CN112968445B - Multifunctional complementary demand response control method for residential building - Google Patents
Multifunctional complementary demand response control method for residential building Download PDFInfo
- Publication number
- CN112968445B CN112968445B CN202110226687.0A CN202110226687A CN112968445B CN 112968445 B CN112968445 B CN 112968445B CN 202110226687 A CN202110226687 A CN 202110226687A CN 112968445 B CN112968445 B CN 112968445B
- Authority
- CN
- China
- Prior art keywords
- day
- load
- temperature
- demand response
- time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
- H02J3/144—Demand-response operation of the power transmission or distribution network
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
- Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
- Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/20—End-user application control systems
- Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
Abstract
The invention relates to a multifunctional complementary demand response control method for residential buildings, which selects corresponding weights and reference loads according to different types of control targets of the residential buildings, such as maximum economy, maximum peak reduction and the like, and finally realizes the control of demand response through the optimization of a uniform objective function. Compared with the traditional demand response control strategy method, the method adopts a uniform optimization objective function, and can improve the universality of the demand response control method.
Description
Technical Field
The invention relates to the technical field of demand response, in particular to a multifunctional complementary demand response control method for a residential building.
Background
In recent years, with the continuous promotion of domestic industrialization and urbanization construction, the problem of imbalance between the supply side and the demand side of a power grid becomes more and more serious. According to statistics, the generating and loading machines in 2018 of China reach 19 hundred million kW and are located in the first world, but the average utilization hours of generating equipment in the whole country in 2017 are only 3670 hours, which means that more than half of power plants are not fully utilized all the year round. In addition, widespread use of renewable energy sources further exacerbates the problem of imbalance on the supply side and the demand side.
Demand Response (DR) alleviates the problem of power supply and demand imbalance during peak periods of electricity by curtailing or shifting peak loads on the customer side. For the supply side, the demand response can slow down newly added peak regulation equipment, further improves the utilization hours of the existing equipment, and reduces the operation and maintenance cost; meanwhile, the land resources originally used for newly-built power plants and power grids are reserved. For the user on the demand side, a corresponding subsidy or reward may generally be available by participating in the demand response.
Previous demand response control strategies require different objective functions for different demand response objectives, such as maximum load shedding or minimum cost, which is not conducive to the popularization and implementation of demand response. Therefore, it is necessary to develop a set of general residential building demand response control strategies, and to perform optimization control by using the same optimization objective function for different demand response objectives.
Disclosure of Invention
The invention aims to solve the technical problem of overcoming the defects in the prior art and provides a demand response control method suitable for residential buildings.
In order to solve the technical problem, the solution of the invention is as follows:
a multifunctional complementary demand response control method for residential buildings can meet the maximum economic benefit (charging according to time-of-use electricity price), the maximum reduction at peak time (subsidy according to the maximum reduction at peak time), the most stable reduction all day, or the maximum reduction all day (namely the maximum energy-saving control). If the actual net electrical load in one day is X ═ X (X) 1 ，x 2 ，…，x n ) Then, thenThe optimal optimization objective function in the whole time of a day is as follows:
wherein x is i Is the actual net electrical load in a day, X ═ X 1 ，x 2 ，…，x n ) The value corresponding to the middle time i; y is i For reference, the electrical load Y ═ Y 1 ，y 2 ，…，y n ) The value corresponding to the middle time i; dist (x) i -y i ) Denotes x i And y i A distance function between; w is a i Is the weight corresponding to time i.
Specifically, the values of the terms in the objective function may be determined and solved by:
step 1: dividing the actual net electric load X into basic loads X according to the usage and the use property ba Weather sensitive load X we Transferable type load X sh And a convertible load X ch The method comprises the following steps of totally four types of loads, and determining values and constraint conditions through a calculation formula of each type of load:
X＝X ba +X we +X sh +X ch
step 2: according to different demand response control purposes, selecting corresponding one-day reference electric load Y (Y) 1 ，y 2 ，…，y n )；
And step 3: selecting corresponding distance function dist (x) according to different demand response control purposes i -y i ) And a weight w i ；
And 4, step 4: the parameters (start-stop state, temperature setting and the like) related to the equipment obtained by optimization are output as control signals.
