cooling load prediction value is represented,
Unit：kW；Represent the upper limit deviation ratio of cooling load prediction value；Represent the upper limit deviation of cooling load prediction value, unit：
kW；The a reference value for being used for adjusting refrigeration duty uncertain region is represented,Meet formula (16)：
Step 302) set up the uncertain constraint of heating power balance as shown in formula (17)：
In formula,Represent power output of the waste-heat recovery device in the t periods, unit：kW；Represent that gas fired-boiler exists
The power output of t periods, unit：kW；Represent input power of the absorption refrigerating equipment in the t periods, unit：kW；Represent heat release power of the regenerative apparatus in the t periods, unit：kW；Represent heat accumulation of the regenerative apparatus in the t periods
Power, unit：kW；Represent probable value of the thermic load in the t periods, unit：kW；ηheRepresent heat-exchanger rig efficiency；Table
Thermic load power prediction value when showing t, unit：kW；Represent the lower limit deviation ratio of heat load prediction value；Represent that heat is negative
The lower limit deviation of lotus predicted value, unit：kW；Represent the upper limit deviation ratio of heat load prediction value；Represent that thermic load is pre-
The upper limit deviation of measured value, unit：kW；Represent a reference value for being used for adjusting thermic load uncertain region；Meet formula (18)：
Step 303) set up the uncertain constraint that the electrical power as shown in formula (19) is balanced：
In formula,Represent power output of the miniature gas turbine in the t periods, unit：kW；Represent t period microgrids
From the electrical power of power network purchase, unit：kW；Represent the electrical power that t periods microgrid is sold to power network, unit：kW；Table
Show electric refrigerating machine input power, unit：kW；Represent the discharge power of t period batteries, unit：kW；Represent the
The charge power of t period batteries, unit：kW；Represent the probable value of t period electric load power, unit：kW；Table
Show the probable value of t period photovoltaic powers, unit：kW；Pl tRepresent t period electric load power prediction values, unit：kW；Table
Show t period photovoltaic power predicted values, unit：kW；Represent the lower limit deviation ratio of electric load predicted value；Pl ldRepresent that electricity is negative
The lower limit deviation of lotus predicted value, unit：kW；Represent the upper limit deviation ratio of electric load predicted value；Pl udRepresent that electric load is pre-
The upper limit deviation of measured value, unit：kW；Represent the lower limit deviation ratio of photovoltaic power predicted value；Represent photovoltaic power prediction
The lower limit deviation of value, unit：kW；Represent the upper limit deviation ratio of photovoltaic power predicted value；Represent photovoltaic power prediction
The upper limit deviation of value, unit：kW；Represent electric uncertainty metric；
WithMeet formula (20)：
Beneficial effect：Compared with prior art, the embodiment of the present invention has advantages below：It is proposed by the present invention based on robust
The supply of cooling, heating and electrical powers type microgrid operation method of optimization, initially sets up supply of cooling, heating and electrical powers type microgrid economical operation object function, the mesh
Scalar functions consider microgrid from power network purchases strategies, microgrid to power network sale of electricity income, microgrid purchase gas cost, battery into
Originally with microgrid maintenance cost；Then set up qualitative constraint really in the operation constraint of supply of cooling, heating and electrical powers type microgrid, including microgrid combustion gas
The thermoelectric (al) power constraint of turbine, Climing constant, microgrid interact power constraint, the operation constraint of battery and heat storage tank with power network
Deng；The uncertain constraint finally set up in the operation constraint of supply of cooling, heating and electrical powers type microgrid, including cold power-balance constraint, thermal power
Constraints of Equilibrium and electrical power Constraints of Equilibrium etc..Each equipment in supply of cooling, heating and electrical powers type microgrid is obtained by solving above-mentioned Optimized model
Operation controlled quentity controlled variable, corresponding control and operation are carried out according to this result.This method can overcome regenerative resource and cool and thermal power negative
Lotus is fluctuated and gives the supply of cooling, heating and electrical powers type microgrid adverse effect brought of operation, improves system run all right, it is ensured that system operation
Economy.
Brief description of the drawings
Fig. 1 is supply of cooling, heating and electrical powers type micro-capacitance sensor structural representation in the present invention；
Fig. 2 is the thermoelectrical efficiency curve segmentation linearisation schematic diagram of miniature gas turbine.
Specific embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, below in conjunction with accompanying drawing and case study on implementation
The present invention is in depth described in detail.It should be appreciated that specific implementation case described herein is only used to explain this hair
It is bright, it is not used to limit invention.
A kind of supply of cooling, heating and electrical powers type microgrid operation method based on robust optimization of the embodiment of the present invention, belongs to cold and hot Electricity Federation
For system regions.It is as shown in Figure 1 supply of cooling, heating and electrical powers type microgrid structural representation, the system is by miniature gas turbine, recuperation of heat
The compositions such as device, boiler, electric refrigeration plant, absorption refrigerating equipment, heat exchanger, battery and heat storage can, the system connects simultaneously
Inlet air such as electrically or optically lies prostrate at the regenerative resource.In the system, miniature gas turbine, boiler, regenerative resource are energy producing unit,
Bulk power grid is to the not enough electrical energy demands of replenishment system or absorbs unnecessary electric energy.System provides electric, hot, cold three to load simultaneously
Plant energy requirement.Be shown in Fig. 2 microgrid gas turbine in supply of cooling, heating and electrical powers type microgrid thermoelectrical efficiency curve segmentation linearisation show
It is intended to, carries out linear interpolation in Fig. 2 to miniature gas turbine thermoelectrical efficiency curve with a number of line segment, realizes to non-linear
The linearisation of efficiency curve.
