CN109409617B - Power utilization robust optimization method for public building environment factor prediction uncertainty - Google Patents
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Abstract
The invention provides a power utilization robust optimization method for prediction uncertainty of public building environmental factors, which establishes an energy consumption model of a lighting system, an energy consumption model of an air conditioning system and an energy consumption model of an elevator system. And meanwhile, an optimized objective function which enables the electricity consumption cost to be minimum is established, electricity utilization robust indexes and building comprehensive robust indexes of each electricity utilization system are established respectively, and then an electricity load scheduling strategy which can reach an expected robust level is worked out according to the robust indexes. The invention analyzes the uncertainty caused by environmental factors in the public building demand response, reduces the deviation of the predicted data and the actual data, reduces the loss of users and reduces the electricity consumption cost.
Description
Technical Field
The invention belongs to the technical field of intelligent power utilization, and particularly relates to a power utilization robust optimization method for public building environmental factor prediction uncertainty.
Background
With the increasing dual pressures of global resources and environment, the energy efficiency problem attracts more and more attention. The global power industry gradually enters the era of smart power grids, and the smart power grids are mainly characterized in that power resources can be comprehensively and reasonably configured, so that the concept of intelligent power utilization is proposed in the field of energy management. In the optimization of building electricity utilization, the main purpose of intelligent electricity utilization is to reduce electricity utilization cost under the condition of meeting electricity utilization requirements. According to statistics, public buildings are main structures of urban buildings, energy consumption of the public buildings accounts for nearly 33% of total social energy consumption, and therefore the purpose of reducing the overall electricity consumption cost of the buildings is achieved by researching electricity utilization optimization operation of the public buildings, and the public buildings have great practical value.
At present, more researches are carried out on the intelligent electricity utilization optimization problem of residential users, and less analysis is carried out on the participation demand response management of public buildings. In addition, due to a plurality of factors, the prediction data and the actual data have deviation, the uncertainty of the prediction data may bring certain loss to users, and for the research of the uncertainty problem, the power load under the condition of uncertain power price and load is mainly controlled at present, but a robust optimization method for uncertain prediction of environmental factors is not discussed.
Disclosure of Invention
The purpose of the invention is as follows: the invention provides a power utilization robust optimization method for prediction uncertainty of public building environmental factors, and reduces errors of public building demand response prediction.
The technical scheme is as follows: the invention provides a power utilization robust optimization method for prediction uncertainty of public building environmental factors, which comprises the following steps of:
1) establishing an energy consumption model of the lighting system;
2) establishing an energy consumption model of the air conditioning system;
3) establishing an energy consumption model of an elevator system;
4) establishing constraint conditions according to actual conditions, and establishing an optimized objective function which enables electricity consumption to be minimum;
5) and (4) considering the prediction uncertainty of environmental factors, establishing respective power utilization robust indexes aiming at the lighting system, the air conditioning system and the elevator system respectively, and establishing a building comprehensive robust index.
And 6) working out an electric load scheduling strategy which can reach the expected robustness level according to the robustness index.
The average indoor illuminance in the step 1) can be calculated by the following equation:
in the formula, EavThe average illumination of the working surface is lx; phi is the rated total luminous flux of the light source in each lamp, and the unit is lm; n is the number of lamps; u is an illumination utilization coefficient; a is the area of the working surface and is given by m2(ii) a K is the lamp maintenance coefficient and K is the maintenance coefficient of the lamp,
generally, to ensure a comfortable working environment of a human body, the illumination intensity in the environment needs to be kept above a certain level, that is, the superposition result of natural illumination and artificial light source illumination can meet the requirement of indoor personnel on the illumination of the light source, and the indoor comprehensive illumination value can be calculated by the following formula:
Es,τ＝Eb,τ+ηeEe,τ
in the formula, Es,τIs an indoor integrated illuminance value in lx; eb,τSupplementing the artificial lighting with the generated illuminance value in lx; ee,τIs the illuminance of natural light, in lx; etaeIn order to utilize the coefficient of the natural light,
therefore, the lighting load per unit time inside the public building is calculated as follows:
in the formula, Pl,tThe power for supplementing illumination for the artificial light source of the illumination system at the moment t; plPower of a single artificial light source; esetIs a target illuminance value; eeIs the natural illuminance, tworkIs the working time.