The base load X in the step 1 ba ＝(x ba，1 ，x ba，2 ，...，x ba，n ) In order to maintain the electrical load necessary for residential building use, which is not flexible in the time dimension, such as room LED lighting, desktop outlet power, etc., the value of a day is available according to the daily usage habits of the user.
The weather sensitive load X in the step 1 we ＝(x we，1 ，x we，2 ，...，x we，n ) The air conditioner is mainly used for heating or refrigerating electric loads of residential buildings, and the air conditioner is influenced by outdoor weather and indoor set temperature.
X we ＝f 1 (T dry ，T dew ，/，P，T set ，S hvac )
Wherein f is 1 Is X we Load prediction model of (1), T dry ＝(t dry，1 ，t dry，2 ，…，t dry，n ) The temperature of the dry bulb outside the whole day room; t is dew ＝(t dew，1 ，t dew，2 ，…，t dew，n ) Is the whole day outdoor wet bulb temperature; j ═ J (J) 1 ，j 2 ，…，j n ) Direct solar radiation outside the whole sky; p ═ P (P) 1 ，p 2 ，…，p n ) Is the atmospheric pressure outside the whole day room; t is set ＝(t set，1 ，t set，2 ，…，t set，n ) Is the room temperature of the whole daySetting a value; s set ＝(s set，1 ，s set，2 ，…，s set，n ) The air conditioning system is in an open state all day long.
The load prediction model f 1 Modeling calculation can be performed on an air conditioning system such as a residential building through a physical modeling method such as EnergyPlus, TRNSYS, Modelica and the like, or prediction calculation can be performed on the basis of historical data through a data-driven model such as an Artificial Neural Network (ANN), a Support Vector Machine (SVM), an extreme learning machine (XGboost) and the like.
The temperature T of the whole day outdoor dry bulb dry All day outdoor wet bulb temperature T dew The total day outdoor direct solar radiation J and the total day outdoor atmospheric pressure P are meteorological parameter adjustments required for predicting the load of the air conditioning system through physical model calculation or a data driving model, and can be obtained through weather forecast data of a local meteorological station.
The set value T of the all-day room temperature set The following four constraints should be satisfied:
t set，min ≤t set，i ≤t set，max
the first constraint condition indicates that the indoor temperature setting value should be within the minimum setting value and the maximum setting value, and the setting range may be slightly larger than the human body comfort temperature range provided by ASHRAE due to the requirement of implementing the demand response. The second constraint indicates that the indoor temperature setpoint should be the minimum temperature step t step Integer multiple of this, the setting of this should match the control accuracy of the air conditioning system. Third andthe fourth constraint represents T set ＝(t set，1 ，t set，2 ，…，t set，n ) Of any arbitrary consecutive a indoor temperature settings, only up to 1 temperature set point adjustment is allowed, which is to prevent the set temperature from being adjusted too frequently.
All-day air conditioning system opening state S set The following two constraints should be satisfied:
s hvac，i ∈{0，1}
wherein, the first constraint condition indicates that the on state of the air conditioning system can be off (0) or off (1). The second constraint denotes S hvac ＝(s hvac，1 ，s hvac，2 ，…，s hvac，n ) In the open states of any continuous beta air conditioning systems, only the adjustment of the open states of the air conditioning systems for at most 1 time is allowed, and the adjustment is set to prevent the air conditioning systems from being started and stopped too frequently.