A kind of supply of cooling, heating and electrical powers type microgrid operation method based on robust optimization of the embodiment of the present invention, the operation method bag
Include following steps：
Step 10) set up the object function of supply of cooling, heating and electrical powers type microgrid economical operation.Wherein, described step 10) in, build
Shown in vertical supply of cooling, heating and electrical powers type microgrid economical operation object function such as formula (1)：
In formula, C represents system operation cost；T represents current time；T represents control time domain；Represent that the t periods are cold and hot
The cost that CCHP type microgrid is interacted with power network,Shown in expression formula such as formula (2)；The fuel of expression system t periods into
This,Shown in expression formula such as formula (3)；The aging cost of battery of t periods is represented,Shown in expression formula such as formula (4)；
The operation expense of expression system t periods,Shown in expression formula such as formula (5)；
In formula,Represent t periods system to power network purchase electricity price, unit：Unit/kWh；Represent t period systems
To power network power purchase power, unit：kW；Represent t periods system to power network sale of electricity electricity price, unit：Unit/kWh；Represent
T periods system is to power network sale of electricity power, unit：kW；Δ t represents time interval.Preferably, described Δ t=1h.
In formula,Represent t period systems buying Gas Prices, unit：Unit/m3；Represent the miniature combustion of t periods
Gas-turbine consumes fuel power, unit：kW；Represent that t period gas fired-boilers consume fuel power, unit：kW；HngTable
Show heating value of natural gas, unit：kWh/m3；
In formula, RbtRepresent the unit interval aging cost of battery, unit：Unit/h；Represent that t period batteries are put
Electricity condition；Represent t period battery states of charge；
In formula,Represent the electrical power of miniature gas turbine t periods, unit：kW；Rmt,rmRepresent miniature gas turbine
Operation and maintenance cost, unit：Unit/kWh；Represent the power of gas fired-boiler t periods, unit：kW；Rb,rmRepresent gas fired-boiler
Operation and maintenance cost, unit：Unit/kWh；Represent the thermic load power of t period systems, unit：kW；ηheRepresent heat exchange
Device efficiency；Rhe,rmRepresent heat exchanger operation and maintenance cost, unit：Unit/kWh；Represent the adsorbent refrigerator t periods
Input power, unit：kW；Rac,rmRepresent adsorbent refrigerator operation and maintenance cost, unit：Unit/kWh；Represent electric refrigerating machine
The input power of t periods, unit：kW；Rec,rmRepresent electric refrigerating machine operation and maintenance cost, unit：Unit/kWh；Represent light
Lie prostrate the predicted value of t periods；Rpv,rmRepresent photovoltaic cell maintenance cost unit：Unit/kWh；Represent the battery t periods
Charge power, unit：kW；Represent battery t period discharge powers, unit：kW；Rbt,rmRepresent battery operation dimension
Shield cost coefficient, unit：Unit/kWh；Represent the accumulation of heat power of heat storage tank t periods, unit：kWh；Represent and store
The heat release power of heat channel t periods, unit：kW；Rtst,rmRepresent heat storage tank operation and maintenance cost coefficient, unit：Unit/kWh.
Step 20) set up qualitative constraint really in the operation constraint of supply of cooling, heating and electrical powers type microgrid.Wherein, described step 20)
Specifically include：
Step 201) set up miniature gas turbine thermoelectric (al) power constraint and Climing constant：
The thermoelectric (al) power constraint of miniature gas turbine is determined, as shown in formula (6)：
In formula,Miniature gas turbine t period running status variables are represented,Represent miniature gas turbine fortune
OK,Represent that miniature gas turbine is shut down；Represent the 1st section after the linearisation of miniature gas turbine thermoelectrical efficiency curve segmentation
Corresponding electrical power lower limit, unit：kW；LmtRepresent the thermoelectrical efficiency curve index set of miniature gas turbine piece-wise linearization；Represent the amount that the electrical power that t periods miniature gas turbine is produced falls in the segmentation of thermoelectrical efficiency curve kth, unit：kW；Represent the binary coding variable of micro-gas-turbine piece-wise linearization thermoelectrical efficiency curve kth segmentation；Represent miniature gas
The binary coding variable of wheel piece-wise linearization thermoelectrical efficiency curve jth segmentation；Represent that miniature gas turbine thermoelectrical efficiency is bent
The corresponding electrical power upper limit of+1 section of kth, unit after line piece-wise linearization：kW；Represent miniature gas turbine piece-wise linearization
The corresponding electrical power lower limit of thermoelectrical efficiency curve kth section, unit：kW；Represent that miniature gas turbine runs product in the t periods
Raw thermal power, unit：kW；Represent the 1st section of corresponding hot merit after the linearisation of miniature gas turbine thermoelectrical efficiency curve segmentation
Rate lower limit；The slope of kth section after the curve segmentation linearisation of expression miniature gas turbine thermoelectrical efficiency.
Miniature gas turbine start and stop Climing constant and continuous operation Climing constant are determined, as shown in formula (7)：
In formula,Represent the lower limit that miniature gas turbine is exerted oneself, unit：kW；Represent what miniature gas turbine was exerted oneself
The upper limit, unit：kW；Represent the electrical power of miniature gas turbine t periods, unit：kW；Represent micro-gas-turbine unit
Maximum drop power in continuous running status, unit：kW；The electrical power of miniature gas turbine t-1 periods is represented, it is single
Position：kW；The power that most increases when representing that micro-gas-turbine unit starts, unit：kW.
Step 202) set up supply of cooling, heating and electrical powers type microgrid and interact power constraint with power network, as shown in formula (8)：
In formula,Represent t period cool and thermal power microgrids from power network power purchase power, unit：kW；Represent that the t periods are cold
Thermoelectricity microgrid from power network power purchase state,The t periods from power network power purchase are represented, Represent the t periods not from power network
Power purchase；The upper limit that expression system is interacted with major network power, unit kW；Represent t period cool and thermal power microgrids to power network
Sale of electricity power, unit：kW；T period cool and thermal power microgrids are represented to power network sale of electricity state,Represent the t periods
To power network sale of electricity,Represent the t periods not from power network sale of electricity.