The heat transferred from the outdoor to the indoor in the step 2) is transmitted from the outdoor through the outer wall and the roof:
Q1＝KA(Tout-Tin)
in the formula, Q is heat transferred into the room from the outdoor through the fence structure, W; k is the heat transfer coefficient of the fence structure and has the unit W/(m)2K); a is the heat transfer area of the enclosure structure, unit m2；Tout、TinOutdoor and indoor temperatures in units;
solar radiant heat entering the air-conditioned room through the architectural glazing:
Q2＝CaACsCiDjmaxCLQ
in the formula, CaIs the effective area coefficient; a is the window area in m2；CsThe shading coefficient is obtained; ciThe shading coefficient of the shading facility in the window; djmaxMaximum solar radiant heat in W/m2；CLQIs the glazing cold load coefficient;
human body heat dissipation cold load:
Q3＝qsnψCLQ
in the formula, qsThe unit W is the heat dissipation capacity of the sensible heat of adult men with different room temperatures and labor properties; n is the total number of people in the room, psi is the clustering coefficient, CLQThe coefficient of the sensible heat and the heat dissipation cold load of the human body is;
the lighting lamp radiates heat:
Q4＝1000PlCLQ
in the formula, PlThe unit kW is the power required by the lighting lamp;
the fresh air load, the fresh air (fresh air) introduced into the air conditioning system is the key summer fresh air cooling load for ensuring good indoor air quality, and is calculated according to the following formula:
Q5＝M(hout-hin)
in the formula, M is fresh air volume and is unit kg/s; h isoutIs the enthalpy value of outdoor air, and the unit kj/kg; h isinIs the enthalpy value of indoor air in kj/kg;
the enthalpy of air is the sum of the enthalpy of dry air and the enthalpy of water vapor in humid air, and comprises sensible heat and latent heat:
h＝1.01T+d(2500+1.84T)
wherein d represents the moisture content, and is usually 0.014kg/(kg. dryair); the indoor required fresh air volume is related to the number of people, and the minimum fresh air volume index of each person is set as RpThen M ═ Rp×n+Rb×s；RbThe minimum fresh air volume standard required by each square meter; n is the number of indoor people; s is the building area;
in summary, the cooling load calculation formula is:
Q＝KA(Tout-Tin)+CaACsCiDjmaxCLQ+qsnψCLQ+
1000PCLQ+M(Rp×n+Rb×S)(hout-hin)
the energy consumption calculation formula of the air conditioning system is as follows:
in the formula, xa,tFor air-conditioning systems, power consumption, Q, during t periodstThe cold load at the time t, and COP is the performance index of the air conditioning system.
In the step 3)
The energy consumption model of the elevator system in the up peak mode is as follows:
ELF(τ)＝365.32-1.115FP,τ-0.199Ncar,τ+0.143FP,τNcar,τ
+0.087FP,τ 2-0.228Ncar,τ 2
the people flow rate condition in the mode meets the following conditions:
in the formula, Pu,τRepresenting the total amount of upstream passenger flow; pd,τRepresenting the total amount of downstream passenger flow;
representing the amount of traffic entering and traveling up an elevator in a building lobby;
indicating a story-off at the top of a buildingPassenger flow for starting the elevator;
similarly, the average waiting time of passengers is the result of the combined action of the passenger flow and the number of running elevators and can be represented by a quadratic curve, so that the average waiting time in this mode can be represented as:
TAW(τ)＝600.403-359.239ln(FP,τ)+86.113ln(Ncar,τ)
-57.995ln(FP,τNcar,τ)+62.096ln(FP,τ)2+36.007ln(Ncar,τ)2
the energy consumption model of the elevator system in the down peak mode is:
ELF(τ)＝-523.806+30.355FP,τ-14.123Ncar,τ+0.241FP,τNcar,τ
-0.188FP,τ 2+0.774Ncar,τ 2
the people flow rate condition in the mode meets the following conditions:
the average latency is:
TAW(τ)＝729.197-429.455ln(FP,τ)+99.519ln(Ncar,τ)
-54.286ln(FP,τNcar,τ)+69.213ln(FP,τ)2+29.390ln(Ncar,τ)2
the energy consumption model of the elevator system in the peak-balancing mode is:
ELF(τ)＝276.885-5.409FP,τ-4.1Ncar,τ+0.067FP,τNcar,τ
+0.058FP,τ 2+0.321Ncar,τ 2
the people flow rate condition in the mode meets the following conditions:
the average latency is:
TAW(τ)＝622.589-352.932ln(FP,τ)+56.316ln(Ncar,τ)
-43.369ln(FP,τNcar,τ)+56.403ln(FP,τ)2+30.878ln(Ncar,τ)2。
the objective function in the step 4) is as follows:
in the formula, ptThe price of electricity at the time t; x is the number oftThe total power consumption of the lighting system, the air conditioning system and the elevator system is t time period;
considering the public building illumination comfort range, the temperature comfort range and the average waiting time of the elevator as constraint conditions:
Emin<＝Eset<＝Emax
Tmin<＝Tset<＝Tmax
Xmin<＝Xset<＝Xmax。
the robust index of the lighting system in the step 5):
El.preoutdoor illuminance value for deterministic prediction at the current moment, El,supFor the outdoor illuminance value, RI, assumed during power optimizationlFor the illumination system robustness index, then:
El.pre–El,sup＝RIl>＝RIl,set
the outdoor illuminance value under the current time interval prediction is [ E ]l.min,El.max]The interval is also El,supThen, the maximum value and the minimum value of the robust index of the illumination system are respectively:
RIl,max＝El.max–El,min
RIl,min＝0
robust index of air conditioning system:
Ta,prepredicting the lower outdoor temperature value, T, for the certainty of the current timea,supFor the outdoor temperature value, RI, assumed during power optimizationaFor the robustness index of the air conditioning system, then:
Ta,sup–Ta,pre＝RIa>＝RIa,set