Transferable load X in said step 1 sh ＝(x sh，1 ，x sh，2 ，...，x sh，n ) The electric loads with elasticity in the time dimension during daily use of residential buildings, such as dryers, dishwashers and the like. X sh Can be seen as a superposition of multiple household electrical appliance transferable loads:
X sh ＝X sh1 +X sh2 +…
X sh1 ＝(x sh1，1 ，x sh1，2 ，...，x sh1，n )
X sh2 ＝(x sh2，1 ，x sh2，2 ，...，x sh2，n )
……
x sh，1 ＝x sh1，1 +x sh2，1 +...
x sh，2 ＝x sh1，2 +x sh2，2 +…
……
with one of the household appliances X sh1 For example, the operating constant power is p sh1 The starting working time is t start The required working time of a day is t span Hour, latest working time t ddl Then X sh1 ＝(x sh1，1 ，x sh1，2 ，...，x sh1，n ) Can be calculated by the following formula:
the constraints that need to be satisfied are as follows:
t start +t span ≤t ddl
convertible load X in said step 1 ch ＝(x ch，1 ，x ch，2 ，...，x ch，n ) The device is a device with a charging and discharging function in a residential building, such as an electric automobile, a storage battery and the like. X ch Can be seen as a superposition of multiple household electrical appliance transferable loads:
X ch ＝X ch1 +X ch2 +…
X ch1 ＝(x ch1，1 ，x ch1，2 ，...，x ch1，n )
X ch2 ＝(x ch2，1 ，x ch2，2 ，...，x ch2，n )
……
x ch，1 ＝x ch1，1 +x ch2，1 +...
x ch，2 ＝x ch1，2 +x ch2，2 +…
with one of the charging and discharging devices X ch1 For example, the full charge capacity is Q ch1 Input power p during charging ch1，ch The charging efficiency is eta ch1，ch With effective power output p at discharge ch1，ex Discharge efficiency of η ch1，ex Increasing penalty efficiency η due to battery discharge causing a decay in battery life ch1，pu At the beginning of workIs m between t start Initial electric quantity is soc start The lowest battery capacity set to soc after the end of the day end Then X ch1 ＝(x ch1，1 ，x ch1，2 ，...，x ch1，n ) Can be calculated by the following formula:
s bat，i ∈{-1，0，1}
wherein s is bat，i The charge and discharge states at time i are represented by-1, 0, and 1, respectively, discharge, power-off, and charge. x' ch1，i Is a strategy for actual charging and discharging of the battery. s bat，i The following three constraints should be satisfied:
the first constraint condition indicates that the charging and discharging equipment is required to ensure that the battery capacity is greater than or equal to Soc at each moment min The lowest battery capacity is considered to be defined.
The second constraint condition indicates that the capacity of the charging and discharging device must not be less than Soc after the end of a day end 。
The third constraint denotes S bat ＝(s bat，1 ，s bat，2 ，…，s bat，n ) In any continuous gamma charge-discharge stateIn the above, only the charge/discharge state adjustment is allowed at most 1 time, and this is provided to prevent the charge/discharge device from being switched over too frequently.
The reference electrical load Y in step 2 is (Y) 1 ，y 2 ，…，y n ) This can be chosen according to the following different needs response control objectives.
1) If the maximum economic benefit (according to the time-of-use electricity price), the maximum reduction amount at the peak moment (which can be considered to be subsidized according to the maximum reduction amount at the peak moment) or the maximum smooth reduction all day needs to be obtained, the x axis can be used as the reference electricity load, namely, Y is equal to (Y) 1 ，y 2 ，…，y n )＝(0，0，…，0)。
2) If the total daily reduction is required to be maximum (namely, the maximum energy saving amount control), the reference power consumption load can be selected as a baseline load, namely, the predicted total daily power consumption load when no demand response is carried out
Distance function dist (x) of said step 3 i -y i ) The selection can be made according to different requirements and response control purposes:
1) if the maximum economic benefit (according to the time-of-use electricity price) needs to be obtained, the maximum reduction at the peak moment (according to the maximum reduction subsidy at the peak moment) or the maximum reduction all day (namely the maximum energy-saving control) needs to be obtained, the distance function can be expressed as an Euclidean distance:
2) if the smoothest reduction is to be achieved throughout the day, the distance function can be expressed as the squared Euclidean distance:
dist(x i -y i )＝(x i -y i ) 2
the weight w in step 3 i The selection can be made according to different requirements and response control purposes:
1) if the maximum economic benefit (charged by time of use price) is to be obtained, the weight can be expressed as:
w i ＝q i
wherein q is i The electricity price corresponding to the time i.
