CN112465212A - Building cluster demand side energy management method and system - Google Patents
Building cluster demand side energy management method and system Download PDFInfo
- Publication number
- CN112465212A CN112465212A CN202011330457.0A CN202011330457A CN112465212A CN 112465212 A CN112465212 A CN 112465212A CN 202011330457 A CN202011330457 A CN 202011330457A CN 112465212 A CN112465212 A CN 112465212A
- Authority
- CN
- China
- Prior art keywords
- building
- model
- energy management
- energy
- load
- 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.)
- Pending
Links
- 238000007726 management method Methods 0.000 title claims abstract description 132
- 238000005265 energy consumption Methods 0.000 claims abstract description 104
- 238000000034 method Methods 0.000 claims abstract description 55
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 44
- 230000017525 heat dissipation Effects 0.000 claims abstract description 11
- 230000020169 heat generation Effects 0.000 claims abstract description 9
- 238000010438 heat treatment Methods 0.000 claims description 88
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 74
- 230000006870 function Effects 0.000 claims description 66
- 238000009423 ventilation Methods 0.000 claims description 66
- 230000009471 action Effects 0.000 claims description 41
- 230000008569 process Effects 0.000 claims description 36
- 238000004364 calculation method Methods 0.000 claims description 18
- 238000010276 construction Methods 0.000 claims description 9
- 230000005611 electricity Effects 0.000 claims description 9
- 238000005286 illumination Methods 0.000 claims description 5
- 230000009545 invasion Effects 0.000 claims description 5
- 238000005057 refrigeration Methods 0.000 claims description 4
- 238000004088 simulation Methods 0.000 claims description 4
- 230000008595 infiltration Effects 0.000 claims 1
- 238000001764 infiltration Methods 0.000 claims 1
- 238000011161 development Methods 0.000 abstract description 6
- 230000002787 reinforcement Effects 0.000 abstract description 5
- 238000006467 substitution reaction Methods 0.000 abstract description 5
- 238000004134 energy conservation Methods 0.000 abstract description 4
- 230000009467 reduction Effects 0.000 abstract description 4
- 238000004590 computer program Methods 0.000 description 6
- 230000035515 penetration Effects 0.000 description 5
- 230000005855 radiation Effects 0.000 description 5
- 230000002829 reductive effect Effects 0.000 description 3
- 238000007664 blowing Methods 0.000 description 2
- 230000000670 limiting effect Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000004378 air conditioning Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 239000003795 chemical substances by application Substances 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000005338 heat storage Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 230000005619 thermoelectricity Effects 0.000 description 1
- 239000012855 volatile organic compound Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- Software Systems (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Evolutionary Computation (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Data Mining & Analysis (AREA)
- Primary Health Care (AREA)
- Medical Informatics (AREA)
- General Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Air Conditioning Control Device (AREA)
Abstract
The application provides a building cluster demand side energy management method and system, which belong to the field of energy management and comprise the following steps: constructing a heat balance model in a building room based on a heat dissipation heat source and a heat generation heat source of the building; constructing a load model based on the thermal balance model and the flexible load types in the building room; constructing an energy management model based on the thermal balance model and the load model; and based on the thermal balance model, the load model and the energy management model, performing energy management on the demand side of the building cluster by adopting a Q learning algorithm. The method and the system for managing the energy of the demand side of the building cluster realize the application of a reinforcement learning algorithm in the energy management of the demand side of the building cluster, can provide effective guidance for the dispatching of power industry departments in China and the energy management work of the demand side of the building cluster, reduce the energy consumption cost of users, and promote the realization of development targets of energy conservation, emission reduction, electric energy substitution and the like.
Description
Technical Field
The application belongs to the field of energy management, and particularly relates to a method and a system for managing energy of a demand side of a building cluster.
Background
With the development of distributed energy technology, the high-proportion penetration of renewable energy and the continuous promotion of the market reformation of Chinese electric power, the influence of terminal users on the demand side of a building cluster on the operation state of an electric power system is larger and larger, and the rapid development of demand response services towards diversification and normalization is promoted.
However, the variety of the end users is various, the energy consumption characteristics are complex, the measured data are numerous, the energy interaction information amount is large, and a new challenge is provided for the energy management of the building cluster demand side of the end users. In the prior art, the energy management and scheduling mode mostly adopts a traditional numerical iteration solving method, the process is easily influenced by personnel experience, a plurality of control variables needing to be considered are provided, and the problem of dimension disaster is limited to a certain extent.
Disclosure of Invention
The application aims to provide a building cluster demand side energy management method and system, application of a reinforcement learning algorithm in building cluster demand side energy management is achieved, and effective guidance can be provided for scheduling of power industry departments and building cluster demand side energy management work.
In order to achieve the above object, a first aspect of the present application provides a method for demand-side energy management based on building clusters, including
Constructing a heat balance model in a building room based on a heat dissipation heat source and a heat generation heat source of the building;
constructing a load model based on the thermal balance model and the flexible load types in the building room;
constructing an energy management model based on the thermal balance model and the load model;
and based on the heat balance model, the load model and the energy management model, performing energy management on the demand side of the building cluster by adopting a Q learning algorithm.
Based on the first aspect of the present application, in a first possible implementation manner, the heat dissipation heat source includes: the inner surface of the wall of the building exchanges heat with air in a convection way Q1The heat consumption of the window body penetration of the building
And heat consumption of cold air invasion/ventilation Q of the above building3；
The heat generating heat source includes: the radiation heat of the outside window illumination of the building
Indoor heat source and indoor air heat exchange power Q of the building5And the heat exchange power Q between the heating equipment in the building and the indoor air6；
The above mentioned heat-clearing tabletThe equation expression of the balance model is specifically as follows:
wherein Q is4The sensible heat of the air of the building is increased in unit time.