the outdoor temperature value predicted at the current time interval is [ T ]a,min,Ta,max]The interval is also Ta,supThen, the maximum value and the minimum value of the robustness index of the air conditioning system are respectively:
RIa,max＝Ta,max–Ta,min
RIa,min＝0
elevator system robustness index:
Fp,prepredicting the lower indoor human flow value for the certainty of the current moment, Fp,supFor the indoor traffic value, RI, assumed during optimization of electricity consumptionpFor elevator system robustness index, then:
Fp,sup–Fp,pre＝RIp>＝RIp,set
the indoor pedestrian volume under the prediction of the current time interval is Fp,min,Fp,max]In this interval also being Fp,supThen, the maximum value and the minimum value of the robust index of the elevator system are respectively:
RIp,max＝Fp,max–Fp,min
RIp,min＝0
the units of the robust indexes of the power utilization systems are different, and the robust levels are difficult to compare, so that the robust indexes of the power utilization systems are normalized, the value range is [0,1], and the normalization processing process is as follows:
in the formula, RINIs the normalized robust index value; RI (Ri)rawIs the original robust index value before normalization; RI (Ri)minAnd RImaxRespectively the minimum value and the maximum value of the original robust index;
the robust indexes are established for the lighting system, the air conditioning system and the elevator system, the overall robust level of the public building can be evaluated by establishing a comprehensive robust index, and the comprehensive robust index can be represented by setting the weight value of each system:
in the formula, δ represents a power consumption system type; RI (Ri)δIs a robust index of the power utilization system; v. ofδIs the corresponding weight coefficient; RI (Ri)mixIs a composite robustness index; in addition, the comprehensive robust index is restricted to be not less than the preset value RIreqThe constraint may reflect the overall robustness level of the public building.
Has the advantages that: the invention analyzes the uncertainty caused by environmental factors in the public building demand response, reduces the deviation of the predicted data and the actual data, reduces the loss of users and reduces the electricity consumption cost.
Drawings
FIG. 1 is a graph of outdoor illuminance prediction values used in an embodiment of the present invention;
FIG. 2 is a graph of outdoor temperature predictions as used in an embodiment of the present invention;
FIG. 3 is a graph of a prediction of pedestrian traffic as used in accordance with an embodiment of the present invention.
Detailed Description
The present invention is further illustrated by the following examples, which are intended to be purely exemplary and are not intended to limit the scope of the invention, as various equivalent modifications of the invention will occur to those skilled in the art upon reading the present disclosure and fall within the scope of the appended claims.
The invention relates to a power utilization robust optimization method for prediction uncertainty of public building environmental factors, which specifically comprises the following steps: and 6) working out an electric load scheduling strategy capable of achieving the expected robustness level according to the robustness index.
Step 1) analyzing the energy consumption characteristics of the lighting system in the public building, and establishing a power consumption model of the lighting system.
The indoor average illuminance can be calculated by the following equation:
in the formula, EavThe average illumination of the working surface is lx; phi is the rated total luminous flux of the light source in each lamp, and the unit is lm; n is the number of lamps; u is an illumination utilization coefficient; a is the area of the working surface and is given by m2(ii) a K is the lamp maintenance coefficient and K is the maintenance coefficient of the lamp,
generally, to ensure a comfortable working environment of a human body, the illumination intensity in the environment needs to be kept above a certain level, that is, the superposition result of natural illumination and artificial light source illumination can meet the requirement of indoor personnel on the illumination of the light source, and the indoor comprehensive illumination value can be calculated by the following formula:
Es,τ＝Eb,τ+ηeEe,τ
in the formula, Es,τIs an indoor integrated illuminance value in lx; eb,τSupplementing the artificial lighting with the generated illuminance value in lx; ee,τIs the illuminance of natural light, in lx; etaeIs a natural light utilization factor.
Therefore, the lighting load per unit time inside the public building is calculated as follows:
in the formula, Pl,tThe power for supplementing illumination for the artificial light source of the illumination system at the moment t; plPower of a single artificial light source; esetIs a target illuminance value; eeNatural illuminance; t is tworkIs the working time.
And 2) analyzing the energy consumption characteristics of the air conditioning system in the public building, and establishing an energy consumption model of the air conditioning system.
Further consider the energy consumption of air conditioning system in the public building in summer, the inside heat of building in summer mainly comes from: heat conducted from the outside to the inside through the outer wall and the roof, solar radiant heat entering the inside through the architectural glass window, heat dissipated by human bodies and heat dissipated by illumination.
