CN109752953A - A kind of building energy supplying system model prediction regulation method of integrated electric refrigerating machine - Google Patents
A kind of building energy supplying system model prediction regulation method of integrated electric refrigerating machine Download PDFInfo
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Abstract
The present invention relates to a kind of building energy supplying system model predictions of integrated electric refrigerating machine to regulate and control method, and technical characterstic is: the following steps are included: the building energy supplying system model that step 1, foundation are made of building virtual energy storage system, electric refrigerating machine, diesel generation unit, fuel cell, battery；Step 2 is established after scheduling model frame and is provided respectively a few days ago, in a few days and rolling portion model, and then constructs building energy supplying system Multiple Time Scales forecast dispatching model, carries out Multiple Time Scales Optimized Operation according to the demand of building energy supplying system and building user.The present invention is integrated with the building virtual energy storage model containing electric refrigerating machine, and virtual energy storage model integrated has been arrived in the model prediction regulation method of building energy supplying system, and the flexibility of building energy is further excavated under the premise of guaranteeing building room temperature comfort level.
Description
Technical field
The invention belongs to the Optimized-control Technique fields of the energy supplying system of substitutional load containing electric energy, and it is more to be related to building energy supplying system
Time scale model prediction regulates and controls method, the building energy supplying system model prediction regulation side of especially a kind of integrated electric refrigerating machine
Method.
Background technique
As the continuous of energy and environmental problem highlights in global range, positive Renewable Energy Development simultaneously improves energy benefit
Have become global common recognition with efficiency." 2016 international energies prospect " display of american energy information administration publication, the energy consumption of building
Account for about the 20% of the transportable energy total amount in the whole world." report of Chinese architecture Energy Conservation " data are shown within 2016, China's building consumption
Share of the energy total amount in China energy consumption total amount is more than 27%, with the continuous improvement and production of Chinese Urbanization level
The adjustment of industry structure, the ratio still will continue to increase.It therefore, is the terminal energy consumption system of representative with huge to build building
Energy-saving and emission-reduction potentiality.It sufficiently excavates to build Demand-side energy-saving potential of the building as representative, to solution human social development process
In increasingly prominent energy demand growth be of great significance with energy shortages contradiction, using energy source and environmental protection contradiction.
In recent years, more and more electric energy substitutional loads are integrated in building side, form the energy supply system based on building
System.Existing research shows that carrying out the Optimized Operation and energy of building energy supplying system under the premise of without investing transformation on a large scale
Buret reason, the safety that can effectively facilitate distributed new in user side dissolve.Traditional single method of regulation a few days ago can not
Influence of the error of reflection generation of electricity by new energy and load prediction to the optimization operation of building energy supplying system completely, and scheduling is gone downstairs a few days ago
Space energy supplying system is difficult to consider in actual motion under different time scales frame for the single time scale modeling of energy equipment
Time coupled characteristic, so that optimum results may not be inconsistent with building energy supplying system practical operation situation.Meanwhile robust optimizes
As a result there is certain conservative, and calculation amount is larger, is difficult to restrain；Random optimization depends on the probability distribution of stochastic variable,
The selection and design of magnanimity scene also increase calculation amount.
To solve the above-mentioned problems, operation plan and automatic generation control before the man-day that reference bulk power grid active power dispatch uses
The scheduling mode combined is made, the Optimized Operation that building energy supplying system carries out Multiple Time Scales is studied to have obtained more and more
Concern.Its main thought is: being based on prediction data in last stage day and formulates Unit Combination and operational plan reference value, transports in real time
The deviation that row order section is left higher level based on real time data is to controlled distribution formula power supply (Distributed Generator, DG)
Carry out power adjustment.Multiple Time Scales method is further applied to building energy supplying system, it is existing to have researched and proposed based on mostly generation
The business premises Multiple Time Scales energy management method of reason method, reduces the operation of building under prediction data uncertain environment
Cost.Existing research also proposed the double-deck energy management method of the Multiple Time Scales for office type building, by a few days ago
Scheduling and in a few days DG controllable to building and controllable electric energy substitutional load (building temperature control system and electronic vapour respectively of real-time adjusting stage
Vehicle) it is scheduled, realize the energy management to building.Energy of the studies above to building energy supplying system under prediction uncertain environment
Management plays an important role, but the open loop Optimization Scheduling that in a few days Real-Time Scheduling uses is that the optimal load flow based on single section is asked
The in a few days operating scheme of building energy supplying system is solved, and disposably issues all optimization instructions, it is difficult to when sensed in advance is one section following
The state change situation of interior building energy supplying system, gained prioritization scheme robustness are poor.
Summary of the invention
It is an object of the invention to overcome the deficiencies in the prior art, propose a kind of design rationally, using simple and quick, practical
Value is strong, flexibility is high and the building energy supplying system model prediction regulation of the integrated electric refrigerating machine of gained prioritization scheme strong robustness
Method.
The present invention solves its realistic problem and adopts the following technical solutions to achieve:
A kind of building energy supplying system model prediction regulation method of integrated electric refrigerating machine, comprising the following steps:
Step 1 is established by building virtual energy storage system, electric refrigerating machine, diesel generation unit, fuel cell, battery group
At building energy supplying system model；
Step 2 is established after scheduling model frame and is provided respectively a few days ago, in a few days and rolling portion model, and then constructs building
Energy supplying system Multiple Time Scales forecast dispatching model carries out Multiple Time Scales according to the demand of building energy supplying system and building user
Optimized Operation.
Moreover, the specific steps of the step 1 include:
(1) building virtual energy storage system model is established；
(2) building energy supplying system is established for energy model of element；
Moreover, the specific steps of the step 2 include:
(1) building energy supplying system Multiple Time Scales forecast dispatching frame is constructed；
(2) economic load dispatching model a few days ago is constructed；
(3) in a few days rolling amendment is carried out；
(4) Multiple Time Scales Optimized Operation is carried out according to the demand of building energy supplying system and building user.
The advantages of the present invention:
1, the present invention can under the premise of guaranteeing building room temperature comfort level, by model prediction regulate and control effectively solve by
In the problem that building energy supplying system Optimized Operation scheme caused by error of predicting and actual motion scene deviation are larger, further drop
Low energy cost；
2, the present invention is integrated with the building virtual energy storage model containing electric refrigerating machine, and virtual energy storage model integrated has been arrived building
In the model prediction regulation method of space energy supplying system, building are further excavated under the premise of guaranteeing building room temperature comfort level
With the flexibility of energy.
Detailed description of the invention
Fig. 1 is the building energy supplying system schematic diagram of integrated electric refrigeration system of the invention；
Fig. 2 is building energy supplying system Multiple Time Scales Optimized Operation frame diagram of the invention；
Fig. 3 is building energy supplying system Multiple Time Scales Optimization Scheduling flow chart of the invention；
Fig. 4 (a) is that scheduling scheme figure-does not consider under virtual energy storage scheduling before building energy supplying system of the invention is DG days controllable
Unit plan a few days ago；
Fig. 4 (b) is under scheduling scheme figure before building energy supplying system of the invention is DG days controllable-consideration virtual energy storage scheduling
Unit plan a few days ago；
Fig. 5 (a) is the refrigeration scheduling scheme that refrigeration scheduling scheme figure-does not consider virtual energy storage before of the invention building 3 days；
Fig. 5 (b) is scheduling scheme figure-consideration virtual energy storage refrigeration scheduling scheme that freezes before of the invention building 3 days；
Fig. 6 is 3 virtual energy storage system of building of the invention scheduling result figure a few days ago；
Fig. 7 is building energy supplying system dominant eigenvalues tracking effect comparison diagram of the invention；
Fig. 8 (a) is the in a few days Optimized Operation result figure of controllable DG and battery of the invention；
Fig. 8 (b) is battery SOC tracking effect comparison diagram of the invention；
Fig. 9 is building energy supplying system dominant eigenvalues tracking effect comparison diagram of the invention；
Figure 10 (a) is the virtual energy storage in a few days amendment scheme figure of building 3 of the invention；
Figure 10 (b) is the in a few days refrigeration scheduling scheme figure for not considering virtual energy storage of building 3 of the invention；
The in a few days refrigeration scheduling scheme figure of the considerations of Figure 10 (c) is building 3 of the invention virtual energy storage.
Specific embodiment
The embodiment of the present invention is described in further detail below in conjunction with attached drawing:
The present invention is different from traditional open loop optimization method, proposes a kind of building energy supplying system model of integrated electric refrigerating machine
Predict regulation method.Building energy supplying system is proposed based on model prediction (model predictive control, MPC) method
The Multiple Time Scales Optimization Scheduling that economic load dispatching is combined in a few days roll correction a few days ago.