2) The peak time maximum reduction (or subsidized by peak time maximum reduction) weight may be expressed as:
where t dr，start ，t dr，start +1，...，t dr，end The peak time (demand response) interval; t is t dr，start Is the demand response start time; t is t dr，end Is the end time of the demand response.
1) The total daily reduction is maximum (i.e. maximum energy saving control), and the distance function can be expressed as:
w i ＝1
2) if the smoothest reduction is to be achieved throughout the day, the distance function can be expressed as:
w i ＝1
drawings
FIG. 1 is a flow chart of a demand response control method for multi-energy complementation of residential buildings.
FIG. 2 illustrates load shedding in response to a residential building case demand.
Detailed Description
FIG. 1 is a flow chart of a demand response control method for multi-energy complementation of residential buildings according to the present invention.
The invention is further described in detail below with reference to the drawings and specific embodiments. The method specifically comprises the following steps:
step 1: and according to the demand response control purpose, sequentially determining the reference electric load, the distance function and the weight of the target function. In this embodiment, the control purpose selected by the residential building is the maximum reduction amount of the demand response time, demand responseThe response time is 19:00-21:00, and the reference electric load of the objective function is selected from Y ═ Y 1 ，y 2 ，…，y n ) (0, 0, …, 0), the distance function is chosen to be the euclidean distance
The weight can be expressed as:
step 2: calculating X as X according to the information of the residential building ba +X we +X sh +X ch The values of each item are shown in Table 1. Base load X ba Mainly comprises a refrigerator and a building lighting load, wherein the refrigerator is started all day long, the power is 100W, the lighting power is 660W, and the indoor use time of personnel is 7:00-22: 00. Weather sensitive load X we The hourly load needs to be calculated through EnergyPlus, and the ground source heat pump type air conditioner is adopted for the building. In the simulation, the upper limit t of the temperature set value set，max And a lower limit t set，min Set at 24 ℃ and 28 ℃ respectively. And the parameter alpha for limiting frequent temperature adjustment and the parameter beta for limiting frequent start and stop are 3. Transferable type load X sh Comprising a washing machine, a dish washer and a dryer, respectively using X sh1 ，X sh2 ，X sh3 Is shown, its rated power p sh1 ，p sh2 ，p sh3 1.34kW, 1.25kW and 3.65kW respectively, and the working time t span Are respectively 2h, 2h and 1h, t start 19:00, 20:00 and 21:00 respectively, and the latest working time t ddl 4:00, 5:00 and 6:00, respectively. Convertible load X ch The equipment is an electric automobile with a capacity Q ch1 30kWh, charging input power of 7kW, and charging efficiency eta ch1，ch 85%, the effective power output by battery discharge is 5kW, the efficiency is 80%, and the penalty efficiency eta is ch1，pu 70% of the initial charge, soc start Is 10 percent of the total weight of the composition,minimum battery capacity soc min 20%, the lowest capacity set for the battery capacity after the end of the day is soc end Is 100%.
And 3, step 3: the approximate solution is solved for the objective function through a genetic algorithm, the load reduction situation after the demand response is obtained is shown in figure 2, and the load is transferred and adjusted and controlled in a ratio of 19:00-21: the 12.87kW of the base line load in the 00 period is reduced to 2.60kW, and the reduction amplitude reaches 79.83%.