The cold load in the public building is the cold quantity which needs to be provided by the air conditioning system in order to maintain or change the indoor temperature, and the cold load is calculated as follows,
heat transfer from the outside to the inside through the outside walls and roof:
Q1＝KA(Tout-Tin)
in the formula, Q is heat transferred into the room from the outdoor through the fence structure, W; k is the heat transfer coefficient of the fence structure and has the unit W/(m)2K); a is the heat transfer area of the enclosure structure, unit m2；Tout、TinIs the outdoor and indoor temperature in units of deg.C.
Solar radiant heat entering the air-conditioned room through the architectural glazing:
Q2＝CaACsCiDjmaxCLQ
in the formula, CaIs the effective area coefficient; a is the window area in m2；CsThe shading coefficient is obtained; ciThe shading coefficient of the shading facility in the window; djmaxMaximum solar radiant heat in W/m2；CLQThe window glass cold load coefficient.
Human body heat dissipation cold load:
Q3＝qsnψCLQ
in the formula, qsFor different room temperature and laborSensible heat dissipation capacity for adult men, unit W; n is the total number of people in the room, psi is the clustering coefficient, CLQAnd the heat dissipation cold load coefficient is the sensible heat of the human body.
The lighting lamp radiates heat:
Q4＝1000PlCLQ
in the formula, PlThe unit kW is the power needed by the lighting lamp.
The fresh air load, the fresh air (fresh air) introduced into the air conditioning system is the key summer fresh air cooling load for ensuring good indoor air quality, and is calculated according to the following formula:
Q5＝M(hout-hin)
in the formula, M is fresh air volume and is unit kg/s; h isoutIs the enthalpy value of outdoor air, and the unit kj/kg; h isinIs the enthalpy value of indoor air, and has unit kj/kg.
The enthalpy of air is the sum of the enthalpy of dry air and the enthalpy of water vapor in humid air, and comprises sensible heat and latent heat:
h＝1.01T+d(2500+1.84T)
wherein d represents the moisture content, and is usually 0.014kg/(kg. dryair); the indoor required fresh air volume is related to the number of people, and the minimum fresh air volume index of each person is set as RpThen M ═ Rp×n+Rb×s；RbThe minimum fresh air volume standard required by each square meter; n is the number of indoor people; and s is the building area.
In summary, the cooling load calculation formula is:
Q＝KA(Tout-Tin)+CaACsCiDjmaxCLQ+qsnψCLQ+
1000PCLQ+M(Rp×n+Rb×S)(hout-hin)
the energy consumption calculation formula of the air conditioning system is as follows:
in the formula, xa,tFor air-conditioning systems tElectricity consumption by time period, QtThe cold load at the time t, and COP is the performance index of the air conditioning system.
And 3) analyzing the energy consumption characteristics of the elevator system in the public building, and establishing an energy consumption model of the elevator system.
For a certain public building, the in-and-out time of building personnel and the use habit of the elevator have certain regularity, so that an operation model of a building elevator system can be established based on passenger flow data, and the operation modes of the elevator can be classified according to the change of the passenger flow on and off the elevator, and can be divided into the following modes:
1) and (3) an uplink peak mode: the up-peak mode generally occurs in the morning working hours, and a large number of people flow into the building to work.
2) A downlink peak mode: the main passenger flow direction is a descending direction, main passengers descend from each floor of the building to the bottom of the building to leave the building by taking elevators, the descending peak mode is called as a descending peak mode, generally occurs in the off-duty time at night, personnel finish the work of one day, and the passengers leave the building by taking the elevators in a centralized mode.
3) Flat peak mode: the up-going passenger flow volume and the down-going passenger flow volume are stable, the difference is not large, the leading passenger flow is absent, the passenger flow volume is small, and the operation mode is the basic operation condition in most time in the building operation period.
In general public buildings, a plurality of elevators are usually set to run simultaneously in the operation period, and meanwhile, the running energy consumption of an elevator system is directly related to the size of the flow of people in the same traffic mode, so that the following points are required to be clear in order to determine the relation between the electricity consumption of the elevator system, the flow of people and the number of running elevators in different elevator traffic modes: 1) the electricity consumption of the elevator is positively correlated with the flow of people, the increase of the flow of people tends to increase the number of operation cycles of the elevator and the number of start and stop of the elevator, and the acceleration and deceleration processes of the elevator consume a large amount of electric energy, namely the increase of the number of start and stop of the elevator increases the electricity consumption, so the electricity consumption of the elevator increases along with the increase of the flow of people; 2) the total electricity consumption of the elevator group system is positively correlated with the number of running elevators in the elevator group system, and the increase of the number of running elevators due to the basic running power consumption and standby power consumption of the elevators causes the increase of the total basic energy consumption and standby energy consumption of the elevator system, so that the more the running elevators in the elevator system, the greater the overall running energy consumption of the elevator system; 3) the passenger flow comprises an uplink passenger flow and a downlink passenger flow, and the quantity and the proportion of the uplink passenger flow and the downlink passenger flow are different, so that the power consumption of the elevator system is different, and therefore, the elevator energy consumption modeling should be respectively carried out according to the difference of traffic modes.