Model prediction (model predictive control, MPC) method uses Rolling optimal strategy and feedback compensation
Thought, can effectively avoid under open loop optimization method the regulation of building energy supplying system it is strong to prediction dependence, it is big by such environmental effects,
Regulation and control scheme and the larger problem of actual motion demand disruption a few days ago, while gained optimization method has preferable anti-interference ability
And robustness.
For this purpose, the present invention on the basis of the studies above, is based further on MPC technique study building energy supplying system a few days ago
The Multiple Time Scales Optimization Scheduling that economic load dispatching is combined in a few days roll correction.In a few days Real-Time Scheduling stage, building supply
Can system a few days ago optimize economic load dispatching model on the basis of, based on renewable energy, outdoor environment and with energy the short-term function of load
Rate predictive information is replaced traditional single disconnected in a few days Real-Time Scheduling with the rolling optimal dispatching based on MPC in finite time window
Face Optimized Operation, the more preferable state change situation for perceiving building energy supplying system in following a period of time, and then in advance to each controllable
The power output of distributed generation resource and controllable burden and with can plan to optimize and revise.In addition, it is contemplated that building construction building enclosure
Thermal storage effect can be adjusted its heat supply/cooling load within the scope of building room temperature comfort level, so that building use can have
There is certain flexibility.In order to efficiently use its flexibility, the present invention constructs the building virtual energy storage mould of integrated electric refrigerating machine
Type, and virtual energy storage model integrated has been arrived in the Multiple Time Scales Optimal Operation Model of building energy supplying system, guaranteeing building
The flexibility of building energy is further excavated under the premise of room temperature comfort level.
Building energy supplying system schematic diagram is as shown in Figure 1, include multiple building, controllable distributed power generation unit
(Distributed Generator, DG), energy-storage system and communication link.Each building system includes electric refrigerating machine, routine
Electrical equipment and rooftop photovoltaic systems.Building energy supplying system Energy Management System (Building Microgrid Energy
Management System, MEMS) and each building Energy Management System (Building Energy Management
System, BEMS) it interacts, each building are predicted and monitored with energy behavior, realization is meeting users'comfort
Under the premise of, reduce the operating cost of each building.Each unit function introduction is as follows:
BEMS: the BEMS of each building according to the prediction time domain of setting and control time domain, in each control time domain by leading to
Believe that link obtains the prediction data of the intensity of illumination and outdoor temperature in prediction time domain from weather station.BEMS is set by building simultaneously
The sensor set obtains current building room temperature, uncontrollable load, the operation data of rooftop photovoltaic systems and operation information.
By simulation and prediction and data prediction, calculated result is uploaded to by MEMS by communication link.
MEMS: collecting the prediction result that each BEMS is uploaded, according to preset regulation goal and electricity price information into
Row optimization calculates, and the electricity consumption curve after then calculating optimization is issued to each BEMS, and each building then execute command adapted thereto.
Power transmission direction: there are bi-directional power flows between building energy supplying system and external electrical network, if building energy supply system
The workload demand of system is more than total electricity provided by internal system power supply, then absorbs power from power grid；If building energy supplying system
Workload demand is not up to total electricity provided by internal system power supply, then extra electricity is sent into power grid.
Communication control mode: there are two-way communication demands between the BEMS and MEMS of each building.Meanwhile weather station and
Unidirectional communications link is also required between BEMS.
A kind of building energy supplying system model prediction regulation method of integrated electric refrigerating machine, comprising the following steps:
Step 1 is established by building virtual energy storage system, electric refrigerating machine, diesel generation unit, fuel cell, battery group
At building energy supplying system model；
The specific steps of the step 1 include:
(1) building virtual energy storage system model is established
The specific steps of step 1 (1) step include:
1. constructing the hot dynamic model of building
In view of the thermal storage effect of building construction, the virtual energy storage unit exchanged with outdoor carry out heat can be modeled as；
The quantitative mathematical relationship that building room temperature, refrigeration demand and outdoor temperature can be obtained based on building equation of heat balance, such as formula
(1) shown in:
In above formula, ρ is atmospheric density；C is air specific heat capacity；V is room air capacity；TinFor room temperature；
(i)It indicates the heat (kW) of external wall and outdoor transmitting, the sum of heat is transmitted for all exterior walls, such as formula (2)
It is shown；
(ii)It indicates the heat (kW) of external window of building and outdoor transmitting, the sum of heat is transmitted for all exterior windows, such as formula
(3) shown in；
(iii)For the calorific value (kW) of indoor airflow, can be obtained by predicting or measuring；
(iv)The heat (kW) transmitted for sun heat radiation by exterior wall passes through according to ISO 13790 for solar radiation
The sum of all exterior wall transmitting heats, as shown in formula (4)；
(v)The heat (kW) transmitted for solar radiation by exterior window can be calculated by formula (5)；Present invention assumes that
All unofficial biography of building construction are evenly distributed on the four sides wall of the four corners of the world；
(vi)For the refrigeration work consumption (kW) of refrigeration equipment；
ToutFor outdoor temperature；UwallFor external wall heat transfer coefficient；UwinFor external window of building heat transfer coefficient；Fwall,jTo build
Build exterior wall j area；Fwin,jFor external window of building j area；SC is shading coefficient, value with whether have sunshading board, glass material etc.
It is related；Rse,jFor the thermal resistance of exterior wall and outdoor air thermal convection and heat radiation；αwFor wall heat transfer loss coefficient；τwinFor wall biography
Heat loss factor；IT,jTotal solar radiation intensity (the kW/m received for every square metre of wall of exterior wall j/window2), it expresses such as formula (6) institute
Show；
Wherein Ib、IdThe direct radiation intensity of illumination on same level surface, scattering radiation intensity and total are respectively indicated with I
Radiation intensity, unit kW/m2；ρgFor ground surface reflectance, 0.2 is taken in the present invention；θ θ is illumination incidence angle；RbIt is that illumination is several
What factor, for describing direct radiation intensity of the illumination on clinoplain and the direct radiation intensity of illumination in the horizontal plane
Ratio, calculation expression is such as shown in (7)；
Wherein, θzFor illumination zenith angle；；
2. constructing building virtual energy storage system model；
In turn, it is based on building thermal storage effect, constructs its virtual energy storage system model；In order to guarantee that building user's is comfortable
Degree, building virtual energy storage model need to consider that the range of room temperature set point, basic ideas are that the refrigeration demand of building can
It is centainly adjusted within the scope of users'comfort；For this purpose, can increase refrigerating capacity when electricity price is lower, (i.e. refrigeration machine can
Its electrical power is opened or increased in advance), extra cold energy is stored in inside building, virtual energy storage system is equivalent to and increases
Electricity consumption, building energy supplying system charge to virtual energy storage system；Similarly, refrigerating capacity can be reduced (i.e. when electricity price is higher
Refrigeration machine can close or reduce in advance its electrical power), using the cold energy stored in advance, it is equivalent to the reduction of virtual energy storage system
Electricity consumption, building energy supplying system is to virtual energy storage system discharge；The charge-discharge electric power that virtual energy storage system can be obtained accordingly, such as formula
(8) shown in；
Wherein,For t moment building virtual energy storage system charge-discharge electric power, electric discharge is positive, and charging is negative；For
The building refrigeration electrical power of room temperature is not adjusted；To consider to adjust the building of room temperature within the scope of temperature pleasant degree
Space refrigeration electrical power；
Formula (1) and formula (8) together constitute building virtual energy storage system mathematic model: within the scope of user temperature comfort level
When building room temperature is adjusted, the refrigeration demand (refrigeration with refrigeration equipment of building is obtained by formula (1) and formula (8) respectively
Power is equal) and virtual energy storage system charge-discharge electric power, and then virtual energy storage system charge-discharge electric power is effectively managed,
Participate in it in Optimized Operation of building energy supplying system.
(2) building energy supplying system is established for energy model of element；
The specific steps of step 1 (2) step include:
1. establishing generator model
The fuel cost of generator is determined by machine unit characteristic parameter and output power, as shown in formula (9):
In formula: ffuel() is the fuel cost function of generator；PDE,tFor the power output of generator；A, b, c are diesel oil
The fuel cost coefficient of generator；
2. establishing fuel cell mode
Fuel-cell fuel cost is by output power and efficiency, parameter ηFCIt determines, as shown in formula (10):
ffuel(PFC,t)=Cgas×Pgas,t=Cgas×(PFC,tΔt/ηFC) (10)
In formula: PFC,tFor the power output of fuel cell；ηFCFor the efficiency of fuel cell；CgasFor Gas Prices；Pgas,t
For the gas horsepower of fuel cell consumption；△ t is the corresponding power output period；
3. establishing electric refrigerating machine model
Building energy supplying system shown in Fig. 1, refrigeration demand consume electric energy by electric refrigerating machine to meet；Building supply in the present invention
The refrigeration equipment that energy system uses is compression electric refrigerating machine (hereinafter referred to as electric refrigerating machine), refrigeration work consumption such as formula (11) institute
Show:
QEC,t=PEC,t×COPEC (11)
In formula: QEC,tIt is exported for the refrigeration work consumption of electric refrigerating machine；PEC,tFor the electrical power of electric refrigerating machine consumption；COPECFor electricity
The Energy Efficiency Ratio of refrigeration machine；
4. establishing battery model
State-of-charge (State of Charge, SOC) is the ratio that battery remaining capacity accounts for rated capacity, indicates to store
The state-of-charge of battery；The SOC value SOC of battery t momenttAs shown in formula (12):
In formula: Pbt,tFor the specified charge-discharge electric power of battery (electric discharge is positive, and charging is negative)；CAPbtFor the volume of battery
Constant volume；ηch, ηdisFor the efficiency for charge-discharge of battery；δ is the self-discharge rate of battery；△ t is the corresponding charge and discharge period.
Step 2 is established after scheduling model frame and is provided respectively a few days ago, in a few days and rolling portion model, and then constructs building
Energy supplying system Multiple Time Scales forecast dispatching model carries out Multiple Time Scales according to the demand of building energy supplying system and building user
Optimized Operation；
The Optimized Operation of integrated electric refrigerating machine building energy supplying system is divided into scheduling and in a few days rolling amendment a few days ago by the present invention
Two stages.
The specific steps of the step 2 include:
(1) building energy supplying system Multiple Time Scales forecast dispatching frame as shown in Figure 2 is constructed；
The specific steps of step 2 (1) step include:
1. economic load dispatching a few days ago
As shown in Fig. 2, economic dispatch stage a few days ago, based on a few days ago each building electric load power prediction value of hour rank,
Outdoor temperature predicted value, intensity of illumination predicted value and generation of electricity by new energy predicted value are minimum with building energy supplying system operating cost
For target, each building user indoor temperature comfort level constraint, the controllable DG (Distributed of building energy supplying system are comprehensively considered
Generator, DG) information such as technical characteristic and ahead market electricity price, obtain n a few days ago1Building energy supply in a scheduling time section
The Optimized Operation scheme of system；
Scheduling scheme specifically includes: Optimized Operation scheme (refrigeration machine scheduling scheme and the room temperature tune of each building
Degree scheme), the controllable DG Optimized Operation scheme of building energy supplying system, energy-storage system Optimized Operation scheme and dominant eigenvalues exchange
Set point；
2. in a few days rolling amendment
In order to embody the meaning of economic optimization scheduling a few days ago, in a few days forecast dispatching plan should defer to be planned a few days ago；However, by
In electric load power prediction value, outdoor temperature predicted value, intensity of illumination predicted value and the generation of electricity by new energy of hour rank a few days ago
The prediction error of predicted value, the in a few days actual motion plan of building energy supplying system and there are deviations for operation plan a few days ago；In order to disappear
Except deviation, increase of the present invention in a few days rolling amendment link is based on building energy supplying system current operating conditions and short-term time scale
Prediction data the Optimized Operation result of long time scale a few days ago is modified；
As shown in Fig. 2, in a few days the amendment stage, using 15min as period progress rolling optimization, entire scheduling time axis was divided into
n2A scheduling time section, wherein prediction time domain is NpA period, control time domain are NcA period, Np≥Nc；
In t moment, current time section to next prediction time domain N is utilizedpInterior short-term forecast information, is not changing day
Preceding DG plan for start-up and shut-down controllable in the works and energy storage charging and discharging state, and under the premise of meeting power-balance and various constraints, with
The minimum target of error between building energy supplying system dominant eigenvalues and a few days ago planned value is based on MPC (Model
Predictive Control, MPC) method optimization seek NcAll controllable DG of building energy supplying system in a period, energy storage
The amendment programmed sequence of system and building power load；
In t, discontinuity surface only issues the amendment plan to the latter time cycle, i.e. NcBuilding energy supply system in a period
First control sequence in the amendment programmed sequence of system；When next dispatching cycle arrives, the above process is repeated；
By rolling amendment in a few days, make the planned value of building energy supplying system dominant eigenvalues tracking a few days ago, to realize
The safe and economic operation of building energy supplying system.
Optimization aim had both considered in a few days dominant eigenvalues tracking planned value a few days ago, and also made overall plans in a few days energy storage SOC
Track the demand of planned value a few days ago, it is ensured that energy-storage system is dispatched under the premise of meeting the constraint of day operation energy balance in a few days
Middle performance ' peak load shifting ' effect.
(2) economic load dispatching model a few days ago is constructed；
The specific steps of step 2 (2) step include:
1. the target of scheduling phase is minimized on the basis of guaranteeing user temperature comfort level to building energy supplying system a few days ago
Operating cost；
Therefore regulation goal function is set as operating cost minimum to a kind of building energy supplying system of integrated electric refrigerating machine a few days ago, such as
Shown in formula (13):
In formula: T is entire dispatching cycle, can use one day for 24 hours；First item be building energy supplying system from power distribution network power purchase at
This；Pgrid,tFor the electrical power that building energy supplying system is exchanged with power distribution network, power purchase is positive, and sale of electricity is negative；Cph,tAnd Cse,tRespectively
Building energy supplying system is from the price of power distribution network power purchase and to the price of power distribution network sale of electricity；Section 2 is controllable in building energy supplying system
Fuel cost, maintenance cost and the start-up cost of DG, as shown in formula (9), (10), PDG,i,tIndicate DGiIn time period t
Power output；ρDG,iIndicate DGiWorking service cost；ρsu,iIndicate DGiStart-up cost；U′DG,i,tIndicate that i platform is distributed
Starting state of the power supply in the t period (" 1 " indicates starting, and " 0 " indicates not start)；Section 3 is energy storage in building energy supplying system
The working service cost of system, photovoltaic system and refrigeration system；PPV,tAnd PEC,tRespectively t moment photovoltaic power output and refrigeration machine
Electrical power；ρPV、ρbtAnd ρECRespectively represent the working service of photovoltaic, battery and electric refrigerating machine unit interval unit power at
This；
2. constraint condition is made of building energy supplying system operation constraint and building operation constraint two parts；
1) economic load dispatching model constraint condition a few days ago are as follows:
The constraint of building energy supplying system power purchase:
Wherein,WithThe respectively bound of building energy supplying system power purchase power；
Electrical power Constraints of Equilibrium:
PPV,tAnd PEC,tRespectively t moment photovoltaic power output and refrigeration electric power；Pbt,tFor the charge and discharge of t moment energy-storage battery
Electrical power；Pel,tFor t moment building electric load；
The operation constraint of controllable distributed power generation unit:
Wherein, UDG,i,tWith U "DG,i,tRespectively indicating operating status of the i platform distributed generation resource in the t period, (" 1 " indicates to open
State is opened, " 0 " indicates closed state) and off state (" 1 " indicates to close, and " 0 " indicates not close)；WithIt is point
The power output bound of cloth power supply i；
Meanwhile DG is also by the minimum available machine timeWith the minimum unused timeConstraint:
Battery operation constraint
Wherein,WithThe respectively bound of energy-storage system charge-discharge electric power；Ebt,tFor the electricity of energy-storage system t moment
Amount；
2) each building operation constraint:
Refrigeration duty Constraints of Equilibrium
Wherein,For electric refrigerating machine refrigeration electrical power；To consider to adjust Indoor Temperature within the scope of temperature pleasant degree
The building refrigeration electrical power of degree；
The constraint of building thermal balance:
The constraint of building room temperature bound:
(3) in a few days rolling amendment is carried out
The specific steps of step 2 (3) step include:
As shown in Fig. 2, in a few days amendment uses MPC method, amendment in a few days actual motion plan exists with operation plan a few days ago
Deviation；Rolling amendment method key step based on MPC are as follows: be based respectively on building in current time t ', MEMS and each BEMS
Space energy supplying system current state x (t ') and each building current indoor temperature Tin(t '), using prediction time domain NpIn a period
Short-term forecast information, predict building energy supplying system and the state in building room temperature future；Next, MEMS is meeting currently
With solving optimization problem under the premise of following constraint condition, the following control time domain N is obtainedcBuilding energy supplying system in a period
Control instruction sequence；Then, first value of control instruction sequence is applied in each BEMS and each DG controller；Together
When, at+1 moment of t ', updating building energy supplying system state is x (t '+1) and building room temperature Tin(t '+1), and repeat
Above-mentioned steps；
Next its mathematical model is introduced:
1. establishing building energy supplying system prediction model
With current time t ' building energy supplying system and power distribution network Tie line Power (Pgrid(t ')), controllable DG power output
(PDG(t ')), energy storage charge-discharge electric power (Pbt(t ')), energy-storage system SOC (SOC (t ')) and building refrigeration machine consume power
(PEC(t ')) constitute vector x (t ') be building energy supplying system state variable, see formula (30)；With controllable DG power output increment (Δ
PDG(t ')), energy storage contribute increment (Δ Pbt(t ')) and building refrigeration machine consumption of electric power increment (Δ PEC(t ')) constitute to
The control variable that u (t ') is building energy supplying system is measured, sees formula (31)；With photovoltaic and blower fan system power output short-term forecast power increment
(ΔPPV(t′)、ΔPWT(t ')), building electric power short-term forecast power increment (Δ Pel(t ')) constitute vector r (t ') be
The disturbance input of building energy supplying system is shown in formula (32)；Using building energy supplying system Tie line Power as building energy supplying system
Output variable is shown in formula (33).Shown in the state space prediction model such as formula (34) that building energy supplying system can then be established.
X (t ')=[Pgrid(t′),PDG(t′),Pbt(t′),SOC(t′),PEC(t′)]T (30)
U (t ')=[Δ PDG(t′),ΔPbt(t′),ΔPEC(t′)]T (31)
R (t ')=[Δ PPV(t′),ΔPWT(t′),ΔPel(t′)]T (32)
Y (t ')=Pgrid(t′) (33)
In formula: shown in the expression of state space matrices A, B, C and D such as formula (35)-(39).
Wherein, EnAnd EmFor unit matrix, n and m are respectively the controllable quantity of DG and the number of building in building energy supplying system
Amount；For the SOC recursion coefficient of battery, it is expressed as follows:
D=[1 000 0] (39)
According to formula (34) it is found that based on photovoltaic system and load in NpShort-term forecast information in a period, by right
The state space prediction model iterates, and can predict to obtain NpBuilding energy supplying system and power distribution network interconnection in a period
Exchange power and each building refrigeration machine consumption of electric power.
2. establishing building room temperature prediction model
The recursive equation of building indoor temperature change generated in case amount, as shown in formula (40):
In formula:WithRespectively meter and building input variable short-term forecast increment information λ (t ')
External wall and the heat of outdoor transmitting, external window of building and heat, indoor heat gain, the sun heat radiation of outdoor transmitting pass through
The heat that the heat of exterior wall transmitting and solar radiation are transmitted by exterior window；For meter and building refrigeration machine consumption of electric power increment
(ΔPEC(t ')) refrigeration work consumption output；Building input variable short-term forecast predicts increment information λ (t ') as shown in formula (41)；
According to formula (40) and (41) it is found that being based on building short-term forecast information λ (t ') and NpBuilding freeze in a period
Machine consumption of electric power predictive information (is rolled to solve and be obtained) by the building energy supplying system state space prediction model of formula (34), can be with
In NpIn a period, room temperature is carried out to roll solution and prediction；
3. establishing rolling optimization model
In order to eliminate the plan of in a few days actual motion and the deviation of operation plan a few days ago of building energy supplying system, in a few days roll excellent
The target of change is that building energy supplying system dominant eigenvalues value tracks planned value a few days ago in each control time domain；Meanwhile in order to true
It protects energy-storage system and plays ' peak load shifting ' effect in a few days scheduling, and reduce frequent charge and discharge number, extend battery and use
Service life increases the penalty term of accumulator cell charging and discharging power increment in a few days rolling optimization objective function, so that in a few days
The SOC of battery tracks planned value a few days ago；
Building energy supplying system prediction model and a prediction time domain NpBuilding energy supply system can be obtained in interior short-term forecast information
System is in currently control time domain NcBuilding energy supplying system dominant eigenvalues value in interior output variable Y, namely current control time domain,
Expression is as shown in formula (42)；Based on scheduling model a few days ago, building energy supplying system can be obtained in currently control time domain NcIt is interior a few days ago
Track object vector FdayNamely the planned value a few days ago of building energy supplying system dominant eigenvalues, it expresses as shown in formula (43)；
The objective function that rolling optimization then can be obtained is as follows:
In formula:For the penalty value of the corresponding accumulator cell charging and discharging power increment of moment t ', it is expressed as follows:
In formula: θbtFor the corresponding penalty factor of penalty term；
In a few days constraint condition with formula (14)~(29) of rolling optimization are identical, repeat no more；Based on rolling optimization model,
EMES can solve to obtain Correction and Control sequence u of the building energy supplying system in the corresponding control time domain of each scheduling instance t '
First value of optimum results is then added on building energy supplying system by (t '), at+1 moment of t ', is believed based on new short-term forecast
Breath repeats above-mentioned entire optimization process；The present invention calls CPLEX to above-mentioned Multiple Time Scales Optimal Scheduling at MATLAB
It is solved.
(4) Multiple Time Scales Optimized Operation is carried out according to the demand of building energy supplying system and building user；
The mentioned multi-time scale model forecast dispatching method flow diagram of the present invention is as shown in Figure 3.Including economic optimization a few days ago
Scheduling and in a few days two stages of rolling amendment, detailed process are as follows:
The specific steps of step 2 (4) step include:
Construct building energy supplying system Multiple Time Scales forecast dispatching model；
1. system initialization: setting each stage building energy supplying system according to the demand of building energy supplying system and building user
Optimization aim and scheduling time scale, if optimization aim is building energy supplying system operating cost minimum (see formula in scheduling a few days ago
(13)), if in a few days amendment perfecting by stage target is the building energy supplying system dominant eigenvalues that tracking is dispatched a few days ago (see formula (44)).
2. economic load dispatching a few days ago: each building electric load power prediction value, outdoor temperature prediction based on hour rank a few days ago
Value, intensity of illumination predicted value and generation of electricity by new energy predicted value, with the minimum target of building energy supplying system operating cost, synthesis is examined
Consider each building user indoor temperature comfort level constraint, building energy supplying system controllable distributed power generation monotechnics characteristic and day
The information such as preceding market guidance obtain the operation plan a few days ago in the n1 period a few days ago；
3. updating system mode: according in a few days short-term forecast information (building load, renewable energy power output, outdoor environment
And indoor airflow obtains heat), update system mode；
4. in a few days rolling amendment: based in a few days short-term predictive information, by each building cooling load and building confession
The controllable DG of energy system carries out rolling optimal dispatching, thus existing for amendment in a few days actual motion plan and a few days ago operation plan partially
Difference.
In the scheduling of upper layer, main task is:
MEMS is based on the short-term forecast information and current preceding control in building energy supplying system prediction model, prediction time domain Np
Dominant eigenvalues setting value a few days ago in time domain Nc obtains currently controlling each in time domain Nc with formula (44) for rolling optimization target
The in a few days Correction and Control sequence of the cooling load of a building and each controllable DG.And first value of Correction and Control sequence is issued
To the control centre of each building BEMS and each DG, thereby executing in a few days amendment plan.
In bottom management, main task is:
Each BEMS, on the one hand receiving the building cooling load that MEMS is issued, in a few days Correction and Control instructs, and to building system
Cold does amendment regulation；On the other hand the operation constraint of the comfort level demand of acquisition building user and refrigeration machine, and will be related
Information upload value MEMS is used for upper layer scheduling system-computed.
On the one hand each controllable DG controller receives the controllable DG power output Correction and Control instruction that MEMS is issued and repairs to DG
Positive regulation；On the other hand the operating parameter of each DG and related constraint are uploaded to MEMS, are used for upper layer scheduling system-computed.
In the present embodiment, the analysis of Multiple Time Scales Optimized Operation is carried out to the building energy supplying system in Fig. 1 containing more building.
Scheduling prediction data is hour grade prediction data a few days ago, and in a few days dispatching short term predicted data is 15min grades of data.Present invention assumes that
Its short-term forecast power is superimposed stochastic prediction error by prediction power a few days ago respectively and is simulated, and embodies such as formula (46) institute
Show:
In formula:AndThe short-term forecast for respectively indicating input variable does not know threshold value；R (t) is one
It is a to obey the random number for being uniformly distributed U (- 1,1).
Building energy supplying system of the present invention contains four kinds of different types of building, and wherein building 1 are residential building, cooling time
For 0:00~9:00 and 18:00~23:00；Building 2 are office type building, and cooling time is 8:00~20:00；Building 3 are public affairs
Residence building, cooling time is whole day；Building 4 are business premises, and cooling time is 10:00~22:00.It is indoor in cooling time
Temperature set-point is 22.5 DEG C, and comfort level range is set as 20 DEG C~25 DEG C.Atmospheric density ρ and air specific heat capacity C take respectively
1.2kg/m3 with 1000J/ (kg DEG C).The coal consumption coefficient of diesel-driven generator is set are as follows: a=44 (/h/MW2), b=65.34
($/h/MW), c=1.1825 ($/h).The purchasing price of natural gas is set as 42.5 $/MWh.
