CN110516863A - A kind of more microgrid active distribution system dual blank-holders of supply of cooling, heating and electrical powers type - Google Patents
A kind of more microgrid active distribution system dual blank-holders of supply of cooling, heating and electrical powers type Download PDFInfo
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Abstract
The invention discloses a kind of more microgrid active distribution system dual blank-holders of supply of cooling, heating and electrical powers type, carry out mathematical modeling to equipment various in representative heat-cool electricity supply type microgrid first；Then the Optimized model of upper layer active distribution network layer is analyzed；Secondly the Optimized model of the more microgrid layers of lower layer's supply of cooling, heating and electrical powers type is analyzed；Bi-level optimal model is finally solved using genetic algorithm and mixed integer linear programming.The present invention provides a kind of effective, practical, scientific energy source optimization dispatching method, the popularization and application being beneficial to energy conservation.
Description
Technical field
The invention belongs to technical field of power systems, and in particular to a kind of more microgrid active distribution systems of supply of cooling, heating and electrical powers type
Dual blank-holder.
Background technique
The fast development of industrial technology proposes increasingly higher demands to energy supply.The fossils such as traditional petroleum, coal
Fuel, it is non-renewable although total amount is abundant.And in China, although fossil energy total amount is big, occupancy volume per person is far below
World average level, therefore improve important method of the efficiency of energy utilization as alleviating energy crisis.Cooling heating and power generation system
It energizes various informative, primary energy utilization ratio can be made to be up to 90% by the cascade utilization of energy, become and pay close attention to both at home and abroad
Emphasis.
But the promotion of existing more its energy utilization rate of microgrid active distribution system of supply of cooling, heating and electrical powers type has received bottleneck,
So needing a new technical solution to solve this problem.
Summary of the invention
Goal of the invention: the present invention provides a kind of more microgrid active distribution system dual blank-holders of supply of cooling, heating and electrical powers type, can
The dual-layer optimization of the more microgrids of supply of cooling, heating and electrical powers type and active distribution network is obtained as a result, effectively improving efficiency of energy utilization, reduces system
System operating cost.
Technical solution: to achieve the above object, the present invention provides a kind of more microgrid active distribution systems of supply of cooling, heating and electrical powers type
Dual blank-holder, comprising the following steps:
S1: mathematical modeling is carried out to the equipment of supply of cooling, heating and electrical powers type microgrid；
S2: the upper layer active distribution network layer Optimized model of bi-level optimal model is established；
S3: the more microgrid layer Optimized models of lower layer's supply of cooling, heating and electrical powers type of bi-level optimal model are established；
S4: being solved using genetic algorithm and mixed integer linear programming, obtains the day of layer model and underlying model
Preceding Optimized Operation plan.
Further, equipment includes miniature gas turbine, combustion gas in the more microgrids of supply of cooling, heating and electrical powers type in the step S1
Boiler, waste heat boiler, absorption refrigeration unit, steam heat exchanger, electric refrigerating machine, energy storage device and renewable energy power generation dress
It sets, specific step is as follows for the mathematical modeling:
S1-1: the mathematical model of gas turbine is established:
ηc=(8.935+33.157 β -27.081 β2+17.989β3)/100 × 100%
ηr=(+24.644 β of 82.869-30.173 β2-16.371β3)/100 × 100%