According to the points and based on data obtained by testing, the relation between the electricity consumption of the elevator system and the flow of people and the number of running elevators can be described by a binary secondary mathematical model containing interactive items:
ELF(τ)＝a+b1FP,τ+b2Ncar,τ+b3FP,τNcar,τ+b4(FP,τ)2+b5(Ncar,τ)2
wherein E isLF(τ) elevator power consumption; fP,τAnd Ncar,τThe energy consumption of the elevator is a result under the combined action of two different factors, namely the flow of people and the number of running elevators, respectively.
The following models are made for different models of elevator energy consumption:
1) the energy consumption model of the elevator system in the up peak mode is as follows:
ELF(τ)＝365.32-1.115FP,τ-0.199Ncar,τ+0.143FP,τNcar,τ
+0.087FP,τ 2-0.228Ncar,τ 2
the people flow rate condition in the mode meets the following conditions:
in the formula, Pu,τRepresenting the total amount of upstream passenger flow; pd,τRepresenting the total amount of downstream passenger flow;
representing the amount of traffic entering and traveling up an elevator in a building lobby;
indicating the amount of traffic leaving the elevator at the top floor of the building.
Similarly, the average waiting time of passengers is the result of the combined action of the passenger flow and the number of running elevators, and can be represented by a quadratic curve. Thus, the average latency in this mode can be expressed as:
TAW(τ)＝600.403-359.239ln(FP,τ)+86.113ln(Ncar,τ)
-57.995ln(FP,τNcar,τ)+62.096ln(FP,τ)2+36.007ln(Ncar,τ)2
2) the energy consumption model of the elevator system in the down peak mode is:
ELF(τ)＝-523.806+30.355FP,τ-14.123Ncar,τ+0.241FP,τNcar,τ
-0.188FP,τ 2+0.774Ncar,τ 2
the people flow rate condition in the mode meets the following conditions:
the average latency is:
TAW(τ)＝729.197-429.455ln(FP,τ)+99.519ln(Ncar,τ)
-54.286ln(FP,τNcar,τ)+69.213ln(FP,τ)2+29.390ln(Ncar,τ)2
3) the energy consumption model of the elevator system in the peak-balancing mode is:
ELF(τ)＝276.885-5.409FP,τ-4.1Ncar,τ+0.067FP,τNcar,τ
+0.058FP,τ 2+0.321Ncar,τ 2
the people flow rate condition in the mode meets the following conditions:
the average latency is:
TAW(τ)＝622.589-352.932ln(FP,τ)+56.316ln(Ncar,τ)
-43.369ln(FP,τNcar,τ)+56.403ln(FP,τ)2+30.878ln(Ncar,τ)2
after the recommended running numbers of the elevators of the elevator system of the public building are obtained through calculation, the running priorities of the elevators can be set according to the new and old degrees, the maximum working time, the overhaul requirement and the running efficiency curves of the elevators which can be actually run by the public building and the running efficiency according to the difference of the new and old degrees of the elevators, all elevator devices are alternately run, the service life of the elevators is prolonged, and the running safety of the elevators is ensured, so that a specific running/shutdown control scheme is formulated for all the elevators on a device layer, the total number of the elevators which normally run in each time interval meets the running number of the elevators required by the elevator system, the running efficiency of the elevator system of the public building is improved, the total energy consumption level of the elevator system is reduced, and meanwhile, the anxiety degree of passengers is known to be increased in an exponential function form along with the increase of waiting time according to statistical results, the average waiting time of the passenger can thus be used to evaluate the passenger's satisfaction with the elevator system.
And 4) establishing a constraint condition according to the actual situation, and establishing an optimized objective function which enables the electricity consumption cost to be minimum.
To minimize the total electricity cost, the objective function is:
in the formula, ptThe price of electricity at the time t; x is the number oftThe total power consumption of the lighting system, the air conditioning system and the elevator system is calculated in the t period.
The modeling formulas of the steps 1), 2) and 3) are constraint conditions of the objective function, and meanwhile, the illumination comfort range, the temperature comfort range and the average waiting time of the elevator of the public building are taken as the constraint conditions:
Emin<＝Eset<＝Emax
Tmin<＝Tset<＝Tmax
Xmin<＝Xset<＝Xmax。
and 5) considering the prediction uncertainty of the environmental factors, and establishing the electricity utilization robust index and the building comprehensive robust index of each electricity utilization system respectively.