A) economic load dispatching a few days ago
The result of Optimized Operation a few days ago of the controllable DG of building energy supplying system and battery is as shown in Figure 4.There it can be seen that working as
When electricity price is higher (11:00~12:00 and 14:00~18:00), building energy supplying system purchases strategies are higher, battery also with
Maximum power electric discharge, diesel-driven generator and fuel cell are generated electricity with rated output power at this time.When electricity price is lower (1:00~
10:00 and 19:00~23:00), building energy supplying system purchases strategies are lower, and controllable DG is shut down；In addition, blower, photovoltaic and electricity
Online shopping electrical power charges to battery while meeting workload demand, rationally to carry out " arbitrage " using energy storage, improves
Building energy supplying system performance driving economy.
However, due to power grid power purchase power limit (formula (15)), not considering virtual energy storage tune in 08:00~09:00
Under the scene of degree, building energy supplying system needs to dispatch controllable DG and carrys out supplemental capacity vacancy.Since fuel cell is compared to diesel engine
It is lower to run fuel cost, therefore, building energy supplying system dispatches fuel cell supplemental capacity vacancy under the scene, such as Fig. 4 (a) institute
Show.Consider under virtual energy storage scheduling scenario, building cooling load is adjusted within the scope of temperature pleasant degree indoors, so that electric
Refrigeration machine power consumption reduces, therefore 08:00~09:00 period does not have power shortage, and building energy supplying system does not need scheduling can
DG (shown in such as Fig. 4 (b)) is controlled, to reduce building energy supplying system operating cost.
Each building freeze a few days ago shown in scheduling result such as Fig. 5 (a) and Fig. 5 (b) in building energy supplying system.It can therefrom see
Out, electric refrigerating machine only works in cooling time.In the cooling time for not considering virtual energy storage scheduling, room temperature is maintained at temperature
22.5 DEG C of set point of degree.In the cooling time for considering energy storage scheduling, room temperature adjusts within the scope of 20 DEG C~25 DEG C, building
Cooling load adjusts accordingly, to optimize scheduling to building virtual energy storage.The building of virtual energy storage scheduling are not considered
Energy supplying system day operation cost is $ 327.93, considers that the operating cost of building virtual energy storage scheduling is $ 298.88, compared to not drawing
The management and running cost for entering building virtual energy storage has dropped 8.86%.Such as Fig. 6 of scheduling result a few days ago of building virtual energy storage system
It is shown.
B) rolling amendment in day
In a few days rolling amendment stage, discontinuity surface when 15min takes one, i.e. rolling optimization are primary every 15min starting.
When selection prediction time domain, it is contemplated that it is on the one hand slower relative to electric characteristic amount variation due to room temperature, it needs longer pre-
Time domain is surveyed to increase the information content of future trends, set target is made more to tally with the actual situation；On the other hand, number is predicted
According to prediction error also can with prediction time domain increase and increase, so as to cause increased costs.In summary from the aspect of two and
The time interval of 15min of the present invention and entire Optimum Regulation time scale for 24 hours, when the present invention selects prediction in simulation process
Domain and control time domain are 4h, i.e. Np=Nc=16 are emulated, namely in a few days the amendment stage is total to rolling optimization 96 times.
In order to verify the proposed Multiple Time Scales Optimized model forecast dispatching method of the present invention for stabilizing building energy supplying system
The effect of interconnection tie power fluctuation is based respectively on DA-P tactful (Day-ahead programming strategy), based on biography
Single section open loop optimisation strategy of uniting and the in a few days Rolling optimal strategy based on MPC set following three kinds of comparisons scene:
Scene one (DA-P strategy): in the in a few days actual motion stage, the building energy supplying system as caused by prediction data error
Dominant eigenvalues difference is all stabilized by external electrical network, without to the controllable DG of building energy supplying system, battery and virtual energy storage system
System optimizes scheduling.
Scene two (traditional single section open loop optimisation strategy): according to operation plan a few days ago, based on traditional single section open loop
Optimisation strategy solves the in a few days amendment scheme of the controllable DG of building energy supplying system, battery and virtual energy storage system, and disposable
It issues all optimization instructions building energy supplying system is planned in a few days to be corrected a few days ago.
Scene three (MPC Rolling optimal strategy): according to operation plan a few days ago, building energy supply is solved based on MPC optimisation strategy
The in a few days amendment scheme of the controllable DG of system, battery and virtual energy storage system hair, with the rolling optimization generation repeatedly of limited period of time
Optimize for primary offline all the period of time, realizes and building energy supplying system is planned a few days ago to carry out in a few days rolling amendment.
Building energy supplying system dominant eigenvalues comparison under three kinds of scenes is as shown in Figure 7.Therefrom as it can be seen that scene one uses
DA-P strategy, building energy supplying system dominant eigenvalues big ups and downs near in a few days planned value, it is difficult to realize building energy supplying system
Access the controllable friendly scheduling of power distribution network.Scene two and scene three by the in a few days operation phase to controllable DG, battery and
The power output of virtual energy storage system optimizes adjustment, can significantly reduce building energy supplying system interconnection tie power fluctuation, interconnection function
Rate tracking effect is preferable.However, comparison scene two and scene three as a result, it has been found that:
1) in building energy supplying system dominant eigenvalues steady period (such as 06:00~09:00), the connection of scene two and scene three
Winding thread tracking effect is not much different.
2) in the building energy supplying system interconnection tie power fluctuation biggish period (such as Fig. 7 irises out period 10:00~12:00),
Due to scene three use MPC method, the dispatching requirement for the following a period of time that can more preferably look forward to the prospect, thus can in advance to controllable DG,
The power output of battery and virtual energy storage system is adjusted；And scene two uses single section open loop optimization method, it is difficult in advance
It adjusts to control variable, so as to cause because of DG climbing limitation, accumulator capacity limitation and virtual energy storage system comfort level limit
The problem of power output caused by system is adjusted not in time, need the long period that could track planned value a few days ago.
In a few days Optimized Operation result such as Fig. 8 of controllable DG and battery that scene three is obtained after being adjusted using rolling optimization
(a) shown in.Therefrom as it can be seen that the controllable DG of building energy supplying system and battery are guaranteeing plan for start-up and shut-down and the constant base of charge and discharge plan
Respective power output is corrected on plinth, to respond the scheduling need that building energy supplying system interconnection tracks planned value a few days ago
It asks.Different batteries in a few days charge and discharge penalty factor θbtUnder SOC tracking effect such as Fig. 8 (b) shown in.Therefrom as it can be seen that value compared with
The corresponding SOC tracking effect of big penalty factor is preferable, and vice versa.Therefore, battery in a few days SOC tracking effect catch hell because
Sub- value is affected, building energy supplying system should flexibly choose according to the actual operation battery in a few days charge and discharge punishment because
Son.
In order to further verify virtual energy storage system optimization scheduling for stabilizing building energy supplying system interconnection tie power fluctuation
Effect, increase comparison scene four: according to operation plan a few days ago, the controllable DG of building energy supplying system being solved based on MPC optimisation strategy
And the in a few days amendment scheme of battery, without doing Dispatching adjustment to virtual energy storage system.Newly-increased scene four and scene three, scene one
Building energy supplying system dominant eigenvalues comparison as shown in figure 9, comparing result find:
1) virtual energy storage system is not dispatched due to scene four in the in a few days rolling amendment stage, building energy supplying system
Dominant eigenvalues tracking effect does not have scene three good.But the building energy supplying system controllable DG booting period (such as 10:00~19:
It 00), can be to a certain extent, since the power output of the DG controllable to building energy supplying system of scene four and battery do in a few days rolling amendment
Building energy supplying system interconnection tie power fluctuation is reduced, therefore dominant eigenvalues tracking effect is better than scene one.
2) in controllable DG shutdown period (such as Fig. 9 red boxes period 09:00~10:00) of building energy supplying system, due to building
The schedulable DG unit of space energy supplying system reduces and scene four does not dispatch virtual energy storage system, so as to cause building energy supplying system
Schedulable ability decline, so that interconnection tracking effect is poor, the interconnection tracking effect with the DA-P strategy of scene one
It is very nearly the same.
Scene three is using the in a few days Optimized Operation result of each virtual energy storage system obtained after rolling optimization adjustment as schemed
Shown in 10 (a) and annex figure A6.Therefrom as it can be seen that in a few days the rolling amendment stage, each BEMS was based on adjusting in bottom management a few days ago
The each building room temperature dispatch command generated in the works and in a few days short term predicted data are spent, according to the virtual of 1.1 section introductions
Energy-storage system mathematical model be calculated virtual energy storage system the in a few days operation phase be not involved in a few days modified refrigeration demand (see
Orange solid line in Figure 10 (a)), and pass to upper layer MEMS.In the scheduling of upper layer, MEMS in a few days corrects optimal tune by solving
Degree model obtains virtual energy storage and participates in a few days modified building refrigeration demand (see the blue solid lines in Figure 10 (a))；Then it is based on
The virtual energy storage system that each BEMS of bottom is uploaded is not involved in a few days modified refrigeration demand, obtains virtual energy storage according to formula (8)
System charge-discharge electric power in a few days revision directive, as shown in black histogram in Figure 10 (a).As can be seen from the figure virtual storage is introduced
After in a few days amendment can be arrived, the refrigeration demand load of building upper and lower wave on the basis of refrigeration demand load curve when no virtual energy storage
It is dynamic.The part for being higher by benchmark is cold-storage, i.e., " charges "；Part lower than benchmark is to let cool, i.e., " discharges ".Building in the case of two kinds
The difference of refrigeration demand load is the virtual energy storage system charge-discharge electric power based on building.By being filled to virtual energy storage system
Electric discharge is scheduled, and can stabilize building energy supplying system interconnection tie power fluctuation to a certain extent.
Each building in a few days modified solutions for refrigeration such as Figure 10 (b), 10 (c) and annex figure A7 institute in building energy supplying system
Show.By comparing 10 (b) and 10 (c) discoveries, after introducing virtual energy storage in a few days amendment, the room temperatures of building and refrigeration machine
Power consumption is compared with planned value a few days ago there is significant change, without introducing virtual energy storage to the in a few days corresponding room of modified scheme
Interior temperature is consistent with room temperature plan value a few days ago.Therefore, BEMS adjusts adjustable virtual energy storage by room temperature
The charge-discharge electric power of system, to realize the mesh for stabilizing building energy supplying system interconnection tie power fluctuation in the in a few days amendment stage
Mark.
The present invention utilizes the thermal storage effect of building, constructs building virtual energy storage system model.Then, by virtual energy storage
System is applied in building microgrid multi-time scale model prediction optimization scheduling model, and conclusion is as follows:
1) in scheduling phase a few days ago, virtual energy storage system model is integrated into building microgrid a few days ago in Optimal Operation Model,
The flexibility of building energy can be made full use of, and reduces the operating cost of building energy supplying system to a certain extent.
2) virtual energy storage system model is integrated into building microgrid in a few days rolling optimal dispatching mould by the rolling amendment stage in day
In type, by the way that building room temperature is adjusted within the scope of temperature pleasant degree, can effectively it stabilize as predicting caused by error a few days ago
Interconnection tie power fluctuation.
3) rolling optimization uses MPC method, power output that can in advance to controllable DG, battery and virtual energy storage system in day
It is adjusted, avoids traditional single section open loop optimization method because of DG climbing limitation, accumulator capacity limitation and virtual energy storage system
The problem of power output caused by comfort level of uniting limitation adjusts not in time.Meanwhile MPC rolling optimization method is with the rolling repeatedly of limited period of time
Dynamic optimization replaces traditional primary offline all the period of time open loop optimization, and it is more preferable to be formed by dispatching method robustness, is more suitable for pre-
Survey the building energy supplying system Optimized Operation under uncertain environment.
It is emphasized that embodiment of the present invention be it is illustrative, without being restrictive, therefore the present invention includes
It is not limited to embodiment described in specific embodiment, it is all to be obtained according to the technique and scheme of the present invention by those skilled in the art
Other embodiments, also belong to the scope of protection of the invention.
Claims (9)
1. a kind of building energy supplying system model prediction of integrated electric refrigerating machine regulates and controls method, it is characterised in that: the following steps are included:
Step 1, foundation are made of building virtual energy storage system, electric refrigerating machine, diesel generation unit, fuel cell, battery
Building energy supplying system model；
Step 2 is established after scheduling model frame and is provided respectively a few days ago, in a few days and rolling portion model, and then constructs building energy supply
System Multiple Time Scales forecast dispatching model carries out Multiple Time Scales optimization according to the demand of building energy supplying system and building user
Scheduling.
2. a kind of building energy supplying system model prediction of integrated electric refrigerating machine according to claim 1 regulates and controls method, special
Sign is: the specific steps of the step 1 include:
(1) building virtual energy storage system model is established；
(2) building energy supplying system is established for energy model of element.
3. a kind of building energy supplying system model prediction of integrated electric refrigerating machine according to claim 2 regulates and controls method, special
Sign is: the specific steps of step 1 (1) step include:
1. constructing the hot dynamic model of building
In view of the thermal storage effect of building construction, the virtual energy storage unit exchanged with outdoor carry out heat can be modeled as；It is based on
The quantitative mathematical relationship of building room temperature, refrigeration demand and outdoor temperature can be obtained in building equation of heat balance, such as formula (1) institute
Show:
In above formula, ρ is atmospheric density；C is air specific heat capacity；V is room air capacity；TinFor room temperature；
(i)Q& wallIt indicates the heat (kW) of external wall and outdoor transmitting, the sum of heat is transmitted for all exterior walls, such as formula (2) institute
Show；
(ii)Q& winIt indicates the heat (kW) of external window of building and outdoor transmitting, the sum of heat is transmitted for all exterior windows, such as formula (3) institute
Show；
(iii)Q& inFor the calorific value (kW) of indoor airflow, can be obtained by predicting or measuring；
(iv)Q& swThe heat (kW) transmitted for sun heat radiation by exterior wall passes through institute according to ISO 13790 for solar radiation
There is the sum of exterior wall transmitting heat, as shown in formula (4)；
(v)The heat (kW) transmitted for solar radiation by exterior window can be calculated by formula (5)；Present invention assumes that building
All unofficial biography of building are evenly distributed on the four sides wall of the four corners of the world；
(vi)For the refrigeration work consumption (kW) of refrigeration equipment；
ToutFor outdoor temperature；UwallFor external wall heat transfer coefficient；UwinFor external window of building heat transfer coefficient；Fwall,jFor external wall
J area；Fwin,jFor external window of building j area；SC is shading coefficient, value with whether have sunshading board, glass material etc. related；
Rse,jFor the thermal resistance of exterior wall and outdoor air thermal convection and heat radiation；αwFor wall heat transfer loss coefficient；τwinFor wall heat transfer damage
Lose coefficient；IT,jTotal solar radiation intensity (the kW/m received for every square metre of wall of exterior wall j/window2), it expresses as shown in formula (6)；
Wherein Ib、IdThe direct radiation intensity of illumination, scattering radiation intensity and the global radiation on same level surface are respectively indicated with I
Intensity, unit kW/m2；ρgFor ground surface reflectance, 0.2 is taken in the present invention；θ is illumination incidence angle；RbFor illumination geometrical factor,
For describing the ratio of direct radiation intensity of the illumination on clinoplain Yu the direct radiation intensity of illumination in the horizontal plane,
Calculation expression is such as shown in (7)；
Wherein, θzFor illumination zenith angle；
2. constructing building virtual energy storage system model；
The charge-discharge electric power of virtual energy storage system is obtained, as shown in formula (8):
Wherein,For t moment building virtual energy storage system charge-discharge electric power, electric discharge is positive, and charging is negative；It is uncomfortable
Save the building refrigeration electrical power of room temperature；To consider to adjust the building system of room temperature within the scope of temperature pleasant degree
Cold electrical power；
Formula (1) and formula (8) together constitute building virtual energy storage system mathematic model.