Based on the first aspect of the present application or the first possible implementation manner of the first aspect, in a second possible implementation manner, the flexible load types include: heating and ventilation equipment and water heater equipment;
the constructing of the load model based on the thermal balance model and the flexible load type in the building room includes:
constructing a heating and ventilating energy consumption function based on the heat balance model and the energy consumption characteristics of the heating and ventilating equipment so as to calculate the electric energy consumed by the heating and ventilating equipment in unit time;
constructing a water heater energy consumption function based on the energy consumption characteristics of the water heater equipment to calculate the electric energy consumed by the water heater equipment in unit time;
and constructing a load model based on the heating and ventilation energy consumption function and the water heater energy consumption function so as to calculate the total electric energy consumed in the unit time of the building.
Based on the second possible implementation manner of the first aspect of the present application, in a third possible implementation manner, the constructing the heating and ventilation energy consumption function based on the heat balance model and the energy consumption characteristics of the heating and ventilation device includes:
determining the air flow and the air supply temperature of the heating and ventilation equipment according to the heat balance model;
determining a first energy consumption function according to the air flow so as to calculate the electric energy consumed in unit time when the heating and ventilation equipment carries out fresh air circulation;
determining a second energy consumption function according to the air supply temperature so as to calculate the electric energy consumed in unit time when the heating and ventilation equipment performs refrigeration/heating;
and constructing the heating and ventilation energy consumption function based on the first energy consumption function and the second energy consumption function so as to calculate the total electric energy consumed by the heating and ventilation equipment in unit time.
Based on the second possible implementation manner of the first aspect of the present application, in a fourth possible implementation manner, the constructing a water heater energy consumption function based on the energy consumption characteristics of the water heater equipment includes:
and determining the energy consumption characteristics of the water heater equipment according to the inlet water temperature and the preset service temperature of the building so as to construct the energy consumption function of the water heater.
Based on the second possible implementation manner of the first aspect of the present application, in a fifth possible implementation manner, the building further includes other inflexible loads;
the building of the load model based on the thermal balance model and the flexible load type in the building room further includes: constructing other load energy consumption functions based on the other inflexible loads to calculate the electric energy consumed by the other inflexible loads in unit time;
the load model constructed based on the heating and ventilation energy consumption model and the water heater energy consumption model is as follows: and constructing the load model based on the heating and ventilation energy consumption model, the water heater energy consumption model and the other load energy consumption functions.
Based on any possible implementation manner of the third possible implementation manner to the fifth possible implementation manner of the first aspect of the present application, in a sixth possible implementation manner, the building an energy management model based on the thermal balance model and the load model includes:
acquiring the time-of-use electricity price of the building in the unit time;
determining a scheduling period of energy management;
and constructing the energy management model based on the time-of-use electricity price, the dispatching cycle and the load model.
Based on the sixth possible implementation manner of the first aspect of the present application, in a seventh possible implementation manner, the building an energy management model based on the thermal balance model and the load model further includes: setting constraint conditions of the energy management model;
the constraint conditions include: indoor heat balance constraint, equipment adjustment constraint and flexible load total amount constant constraint;
the indoor heat balance constraint is that the heat balance model constraint is required to be met in the energy management process, the equipment adjustment constraint is that the operation condition constraint of the heating and ventilation equipment and the water heater equipment is required to be met in the energy management process, and the flexible load total quantity constant constraint is that the requirement constant constraint of the load electric quantity is required to be met in the energy management process.
Based on any one possible implementation manner of the third possible implementation manner to the fifth possible implementation manner of the first aspect of the present application, in an eighth possible implementation manner, the performing energy management on the demand side of the building cluster by using the Q learning algorithm includes:
discretizing continuous state variables and action variables to enable the Q learning algorithm to learn, wherein the state variables are actual load demand conditions of the building, and the action variables are action strategies in an energy management process;
generating a simulated sample by a Markov decision process;
selecting a current state variable based on the simulation sample and the energy management model, and determining a current action variable according to the current state variable and the action variable selection probability;
corresponding a reward value calculation function in the Q learning algorithm to the energy management model to determine a reward value obtained through a current action variable under a current state variable;
and performing energy management on the building based on the state variable, the action variable and the reward value.
The second aspect of the present application provides a demand side energy management system based on building cluster, including:
the first model building module is used for building an indoor heat balance model of the building based on a heat dissipation heat source and a heat generation heat source of the building;
the second model building module is used for building a load model based on the heat balance model and the flexible load types in the building room;
a third model building module for building an energy management model based on the thermal balance model and the load model;
and the management module is used for performing energy management on the demand side of the building cluster by adopting a Q learning algorithm based on the heat balance model, the load model and the energy management model.
In view of the above, according to the method and the system for managing the energy on the demand side of the building cluster, a heat balance model in a building room is constructed based on a heat dissipation heat source and a heat generation heat source of a building; constructing a load model based on the thermal balance model and the flexible load types in the building room; constructing an energy management model based on the thermal balance model and the load model; and finally, based on the thermal balance model, the load model and the energy management model, performing energy management on the demand side of the building cluster by adopting a Q learning algorithm. By constructing the energy management model and combining with the Q learning algorithm, the application of the reinforcement learning algorithm in the energy management of the demand side of the building cluster is realized, effective guidance can be provided for the scheduling of the power industry department in China and the energy management work of the demand side of the building cluster, the energy consumption cost of users is reduced, and the realization of development targets such as energy conservation, emission reduction, electric energy substitution and the like is promoted.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for managing energy on demand side of a building cluster according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of a process for constructing a load model according to an embodiment of the present disclosure;
fig. 3 is a schematic flow chart illustrating energy management on a demand side of a building cluster by using a Q learning algorithm according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a building cluster demand side energy management system according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The technical solutions in the embodiments of the present application are clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, but the present application may be practiced in other ways than those described herein, and it will be apparent to those of ordinary skill in the art that the present application is not limited by the specific embodiments disclosed below.