When power utilization optimization is carried out, the environmental factor predicted value can generate great influence on the optimization result, therefore, a robust index method is introduced to analyze the prediction uncertainty, the higher the robust index value is, the stronger the robustness of the power utilization optimization is, the smaller the negative influence of the uncertainty factor on the optimization result is, the robust index is taken as an additional constraint to be introduced into the constraint condition of the power utilization optimization,
illumination system robustness index:
El.preoutdoor illuminance value for deterministic prediction at the current moment, El,supFor the outdoor illuminance value, RI, assumed during power optimizationlFor the illumination system robustness index, then:
El.pre–El,sup＝RIl>＝RIl,set
the outdoor illuminance value under the current time interval prediction is [ E ]l.min,El.max]The interval is also El,supThen the maximum and minimum values of the robust index of the illumination systemRespectively, the following steps:
RIl,max＝El.max–El,min
RIl,min＝0
robust index of air conditioning system:
Ta,prepredicting the lower outdoor temperature value, T, for the certainty of the current timea,supFor the outdoor temperature value, RI, assumed during power optimizationaFor the robustness index of the air conditioning system, then:
Ta,sup–Ta,pre＝RIa>＝RIa,set
the outdoor temperature value predicted at the current time interval is [ T ]a,min,Ta,max]The interval is also Ta,supThen, the maximum value and the minimum value of the robustness index of the air conditioning system are respectively:
RIa,max＝Ta,max–Ta,min
RIa,min＝0
elevator system robustness index:
Fp,prepredicting the lower indoor human flow value for the certainty of the current moment, Fp,supFor the indoor traffic value, RI, assumed during optimization of electricity consumptionpFor elevator system robustness index, then:
Fp,sup–Fp,pre＝RIp>＝RIp,set
the indoor pedestrian volume under the prediction of the current time interval is Fp,min,Fp,max]In this interval also being Fp,supThen, the maximum value and the minimum value of the robust index of the elevator system are respectively:
RIp,max＝Fp,max–Fp,min
RIp,min＝0
the units of the robust indexes of the power utilization systems are different, and the robust levels are difficult to compare, so that the robust indexes of the power utilization systems are normalized, the value range is [0,1], and the normalization processing process is as follows:
in the formula, RINIs the normalized robust index value; RI (Ri)rawIs the original robust index value before normalization; RI (Ri)minAnd RImaxThe minimum and maximum of the original robust index, respectively.
The robust indexes are established for the lighting system, the air conditioning system and the elevator system, the overall robust level of the public building can be evaluated by establishing a comprehensive robust index, and the comprehensive robust index can be represented by setting the weight value of each system:
in the formula, δ represents a power consumption system type; RI (Ri)δIs a robust index of the power utilization system; v. ofδIs the corresponding weight coefficient; RI (Ri)mixIs a composite robustness index. In addition, the comprehensive robust index is restricted to be not less than the preset value RIreqThe constraint may reflect the overall robustness level of the public building.
And 6) working out an electric load scheduling strategy capable of achieving the expected robustness level according to the robustness index.
Examples of the design
In order to verify the feasibility of the model, the invention performs an example simulation analysis on an assumed scene. The working time of a certain public building is 9:00-21:00, 20 layers of the building are on the ground, and 2 layers of the building are underground. The LED energy-saving lamp is adopted for illumination, and the illumination area is 17768m2The illumination power per unit area was 3W, the luminous flux was 100lm/W, the illumination utilization factor was 0.5, and the illumination maintenance factor was 0.8. Δ t is 1 h. The total refrigerating area of the air conditioning system of the public building is 17768m2The total area of the building exterior wall is 7000m2Average windowThe wall ratio was 0.38. The building is provided with 8 elevators in total, and the power of each elevator is 24 kW.
First, the prediction uncertainty of the building external environment is considered and the robustness level of each system is evaluated. The electricity rates of the lighting system at different robustness indexes are shown in table 1. As can be seen from table 1, the power rate of the lighting system increases as the robust index increases, since the feasible region of load optimization becomes smaller as the robust index of the lighting system increases, thereby decreasing the economy.
The electric charge expenses of the air conditioning system under different robustness indexes are shown in table 2. As can be seen from table 2, the electricity cost increases with the increase of the robustness index value of the air conditioning system, because more electric energy needs to be consumed to keep the building temperature at a lower level, so as to better cope with the uncertainty problem of the prediction of the environmental factors. It follows that an increase in the level of system robustness is accompanied by a decrease in economy.
For elevator systems, the analysis is only performed for up-peak and down-peak hours. Table 3 shows the electricity charges of the elevator system at different robustness indexes during the up peak, and table 4 shows the electricity charges of the elevator system at different robustness indexes during the down peak. As can be seen from tables 3 and 4, the elevator system electricity cost expenditure increases as the robust index value increases. This is because the larger the robust index is, the more elevators can be controlled to meet the uplink and downlink requirements of more people, but the result of the increase of the electricity charge is caused.
TABLE 1 electric charges for lighting systems at different robustness indexes
TABLE 2 electric charges of air conditioning system under different robust indexes
Table 3 electric charge (up) of elevator system under different robust indexes
Table 4 electric charge (down) of elevator system under different robust indexes
When the comprehensive robust index is considered, firstly, the weight coefficients of all systems and the comprehensive robust index value are set, then, three different scenes are analyzed, the weight coefficient of each power utilization system is shown in a table 5, the robust index value is shown in a table 6, and the power charge under different scenes is calculated and shown in a table 7.
The setting of the load weight coefficients of the scene 1 and the scene 2 is the same, and the setting of the comprehensive robustness index is different. As can be seen from table 6, the higher the setting of the comprehensive robust index is, the larger the robust index value of each optimized system is, and the electricity fee is increased accordingly. The reason is that as the setting of the comprehensive robustness index is increased, each system can better deal with the occurrence of the uncertain events predicted by the environmental factors, and the self robustness level is improved by sacrificing the economy.
The setting of the comprehensive robust indexes of the scene 2 and the scene 3 is the same, and the setting of the weight coefficient of each power utilization system is different. As can be seen from table 6, the power utilization system has different weight settings, so that the optimized system robustness index changes, which indicates that the power utilization optimization result is affected by the different load weight settings. In addition, the power rates of scenario 2 and scenario 3 are higher than scenario 1 because scenario 2 and scenario 3 increase the building's integrated power usage robustness level at the expense of economy (paying more power rates).
TABLE 5 weight coefficients of systems under different scenarios
TABLE 6 robust index values for various systems
TABLE 7 Total Electricity charges
In summary, the above method for robust optimization strategy of electricity consumption for public buildings considering the uncertainty of prediction of environmental factors is effective. Robust indexes are respectively established according to the power utilization characteristics of the lighting system, the air conditioning system and the elevator system, and a building comprehensive robust index is established, so that a power utilization system optimization strategy capable of achieving an expected robust level is worked out. However, the optimization result is only the effect which can be realized theoretically, and the specific effect needs to be tested in practical application.
Cplex is used in the above simulation, and Corei53.20Ghz, 4G RAM is used as a computer.
Claims (5)
1. A power utilization robust optimization method for prediction uncertainty of public building environmental factors is characterized by comprising the following steps of:
1) establishing an energy consumption model of the lighting system;
2) establishing an energy consumption model of the air conditioning system;
3) establishing an energy consumption model of an elevator system;
4) establishing constraint conditions according to actual conditions, and establishing an optimized objective function which enables electricity consumption to be minimum;
5) considering the prediction uncertainty of environmental factors, establishing respective electricity utilization robust indexes for the lighting system, the air conditioning system and the elevator system respectively, and establishing a building comprehensive robust index;
wherein, the energy consumption model of the lighting system in the step 1) is as follows:
in the formula, Pl,tThe power for supplementing illumination for the artificial light source of the illumination system at the moment t; plPower of a single artificial light source; esetIs a target illuminance value; eeNatural illuminance; t is tworkWorking time is taken; phi is the rated total luminous flux of the light source in each lamp; u is an illumination utilization coefficient; a is the area of the working surface and is given by m2(ii) a K is a lamp maintenance coefficient;
the energy consumption model of the air conditioning system in the step 2) is as follows:
in the formula, xa,tFor air-conditioning systems, power consumption, Q, during t periodstThe time t is the cold load, and COP is the performance index of the air conditioning system;
the elevator system energy consumption model in the step 3) comprises an elevator system energy consumption model in an up peak mode, an elevator system energy consumption model in a down peak mode and an elevator system energy consumption model in a flat peak mode:
the energy consumption model of the elevator system in the up peak mode is as follows:
ELF(τ)＝365.32-1.115FP,τ-0.199Ncar,τ+0.143FP,τNcar,τ+0.087FP,τ 2-0.228Ncar,τ 2
the people flow rate condition in the mode meets the following conditions:
the energy consumption model of the elevator system in the down peak mode is as follows:
ELF(τ)＝-523.806+30.355FP,τ-14.123Ncar,τ+0.241FP,τNcar,τ-0.188FP,τ 2+0.774Ncar,τ 2
the people flow rate condition in the mode meets the following conditions:
the energy consumption model of the elevator system in the peak-balancing mode is as follows:
ELF(τ)＝276.885-5.409FP,τ-4.1Ncar,τ+0.067FP,τNcar,τ+0.058FP,τ 2+0.321Ncar,τ 2
the people flow rate condition in the mode meets the following conditions:
in the above formulae, FP,τIs the human flow; n is a radical ofcar,τThe number of running elevators is counted; pu,τRepresenting the total amount of upstream passenger flow; pd,τRepresenting the total amount of downstream passenger flow;
representing the amount of traffic entering and traveling up an elevator in a building lobby;
indicating the amount of traffic leaving the elevator at the top floor of the building;
to representThe amount of traffic leaving the elevator in the building lobby,
indicating the amount of passenger entering the elevator and traveling down the top floor of the building;
the electricity utilization robust index of the illumination system in the step 5) is as follows:
El.pre–El,sup＝RIl>＝RIl,se
in the formula, El.prePredicting an outdoor illuminance value for the certainty of the current moment; el,supThe outdoor illumination value assumed when power utilization optimization is carried out; RI (Ri)lIs the electricity usage robustness index of the lighting system; RI (Ri)l,seA preset value of the electricity utilization robust index of the lighting system;
the electricity utilization robust index of the air conditioning system is as follows:
Ta,sup–Ta,pre＝RIa>＝RIa,set
in the formula, Ta,prePredicting an outdoor temperature value for the certainty of the current moment; t isa,supThe outdoor temperature value is assumed when power utilization optimization is carried out; rIaThe electricity utilization robust index of the air conditioning system; RI (Ri)a,setThe power utilization robustness index of the air conditioning system is preset;
the electricity utilization robust index of the elevator system is as follows:
Fp,sup–Fp,pre＝RIp>＝RIp,set
in the formula, Fp,prePredicting the indoor pedestrian flow value for the certainty of the current time; fp,supThe indoor pedestrian flow value is assumed during power utilization optimization; RI (Ri)pIs an elevator system robustness index; RI (Ri)p,setThe electricity utilization robust index of the elevator system is preset;
the building comprehensive robust index is as follows:
vδ∈[0,1]，
in the formula, δ represents a power consumption system type; RI (Ri)δIs a robust index of the power utilization system; v. ofδIs the corresponding weight coefficient; RI (Ri)reqThe building comprehensive robustness index is a preset value.
2. The robust optimization method for electricity utilization according to prediction uncertainty of public building environmental factors of claim 1, further comprising step 6) of working out an electricity utilization load scheduling strategy capable of achieving a desired robustness level according to the robustness index.
3. The robust power utilization optimization method for predicting uncertainty of environmental factors of public buildings according to claim 1, wherein the cold load Q in the step 2)tThe calculation formula of (2) is as follows:
Qt＝KA1(Tout-Tin)+CaA2CsCiDjmaxCLQ1+qsnψCLQ2+1000PCLQ3+M(Rp×n+Rb×S)(hout-hin)
wherein K is the heat transfer coefficient of the fence structure and the unit W/(m)2·k)；A1Is the heat transfer area of the enclosure structure, unit m2；Tout、TinOutdoor and indoor temperatures in units; caIs the effective area coefficient; a. the2Is the window area in m2；CsThe shading coefficient is obtained; ciThe shading coefficient of the shading facility in the window; djmaxMaximum solar radiant heat in W/m2；CLQ1Is the glazing cold load coefficient; q. q.ssThe unit W is the heat dissipation capacity of the sensible heat of adult men with different room temperatures and labor properties; n is the total number of people in the room; psi is the cluster coefficient; cLQ2The coefficient of the sensible heat and the heat dissipation cold load of the human body is; cLQ3The cold load coefficient of the lighting fixture; plIs a lightThe power required by the lighting fixture is kW; m is fresh air volume, unit kg/s; h isoutIs the enthalpy value of outdoor air, and the unit kj/kg; h isinIs the enthalpy value of indoor air in kj/kg; rpThe minimum new air volume index of each person is assumed; rbThe minimum fresh air volume standard required by each square meter; s is the building area;
wherein the enthalpy value h of outdoor airoutAnd the enthalpy value h of indoor airinCan be calculated from the following equation:
h＝1.01T+d(2500+1.84T)
wherein h represents the air enthalpy; d represents the moisture content, and is usually taken as 0.014kg/(kg. dryair); t represents temperature.
4. The robust optimization method for electricity consumption based on prediction uncertainty of public building environmental factors as claimed in claim 1, wherein the average waiting time of the elevator in the up-peak mode is as follows:
TAW(τ)＝600.403-359.239ln(FP,τ)+86.113ln(Ncar,τ)-57.995ln(FP,τNcar,τ)+62.096ln(FP,τ)2+36.007ln(Ncar,τ)2；
the average waiting time of the elevator in the down peak mode is as follows:
TAW(τ)＝729.197-429.455ln(FP,τ)+99.519ln(Ncar,τ)-54.286ln(FP,τNcar,τ)+69.213ln(FP,τ)2+29.390ln(Ncar,τ)2；
the average waiting time of the elevator in the peak smoothing mode is as follows:
TAW(τ)＝622.589-352.932ln(FP,τ)+56.316ln(Ncar,τ)-43.369ln(FP,τNcar,τ)+56.403ln(FP,τ)2+30.878ln(Ncar,τ)2。
5. the robust power utilization optimization method for predicting uncertainty of environmental factors of public buildings according to claim 1, wherein the optimization objective function in the step 4) is as follows:
in the formula, ptThe price of electricity at the time t; x is the number oftThe total power consumption of the lighting system, the air conditioning system and the elevator system is t time period;
considering the public building illumination comfort range, the temperature comfort range and the average waiting time of the elevator as constraint conditions:
Emin<＝Eset<＝Emax
Tmin<＝Tset<＝Tmax
Xmin<＝Xset<＝Xmax
in the formula, Emin、EmaxAnd EsetRespectively is the minimum value, the maximum value and the target value of the illumination comfort range of the public building; t ismin、TmaxAnd TsetRespectively the minimum value, the maximum value and the target value of the temperature comfort range; xmin、XmaxThe minimum value, the maximum value and the target value of the average waiting time of the elevator are respectively.
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