Step 203) constraints that battery runs is set up, as shown in formula (9)：
In formula,The charged state of battery t periods is represented,Represent that battery charges；Table
Show that battery does not charge；Represent the charge power minimum value of battery, unit：kW；Represent t period batteries
Charge power, unit：kW；Represent the charge power maximum of battery, unit：kW；Represent the battery t periods
Discharge condition,Represent battery discharging；Represent that battery does not discharge；Represent the electric discharge of battery
Power minimum, unit：kW；Represent the discharge power of t period batteries, unit：kW；Represent putting for battery
Electrical power maximum, unit：kW；Represent the energy of t periods in battery, unit：kWh；Represent the in battery
The energy of t-1 periods, unit：kWh；σbtRepresent the self-energy proportion of goods damageds of battery；Represent the charge efficiency of battery；Represent battery discharging efficiency；Represent that battery stores the lower limit of energy, unit：kWh；Represent battery storage
The upper limit of energy, unit：kWh；Δ t represents time interval.
Step 204) constraints that heat storage tank runs is set up, as shown in formula (10)：
In formula,The heat release state of heat storage tank t periods is represented,Heat storage tank heat release is represented,Table
Show heat storage tank not heat release；Represent the accumulation of heat lower limit of the power of heat storage tank, unit：kW；Represent the heat storage tank t periods
Heat release power, unit：kW；Represent the accumulation of heat upper limit of the power of heat storage tank, unit：kW；Represent the heat storage tank t periods
Heat storage state,Heat storage tank accumulation of heat is represented,Represent heat storage tank not accumulation of heat；Represent putting for heat storage tank
Thermal power lower limit, unit：kW；Represent the accumulation of heat power of heat storage tank t periods, unit：kWh；Represent heat storage tank
The heat release upper limit of the power, unit：kW；Represent the energy of t periods in heat storage tank, unit：kWh；Represent in heat storage tank
The energy of t-1 periods, unit：kWh；σtstRepresent the self-energy proportion of goods damageds of heat storage tank；Represent the accumulation of heat effect of heat storage tank
Rate；Represent that heat storage tank discharges the efficiency of heat；Represent that heat storage tank stores the upper limit of energy, unit：kWh；Represent
Heat storage tank stores the lower limit of energy, unit：kWh.
Step 205) set up auxiliary equipment operation constraint, as shown in formula (11) to formula (14)：
Set up the gas fired-boiler operation constraints as shown in formula (11)：
In formula,Represent that gas fired-boiler goes out the lower limit of activity of force, unit：kW；Represent the work(of gas fired-boiler t periods
Rate, unit：kW；Represent that gas fired-boiler goes out the upper limit of activity of force, unit：kW.
Set up the electric refrigerating plant operation constraints as shown in formula (12)：
In formula,Represent electric refrigerating plant input power lower limit, unit：kW；Represent the electric refrigerating plant t periods
Input electric power, unit：kW；Represent the electric refrigerating plant input power upper limit, unit：kW.
Set up the absorption refrigerating equipment operation constraints as shown in formula (13)：
In formula,Represent absorption refrigerating equipment input power lower limit, unit：kW；Represent absorption refrigerating equipment
The input electric power of t periods, unit：kW；Represent the absorption refrigerating equipment input power upper limit, unit：kW.
Set up the heat-exchanger rig operation constraints as shown in formula (14)：
In formula,Represent heat-exchanger rig input power lower limit, unit：kW；Represent the defeated of heat-exchanger rig t periods
Enter electrical power, unit：kW；Represent the heat-exchanger rig input power upper limit, unit：kW.
Step 30) set up the uncertainty constraint that supply of cooling, heating and electrical powers type microgrid is run in constraint.Wherein, described step
30) specifically include：
Step 301) set up as shown in formula (15) cold power-balance uncertain constraint：
In formula, COPacRepresent absorption refrigerating equipment Energy Efficiency Ratio；Represent that absorption refrigerating equipment is input into the t periods
Thermal power, unit：kW；COPecRepresent electric refrigeration plant Energy Efficiency Ratio；Represent the electric work that electric refrigeration plant is input into the t periods
Rate, unit：kW；Represent probable value of the refrigeration duty in the t periods, unit：kW；Represent t period refrigeration duty power predictions
Value, unit：kW；Represent the lower limit deviation ratio of cooling load prediction value；The lower limit deviation of cooling load prediction value is represented, it is single
Position：kW；Represent the upper limit deviation ratio of cooling load prediction value；Represent the upper limit deviation of cooling load prediction value, unit：kW；Represent a reference value for being used for adjusting refrigeration duty uncertain region.The selection of value needs robustness and solution in problem
Conservative between compromised.Meet formula (16)：
Step 302) set up the uncertain constraint of heating power balance as shown in formula (17)：
In formula,Represent power output of the waste-heat recovery device in the t periods, unit：kW；Represent that gas fired-boiler exists
The power output of t periods, unit：kW；Represent input power of the absorption refrigerating equipment in the t periods, unit：kW；Represent heat release power of the regenerative apparatus in the t periods, unit：kW；Represent heat accumulation of the regenerative apparatus in the t periods
Power, unit：kW；Represent probable value of the thermic load in the t periods, unit：kW；ηheRepresent heat-exchanger rig efficiency；Table
Thermic load power prediction value when showing t, unit：kW；Represent the lower limit deviation ratio of heat load prediction value；Represent that heat is negative
The lower limit deviation of lotus predicted value, unit：kW；Represent the upper limit deviation ratio of heat load prediction value；Represent that thermic load is pre-
The upper limit deviation of measured value, unit：kW；Represent a reference value for being used for adjusting thermic load uncertain region；The selection of value needs
Compromised between the robustness of problem and the conservative of solution.Meet formula (18)：
Step 303) set up the uncertain constraint that the electrical power as shown in formula (19) is balanced：
In formula,Represent power output of the miniature gas turbine in the t periods, unit：kW；Represent that the t periods are micro-
The electrical power that net is bought from power network, unit：kW；Represent the electrical power that t periods microgrid is sold to power network, unit：kW；
Represent electric refrigerating machine input power, unit：kW；Represent the discharge power of t period batteries, unit：kW；Represent
The charge power of t period batteries, unit：kW；Represent the probable value of t period electric load power, unit：kW；
Represent the probable value of t period photovoltaic powers, unit：kW；Pl tRepresent t period electric load power prediction values, unit：kW；
Represent t period photovoltaic power predicted values, unit：kW；Represent the lower limit deviation ratio of electric load predicted value；Pl ldRepresent electricity
The lower limit deviation of predicted load, unit：kW；Represent the upper limit deviation ratio of electric load predicted value；Pl udRepresent that electric load is pre-
The upper limit deviation of measured value, unit：kW；Represent the lower limit deviation ratio of photovoltaic power predicted value；Represent photovoltaic power prediction
The lower limit deviation of value, unit：kW；Represent the upper limit deviation ratio of photovoltaic power predicted value；Represent photovoltaic power predicted value
Upper limit deviation, unit：kW；Represent electric uncertainty metric；
WithMeet formula (20)
Step 40) solving-optimizing model, system operation controlled quentity controlled variable is obtained, and accordingly finger is sent to each equipment in system
Order；The Optimized model includes step 10) object function, the step 20 set up) set up qualitative constraint and step 30 really) set up
Uncertain constraint.