Example one
The embodiment of the application provides a method for managing energy of demand sides of a building cluster, as shown in fig. 1, the method for managing energy of demand sides of the building cluster comprises the following steps:
step 11: constructing a heat balance model in a building room based on a heat dissipation heat source and a heat generation heat source of the building;
in one application scenario, the building may be an intelligent building, and the main flexibility controllable load (i.e. flexible load) equipped in the building room is combined, so that at least one intelligent building with different functions forms a building cluster with a demand side capable of performing energy management. When a heat balance model is constructed based on the building cluster, the interior of an intelligent building is simulated into a single isothermal air conditioning area, the relation between a heat generating heat source and a heat dissipating heat source in the intelligent building is comprehensively considered, and a thermal resistance-thermal capacity network model is adopted to model the single refrigerating/heating area in the building, wherein the thermal resistance and the thermal capacity have the capabilities of heat transmission and heat storage respectively, the building comprises wall nodes and indoor air nodes, and the nodes are connected with each other through the thermal resistance and are grounded through the thermal capacity.
Optionally, the heat dissipation heat source includes: the inner surface of the wall of the building exchanges heat with air in a convection way Q1The heat consumption of the window body penetration of the building
And heat consumption of cold air invasion/ventilation Q of the above building3(ii) a The heat generating heat source includes: the radiation heat of the outside window illumination of the building
Indoor heat source and indoor air heat exchange power Q of the building5And the heat exchange power Q between the heating equipment in the building and the indoor air6(ii) a The equation expression of the thermal equilibrium model is specifically:
wherein Q is4Is the above in a unit timeThe air of the building is added with heat.
Further, in the heat balance model, the inner surface of the wall of the building exchanges heat with air in a convection mode Q1The calculation formula of (2) is as follows:
the heat consumption of the window body penetration of the building
The calculation formula of (2) is as follows:
the heat consumption Q of cold air invasion/ventilation of the building3The calculation formula of (2) is as follows: q3＝0.278Cp,airρwV(t)(T2-T∞) (ii) a The radiation heat of the outside window illumination of the building
The calculation formula of (2) is as follows:
the air heat value of the building in unit time is increased by Q4The calculation formula of (2) is as follows:
indoor heat source and indoor air heat exchange power Q of the building5The calculation formula of (2) is as follows: q5＝3.8W/m2×Sroom(ii) a The heat exchange power Q between the heating equipment in the building and the indoor air6The calculation formula of (2) is as follows: q6＝mHVAC×Cp,air×(THVAC-T2)：
In the formula (I), the compound is shown in the specification,
total number of walls between rooms 1, 2, T2Is the indoor temperature of the room 2,
is the surface temperature of the wall between the rooms 1, 2,
is the thermal resistance of the wall between the rooms 1, 2, Cp,airIs the specific heat capacity of air, ρwIs the air density, L is the outdoor air permeation, T∞V (t) is the ventilation per unit time, R is the ambient temperaturewaIs the thermal resistance of wall body, pi1,2Marking the coefficient for the wall, wherein pi is when there is a window1,2Is 1, no window time pi1,2Is a non-volatile organic compound (I) with a value of 0,
as a function of the window permeability,
is the volume of the window body,
the intensity of the light of the window is,
is the zone 2 heat capacity; sroomIs the area of a room, mHVACFor heating and ventilating apparatus air flow, THVACThe air supply temperature of the heating and ventilation equipment is controlled.
Step 12: constructing a load model based on the thermal balance model and the flexible load types in the building room;
optionally, the flexible load includes: the heating and ventilation device is used for adjusting indoor temperature, the energy consumption characteristic of the heating and ventilation device is related to the operating parameters, the indoor and outdoor temperatures, the functional attributes of the building and other factors, and the energy consumption characteristic of the water heater device is related to the temperature, the water consumption and other factors.
Optionally, as shown in fig. 2, the constructing a load model based on the thermal balance model and the flexible load type in the building room includes:
step 121: constructing a heating and ventilating energy consumption function based on the heat balance model and the energy consumption characteristics of the heating and ventilating equipment so as to calculate the electric energy consumed by the heating and ventilating equipment in unit time;
optionally, determining the air flow rate and the air supply temperature of the heating and ventilation equipment according to the heat balance model;
specifically, based on the calculation formulas of the heat generating heat source and the heat dissipating heat source, the air flow of the heating and ventilation equipment (i.e. the air flow m of the heating and ventilation equipment) can be obtained by combining the heat balance modelHVAC) And the temperature of the air supply (i.e. the temperature T of the air supply of the heating and ventilation equipment)HVAC) Thus realizing the centralized control of the heating and ventilation equipment in the building.