TABLE 1 residential architecture various kinds of electricity consumption information
Claims (1)
1. A multi-energy complementary demand response control method for residential buildings is characterized in that the method can meet the maximum economic benefit (charging according to time-of-use electricity price), the maximum reduction amount at peak time (subsidy according to the maximum reduction amount at peak time), the most stable reduction all day, or the maximum reduction amount all day; the actual net electrical load in a day is X ═ X 1 ,x 2 ,…,x n )，All the time of day The optimized objective function is:
wherein x is i Is the actual net electrical load in a day, X ═ X 1 ,x 2 ,…,x n ) The value corresponding to the middle time i; y is i For reference, the electrical load Y ═ Y 1 ,y 2 ,…,y n ) The value corresponding to the middle time i; dist (x) i -y i ) Denotes x i And y i A distance function between; w is a i The weight corresponding to the moment i; characterised in that, in particular, the determination of the objective function is carried out byThe values of the terms are solved:
step 1: dividing the actual net electric load X into basic loads X according to the usage and the use property ba Weather sensitive load X we Transferable type load X sh And a convertible load X ch The method comprises the following steps of totally four types of loads, and determining values and constraint conditions through a calculation formula of each type of load:
X＝X ba +X we +X sh +X ch
step 2: according to different demand response control purposes, selecting corresponding one-day reference electric load Y (Y ═ Y) 1 ,y 2 ,…,y n )；
And step 3: selecting corresponding distance function dist (x) according to different demand response control purposes i -y i ) And a weight w i ；
And 4, step 4: searching through a group optimization algorithm to obtain optimized equipment-related parameters (start-stop state and temperature setting) which are output as control signals;
the base load X in the step 1 ba ＝(x ba，1 ，x ba，2 ，...，x ba，n ) In order to maintain the electricity load necessary for the use of the residential building, the elasticity in the time dimension is not provided, such as room LED illumination, electricity consumption of a desktop socket and the like, and the value in one day can be obtained according to the daily use habit of a user;
the weather-sensitive load X in the step 1 we ＝(x we，1 ，x we，2 ，...，x we，n ) The electric load is mainly used for heating or refrigerating the residential building, and the electric load is influenced by outdoor weather and indoor set temperature;
X we ＝f 1 (T dry ，T dew ，J，P，T set ，S hvac )
wherein f is 1 Is X we Load prediction model of (1), T dry ＝(t dry，1， t dry，2 ，…，t dry，n ) The temperature of the dry bulb outside the whole day room; t is dew ＝(t dew，1 ，t dew，2 ，…，t dew，n ) Is made ofThe wet bulb temperature outside the sky; j ═ J (J) 1 ，j 2 ，…，j n ) Direct solar radiation outside the whole sky room; p ═ P (P) 1 ，p 2 ，…，p n ) Is the atmospheric pressure outside the whole day room; t is set ＝(t set ，1，t set，2 ，…，t set，n ) The room temperature set value throughout the day; s set ＝(s set，1， s set，2 ，…，s set，n ) The air conditioning system is in a starting state all day long;
the load prediction model f 1 Modeling calculation can be performed on air conditioning systems such as residential buildings through physical modeling methods such as EnergyPlus, TRNSYS, Modelica and the like, or prediction calculation can be performed on historical data through data driving models such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), extreme learning machines (XGboost) and the like;
the temperature T of the whole day outdoor dry bulb dry All day outdoor wet bulb temperature T dew The all-weather outdoor direct solar radiation J and the all-weather outdoor atmospheric pressure P are meteorological parameter adjustment required by predicting the load of the air conditioning system through physical model calculation or a data driving model and can be obtained through weather forecast data of a local meteorological station;
the set value T of the all-day room temperature set The following four constraints should be satisfied:
t set，min ≤t set，i ≤t set，max
wherein the first constraint representsThe indoor temperature set value should be in the interval of the minimum set value and the maximum set value, and the set range can be slightly larger than the human body comfortable temperature interval provided by ASHRAE due to the requirement of implementing demand response; the second constraint indicates that the indoor temperature setpoint should be the minimum temperature step t step Integral multiple of the air conditioning system, the setting of which should match the control accuracy of the air conditioning system; the third and fourth constraints represent T set ＝(t set,1 ,t set,2 ,…,t set,n ) In any continuous alpha indoor temperature settings, only at most 1 time of temperature set point adjustment is allowed, and the setting is to prevent the set temperature from being adjusted too frequently;
the all-day air conditioning system is in an open state S set The following two constraints should be satisfied:
s hvac,i ∈{0,1}
wherein, the first constraint condition indicates that the on state of the air conditioning system can be off (0) or (1) off; the second constraint denotes S hvac ＝(s hvac,1 ,s hvac,2 ,…,s hvac,n ) In the opening states of any continuous beta air-conditioning systems, only the opening state adjustment of the air-conditioning system is allowed for at most 1 time, and the setting is to prevent the air-conditioning system from being started and stopped too frequently;
transferable load X in said step 1 sh ＝(x sh,1 ,x sh,2 ,...,x sh,n ) The electric loads with elasticity in the time dimension during daily use of residential buildings, such as dryers, dishwashers and the like; x sh Can be seen as a superposition of multiple household appliance transferable loads:
X sh ＝X sh1 +X sh2 +…
X sh1 ＝(x sh1,1 ,x sh1,2 ,...,x sh1,n )
X sh2 ＝(x sh2,1 ,x sh2,2 ,...,x sh2,n )
……
x sh,1 ＝x sh1,1 +x sh2,1 +...