Existing supply of cooling, heating and electrical powers type microgrid in operation, is exerted oneself predicted value and predicted load enters according to regenerative resource
Row scheduling, being exerted oneself with customer charge due to regenerative resource has certain stochastic behaviour, therefore has very big in real time execution
Predicted value may be deviateed, cause supply of cooling, heating and electrical powers microgrid actually cannot optimal economy run, or even to microgrid
The safety of system constitutes huge threat.The embodiment of the present invention by set up regenerative resource exert oneself it is uncertain with load power
Constraint, constitutes supply of cooling, heating and electrical powers type microgrid Robust Optimization Model.May go out in considering system operation due to Robust Optimization Model
Existing worst case, thus the method can eliminate regenerative resource and negative rules to systematic economy operation bring not
Profit influence, while guarantee system remains able to safely and steadily run when regenerative resource and load have uncertain.
A kind of supply of cooling, heating and electrical powers type microgrid economic load dispatching method based on robust optimization of the present embodiment, initially sets up cold and hot
CCHP type microgrid economical operation object function, the object function considers microgrid and is sold to power network from power network purchases strategies, microgrid
Electric income, microgrid purchase gas cost, battery cost and microgrid maintenance cost；Then set up supply of cooling, heating and electrical powers type microgrid fortune
Really qualitative constraint in row constraint, including microgrid gas turbine thermoelectric (al) power constraint, Climing constant, microgrid interacts work(with power network
Rate is constrained, operation constraint of battery and heat storage tank etc.；Finally set up uncertain in the operation constraint of supply of cooling, heating and electrical powers type microgrid
Property constraint, including cold power-balance constraint, heating power balance constraint and electrical power Constraints of Equilibrium etc..By solving above-mentioned optimization mould
Type obtains the operation controlled quentity controlled variable of each equipment in supply of cooling, heating and electrical powers type microgrid, and corresponding control and operation are carried out according to this result.This
Method can overcome the adverse effect that regenerative resource and cool and thermal power load fluctuation bring to the operation of supply of cooling, heating and electrical powers type microgrid, carry
System run all right high, it is ensured that the economy of system operation.
Claims (5)
1. it is a kind of based on robust optimization supply of cooling, heating and electrical powers type microgrid operation method, it is characterised in that the operation method include with
Lower step：
Step 10) set up the object function of supply of cooling, heating and electrical powers type microgrid economical operation；
Step 20) set up qualitative constraint really in the operation constraint of supply of cooling, heating and electrical powers type microgrid；
Step 30) set up the uncertainty constraint that supply of cooling, heating and electrical powers type microgrid is run in constraint；
Step 40) solving-optimizing model, system operation controlled quentity controlled variable is obtained, and command adapted thereto is sent to each equipment in system；Institute
Optimized model is stated including step 10) object function, the step 20 set up) set up qualitative constraint and step 30 really) set up not
Certainty is constrained.
2. according to the supply of cooling, heating and electrical powers type microgrid operation method optimized based on robust described in claim 1, it is characterised in that institute
The step of stating 10) in, shown in the supply of cooling, heating and electrical powers type microgrid economical operation object function such as formula (1) of foundation：
In formula, C represents system operation cost；T represents current time；T represents control time domain；Represent t periods cold and hot Electricity Federation
For the cost that type microgrid is interacted with power network,Shown in expression formula such as formula (2)；The fuel cost of expression system t periods,
Shown in expression formula such as formula (3)；The aging cost of battery of t periods is represented,Shown in expression formula such as formula (4)；Represent
The operation expense of system t periods,Shown in expression formula such as formula (5)；
In formula,Represent t periods system to power network purchase electricity price, unit：Unit/kWh；Represent t period systems to electricity
Net purchase electrical power, unit：kW；Represent t periods system to power network sale of electricity electricity price, unit：Unit/kWh；Represent t
Period system is to power network sale of electricity power, unit：kW；Dt represents time interval；
In formula,Represent t period systems buying Gas Prices, unit：Unit/m3；Represent t period micro-gas-turbines
Machine consumes fuel power, unit：kW；Represent that t period gas fired-boilers consume fuel power, unit：kW；HngRepresent
Heating value of natural gas, unit：kWh/m3；
In formula, RbtRepresent the unit interval aging cost of battery, unit：Unit/h；Represent t period battery discharging shapes
State；Represent t period battery states of charge；
In formula,Represent the electrical power of miniature gas turbine t periods, unit：kW；Rmt,rmRepresent miniature gas turbine operation
Maintenance cost, unit：Unit/kWh；Represent the power of gas fired-boiler t periods, unit：kW；Rb,rmRepresent gas fired-boiler operation
Maintenance cost, unit：Unit/kWh；Represent the thermic load power of t period systems, unit：kW；ηheRepresent heat exchanger effect
Rate；Rhe,rmRepresent heat exchanger operation and maintenance cost, unit：Unit/kWh；Represent the input of adsorbent refrigerator t periods
Power, unit：kW；Rac,rmRepresent adsorbent refrigerator operation and maintenance cost, unit：Unit/kWh；Represent electric refrigerating machine t
The input power of period, unit：kW；Rec,rmRepresent electric refrigerating machine operation and maintenance cost, unit：Unit/kWh；Represent photovoltaic
The predicted value of t periods；Rpv,rmRepresent photovoltaic cell maintenance cost unit：Unit/kWh；Represent the battery t periods
Charge power, unit：kW；Represent battery t period discharge powers, unit：kW；Rbt,rmRepresent battery operation maintenance
Cost coefficient, unit：Unit/kWh；Represent the accumulation of heat power of heat storage tank t periods, unit：kWh；Represent accumulation of heat
The heat release power of groove t periods, unit：kW；Rtst,rmRepresent heat storage tank operation and maintenance cost coefficient, unit：Unit/kWh.
3. according to the supply of cooling, heating and electrical powers type microgrid operation method optimized based on robust described in claim 1, it is characterised in that institute
The step of stating 20) specifically include：
Step 201) set up miniature gas turbine thermoelectric (al) power constraint and Climing constant：
The thermoelectric (al) power constraint of miniature gas turbine is determined, as shown in formula (6)：
In formula,Miniature gas turbine t period running status variables are represented,Miniature gas turbine operation is represented,Represent that miniature gas turbine is shut down；Represent after the linearisation of miniature gas turbine thermoelectrical efficiency curve segmentation the 1st section it is right
The electrical power lower limit answered, unit：kW；LmtRepresent the thermoelectrical efficiency curve index set of miniature gas turbine piece-wise linearization；
Represent the amount that the electrical power that t periods miniature gas turbine is produced falls in the segmentation of thermoelectrical efficiency curve kth, unit：kW；
Represent the kth section binary coding variable of micro-gas-turbine thermoelectrical efficiency curve segmentation linearisation；Represent micro-gas-turbine point
The jth section binary coding variable of section linearisation thermoelectrical efficiency curve；Represent miniature gas turbine thermoelectrical efficiency curve segmentation
The corresponding electrical power upper limit of+1 section of kth, unit after linearisation：kW；Represent the thermoelectricity effect of miniature gas turbine piece-wise linearization
The corresponding electrical power lower limit of rate curve kth section, unit：kW；Represent that miniature gas turbine runs the heat for producing in the t periods
Power, unit：kW；After the curve segmentation linearisation of expression miniature gas turbine thermoelectrical efficiency under the 1st section of corresponding thermal power
Limit；The slope of kth section after the curve segmentation linearisation of expression miniature gas turbine thermoelectrical efficiency；
Miniature gas turbine start and stop Climing constant and continuous operation Climing constant are determined, as shown in formula (7)：
In formula,Represent the lower limit that miniature gas turbine is exerted oneself, unit：kW；The upper limit that miniature gas turbine is exerted oneself is represented,
Unit：kW；Represent the electrical power of miniature gas turbine t periods, unit：kW；Represent that micro-gas-turbine unit exists
Maximum drop power during continuous running status, unit：kW；Represent the electrical power of miniature gas turbine t-1 periods, unit：
kW；The power that most increases when representing that micro-gas-turbine unit starts, unit：kW；
Step 202) set up supply of cooling, heating and electrical powers type microgrid and interact power constraint with power network, as shown in formula (8)：
In formula,Represent t period cool and thermal power microgrids from power network power purchase power, unit：kW；Represent t period cool and thermal powers
Microgrid from power network power purchase state,The t periods from power network power purchase are represented,Represent the t periods not from power network power purchase；The upper limit that expression system is interacted with major network power, unit kW；Represent t period cool and thermal power microgrids to power network sale of electricity
Power, unit：kW；T period cool and thermal power microgrids are represented to power network sale of electricity state,Represent the t periods to electricity
Net sale of electricity,Represent the t periods not from power network sale of electricity；
Step 203) constraints that battery runs is set up, as shown in formula (9)：
In formula,The charged state of battery t periods is represented,Represent that battery charges；Represent
Battery does not charge；Represent the charge power minimum value of battery, unit：kW；Represent filling for t period batteries
Electrical power, unit：kW；Represent the charge power maximum of battery, unit：kW；Represent the battery t periods
Discharge condition,Represent battery discharging；Represent that battery does not discharge；Represent the electric discharge of battery
Power minimum, unit：kW；Represent the discharge power of t period batteries, unit：kW；Represent battery
Discharge power maximum, unit：kW；Represent the energy of t periods in battery, unit：kWh；Represent in battery
The energy of t-1 periods, unit：kWh；σbtRepresent the self-energy proportion of goods damageds of battery；Represent the charging effect of battery
Rate；Represent battery discharging efficiency；Represent that battery stores the lower limit of energy, unit：kWh；Represent battery
Store the upper limit of energy, unit：kWh；Dt represents time interval；
Step 204) constraints that heat storage tank runs is set up, as shown in formula (10)：
In formula,The heat release state of heat storage tank t periods is represented,Heat storage tank heat release is represented,Represent and store
Heat channel not heat release；Represent the accumulation of heat lower limit of the power of heat storage tank, unit：kW；Represent the heat release of heat storage tank t periods
Power, unit：kW；Represent the accumulation of heat upper limit of the power of heat storage tank, unit：kW；
The heat storage state of heat storage tank t periods is represented,Heat storage tank accumulation of heat is represented,Represent heat storage tank
Not accumulation of heat；Represent the heat release lower limit of the power of heat storage tank, unit：kW；Represent the accumulation of heat work(of heat storage tank t periods
Rate, unit：kWh；Represent the heat release upper limit of the power of heat storage tank, unit：kW；Represent the energy of t periods in heat storage tank
Amount, unit：kWh；Represent the energy of t-1 periods in heat storage tank, unit：kWh；σtstRepresent the self-energy of heat storage tank
The proportion of goods damageds；Represent the heat storage efficiency of heat storage tank；Represent that heat storage tank discharges the efficiency of heat；Represent heat storage tank storage
The upper limit of energy, unit：kWh；Represent that heat storage tank stores the lower limit of energy, unit：kWh；
Step 205) set up auxiliary equipment operation constraint, as shown in formula (11) to formula (14)：
Set up the gas fired-boiler operation constraints as shown in formula (11)：
In formula,Represent that gas fired-boiler goes out the lower limit of activity of force, unit：kW；The power of gas fired-boiler t periods is represented, it is single
Position：kW；Represent that gas fired-boiler goes out the upper limit of activity of force, unit：kW；
Set up the electric refrigerating plant operation constraints as shown in formula (12)：
In formula,Represent electric refrigerating plant input power lower limit, unit：kW；Represent the input of electric refrigerating plant t periods
Electrical power, unit：kW；Represent the electric refrigerating plant input power upper limit, unit：kW；
Set up the absorption refrigerating equipment operation constraints as shown in formula (13)：
In formula,Represent absorption refrigerating equipment input power lower limit, unit：kW；When representing absorption refrigerating equipment t
The input electric power of section, unit：kW；Represent the absorption refrigerating equipment input power upper limit, unit：kW；
Set up the heat-exchanger rig operation constraints as shown in formula (14)：
In formula,Represent heat-exchanger rig input power lower limit, unit：kW；Represent the input electric work of heat-exchanger rig t periods
Rate, unit：kW；Represent the heat-exchanger rig input power upper limit, unit：kW.
4. according to the supply of cooling, heating and electrical powers type microgrid operation method optimized based on robust described in claim 1, it is characterised in that institute
The step of stating 30) specifically include：
Step 301) set up as shown in formula (15) cold power-balance uncertain constraint：
In formula, COPacRepresent absorption refrigerating equipment Energy Efficiency Ratio；Represent the heat that absorption refrigerating equipment is input into the t periods
Power, unit：kW；COPecRepresent electric refrigeration plant Energy Efficiency Ratio；The electrical power that electric refrigeration plant is input into the t periods is represented,
Unit：kW；Represent probable value of the refrigeration duty in the t periods, unit：kW；T period refrigeration duty power prediction values are represented,
Unit：kW；Represent the lower limit deviation ratio of cooling load prediction value；Represent the lower limit deviation of cooling load prediction value, unit：
kW；Represent the upper limit deviation ratio of cooling load prediction value；Represent the upper limit deviation of cooling load prediction value, unit：kW；Represent a reference value for being used for adjusting refrigeration duty uncertain region；Meet formula (16)：
Step 302) set up the uncertain constraint of heating power balance as shown in formula (17)：
In formula,Represent power output of the waste-heat recovery device in the t periods, unit：kW；Represent gas fired-boiler in t
The power output of section, unit：kW；Represent input power of the absorption refrigerating equipment in the t periods, unit：kW；Table
Show heat release power of the regenerative apparatus in the t periods, unit：kW；Heat accumulation power of the regenerative apparatus in the t periods is represented, it is single
Position：kW；Represent probable value of the thermic load in the t periods, unit：kW；ηheRepresent heat-exchanger rig efficiency；When representing t
Thermic load power prediction value, unit：kW；Represent the lower limit deviation ratio of heat load prediction value；Represent heat load prediction value
Lower limit deviation, unit：kW；Represent the upper limit deviation ratio of heat load prediction value；Represent the upper of heat load prediction value
Limit deviation, unit：kW；Represent a reference value for being used for adjusting thermic load uncertain region；Meet formula (18)：
Step 303) set up the uncertain constraint that the electrical power as shown in formula (19) is balanced：
In formula,Represent power output of the miniature gas turbine in the t periods, unit：kW；Represent t periods microgrid from electricity
The electrical power that net purchase is bought, unit：kW；Represent the electrical power that t periods microgrid is sold to power network, unit：kW；Represent electricity
Refrigeration machine input power, unit：kW；Represent the discharge power of t period batteries, unit：kW；When representing t
The charge power of section battery, unit：kW；Represent the probable value of t period electric load power, unit：kW；Represent the
The probable value of t period photovoltaic powers, unit：kW；Represent t period electric load power prediction values, unit：kW；Represent t
Period photovoltaic power predicted value, unit：kW；Represent the lower limit deviation ratio of electric load predicted value；Represent that electric load is pre-
The lower limit deviation of measured value, unit：kW；Represent the upper limit deviation ratio of electric load predicted value；Represent electric load predicted value
Upper limit deviation, unit：kW；Represent the lower limit deviation ratio of photovoltaic power predicted value；Represent photovoltaic power predicted value
Lower limit deviation, unit：kW；Represent the upper limit deviation ratio of photovoltaic power predicted value；Represent photovoltaic power predicted value
Upper limit deviation, unit：kW；Represent electric uncertainty metric；
WithMeet formula (20)
5. according to the supply of cooling, heating and electrical powers type microgrid operation method optimized based on robust described in claim 2, it is characterised in that institute
The Δ t=1h for stating.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611149137.9A CN106786793B (en) | 2016-12-14 | 2016-12-14 | A kind of supply of cooling, heating and electrical powers type microgrid operation method based on robust optimization |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611149137.9A CN106786793B (en) | 2016-12-14 | 2016-12-14 | A kind of supply of cooling, heating and electrical powers type microgrid operation method based on robust optimization |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106786793A true CN106786793A (en) | 2017-05-31 |
| CN106786793B CN106786793B (en) | 2019-04-09 |
Family
ID=58876851
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611149137.9A Active CN106786793B (en) | 2016-12-14 | 2016-12-14 | A kind of supply of cooling, heating and electrical powers type microgrid operation method based on robust optimization |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106786793B (en) |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107358345A (en) * | 2017-06-30 | 2017-11-17 | 上海电力学院 | The distributed triple-generation system optimizing operation method of meter and dsm |
| CN107565605A (en) * | 2017-08-24 | 2018-01-09 | 浙江万克新能源科技有限公司 | A kind of shop equipment based on micro-capacitance sensor tends to the method for optimization automatically |
| CN107609684A (en) * | 2017-08-24 | 2018-01-19 | 浙江万克新能源科技有限公司 | A kind of integrated energy system economic optimization dispatching method based on micro-capacitance sensor |
| CN108197412A (en) * | 2018-02-05 | 2018-06-22 | 东北大学 | A kind of multiple-energy-source coupling Energy Management System and optimization method |
| CN108596525A (en) * | 2018-06-29 | 2018-09-28 | 国家电网有限公司 | The cold and hot micro- energy net robust Optimization Scheduling electrically provided multiple forms of energy to complement each other |
| CN108717594A (en) * | 2018-04-16 | 2018-10-30 | 东南大学 | A kind of more micro-grid system economic optimization dispatching methods of supply of cooling, heating and electrical powers type |
| CN109299823A (en) * | 2018-09-22 | 2019-02-01 | 天津大学 | A kind of micro- energy net multi-objective planning method considering thermal method sea water desalination |
| CN109687529A (en) * | 2018-12-26 | 2019-04-26 | 新奥数能科技有限公司 | A kind of energy scheduling method, device, readable medium and electronic equipment |
| CN109726906A (en) * | 2018-12-21 | 2019-05-07 | 国网浙江省电力有限公司电力科学研究院 | Co-generation unit dispatching method a few days ago based on the constraint of heat supply network partial differential equation |
| CN109840638A (en) * | 2019-02-28 | 2019-06-04 | 华南理工大学 | A kind of meter and the probabilistic combined heat and power of heat supply network robust economic load dispatching method a few days ago |
| CN110059853A (en) * | 2019-03-11 | 2019-07-26 | 上海电气分布式能源科技有限公司 | The configuration method of equipment in a kind of cold, heat and power triple supply system |
| WO2019165701A1 (en) * | 2018-02-28 | 2019-09-06 | 东南大学 | Random robust coupling optimization scheduling method for alternating-current and direct-current hybrid micro-grids |
| CN110361969A (en) * | 2019-06-17 | 2019-10-22 | 清华大学 | A kind of cool and thermal power integrated energy system optimizing operation method |
| CN111219250A (en) * | 2020-01-20 | 2020-06-02 | 上海电力大学 | Mixed distributed energy supply system |
| CN113437744A (en) * | 2021-06-09 | 2021-09-24 | 河海大学 | Photo-thermal-biomass hybrid power station robust optimization scheduling model considering uncertainty |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2779040A1 (en) * | 2013-03-15 | 2014-09-17 | Honeywell International Inc. | Modeling energy conversion in systems |
| CN104616208A (en) * | 2015-02-04 | 2015-05-13 | 东南大学 | Model predication control based cooling heating and power generation type micro-grid operation method |
| CN105550766A (en) * | 2015-12-04 | 2016-05-04 | 山东大学 | Micro-grid robustness multi-target operation optimization method containing renewable energy resources |
-
2016
- 2016-12-14 CN CN201611149137.9A patent/CN106786793B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2779040A1 (en) * | 2013-03-15 | 2014-09-17 | Honeywell International Inc. | Modeling energy conversion in systems |
| CN104616208A (en) * | 2015-02-04 | 2015-05-13 | 东南大学 | Model predication control based cooling heating and power generation type micro-grid operation method |
| CN105550766A (en) * | 2015-12-04 | 2016-05-04 | 山东大学 | Micro-grid robustness multi-target operation optimization method containing renewable energy resources |
Non-Patent Citations (2)
| Title |
|---|
| 戚建文等: "冷热电联供微网环保经济调度研究", 《电测与仪表》 * |
| 石可颂: "冷热电联供系统控制策略与优化调度研究", 《中国优秀硕士学位论文全文数据库（电子期刊）》 * |
Cited By (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107358345A (en) * | 2017-06-30 | 2017-11-17 | 上海电力学院 | The distributed triple-generation system optimizing operation method of meter and dsm |
| CN107358345B (en) * | 2017-06-30 | 2021-07-20 | 上海电力学院 | Distributed combined cooling heating and power system optimization operation method considering demand side management |
| CN107565605A (en) * | 2017-08-24 | 2018-01-09 | 浙江万克新能源科技有限公司 | A kind of shop equipment based on micro-capacitance sensor tends to the method for optimization automatically |
| CN107609684A (en) * | 2017-08-24 | 2018-01-19 | 浙江万克新能源科技有限公司 | A kind of integrated energy system economic optimization dispatching method based on micro-capacitance sensor |
| CN108197412A (en) * | 2018-02-05 | 2018-06-22 | 东北大学 | A kind of multiple-energy-source coupling Energy Management System and optimization method |
| CN108197412B (en) * | 2018-02-05 | 2021-03-26 | 东北大学 | Multi-energy coupling energy management system and optimization method |
| WO2019165701A1 (en) * | 2018-02-28 | 2019-09-06 | 东南大学 | Random robust coupling optimization scheduling method for alternating-current and direct-current hybrid micro-grids |
| CN108717594A (en) * | 2018-04-16 | 2018-10-30 | 东南大学 | A kind of more micro-grid system economic optimization dispatching methods of supply of cooling, heating and electrical powers type |
| CN108717594B (en) * | 2018-04-16 | 2020-02-18 | 东南大学 | Economic optimization scheduling method for combined cooling heating and power type multi-microgrid system |
| CN108596525B (en) * | 2018-06-29 | 2022-07-22 | 国家电网有限公司 | Robust optimization scheduling method for micro-energy network with complementary cold-heat-electricity multi-energy |
| CN108596525A (en) * | 2018-06-29 | 2018-09-28 | 国家电网有限公司 | The cold and hot micro- energy net robust Optimization Scheduling electrically provided multiple forms of energy to complement each other |
| CN109299823A (en) * | 2018-09-22 | 2019-02-01 | 天津大学 | A kind of micro- energy net multi-objective planning method considering thermal method sea water desalination |
| CN109726906A (en) * | 2018-12-21 | 2019-05-07 | 国网浙江省电力有限公司电力科学研究院 | Co-generation unit dispatching method a few days ago based on the constraint of heat supply network partial differential equation |
| CN109687529A (en) * | 2018-12-26 | 2019-04-26 | 新奥数能科技有限公司 | A kind of energy scheduling method, device, readable medium and electronic equipment |
| CN109840638A (en) * | 2019-02-28 | 2019-06-04 | 华南理工大学 | A kind of meter and the probabilistic combined heat and power of heat supply network robust economic load dispatching method a few days ago |
| CN109840638B (en) * | 2019-02-28 | 2022-06-10 | 华南理工大学 | Thermoelectric combined day-ahead robust economic dispatching method considering heat supply network uncertainty |
| CN110059853A (en) * | 2019-03-11 | 2019-07-26 | 上海电气分布式能源科技有限公司 | The configuration method of equipment in a kind of cold, heat and power triple supply system |
| CN110059853B (en) * | 2019-03-11 | 2023-09-05 | 上海电气分布式能源科技有限公司 | Configuration method of equipment in combined cooling heating and power system |
| CN110361969B (en) * | 2019-06-17 | 2021-01-05 | 清华大学 | Optimized operation method of cooling, heating and power comprehensive energy system |
| CN110361969A (en) * | 2019-06-17 | 2019-10-22 | 清华大学 | A kind of cool and thermal power integrated energy system optimizing operation method |
| CN111219250A (en) * | 2020-01-20 | 2020-06-02 | 上海电力大学 | Mixed distributed energy supply system |
| CN113437744A (en) * | 2021-06-09 | 2021-09-24 | 河海大学 | Photo-thermal-biomass hybrid power station robust optimization scheduling model considering uncertainty |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106786793B (en) | 2019-04-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106786793B (en) | A kind of supply of cooling, heating and electrical powers type microgrid operation method based on robust optimization | |
| CN107832979B (en) | Factory comprehensive energy system economic optimization scheduling method considering energy cascade utilization | |
| Li et al. | Optimal scheduling of integrated demand response-enabled community-integrated energy systems in uncertain environments | |
| CN108717594B (en) | Economic optimization scheduling method for combined cooling heating and power type multi-microgrid system | |
| CN106505634B (en) | The supply of cooling, heating and electrical powers type microgrid operation method with control is coordinated and optimized based on two stages | |
| CN103728881B (en) | A kind of optimizing operation method of many edifice control system system | |
| CN111950807B (en) | Comprehensive energy system optimization operation method considering uncertainty and demand response | |
| CN104616208A (en) | Model predication control based cooling heating and power generation type micro-grid operation method | |
| CN109146182A (en) | The economic load dispatching method of meter and the distributed triple-generation system of a variety of energy storage | |
| CN104537443A (en) | Co-generation type micro-grid economy coordination and optimization dispatching method | |
| CN106058942A (en) | Energy hub optimizing model taking wind power nondeterminacy into consideration and including power to gas and CCHP | |
| CN106950840A (en) | Towards the integrated energy system layered distribution type control method for coordinating of power network peak clipping | |
| CN112464477A (en) | Multi-energy coupling comprehensive energy operation simulation method considering demand response | |
| CN114707289B (en) | Multi-objective optimization method of electrothermal coupling comprehensive energy system based on opportunity constraint | |
| CN113256045B (en) | Park comprehensive energy system day-ahead economic dispatching method considering wind and light uncertainty | |
| CN109345012A (en) | Garden energy internet running optimizatin method based on comprehensive evaluation index | |
| CN110620403A (en) | Day-ahead scheduling method and system for collaborative operation of energy system considering renewable energy | |
| CN109583623A (en) | Region multipotency source supply system in a few days rolls collaboration optimizing operation method and device | |
| CN108008629A (en) | The complementary optimizing operation method for utilizing system of one kind of multiple energy | |
| CN113435095A (en) | Method and system for optimizing scheduling of comprehensive energy system | |
| CN113240166A (en) | Day-ahead economic dispatching method of micro-energy network considering high-proportion new energy consumption | |
| CN116341703A (en) | Method, system, medium and equipment for optimizing operation of park comprehensive energy system | |
| CN111008760A (en) | Double-layer optimization method for combined cooling heating and power micro-grid system | |
| Jie et al. | Study of energy scheduling and optimal cost management of a new structure CCHP system: A case study supplying energy for a chemical enterprise in Jiangsu Province | |
| CN114037337A (en) | Micro energy network optimization scheduling method and system based on model predictive control |
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 |