Further, a first energy consumption function is determined according to the air flow so as to calculate the electric energy consumed in unit time when the heating and ventilation equipment circulates fresh air. Specifically, the first energy consumption function is:
in the formula, PHVAC，f(t) is the electric energy consumed in unit time when the heating and ventilation equipment carries out fresh air circulation, mHVAC(t) is the air flow rate per unit time, eta of the heating and ventilation equipmentHVAC,fanAnd ηHVAC,motorRespectively, the operation efficiency of the fan and the operation efficiency of the engine, delta Peq,HVACIs equivalent total pressure drop and has a calculation formula of delta Peq,HVAC＝Pstatic+ρwv2[ 2 ] in the formula, PstaticFor static pressure drop, ρwThe air density and the blowing speed are v.
Further, a second energy consumption function is determined according to the air supply temperature so as to calculate the electric energy consumed in unit time when the heating and ventilation equipment performs cooling/heating. Specifically, the second energy consumption function is:
in the formula, mHVACFor heating and ventilating equipment air flow, Cp,airIs the specific heat capacity of air, CCOPFor the energy efficiency ratio of thermoelectricity, THVAC(T) is the blowing air temperature per unit time of the heating and ventilating device, T2(t) is the indoor temperature per unit time, PHVAC，h(t) isThe heating and ventilation equipment consumes electric energy in unit time during refrigeration/heating.
Further, the heating and ventilation energy consumption function is constructed based on the first energy consumption function and the second energy consumption function so as to calculate the total electric energy consumed by the heating and ventilation equipment in unit time. Specifically, the heating and ventilation energy consumption function is as follows: pHVAC(t)＝PHVAC,h(t)+PHVAC,f(t) in the formula, PHVACAnd (t) is the total electric energy consumed by the heating and ventilation equipment in unit time.
Step 122: constructing a water heater energy consumption function based on the energy consumption characteristics of the water heater equipment to calculate the electric energy consumed by the water heater equipment in unit time;
optionally, the constructing a water heater energy consumption function based on the energy consumption characteristics of the water heater equipment includes: and determining the energy consumption characteristics of the water heater equipment according to the inlet water temperature and the preset service temperature of the building so as to construct the energy consumption function of the water heater.
Specifically, the energy consumption function of the water heater is as follows:
in the formula, EWT(t) is the total power consumed by the water heater unit in a unit time, VGPM,WTVolume of water consumed for water heater equipment, DWT(t) duration of hot water use, cwaterIs the density of water, Cp,waterIs the specific heat capacity of water, TWT(T) is the preset service temperature of the hot water, TinAnd (t) is the water inlet temperature of the building.
Step 123: and constructing a load model based on the heating and ventilation energy consumption function and the water heater energy consumption function so as to calculate the total electric energy consumed in the unit time of the building.
Optionally, the equation expression of the load model is specifically as follows: pB(t)＝PHVAC(t)+EWT(t) in the formula, PB(t) is the total electrical energy consumed in the building unit time.
Further, the above-described building also includes other non-flexible loads (i.e., non-flexible loads); the thermal equilibrium mold based on the aboveThe construction of the load model according to the type and the flexible load type in the building room further comprises the following steps: constructing other load energy consumption functions based on the other inflexible loads to calculate the electric energy P consumed by the other inflexible loads in unit timeO(t); the load model constructed based on the heating and ventilation energy consumption model and the water heater energy consumption model is as follows: and constructing a load model based on the heating and ventilation energy consumption model, the water heater energy consumption model and the other load energy consumption functions. Specifically, the equation expression of the load model is as follows: pB(t)＝PHVAC(t)+EWT(t)+PO(t)。
Step 13: constructing an energy management model based on the thermal balance model and the load model;
optionally, the building an energy management model based on the thermal balance model and the load model includes: acquiring the time-of-use electricity price of the building in the unit time; determining a scheduling period of energy management; and constructing the energy management model based on the time-of-use electricity price, the dispatching cycle and the load model.
Specifically, the equation expression of the energy management model is specifically as follows:
in the formula, EBCost of energy used for construction, pe(t) is the time of use electricity price per unit time of the building, pB(T) total electric energy consumed in the building unit time, T is a scheduling period of energy management, T0Is the starting time of the schedule.
Optionally, the building an energy management model based on the thermal balance model and the load model further includes: setting constraint conditions of the energy management model; the constraint conditions include: indoor heat balance constraint, equipment adjustment constraint and flexible load total amount constant constraint; the indoor heat balance constraint is that the heat balance model constraint is required to be met in the energy management process, the equipment adjustment constraint is that the operation condition constraint of the heating and ventilation equipment and the water heater equipment is required to be met in the energy management process, and the flexible load total quantity constant constraint is that the requirement constant constraint of the load electric quantity is required to be met in the energy management process. Alternatively, the constraint condition may also include an indoor temperature comfort constraint, and the like, which is not limited herein.
Specifically, the above-mentioned equipment adjustment constraints need to satisfy the following equipment operating conditions:
0≤mHVAC≤mHVAC,max
THVAC,min≤THVAC≤THVAC,max
PWT,min≤PWT(t)≤PWT,max
TWT,min≤TWT≤TWT,max
in the formula, mHVAC,maxMaximum value of air flow when supplying air to heating and ventilating equipment, THVAC,maxAnd THVAC,minRespectively the maximum value and the minimum value, P, of the air supply temperature when the heating and ventilation equipment supplies airWT,maxAnd PWT,minRespectively the maximum value and the minimum value of the running power of the water heater equipment; t isWT,maxAnd TWT,minRespectively the maximum value and the minimum value of the temperature in the water tank of the water heater equipment.