Wherein, ηcFor gas turbine power generation efficiency, ηrFor gas turbine heat recovery efficiency, QGTFor combustion turbine exhaustion waste heat
Amount, PGTFor gas turbine power generation power, ηlFor gas turbine radiation loss coefficient, VGTIt is consumed by runing time internal-combustion gas turbine engine
Amount of natural gas, LHVNGFor heating value of natural gas；
S1-2: the mathematical model of energy storage device is established:
Wherein, E (t) is the energy that energy storage device is stored in the t period, and Δ t is the time interval of t period to t+1 period,
PabsIt (t) is t period energy storage power, Prelea(t) be t period exoergic power, μ be energy storage device itself to environment dissipate the loss of energy or
From the energy coefficient of loss, ηabsFor the energy storage efficiency of energy storage device, ηreleaFor energy storage device exergic efficiency；
S1-3: the mathematical model of gas fired-boiler is established:
Wherein, PGBFor the thermal power of gas fired-boiler,For the gas quantity that gas fired-boiler is consumed in the Δ t period, ηGBFor
The thermal efficiency of gas fired-boiler；
S1-4: the mathematical model of heat-exchanger rig is established:
PHX,out=PWH,heatηHX
Wherein, PHX,outHeats power, P are exported for steam and hot water heat-exchanger rigWH,heatFor in the output steam of waste heat boiler
For the thermal power of heating, ηHXFor the transfer efficiency of steam and hot water heat-exchanger rig；
S1-5: the mathematical model of Absorption Refrigerator is established:
PAC,out=PWH,coolηAC
Wherein, PAC,outRefrigeration work consumption, P are exported for steam operated absorption refrigerating machineWH,coolFor the output steam of waste heat boiler
In for refrigeration input power, ηACFor the refrigerating efficiency of steam operated absorption refrigerating machine；
S1-6: the mathematical model of electric refrigerating machine is established:
PEC,out=PEC,inηEC
Wherein, PEC,outFor the output refrigeration work consumption of electric refrigerating machine, PEC,inFor the input electric power of electric refrigerating machine, ηECFor electricity
The Energy Efficiency Ratio of refrigeration machine.
Further, the Optimized model of upper layer active distribution network layer is established in the step S2 the following steps are included:
S2-1: establishing upper layer optimization object function, i.e. the day operation economic cost of active distribution network layer is minimum:
minFDN=FG-Fs
Wherein, FDNFor the total cost of production of power distribution network, FGFor the cost of electricity-generating of generating set in power distribution network, FsActively to match
The expense of electrical power, P are interacted between power grid and each energy mix systemGiIt (t) is power output of i-th conventional power unit in period t,
ai、bi、ciFor corresponding cost coefficient, n indicates the quantity of generating set,For period t active distribution network and supply of cooling, heating and electrical powers
The electrical power of type microgrid j interaction, positive value indicate power grid to more microgrid sales of electricity, and negative value indicates power grid from more microgrid power purchases, τj(t) it is
The real-time deal electricity price of t moment power grid and microgrid j, m are supply of cooling, heating and electrical powers type microgrid number；
S2-2: establishing the constraint condition of active distribution network, specifically includes electrical power Constraints of Equilibrium, conventional power unit power output up and down
Limit constraint, the constraint of active distribution network spinning reserve, tie-line power transmission constraint:
Electrical power Constraints of Equilibrium:
Conventional power unit power output bound constraint:
PGi,min≤PGi(t)≤PGi,max
The constraint of power distribution network spinning reserve:
Tie-line power transmission constraint:
Wherein,Electric load predicted value for active distribution network in the t period, PGi,maxAnd PGi,minFor the active of unit i
Power output bound, RDNIt (t) is stand-by requirement of the active distribution network in the t period,WithIt is active distribution network to each cold and hot
The bound of electricity supply type microgrid transimission power.