x sh,2 ＝x sh1,2 +x sh2,2 +…
……
with one of the household appliances X sh1 For example, it operates at a constant power of p sh1 The starting working time is t start The required working time of a day is t span Hour, latest working time t ddl Then X sh1 ＝(x sh1,1 ,x sh1,2 ,...,x sh1,n ) Can be calculated by the following formula:
the constraints that need to be satisfied are as follows:
t start +t span ≤t ddl
convertible load X in said step 1 ch ＝(x ch,1 ,x ch,2 ,...,x ch,n ) The device is a device with the functions of charging and discharging in a residential building, such as an electric automobile, a storage battery and the like; x ch Can be seen as a superposition of multiple household appliance transferable loads:
X ch ＝X ch1 +X ch2 +…
X ch1 ＝(x ch1,1 ,x ch1,2 ,...,x ch1,n )
X ch2 ＝(x ch2,1 ,x ch2,2 ,...,x ch2,n )
……
x ch,1 ＝x ch1,1 +x ch2,1 +...
x ch,2 ＝x ch1,2 +x ch2,2 +…
……
with one of the charging and discharging devices X ch1 For example, the full charge capacity is Q ch1 Input power of p during charging ch1,ch Air chargerElectrical efficiency of η ch1,ch With effective power output p at discharge ch1,ex Discharge efficiency of η ch1,ex Increasing penalty efficiency η due to battery discharge causing a decay in battery life ch1,pu The starting working time is t start Initial electric quantity is soc start The lowest battery capacity set to soc after the end of the day end Then X ch1 ＝(x ch1,1 ,x ch1,2 ,...,x ch1,n ) Can be calculated by the following formula:
s bat,i ∈{-1,0,1}
wherein s is bat,i The charge-discharge state at the moment i is represented by-1, 0 and 1, which respectively represent discharge, power failure and charge; x' ch1,i The method is a strategy for actual charging and discharging of the battery; s is bat,i The following three constraints should be satisfied:
the first constraint condition indicates that the charging and discharging equipment is required to ensure that the battery capacity is greater than or equal to soc at each moment min To be considered to define the lowest battery capacity;
the second constraint condition indicates that the capacity of the charging and discharging device must not be less than soc after the end of a day end ；
The third constraint denotes S bat ＝(S bat,1 ,S bat,2 ,…,s bat,n ) In any gamma charge-discharge states, only up to 1 charge-discharge state adjustment is allowed, and the charge-discharge state adjustment is set to prevent the charge-discharge equipment from being switched over frequently.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110226687.0A CN112968445B (en) | 2021-03-01 | 2021-03-01 | Multifunctional complementary demand response control method for residential building |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110226687.0A CN112968445B (en) | 2021-03-01 | 2021-03-01 | Multifunctional complementary demand response control method for residential building |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112968445A CN112968445A (en) | 2021-06-15 |
| CN112968445B true CN112968445B (en) | 2022-09-13 |
Family
ID=76276107
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110226687.0A Active CN112968445B (en) | 2021-03-01 | 2021-03-01 | Multifunctional complementary demand response control method for residential building |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112968445B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106779365A (en) * | 2016-12-02 | 2017-05-31 | 合肥工业大学 | A kind of family's Optimization of Load Dispatching method based on demand response |