The above equation expression for the constant constraint of the total flexible load is as follows:
where T is the scheduling period of energy management, T0For the starting time of the scheduling, PHVAC(t) and EWT(t) total electric energy consumed in unit time by the heating and ventilation equipment and the water heater equipment respectively,
and
respectively used energy of heating and ventilation equipment and water heater equipment in unit time after energy management is carried out on the buildingAnd (4) demand.
Step 14: and based on the heat balance model, the load model and the energy management model, performing energy management on the demand side of the building cluster by adopting a Q learning algorithm.
Optionally, as shown in fig. 3, the performing energy management on the demand side of the building cluster by using the Q learning algorithm includes:
step 141: discretizing continuous state variables and action variables to enable the Q learning algorithm to learn;
wherein the state variables are actual load demand conditions of the building, such as indoor temperature, water supply amount and supply conditions of other non-flexible loads of the building in each unit time; the action variables are action strategies in the energy management process, such as the temperature of air supplied by the heating and ventilation equipment or the air supply temperature, the air flow rate, the hot water supply quantity set by the water heater and the like.
Optionally, before discretizing the continuous state variables and the continuous action variables, the Q-value table is initialized, where the initialization rule is that initial values of elements in the Q-value table in the offline pre-learning stage are all 0, so as to obtain a feasible Q-value table, and the feasible Q-value table is initialized to the feasible Q-value table retained in the offline pre-learning stage in the online learning stage.
Specifically, after the Q-value table is initialized, continuous state variables and action variables are discretized into an interval form, and a value combination of < state, action > is constructed so that a Q learning algorithm can learn, and further, the corresponding unique state and action combination can be determined according to the actual load demand condition of the building.
Optionally, the possible state and action combination instructions are checked, the state and action combinations which do not meet the constraint condition of the energy management model are removed, and after the state variable and the action variable of one iteration are determined, the Q values of the time periods to which different agents belong can be calculated.
Step 142: generating a simulated sample by a Markov decision process;
step 143: selecting a current state variable based on the simulation sample and the energy management model, and determining a current action variable according to the current state variable and the action variable selection probability;
the action variable selection probability is the probability of converting the action variable into the next state variable through the current action variable under the current state variable.
Step 144: corresponding a reward value calculation function in the Q learning algorithm to the energy management model to determine a reward value obtained through a current action variable under a current state variable;
the reward value is obtained by converting the current action variable into the next state variable through the current action variable under the current state variable, and in one application scenario, the reward can be represented by the change of the energy cost of the building in the energy management model.
Step 145: and performing energy management on the building based on the state variable, the action variable and the reward value.
In one application scenario, in the process of managing energy to the demand side of the building cluster, the management objective to be considered is the economic optimization of the energy cost of the building, and the management objective can realize energy management of the building based on the state variable, the action variable and the reward value.
Further, the above energy management on the demand side of the building cluster by using the Q learning algorithm further includes: predicting a future state variable, and updating a Q value table by adopting an iterative formula based on the future state variable; and judging whether the learning process of the Q learning algorithm is converged, if so, ending the learning process, and if not, returning to the step 141 to enable the Q learning algorithm to keep learning.
Optionally, whether the learning process of the Q learning algorithm is converged is determined based on whether the Q value table is close to convergence, if the Q value table is close to convergence, it is determined that the learning process of the Q learning algorithm is converged, and if the Q value table is not close to convergence, it is determined that the learning process of the Q learning algorithm is not converged, or it is further determined based on other preset conditions whether the learning process of the Q learning algorithm is converged. Or, it may also be determined whether the learning process of the Q learning algorithm converges based on whether the number of learning times (learning duration) of the Q learning algorithm has reached a preset number threshold (preset duration threshold), and if so, it may be determined that the learning process of the Q learning algorithm converges, otherwise, it may be determined that the learning process of the Q learning algorithm does not converge, or it may be further determined whether the learning process of the Q learning algorithm converges based on other preset conditions. The embodiment of the present application does not limit the specific manner of "determining whether the learning process of the Q learning algorithm converges" or not.
In view of the above, according to the method for managing energy on the demand side of the building cluster provided by the embodiment of the application, a heat balance model in a building room is constructed based on a heat dissipation heat source and a heat generation heat source of a building; constructing a load model based on the thermal balance model and the flexible load types in the building room; constructing an energy management model based on the thermal balance model and the load model; and finally, based on the thermal balance model, the load model and the energy management model, performing energy management on the demand side of the building cluster by adopting a Q learning algorithm. By constructing the energy management model and combining with the Q learning algorithm, the application of the reinforcement learning algorithm in the energy management of the demand side of the building cluster is realized, effective guidance can be provided for the scheduling of the power industry department in China and the energy management work of the demand side of the building cluster, the energy consumption cost of users is reduced, and the realization of development targets such as energy conservation, emission reduction, electric energy substitution and the like is promoted.
Example two
The embodiment of the application provides a demand side energy management system based on a building cluster, as shown in fig. 4, including:
a first model building module 21, configured to build a thermal balance model of a building indoor based on a heat radiation heat source and a heat generation heat source of the building;
a second model building module 22 for building a load model based on the thermal balance model and the flexible load type in the building room;
a third model building module 23, configured to build an energy management model based on the thermal balance model and the load model;
and the management module 24 is configured to perform energy management on the demand side of the building cluster by using a Q learning algorithm based on the thermal balance model, the load model, and the energy management model.
Optionally, the heat dissipation heat source includes: the inner surface of the wall of the building exchanges heat with air in a convection way Q1The heat consumption of the window body penetration of the building
And heat consumption of cold air invasion/ventilation Q of the above building3(ii) a The heat generating heat source includes: the radiation heat of the outside window illumination of the building
Indoor heat source and indoor air heat exchange power Q of the building5And the heat exchange power Q between the heating equipment in the building and the indoor air6(ii) a The first model building module 21 is specifically configured to: constructing an equation expression of a thermal balance model:
wherein Q is4The sensible heat of the air of the building is increased in unit time.
Optionally, the flexible load includes: heating and ventilation equipment and water heater equipment; the second model building module 22 includes: the system comprises a heating and ventilation energy consumption calculation module, a water heater energy consumption calculation module and a load model construction module;
wherein, the heating and ventilation energy consumption calculation module is used for: constructing a heating and ventilating energy consumption function based on the heat balance model and the energy consumption characteristics of the heating and ventilating equipment so as to calculate the electric energy consumed by the heating and ventilating equipment in unit time;
the energy consumption calculation module of the water heater is used for: constructing a water heater energy consumption function based on the energy consumption characteristics of the water heater equipment to calculate the electric energy consumed by the water heater equipment in unit time;
the load model building module is used for: and constructing a load model based on the heating and ventilation energy consumption function and the water heater energy consumption function so as to calculate the total electric energy consumed in the unit time of the building.
Optionally, the heating and ventilation energy consumption calculating module is specifically configured to: determining the air flow and the air supply temperature of the heating and ventilation equipment according to the heat balance model; determining a first energy consumption function according to the air flow so as to calculate the electric energy consumed in unit time when the heating and ventilation equipment carries out fresh air circulation; determining a second energy consumption function according to the air supply temperature so as to calculate the electric energy consumed in unit time when the heating and ventilation equipment performs refrigeration/heating; and constructing the heating and ventilation energy consumption function based on the first energy consumption function and the second energy consumption function so as to calculate the total electric energy consumed by the heating and ventilation equipment in unit time.
Optionally, the water heater energy consumption calculating module is specifically configured to: constructing a water heater energy consumption function based on the energy consumption characteristics of the water heater equipment comprises: and determining the energy consumption characteristic of the water heater according to the inlet water temperature and the preset service temperature of the building so as to construct the energy consumption function of the water heater.
Optionally, the building further comprises other non-flexible loads; the second model building module 22 further includes: the other energy consumption calculation module is used for constructing other load energy consumption functions based on the other inflexible loads so as to calculate the electric energy consumed by the other inflexible loads in unit time; the load model building module is specifically configured to: and constructing a load model based on the heating and ventilation energy consumption model, the water heater energy consumption model and the other load energy consumption functions.
Optionally, the third model building module 23 is specifically configured to: acquiring the time-of-use electricity price of the building in the unit time; determining a scheduling period of energy management; and constructing the energy management model based on the time-of-use electricity price, the dispatching cycle and the load model.
Optionally, the third model building module 23 is further configured to: setting constraint conditions of the energy management model; the constraint conditions include: indoor heat balance constraint, equipment adjustment constraint and flexible load total amount constant constraint; the indoor heat balance constraint is that the heat balance model constraint is required to be met in the energy management process, the equipment adjustment constraint is that the operation condition constraint of the heating and ventilation equipment and the water heater equipment is required to be met in the energy management process, and the flexible load total quantity constant constraint is that the requirement constant constraint of the load electric quantity is required to be met in the energy management process.
Optionally, the management module 24 is specifically configured to: discretizing continuous state variables and action variables to enable the Q learning algorithm to learn, wherein the state variables are actual load demand conditions of the building, and the action variables are action strategies in an energy management process; generating a simulated sample by a Markov decision process; selecting a current state variable based on the simulation sample and the energy management model, and determining a current action variable according to the current state variable and the action variable selection probability; corresponding a reward value calculation function in the Q learning algorithm to the energy management model to determine a reward value obtained through a current action variable under a current state variable; and performing energy management on the building based on the state variable, the action variable and the reward value.
As can be seen from the above, in the energy management system on the demand side of the building cluster provided in the embodiment of the present application, a thermal balance model in a building room is constructed through the first model construction module 21; constructing a load model by a second model construction module 22; and an energy management model is constructed through a third model construction module 23; and finally, performing energy management on the demand side of the building cluster through a management module 24. By combining the energy management model constructed by the third model construction module 23 with the Q learning algorithm in the management model, the application of the reinforcement learning algorithm in the energy management of the demand side of the building cluster is realized, effective guidance can be provided for the scheduling of the power industry department in China and the energy management work of the demand side of the building cluster, the energy consumption cost of users is reduced, and the realization of development targets such as energy conservation, emission reduction, electric energy substitution and the like is promoted.
It should be understood that the above-described integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above may be implemented by a computer program, which may be stored in a computer-readable storage medium and can implement the steps of the embodiments of the methods described above when the computer program is executed by a processor. The computer program includes computer program code, and the computer program code may be in a source code form, an object code form, an executable file or some intermediate form. The computer readable medium may include: any entity or device capable of carrying the above-mentioned computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signal, telecommunication signal, software distribution medium, etc. It should be noted that the contents contained in the computer-readable storage medium can be increased or decreased as required by legislation and patent practice in the jurisdiction.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned functions may be distributed as different functional units and modules according to needs, that is, the internal structure of the apparatus may be divided into different functional units or modules to implement all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
It should be noted that, the methods and the details thereof provided by the foregoing embodiments may be combined with the apparatuses and devices provided by the embodiments, which are referred to each other and are not described again.
Those of ordinary skill in the art would appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described apparatus/device embodiments are merely illustrative, and for example, the division of the above-described modules or units is only one logical functional division, and the actual implementation may be implemented by another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed.
The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. A demand side energy management method based on a building cluster is characterized by comprising the following steps:
constructing a heat balance model in a building room based on a heat dissipation heat source and a heat generation heat source of the building;
constructing a load model based on the thermal balance model and the flexible load types in the building room;
constructing an energy management model based on the thermal balance model and the load model;
and based on the heat balance model, the load model and the energy management model, performing energy management on the demand side of the building cluster by adopting a Q learning algorithm.
2. The method of building cluster demand side energy management of claim 1, wherein the heat sink heat source comprises: the inner surface of the wall of the building exchanges heat with air in a convection way Q1Heat dissipation by window infiltration of said building
And the heat consumption Q of cold air invasion/ventilation of the building3；
The heat-generating heat source comprises: radiant heat of outside window illumination of said building
Indoor heat source and indoor air heat exchange power Q of the building5And the heat exchange power Q between the heating equipment in the building and the indoor air6；
The equation expression of the thermal equilibrium model is specifically as follows:
wherein Q is4The value of the air apparent heat of the building in unit time is increased.
3. The building cluster demand side energy management method of claim 1 or 2, wherein the flexible load categories include: heating and ventilation equipment and water heater equipment;
the building a load model based on the thermal balance model and the flexible load category in the building room comprises:
constructing a heating and ventilating energy consumption function based on the heat balance model and the energy consumption characteristics of the heating and ventilating equipment to calculate the electric energy consumed by the heating and ventilating equipment in unit time;
constructing a water heater energy consumption function based on the energy consumption characteristics of the water heater equipment to calculate the electric energy consumed by the water heater equipment in unit time;
and constructing a load model based on the heating and ventilation energy consumption function and the water heater energy consumption function so as to calculate the total electric energy consumed in the unit time of the building.
4. The building cluster demand side energy management method of claim 3, wherein the constructing an heating and ventilation energy consumption function based on the thermal balance model and the energy consumption characteristics of the heating and ventilation equipment comprises:
determining the air flow and the air supply temperature of the heating and ventilating device according to the heat balance model;
determining a first energy consumption function according to the air flow so as to calculate the electric energy consumed in unit time when the heating and ventilation equipment performs fresh air circulation;
determining a second energy consumption function according to the air supply temperature so as to calculate the electric energy consumed in unit time when the heating and ventilation equipment carries out refrigeration/heating;
and constructing the heating and ventilation energy consumption function based on the first energy consumption function and the second energy consumption function so as to calculate the total electric energy consumed by the heating and ventilation equipment in unit time.
5. The building cluster demand side energy management method of claim 3, wherein the constructing a water heater energy consumption function based on the energy consumption characteristics of the water heater devices comprises:
and determining the energy consumption characteristics of the water heater equipment according to the water inlet temperature and the preset service temperature of the building so as to construct the energy consumption function of the water heater.
6. The method of building cluster demand side energy management of claim 3, wherein the building further comprises other non-compliant loads;
the building a load model based on the thermal balance model and the flexible load category in the building room further comprises: constructing other load energy consumption functions based on the other inflexible loads to calculate the electric energy consumed by the other inflexible loads in unit time;
the load model constructed based on the heating and ventilation energy consumption model and the water heater energy consumption model is as follows: and constructing the load model based on the heating and ventilation energy consumption model, the water heater energy consumption model and the other load energy consumption functions.
7. The building cluster demand side energy management method of any one of claims 4-6, wherein the building an energy management model based on the thermal balance model and the load model comprises:
acquiring the time-of-use electricity price of the building in the unit time;
determining a scheduling period of energy management;
constructing the energy management model based on the time of use electricity prices, the scheduling period, and the load model.
8. The building cluster demand side energy management method of claim 7, wherein the building an energy management model based on the thermal balance model and the load model further comprises: setting constraints of the energy management model;
the constraint conditions include: indoor heat balance constraint, equipment adjustment constraint and flexible load total amount constant constraint;
the indoor heat balance constraint is that the heat balance model constraint needs to be met in the energy management process, the equipment adjustment constraint is that the operation condition constraint of the heating and ventilation equipment and the water heater equipment needs to be met in the energy management process, and the flexible load total quantity constant constraint is that the requirement constant constraint of the load electric quantity needs to be met in the energy management process.
9. The method for energy management on demand side of building cluster according to any one of claims 4 to 6, wherein the energy management on demand side of building cluster by using Q learning algorithm comprises:
discretizing continuous state variables and action variables to enable the Q learning algorithm to learn, wherein the state variables are the actual load demand condition of the building, and the action variables are action strategies in the energy management process;
generating a simulated sample by a Markov decision process;
selecting a current state variable based on the simulation sample and the energy management model, and determining a current action variable according to the current state variable and the action variable selection probability;
corresponding a reward value calculation function in the Q learning algorithm to the energy management model to determine a reward value obtained through a current action variable under a current state variable;
performing energy management on the building based on the state variable, the action variable, and the reward value.
10. A demand side energy management system based on a building cluster is characterized by comprising:
the first model building module is used for building an indoor heat balance model of the building based on a heat dissipation heat source and a heat generation heat source of the building;
a second model construction module for constructing a load model based on the thermal balance model and the flexible load category in the building room;
a third model construction module for constructing an energy management model based on the thermal balance model and the load model;
and the management module is used for performing energy management on the demand side of the building cluster by adopting a Q learning algorithm based on the heat balance model, the load model and the energy management model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011330457.0A CN112465212A (en) | 2020-11-24 | 2020-11-24 | Building cluster demand side energy management method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011330457.0A CN112465212A (en) | 2020-11-24 | 2020-11-24 | Building cluster demand side energy management method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112465212A true CN112465212A (en) | 2021-03-09 |
Family
ID=74799812
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011330457.0A Pending CN112465212A (en) | 2020-11-24 | 2020-11-24 | Building cluster demand side energy management method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112465212A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112990574A (en) * | 2021-03-12 | 2021-06-18 | 中国农业大学 | Assessment method and system based on building energy consumption flexible adjustment potential index |
| CN113701315A (en) * | 2021-09-02 | 2021-11-26 | 国网(苏州)城市能源研究院有限责任公司 | Intelligent energy-saving control method and system for split air conditioner cluster |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104505864A (en) * | 2014-12-12 | 2015-04-08 | 国家电网公司 | Demand response control strategy simulation system and method for absorptive distributed type power generation |
| CN106487011A (en) * | 2016-11-28 | 2017-03-08 | 东南大学 | A kind of based on the family of Q study microgrid energy optimization method |
| CN110460040A (en) * | 2019-07-15 | 2019-11-15 | 天津大学 | A kind of micro-capacitance sensor traffic control method considering intelligent building thermal balance characteristics |
| CN111064229A (en) * | 2019-12-18 | 2020-04-24 | 广东工业大学 | Wind-light-gas-storage combined dynamic economic dispatching optimization method based on Q learning |
| CN111121301A (en) * | 2018-11-01 | 2020-05-08 | 青岛海尔新能源电器有限公司 | Electricity saving amount calculation method of variable-frequency heat pump water heater and heat pump water heater |
-
2020
- 2020-11-24 CN CN202011330457.0A patent/CN112465212A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104505864A (en) * | 2014-12-12 | 2015-04-08 | 国家电网公司 | Demand response control strategy simulation system and method for absorptive distributed type power generation |
| CN106487011A (en) * | 2016-11-28 | 2017-03-08 | 东南大学 | A kind of based on the family of Q study microgrid energy optimization method |
| CN111121301A (en) * | 2018-11-01 | 2020-05-08 | 青岛海尔新能源电器有限公司 | Electricity saving amount calculation method of variable-frequency heat pump water heater and heat pump water heater |
| CN110460040A (en) * | 2019-07-15 | 2019-11-15 | 天津大学 | A kind of micro-capacitance sensor traffic control method considering intelligent building thermal balance characteristics |
| CN111064229A (en) * | 2019-12-18 | 2020-04-24 | 广东工业大学 | Wind-light-gas-storage combined dynamic economic dispatching optimization method based on Q learning |
Non-Patent Citations (2)
| Title |
|---|
| MATTEO MURATORI等: ""A highly resolved modeling technique to simulate residential power demand"", 《APPLIED ENERGY》 * |
| 余涛 等: ""在策略SARSA算法在互联电网CPS最优控制中的应用"", 《电力系统保护与控制》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112990574A (en) * | 2021-03-12 | 2021-06-18 | 中国农业大学 | Assessment method and system based on building energy consumption flexible adjustment potential index |
| CN112990574B (en) * | 2021-03-12 | 2024-04-09 | 中国农业大学 | Evaluation method and system based on building energy flexible adjustment potential index |
| CN113701315A (en) * | 2021-09-02 | 2021-11-26 | 国网(苏州)城市能源研究院有限责任公司 | Intelligent energy-saving control method and system for split air conditioner cluster |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11002457B2 (en) | Variable refrigerant flow, room air conditioner, and packaged air conditioner control systems with cost target optimization | |
| JP6976976B2 (en) | Multilevel model predictive control system and method | |
| Wang et al. | Operating a commercial building HVAC load as a virtual battery through airflow control | |
| Zhang et al. | Optimal design of building environment with hybrid genetic algorithm, artificial neural network, multivariate regression analysis and fuzzy logic controller | |
| CN110460040B (en) | Micro-grid operation scheduling method considering intelligent building heat balance characteristic | |
| Lu et al. | Global optimization for overall HVAC systems––Part II problem solution and simulations | |
| Yoon et al. | Performance based thermal comfort control (PTCC) using deep reinforcement learning for space cooling | |
| US20190236446A1 (en) | Optimization control technology for building energy conservation | |
| Yang et al. | Optimal control strategy for HVAC system in building energy management | |
| Mossolly et al. | Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm | |
| Platt et al. | Adaptive HVAC zone modeling for sustainable buildings | |
| US8396572B2 (en) | System and method for energy plant optimization using mixed integer-linear programming | |
| JP2019196898A (en) | Building device with cost target optimization | |
| US20080277486A1 (en) | HVAC control system and method | |
| CN110736227A (en) | Building management system with online configurable system identification | |
| Maasoumy et al. | Selecting building predictive control based on model uncertainty | |
| CN112465212A (en) | Building cluster demand side energy management method and system | |
| CN113339941B (en) | Control method of variable frequency air conditioner | |
| Baghaee et al. | User comfort and energy efficiency in HVAC systems by Q-learning | |
| Mahbod et al. | Energy saving evaluation of an energy efficient data center using a model-free reinforcement learning approach | |
| Xu et al. | Supply demand coordination for building energy saving: Explore the soft comfort | |
| JP2019163885A (en) | Air-conditioning control device, air-conditioning control method and computer program | |
| CN115882463A (en) | Commercial building air conditioner load schedulable potential evaluation method | |
| JP2013047597A (en) | Heat pump system, method for controlling the same, and program | |
| Radhakrishnan et al. | Token based scheduling of HVAC services in commercial buildings |
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 |