Further, the Optimized model for the more microgrid layers of lower layer's supply of cooling, heating and electrical powers type being established in the step S3 includes following step
It is rapid:
S3-1: the optimization object function in the more microgrid economic optimization scheduling models of supply of cooling, heating and electrical powers type:
Wherein, j is the number of supply of cooling, heating and electrical powers type microgrid, and m is the number of supply of cooling, heating and electrical powers type microgrid, Ffuel,jIt is j-th
The fuel cost of supply of cooling, heating and electrical powers type microgrid, Fdisnet,jThe function interacted for j-th of supply of cooling, heating and electrical powers type microgrid with active distribution network
Rate expense；
S3-2: the constraint condition of the more microgrid economic optimization scheduling models of supply of cooling, heating and electrical powers type is established, including power-balance is about
Beam, place capacity constraint, equipment operation constraint, wherein place capacity and operation constraint condition be to meet the power output of each equipment
The limitation of power bound, the power-balance constraint are specific as follows:
Establish cold power-balance constraint equation:
Wherein,For the refrigeration work consumption of electric refrigerating machine,For Absorption Refrigerator refrigeration work consumption, source is waste heat pot
The gas turbine waste heat that furnace is collected,For the demand of refrigeration duty in supply of cooling, heating and electrical powers type microgrid；
Establish heating power balance constraint equation:
Wherein,For the output thermal power of gas fired-boiler,For the heats power of steam and hot water heat-exchanger rig,For
The demand of thermic load in supply of cooling, heating and electrical powers type microgrid；
Establish electrical power Constraints of Equilibrium equation:
Wherein,For the generated output of gas turbine,For supply of cooling, heating and electrical powers type microgrid and active distribution network by when
Electrical power cross-over value,For wind-power electricity generation power,For photovoltaic generation power,For supply of cooling, heating and electrical powers type microgrid electric load
Power,For electric refrigerating machine power consumption in supply of cooling, heating and electrical powers type microgrid；
It establishes equipment and goes out the constraint of activity of force bound:
Wherein,WithFor the minimum value and maximum value of Gas Turbine Output power,WithFor gas fired-boiler
The minimum value and maximum value of activity of force out,WithGo out the minimum value and maximum value of activity of force for heat-exchanger rig,WithGo out the minimum value and maximum value of activity of force for Absorption Refrigerator,WithGo out the minimum of activity of force for electric refrigerating machine
Value and maximum value,WithThe minimum value and maximum value of power to charge the battery,WithFor electric power storage tank discharge function
The minimum value and maximum value of rate.
Further, the step S4 specifically:
It is solved using genetic algorithm (GA) with the method that mixed integer linear programming software (Cplex) combines, on
Layer uses GA algorithm, purchased to the generating set of active distribution network power output and between each supply of cooling, heating and electrical powers type microgrid sale of electricity power into
Row optimizing, lower layer calls Cplex to calculate the equipment power output of supply of cooling, heating and electrical powers type microgrid each in dispatching cycle, and returns
The performance number of each supply of cooling, heating and electrical powers type microgrid and active distribution network purchase sale of electricity, upper layer calculates fitness value, passes through genetic manipulation
Optimizing obtains the optimal solution under setting target.
Further, specific as follows for the derivation algorithm process step of bi-level optimal model in the step S4:
S4-1: the master datas such as basic device parameter, dispatching cycle, cost coefficient are read；
S4-2: random to generate initial population data；
S4-3: mixed integer linear programming software is called to solve the optimization of the more micro-grid systems of underlying model supply of cooling, heating and electrical powers type
Model；
S4-4: the ideal adaptation angle value of layer model in calculating；
S4-5: judge whether the number of iterations is greater than 30；
S4-6: if it is judged that be it is yes, then export the optimal solution of the upper and lower；
S4-7: if it is judged that be it is no, then re-form genetic groups, step S4-2 repeated, until judging result is
It is.
The utility model has the advantages that compared with prior art, the present invention by establishing bi-level optimal model, analysis active distribution system and
Situations such as economic cost and equipment power output of the more micro-grid systems of supply of cooling, heating and electrical powers type, not only there is important Engineering Guidance meaning,
And can effectively improve the efficiency of energy utilization of the more microgrid active distribution systems of supply of cooling, heating and electrical powers type, reduce system operation at
This.
Detailed description of the invention
Fig. 1 is flow diagram of the present invention；
Fig. 2 is the more microgrid active distribution system structure charts of supply of cooling, heating and electrical powers type of the present invention；
Fig. 3 is the solution flow chart of bi-level optimal model of the present invention.
Specific embodiment
In the following with reference to the drawings and specific embodiments, the present invention is furture elucidated.
As depicted in figs. 1 and 2, the present invention provides a kind of more microgrid active distribution system dual-layer optimization sides of supply of cooling, heating and electrical powers type
Method, comprising the following steps:
S1: mathematical modeling is carried out to the equipment of supply of cooling, heating and electrical powers type microgrid；
S2: the upper layer active distribution network layer Optimized model of bi-level optimal model is established；
S3: the more microgrid layer Optimized models of lower layer's supply of cooling, heating and electrical powers type of bi-level optimal model are established；
S4: being solved using genetic algorithm and mixed integer linear programming, obtains the day of layer model and underlying model
Preceding Optimized Operation plan.
In the more microgrids of supply of cooling, heating and electrical powers type in the present embodiment step S1 equipment include miniature gas turbine, gas fired-boiler,
Waste heat boiler, absorption refrigeration unit, steam heat exchanger, electric refrigerating machine, energy storage device and renewable energy power generation device, number
Learning modeling, specific step is as follows:
S1-1: the mathematical model of gas turbine is established:
ηc=(8.935+33.157 β -27.081 β2+17.989β3)/100 × 100%
ηr=(+24.644 β of 82.869-30.173 β2-16.371β3)/100 × 100%
Wherein, ηcFor gas turbine power generation efficiency, ηrFor gas turbine heat recovery efficiency, QGTFor combustion turbine exhaustion waste heat
Amount, PGTFor gas turbine power generation power, ηlFor gas turbine radiation loss coefficient, VGTIt is consumed by runing time internal-combustion gas turbine engine
Amount of natural gas, LHVNGFor heating value of natural gas；
S1-2: the mathematical model of energy storage device is established:
Wherein, E (t) is the energy that energy storage device is stored in the t period, and Δ t is the time interval of t period to t+1 period,
PabsIt (t) is t period energy storage power, Prelea(t) be t period exoergic power, μ be energy storage device itself to environment dissipate the loss of energy or
From the energy coefficient of loss, ηa bsFor the energy storage efficiency of energy storage device, ηreleaFor energy storage device exergic efficiency；
S1-3: the mathematical model of gas fired-boiler is established:
Wherein, PGBFor the thermal power of gas fired-boiler,For the gas quantity that gas fired-boiler is consumed in the Δ t period, ηGBFor
The thermal efficiency of gas fired-boiler；
S1-4: the mathematical model of heat-exchanger rig is established:
PHX,out=PWH,heatηHX
Wherein, PHX,outHeats power, P are exported for steam and hot water heat-exchanger rigWH,heatFor in the output steam of waste heat boiler
For the thermal power of heating, ηHXFor the transfer efficiency of steam and hot water heat-exchanger rig；
S1-5: the mathematical model of Absorption Refrigerator is established:
PAC,out=PWH,coolηAC
Wherein, PAC,outRefrigeration work consumption, P are exported for steam operated absorption refrigerating machineWH,coolFor the output steam of waste heat boiler
In for refrigeration input power, ηACFor the refrigerating efficiency of steam operated absorption refrigerating machine；
S1-6: the mathematical model of electric refrigerating machine is established:
PEC,out=PEC,inηEC
Wherein, PEC,outFor the output refrigeration work consumption of electric refrigerating machine, PEC,inFor the input electric power of electric refrigerating machine, ηECFor electricity
The Energy Efficiency Ratio of refrigeration machine.
The Optimized model of upper layer active distribution network layer is established in the present embodiment step S2 the following steps are included:
S2-1: establishing upper layer optimization object function, i.e. the day operation economic cost of active distribution network layer is minimum:
minFDN=FG-Fs
Wherein, FDNFor the total cost of production of power distribution network, FGFor the cost of electricity-generating of generating set in power distribution network, FsActively to match
The expense of electrical power, P are interacted between power grid and each energy mix systemGiIt (t) is power output of i-th conventional power unit in period t,
ai、bi、ciFor corresponding cost coefficient, n indicates the quantity of generating set,For period t active distribution network and supply of cooling, heating and electrical powers
The electrical power of type microgrid j interaction, positive value indicate power grid to more microgrid sales of electricity, and negative value indicates power grid from more microgrid power purchases, τj(t) it is
The real-time deal electricity price of t moment power grid and microgrid j, m are supply of cooling, heating and electrical powers type microgrid number；
S2-2: establishing the constraint condition of active distribution network, specifically includes electrical power Constraints of Equilibrium, conventional power unit power output up and down
Limit constraint, the constraint of active distribution network spinning reserve, tie-line power transmission constraint:
Electrical power Constraints of Equilibrium:
Conventional power unit power output bound constraint:
PGi,min≤PGi(t)≤PGi,max
The constraint of power distribution network spinning reserve:
Tie-line power transmission constraint:
Wherein,Electric load predicted value for active distribution network in the t period, PGi,maxAnd PGi,minFor the active of unit i
Power output bound, RDNIt (t) is stand-by requirement of the active distribution network in the t period,WithIt is active distribution network to each cold and hot
The bound of electricity supply type microgrid transimission power.
The Optimized model of the more microgrid layers of lower layer's supply of cooling, heating and electrical powers type is established in the present embodiment step S3 the following steps are included:
S3-1: the optimization object function in the more microgrid economic optimization scheduling models of supply of cooling, heating and electrical powers type:
Wherein, j is the number of supply of cooling, heating and electrical powers type microgrid, and m is the number of supply of cooling, heating and electrical powers type microgrid, Ffuel,jIt is j-th
The fuel cost of supply of cooling, heating and electrical powers type microgrid, Fdisnet,jThe function interacted for j-th of supply of cooling, heating and electrical powers type microgrid with active distribution network
Rate expense；
S3-2: the constraint condition of the more microgrid economic optimization scheduling models of supply of cooling, heating and electrical powers type is established, including power-balance is about
Beam, place capacity constraint, equipment operation constraint, wherein place capacity and operation constraint condition be to meet the power output of each equipment
The limitation of power bound, the power-balance constraint are specific as follows:
Establish cold power-balance constraint equation:
Wherein,For the refrigeration work consumption of electric refrigerating machine,For Absorption Refrigerator refrigeration work consumption, source is waste heat pot
The gas turbine waste heat that furnace is collected,For the demand of refrigeration duty in supply of cooling, heating and electrical powers type microgrid；
Establish heating power balance constraint equation:
Wherein,For the output thermal power of gas fired-boiler,For the heats power of steam and hot water heat-exchanger rig,For
The demand of thermic load in supply of cooling, heating and electrical powers type microgrid；
Establish electrical power Constraints of Equilibrium equation:
Wherein,For the generated output of gas turbine,For supply of cooling, heating and electrical powers type microgrid and active distribution network by when
Electrical power cross-over value,For wind-power electricity generation power,For photovoltaic generation power,For supply of cooling, heating and electrical powers type microgrid electric load
Power,For electric refrigerating machine power consumption in supply of cooling, heating and electrical powers type microgrid；
It establishes equipment and goes out the constraint of activity of force bound:
Wherein,WithFor the minimum value and maximum value of Gas Turbine Output power,WithFor gas fired-boiler
The minimum value and maximum value of activity of force out,WithGo out the minimum value and maximum value of activity of force for heat-exchanger rig,WithGo out the minimum value and maximum value of activity of force for Absorption Refrigerator,WithGo out the minimum of activity of force for electric refrigerating machine
Value and maximum value,WithThe minimum value and maximum value of power to charge the battery,WithFor electric power storage tank discharge function
The minimum value and maximum value of rate.
The present embodiment step S4 specifically:
It is solved using genetic algorithm (GA) with the method that mixed integer linear programming software (Cplex) combines, on
Layer uses GA algorithm, purchased to the generating set of active distribution network power output and between each supply of cooling, heating and electrical powers type microgrid sale of electricity power into
Row optimizing, lower layer calls Cplex to calculate the equipment power output of supply of cooling, heating and electrical powers type microgrid each in dispatching cycle, and returns
The performance number of each supply of cooling, heating and electrical powers type microgrid and active distribution network purchase sale of electricity, upper layer calculates fitness value, passes through genetic manipulation
Optimizing obtains the optimal solution under setting target.
As shown in figure 3, the derivation algorithm process step in step S4 for bi-level optimal model is specific as follows:
S4-1: the master datas such as basic device parameter, dispatching cycle, cost coefficient are read；
S4-2: random to generate initial population data；
S4-3: mixed integer linear programming software is called to solve the optimization of the more micro-grid systems of underlying model supply of cooling, heating and electrical powers type
Model；
S4-4: the ideal adaptation angle value of layer model in calculating；
S4-5: judge whether the number of iterations is greater than 30；
S4-6: if it is judged that be it is yes, then export the optimal solution of the upper and lower；
S4-7: if it is judged that be it is no, then re-form genetic groups, step S4-2 repeated, until judging result is
It is；
S4-8: terminate.
The present embodiment calculates separately two using the more micro-grid system examples of representative heat-cool electricity supply type in nascent state city in Tianjin
More micro-grid system totle drilling costs under the traditional method of operation of kind, and compared with dual-layer optimization dispatching method proposed by the present invention,
The results are shown in Table 1:
More micro-grid system total operating costs under 1 different running method of table
Compared with traditional " electricity determining by heat " and " with the fixed heat of electricity " method of operation, dual-layer optimization dispatching method proposed by the present invention
5.33% and 9.58% total operating cost can be reduced respectively.The Optimization Scheduling proposed through the invention dispatches cool and thermal power
Electric power value, Ke Yixian are interacted between each microgrid equipment power output and each microgrid and power grid, adjacent microgrid in the more micro-grid systems of alliance type
Write the total operating cost for reducing more micro-grid systems.
Claims (6)
1. a kind of more microgrid active distribution system dual blank-holders of supply of cooling, heating and electrical powers type, it is characterised in that: the following steps are included:
S1: mathematical modeling is carried out to the equipment of supply of cooling, heating and electrical powers type microgrid；
S2: the upper layer active distribution network layer Optimized model of bi-level optimal model is established；
S3: the more microgrid layer Optimized models of lower layer's supply of cooling, heating and electrical powers type of bi-level optimal model are established；
S4: being solved using genetic algorithm and mixed integer linear programming, obtains a few days ago excellent of layer model and underlying model
Change operation plan.
2. the more microgrid active distribution system dual blank-holders of a kind of supply of cooling, heating and electrical powers type according to claim 1, special
Sign is: equipment includes miniature gas turbine, gas fired-boiler, waste heat pot in the more microgrids of supply of cooling, heating and electrical powers type in the step S1
Furnace, absorption refrigeration unit, steam heat exchanger, electric refrigerating machine, energy storage device and renewable energy power generation device, the mathematics
Specific step is as follows for modeling:
S1-1: the mathematical model of gas turbine is established:
ηc=(8.935+33.157 β -27.081 β2+17.989β3)/100 × 100%
ηr=(+24.644 β of 82.869-30.173 β2-16.371β3)/100 × 100%
Wherein, ηcFor gas turbine power generation efficiency, ηrFor gas turbine heat recovery efficiency, QGTFor combustion turbine exhaustion excess heat,
PGTFor gas turbine power generation power, ηlFor gas turbine radiation loss coefficient, VGTFor consumed by runing time internal-combustion gas turbine engine
Amount of natural gas, LHVNGFor heating value of natural gas；
S1-2: the mathematical model of energy storage device is established:
Wherein, E (t) is the energy that energy storage device is stored in the t period, and Δ t is time interval of the t period to the t+1 period, Pabs(t)
For t period energy storage power, PreleaIt (t) is t period exoergic power, μ is that energy storage device itself dissipates the loss of energy to environment or is lost certainly
Energy coefficient, ηabsFor the energy storage efficiency of energy storage device, ηreleaFor energy storage device exergic efficiency；
S1-3: the mathematical model of gas fired-boiler is established:
Wherein, PGBFor the thermal power of gas fired-boiler,For the gas quantity that gas fired-boiler is consumed in the Δ t period, ηGBFor gas-fired boiler
The thermal efficiency of furnace；
S1-4: the mathematical model of heat-exchanger rig is established:
PHX,out=PWH,heatηHX
Wherein, PHX,outHeats power, P are exported for steam and hot water heat-exchanger rigWH,heatTo be used in the output steam of waste heat boiler
The thermal power of heating, ηHXFor the transfer efficiency of steam and hot water heat-exchanger rig；
S1-5: the mathematical model of Absorption Refrigerator is established:
PAC,out=PWH,coolηAC
Wherein, PAC,outRefrigeration work consumption, P are exported for steam operated absorption refrigerating machineWH,coolTo be used in the output steam of waste heat boiler
In the input power of refrigeration, ηACFor the refrigerating efficiency of steam operated absorption refrigerating machine；
S1-6: the mathematical model of electric refrigerating machine is established:
PEC,out=PEC,inηEC
Wherein, PEC,outFor the output refrigeration work consumption of electric refrigerating machine, PEC,inFor the input electric power of electric refrigerating machine, ηECFor electricity refrigeration
The Energy Efficiency Ratio of machine.
3. the more microgrid active distribution system dual blank-holders of a kind of supply of cooling, heating and electrical powers type according to claim 1, special
Sign is: the Optimized model of upper layer active distribution network layer is established in the step S2 the following steps are included:
S2-1: establishing upper layer optimization object function, i.e. the day operation economic cost of active distribution network layer is minimum:
min FDN=FG-Fs
Wherein, FDNFor the total cost of production of power distribution network, FGFor the cost of electricity-generating of generating set in power distribution network, FsFor active distribution network
The expense of electrical power, P are interacted between each energy mix systemGiIt (t) is power output of i-th conventional power unit in period t, ai、
bi、ciFor corresponding cost coefficient, n indicates the quantity of generating set,For period t active distribution network and supply of cooling, heating and electrical powers type
The electrical power of microgrid j interaction, positive value indicate power grid to more microgrid sales of electricity, and negative value indicates power grid from more microgrid power purchases, τjIt (t) is t
The real-time deal electricity price of moment power grid and microgrid j, m are supply of cooling, heating and electrical powers type microgrid number；
S2-2: establishing the constraint condition of active distribution network, specifically includes electrical power Constraints of Equilibrium, conventional power unit power output bound about
Beam, the constraint of active distribution network spinning reserve, tie-line power transmission constraint:
Electrical power Constraints of Equilibrium:
Conventional power unit power output bound constraint:
PGi,min≤PGi(t)≤PGi,max
The constraint of power distribution network spinning reserve:
Tie-line power transmission constraint:
Wherein,Electric load predicted value for active distribution network in the t period, PGi,maxAnd PGi,minFor the active power output of unit i
Bound, RDNIt (t) is stand-by requirement of the active distribution network in the t period,WithIt is active distribution network to each cold and hot Electricity Federation
For the bound of type microgrid transimission power.
4. the more microgrid active distribution system dual blank-holders of a kind of supply of cooling, heating and electrical powers type according to claim 1, special
Sign is: the Optimized model of the more microgrid layers of lower layer's supply of cooling, heating and electrical powers type is established in the step S3 the following steps are included:
S3-1: the optimization object function in the more microgrid economic optimization scheduling models of supply of cooling, heating and electrical powers type:
Wherein, j is the number of supply of cooling, heating and electrical powers type microgrid, and m is the number of supply of cooling, heating and electrical powers type microgrid, Ffuel,jIt is cold and hot for j-th
The fuel cost of electricity supply type microgrid, Fdisnet,jThe power expense interacted for j-th of supply of cooling, heating and electrical powers type microgrid with active distribution network
With；
S3-2: establishing the constraint condition of the more microgrid economic optimization scheduling models of supply of cooling, heating and electrical powers type, including power-balance constraint, sets
Standby capacity-constrained, equipment operation constraint, wherein place capacity and operation constraint condition are to meet the going out on activity of force of each equipment
Lower limit limitation, the power-balance constraint are specific as follows:
Establish cold power-balance constraint equation:
Wherein,For the refrigeration work consumption of electric refrigerating machine,For Absorption Refrigerator refrigeration work consumption, source is waste heat boiler receipts
The gas turbine waste heat of collection,For the demand of refrigeration duty in supply of cooling, heating and electrical powers type microgrid；
Establish heating power balance constraint equation:
Wherein,For the output thermal power of gas fired-boiler,For the heats power of steam and hot water heat-exchanger rig,It is cold and hot
The demand of thermic load in electricity supply type microgrid；
Establish electrical power Constraints of Equilibrium equation:
Wherein,For the generated output of gas turbine,For supply of cooling, heating and electrical powers type microgrid and active distribution network by when electric work
Rate cross-over value,For wind-power electricity generation power,For photovoltaic generation power,For supply of cooling, heating and electrical powers type microgrid electric load power,For electric refrigerating machine power consumption in supply of cooling, heating and electrical powers type microgrid；
It establishes equipment and goes out the constraint of activity of force bound:
Wherein,WithFor the minimum value and maximum value of Gas Turbine Output power,WithFor gas fired-boiler power output
The minimum value and maximum value of power,WithGo out the minimum value and maximum value of activity of force for heat-exchanger rig,WithFor
Absorption Refrigerator goes out the minimum value and maximum value of activity of force,WithGo out the minimum value and most of activity of force for electric refrigerating machine
Big value,WithThe minimum value and maximum value of power to charge the battery,WithMost for battery discharge power
Small value and maximum value.
5. the more microgrid active distribution system dual blank-holders of a kind of supply of cooling, heating and electrical powers type according to claim 1, special
Sign is: the step S4 specifically:
It being solved using genetic algorithm with the method that mixed integer linear programming software combines, upper layer uses genetic algorithm,
Generating set power output to active distribution network and the purchase sale of electricity power progress optimizing between each supply of cooling, heating and electrical powers type microgrid, lower layer's tune
It is calculated, and returned with equipment power output of the mixed integer linear programming software to supply of cooling, heating and electrical powers type microgrid each in dispatching cycle
The performance number of each supply of cooling, heating and electrical powers type microgrid and active distribution network purchase sale of electricity is returned, upper layer calculates fitness value, grasps by heredity
Make optimizing, obtains the optimal solution under setting target.
6. the more microgrid active distribution system dual blank-holders of a kind of supply of cooling, heating and electrical powers type according to claim 5, special
Sign is: the derivation algorithm process step in the step S4 for bi-level optimal model is specific as follows:
S4-1: master data is read；
S4-2: random to generate initial population data；
S4-3: mixed integer linear programming software is called to solve the optimization mould of the more micro-grid systems of underlying model supply of cooling, heating and electrical powers type
Type；
S4-4: the ideal adaptation angle value of layer model in calculating；
S4-5: judge whether the number of iterations is greater than 30；
S4-6: if it is judged that be it is yes, then export the optimal solution of the upper and lower；
S4-7: if it is judged that be it is no, then re-form genetic groups, step S4-2 repeated, until judging result is yes.
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