4. a kind of building energy supplying system model prediction of integrated electric refrigerating machine according to claim 2 regulates and controls method, special
Sign is: the specific steps of step 1 (2) step include:
1. establishing generator model
The fuel cost of generator is determined by machine unit characteristic parameter and output power, as shown in formula (9):
In formula: ffuel() is the fuel cost function of generator；PDE,tFor the power output of generator；A, b, c are diesel generation
The fuel cost coefficient of machine；
2. establishing fuel cell mode
Fuel-cell fuel cost is by output power and efficiency, parameter ηFCIt determines, as shown in formula (10):
ffuel(PFC,t)=Cgas×Pgas,t=Cgas×(PFC,tΔt/ηFC) (10)
In formula: PFC,tFor the power output of fuel cell；ηFCFor the efficiency of fuel cell；CgasFor Gas Prices；Pgas,tFor combustion
Expect the gas horsepower of battery consumption；△ t is the corresponding power output period；
3. establishing electric refrigerating machine model
The refrigeration equipment that building energy supplying system uses is compression electric refrigerating machine, shown in refrigeration work consumption such as formula (11):
QEC,t=PEC,t×COPEC (11)
In formula: QEC,tIt is exported for the refrigeration work consumption of electric refrigerating machine；PEC,tFor the electrical power of electric refrigerating machine consumption；COPECFor electricity refrigeration
The Energy Efficiency Ratio of machine；
4. establishing battery model
State-of-charge (State of Charge, SOC) is the ratio that battery remaining capacity accounts for rated capacity, indicates battery
State-of-charge；The SOC value SOC of battery t momenttAs shown in formula (12):
In formula: Pbt,tFor the specified charge-discharge electric power of battery (electric discharge is positive, and charging is negative)；CAPbtFor the specified appearance of battery
Amount；ηch, ηdisFor the efficiency for charge-discharge of battery；δ is the self-discharge rate of battery；△ t is the corresponding charge and discharge period.
5. a kind of building energy supplying system model prediction of integrated electric refrigerating machine according to claim 2 regulates and controls method, special
Sign is: the specific steps of the step 2 include:
(1) building energy supplying system Multiple Time Scales forecast dispatching frame is constructed；
(2) economic load dispatching model a few days ago is constructed；
(3) in a few days rolling amendment is carried out；
(4) Multiple Time Scales Optimized Operation is carried out according to the demand of building energy supplying system and building user.
6. a kind of building energy supplying system model prediction of integrated electric refrigerating machine according to claim 5 regulates and controls method, special
Sign is: the specific steps of step 2 (1) step include:
1. economic load dispatching a few days ago
Economic dispatch stage a few days ago, based on a few days ago each building electric load power prediction value of hour rank, outdoor temperature predicted value,
Intensity of illumination predicted value and generation of electricity by new energy predicted value are comprehensively considered with the minimum target of building energy supplying system operating cost
Each building user indoor temperature comfort level constraint, building energy supplying system controlled distribution formula power technology characteristic and ahead market
The information of electricity price obtains n a few days ago1The Optimized Operation scheme of building energy supplying system in a scheduling time section；
2. in a few days rolling amendment
In a few days the amendment stage carries out rolling optimization by the period of 15min, and entire scheduling time axis is divided into n2A scheduling time section,
Wherein prediction time domain is NpA period, control time domain are NcA period, Np≥Nc；
In t moment, current time section to next prediction time domain N is utilizedpInterior short-term forecast information, is counted a few days ago not changing
Controllable DG plan for start-up and shut-down and energy storage charging and discharging state in drawing, and under the premise of meeting power-balance and various constraints, with building
The minimum target of error between energy supplying system dominant eigenvalues and a few days ago planned value is asked based on model prediction regulation method optimization
Take NcThe amendment plan sequence of all controllable DG of building energy supplying system, energy-storage system in a period and building power load
Column；
In t, discontinuity surface only issues the amendment plan to the latter time cycle, i.e. NcBuilding energy supplying system repairs in a period
First control sequence in positive programmed sequence；When next dispatching cycle arrives, the above process is repeated.
7. a kind of building energy supplying system model prediction of integrated electric refrigerating machine according to claim 5 regulates and controls method, special
Sign is: the specific steps of step 2 (2) step include:
1. by a kind of building energy supplying system of integrated electric refrigerating machine, regulation goal function is set as operating cost minimum a few days ago, such as formula
(13) shown in:
In formula: T is entire dispatching cycle, can use one day for 24 hours；First item is cost of the building energy supplying system from power distribution network power purchase；
Pgrid,tFor the electrical power that building energy supplying system is exchanged with power distribution network, power purchase is positive, and sale of electricity is negative；Cph,tAnd Cse,tRespectively building
Energy supplying system is from the price of power distribution network power purchase and to the price of power distribution network sale of electricity；Section 2 is controllable DG in building energy supplying system
Fuel cost, maintenance cost and start-up cost, as shown in formula (9), (10), PDG,i,tIndicate DGiFunction in time period t
Rate output；ρDG,iIndicate DGiWorking service cost；ρsu,iIndicate DGiStart-up cost；U′DG,i,tIndicate i platform distributed generation resource
In the starting state of t period；Section 3 is the use of energy-storage system, photovoltaic system and refrigeration system in building energy supplying system
Maintenance cost；PPV,tAnd PEC,tRespectively t moment photovoltaic power output and refrigeration electric power；ρPV、ρbtAnd ρECIt respectively represents photovoltaic, store
The working service cost of battery and electric refrigerating machine unit interval unit power；
2. constraint condition is made of building energy supplying system operation constraint and building operation constraint two parts；
1) economic load dispatching model constraint condition a few days ago are as follows:
The constraint of building energy supplying system power purchase:
Wherein,Pgrid WithThe respectively bound of building energy supplying system power purchase power；
Electrical power Constraints of Equilibrium:
PPV,tAnd PEC,tRespectively t moment photovoltaic power output and refrigeration electric power；Pbt,tFor the charge and discharge electric work of t moment energy-storage battery
Rate；Pel,tFor t moment building electric load；PEC,n,tFor refrigeration machine n t moment power output；
The operation constraint of controllable distributed power generation unit:
Wherein, UDG,i,tWith U "DG,i,tRespectively indicating operating status of the i platform distributed generation resource in the t period, (" 1 " indicates opening state
State, " 0 " indicate closed state) and off state (" 1 " indicates to close, and " 0 " indicates not close)；P DG,i,tWithIt is distributed
The power output bound of power supply i；
Meanwhile DG is also by the minimum available machine timeWith the minimum unused timeConstraint:
Battery operation constraint
Wherein,P btWithThe respectively bound of energy-storage system charge-discharge electric power；Ebt,tFor the electricity of energy-storage system t moment；
2) each building operation constraint:
Refrigeration duty Constraints of Equilibrium
Wherein,For electric refrigerating machine refrigeration electrical power；To consider to adjust room temperature within the scope of temperature pleasant degree
Building refrigeration electrical power；
The constraint of building thermal balance:
The constraint of building room temperature bound:
8. a kind of building energy supplying system model prediction of integrated electric refrigerating machine according to claim 5 regulates and controls method, special
Sign is: the specific steps of step 2 (3) step include:
Building energy supplying system current state x (t ') is based respectively in current time t ', MEMS and each BEMS and each building are worked as
Preceding room temperature Tin(t '), using prediction time domain NpShort-term forecast information in a period predicts building energy supplying system and building
The state in space room temperature future；Next, MEMS solving optimization under the premise of meeting the constraint condition of current and future is asked
Topic obtains the following control time domain NcThe control instruction sequence of building energy supplying system in a period；Then, by control instruction sequence
First value be applied in each BEMS and each DG controller；Meanwhile at+1 moment of t ', building energy supplying system state is updated
For x (t '+1) and building room temperature Tin(t '+1), and repeat above-mentioned steps；
1. establishing building energy supplying system prediction model
With current time t ' building energy supplying system and power distribution network Tie line Power (Pgrid(t ')), controllable DG contribute (PDG
(t ')), energy storage charge-discharge electric power (Pbt(t ')), energy-storage system SOC (SOC (t ')) and building refrigeration machine consume power (PEC
(t ')) constitute vector x (t ') be building energy supplying system state variable, see formula (30)；With controllable DG power output increment (Δ PDG
(t ')), energy storage contribute increment (Δ Pbt(t ')) and building refrigeration machine consumption of electric power increment (Δ PEC(t ')) constitute vector
U (t ') is the control variable of building energy supplying system, sees formula (31)；With photovoltaic and blower fan system power output short-term forecast power increment
(ΔPPV(t′)、ΔPWT(t ')), building electric power short-term forecast power increment (Δ Pel(t ')) constitute vector r (t ') be
The disturbance input of building energy supplying system is shown in formula (32)；Using building energy supplying system Tie line Power as building energy supplying system
Output variable is shown in formula (33)；Shown in the state space prediction model such as formula (34) that building energy supplying system can then be established；
X (t ')=[Pgrid(t′),PDG(t′),Pbt(t′),SOC(t′),PEC(t′)]T (30)
U (t ')=[Δ PDG(t′),ΔPbt(t′),ΔPEC(t′)]T (31)
R (t ')=[Δ PPV(t′),ΔPWT(t′),ΔPel(t′)]T (32)
Y (t ')=Pgrid(t′) (33)
In formula: shown in the expression of state space matrices A, B, C and D such as formula (35)-(39)；
Wherein, EnAnd EmFor unit matrix, n and m are respectively the controllable quantity of DG and the quantity of building in building energy supplying system；For
The SOC recursion coefficient of battery, is expressed as follows:
D=[1 000 0] (39)
According to formula (34) it is found that based on photovoltaic system and load in NpShort-term forecast information in a period, by the state
Spatial prediction model iterates, and can predict to obtain NpBuilding energy supplying system exchanges function with power distribution network interconnection in a period
Rate and each building refrigeration machine consumption of electric power；
2. establishing building room temperature prediction model
The recursive equation of building indoor temperature change generated in case amount, as shown in formula (40):
In formula:WithRespectively meter and building input variable short-term forecast increment information λ (t ') are built
The heat, external window of building and heat, indoor heat gain, the sun heat radiation of outdoor transmitting for building exterior wall and outdoor transmitting pass through exterior wall
The heat that the heat of transmitting and solar radiation are transmitted by exterior window；For meter and building refrigeration machine consumption of electric power increment (Δ PEC
(t ')) refrigeration work consumption output；Building input variable short-term forecast predicts increment information λ (t ') as shown in formula (41)；
According to formula (40) and (41) it is found that being based on building short-term forecast information λ (t ') and NpBuilding refrigeration machine consumes in a period
Electrical power predictive information (is rolled to solve and be obtained) by the building energy supplying system state space prediction model of formula (34), can be in NpIt is a
In period, room temperature is carried out to roll solution and prediction；
3. establishing rolling optimization model
Building energy supplying system prediction model and a prediction time domain NpInterior short-term forecast information can be obtained building energy supplying system and exist
Current control time domain NcBuilding energy supplying system dominant eigenvalues value in interior output variable Y, namely current control time domain, expression
As shown in formula (42)；Based on scheduling model a few days ago, building energy supplying system can be obtained in currently control time domain NcInterior tracking a few days ago
Object vector FdayNamely the planned value a few days ago of building energy supplying system dominant eigenvalues, it expresses as shown in formula (43)；
The objective function that rolling optimization then can be obtained is as follows:
In formula:For the penalty value of the corresponding accumulator cell charging and discharging power increment of moment t ', it is expressed as follows:
In formula: θbtFor the corresponding penalty factor of penalty term；
In a few days constraint condition with formula (14)~(29) of rolling optimization are identical；Based on rolling optimization model, EMES can be solved
It, then will optimization to Correction and Control sequence u (t ') of the building energy supplying system in the corresponding control time domain of each scheduling instance t '
As a result first value is added on building energy supplying system, at+1 moment of t ', is repeated based on new short-term forecast information above-mentioned entire
Optimization process；The present invention calls CPLEX to solve above-mentioned Multiple Time Scales Optimal Scheduling at MATLAB.
9. a kind of building energy supplying system model prediction of integrated electric refrigerating machine according to claim 5 regulates and controls method, special
Sign is: the specific steps of step 2 (4) step include:
1. system initialization: setting the optimization of each stage building energy supplying system according to the demand of building energy supplying system and building user
Target and scheduling time scale, if optimization aim is that building energy supplying system operating cost is minimum in scheduling a few days ago, if in a few days correcting
Perfecting by stage target is the building energy supplying system dominant eigenvalues that tracking is dispatched a few days ago；
2. economic load dispatching a few days ago: each building electric load power prediction value, outdoor temperature predicted value, light based on hour rank a few days ago
According to prediction of strength value and generation of electricity by new energy predicted value, with the minimum target of building energy supplying system operating cost, comprehensively consider each
The constraint of a building user indoor temperature comfort level, building energy supplying system controllable distributed power generation monotechnics characteristic and city a few days ago
The information such as field electricity price, obtain n a few days ago1Operation plan a few days ago in a period；
3. updating system mode: according in a few days short-term forecast information (building load, renewable energy power output, outdoor environment and room
Inner heat source obtains heat), update system mode；
4. in a few days rolling amendment: based in a few days short-term predictive information, by each building cooling load and building energy supply system
The controllable DG that unites carries out rolling optimal dispatching, thus amendment in a few days actual motion plan and a few days ago deviation existing for operation plan.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110288271A (en) * | 2019-07-11 | 2019-09-27 | 北京全来电科技有限公司 | A kind of platform area grade charging load control strategy and method based on Model Predictive Control |
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102132223A (en) * | 2008-07-31 | 2011-07-20 | 罗伯特·博世有限公司 | Sensor-based occupancy and behavior prediction method for intelligently controlling energy consumption within a building |
| US20130304235A1 (en) * | 2012-05-11 | 2013-11-14 | Siemens Corporation | System and method for fault prognostics enhanced mpc framework |
| CN104537579A (en) * | 2014-12-29 | 2015-04-22 | 河海大学 | Smart distribution grid greenness assessment method |
| CN106527135A (en) * | 2016-11-21 | 2017-03-22 | 云南电网有限责任公司电力科学研究院 | Building energy management system optimization control method and device |
| CN106960272A (en) * | 2017-02-28 | 2017-07-18 | 天津大学 | Building microgrid Multiple Time Scales Optimization Scheduling containing virtual energy storage |
| CN108241295A (en) * | 2016-12-23 | 2018-07-03 | Abb股份公司 | For the adaptive modeling method and system of the building energy control based on MPC |
-
2018
- 2018-10-08 CN CN201811168372.XA patent/CN109752953B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102132223A (en) * | 2008-07-31 | 2011-07-20 | 罗伯特·博世有限公司 | Sensor-based occupancy and behavior prediction method for intelligently controlling energy consumption within a building |
| US20130304235A1 (en) * | 2012-05-11 | 2013-11-14 | Siemens Corporation | System and method for fault prognostics enhanced mpc framework |
| CN104537579A (en) * | 2014-12-29 | 2015-04-22 | 河海大学 | Smart distribution grid greenness assessment method |
| CN106527135A (en) * | 2016-11-21 | 2017-03-22 | 云南电网有限责任公司电力科学研究院 | Building energy management system optimization control method and device |
| CN108241295A (en) * | 2016-12-23 | 2018-07-03 | Abb股份公司 | For the adaptive modeling method and system of the building energy control based on MPC |
| CN106960272A (en) * | 2017-02-28 | 2017-07-18 | 天津大学 | Building microgrid Multiple Time Scales Optimization Scheduling containing virtual energy storage |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110288271A (en) * | 2019-07-11 | 2019-09-27 | 北京全来电科技有限公司 | A kind of platform area grade charging load control strategy and method based on Model Predictive Control |
| CN110288271B (en) * | 2019-07-11 | 2022-10-11 | 北京全来电科技有限公司 | Transformer district level charging load regulation and control strategy and method based on model predictive control |
| CN110531717B (en) * | 2019-08-13 | 2022-04-19 | 天津大学 | Energy-saving optimization scheduling method of coal mine belt type conveying system with fused silo virtual energy storage |
| CN110531717A (en) * | 2019-08-13 | 2019-12-03 | 天津大学 | Merge the coal mine belt conveyor systems energy saving optimizing dispatching method of silo virtual energy storage |
| CN111415030A (en) * | 2019-11-19 | 2020-07-14 | 广西电网有限责任公司 | Building energy system comprehensive demand response optimization method considering energy utilization comfort level |
| CN111415030B (en) * | 2019-11-19 | 2022-11-29 | 广西电网有限责任公司 | Building energy system comprehensive demand response optimization method considering energy utilization comfort level |
| CN111832898A (en) * | 2020-06-11 | 2020-10-27 | 华中科技大学 | Air-conditioning-based multifunctional demand response scheduling method for power system |
| CN113872180A (en) * | 2020-06-30 | 2021-12-31 | 华为技术有限公司 | Method and system for supplying power to equipment and related equipment |
| WO2022001304A1 (en) * | 2020-06-30 | 2022-01-06 | 华为技术有限公司 | Device power supply method, system, and related device |
| CN111738621B (en) * | 2020-07-17 | 2020-11-24 | 深圳华工能源技术有限公司 | Method for demand side to adjust resource time-scale aggregation participation demand response |
| CN111738621A (en) * | 2020-07-17 | 2020-10-02 | 深圳华工能源技术有限公司 | Method for demand side to adjust resource time-scale aggregation participation demand response |
| CN112415963B (en) * | 2020-11-13 | 2022-02-08 | 珠海格力电器股份有限公司 | Building-level energy control method, control device and system and processor |
| CN112415963A (en) * | 2020-11-13 | 2021-02-26 | 珠海格力电器股份有限公司 | Building-level energy control method, control device and system and processor |
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| CN115685759B (en) * | 2022-10-28 | 2024-04-16 | 武汉理工大学 | Energy-saving building system control method based on energy priority |
| CN115689253A (en) * | 2022-12-30 | 2023-02-03 | 北京智能建筑科技有限公司 | Comprehensive energy scheduling optimization method taking total carbon emission of building as target |
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