| CN108155639A (en) * | 2018-03-13 | 2018-06-12 | 华北电力大学 | The discrimination method of load is adjusted in a kind of wide area |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111884216B (en) * | 2020-07-30 | 2022-01-14 | 上海理工大学 | Multi-target control method based on building power demand response |
-
2021
- 2021-03-01 CN CN202110226687.0A patent/CN112968445B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106779365A (en) * | 2016-12-02 | 2017-05-31 | 合肥工业大学 | A kind of family's Optimization of Load Dispatching method based on demand response |
| CN108155639A (en) * | 2018-03-13 | 2018-06-12 | 华北电力大学 | The discrimination method of load is adjusted in a kind of wide area |
Non-Patent Citations (4)
| Title |
|---|
| "自适应多场景的单用户柔性负控效果评价算法";郑文明 等;《电力需求侧管理》;20220531;第21卷(第3期);第32-36页 * |
| 中国公共建筑的建筑典型模型建立;陈智博等;《建筑节能》;20200225(第02期);全文 * |
| 单体建筑需求响应研究;罗为等;《建筑节能》;20191125(第11期);第31-35,64页 * |
| 面向电网调峰辅助服务的规模化空调负荷优化调度研究;李作锋等;《电器与能效管理技术》;20180130(第02期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112968445A (en) | 2021-06-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110866641B (en) | Two-stage optimization scheduling method and system for multi-energy complementary system considering source storage load coordination | |
| CN106527135B (en) | Optimization control method and device for building energy management system | |
| CN104850013A (en) | Intelligent electricity utilization method of household appliances | |
| CN102252454B (en) | Combined type renewable building energy source supplying system | |
| CN109340904B (en) | Electric heating collaborative optimization operation method | |
| CN109787262A (en) | A kind of resident's building system active response micro-capacitance sensor Optimization Scheduling | |
| GR1010085B (en) | Method for improving the energy management of a nearly zero energy building | |
| CN115857348A (en) | Distributed energy system capacity optimization method considering comfortable energy supply of two-combined heat pump | |
| CN103762611A (en) | Method for smoothing microgrid call wire power | |
| CN115241927B (en) | Operation control method, device, equipment and medium for household intelligent energy system | |
| CN110752629A (en) | Energy optimization management method for AC/DC hybrid household micro-grid | |
| CN112128945A (en) | Method for providing active power compensation based on battery equivalent model | |
| CN102103362B (en) | Micro-grid system and micro-grid system-based fuzzy control method | |
| CN103512151A (en) | Method and device for having control over air conditioner installed in area | |
| CN111915107A (en) | Load clustering control method based on dynamic clustering | |
| CN112883629A (en) | Building power utilization load optimal scheduling method based on improved whale algorithm | |
| CN112968445B (en) | Multifunctional complementary demand response control method for residential building | |
| CN113131519A (en) | Family energy management optimization method based on mixed integer linear programming | |
| CN110661253B (en) | Multi-type electric power elastic load combined adjusting method in building group | |
| CN112880133A (en) | Flexible energy utilization control method for building air conditioning system | |
| CN107563547B (en) | Comprehensive energy management and control method for optimizing depth of energy consumption of user side | |
| CN110729728A (en) | Demand response method considering real-time comfort of user and load rate of power grid | |
| CN113420413B (en) | Flexible load adjustability quantification method and system based on load plasticity | |
| CN105549389A (en) | Household energy management algorithm based on building thermodynamic model | |
| CN202101470U (en) | Composite type renewable building energy supplying system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |