CN112926797A - Public building power demand response double optimization method based on response priority - Google Patents
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Abstract
The utility model provides a public building power demand response double optimization method based on response priority, which comprises the steps of obtaining parameter data of each power load of a public building, and determining the power load which can participate in demand side response in the public building; determining a plurality of response strategies according to the obtained power load, calculating the response capability of each strategy, and determining the priority of each response strategy; performing double optimization according to the obtained control strategy, response capability and priority, wherein the first re-optimization adjusts the operation parameters of the response strategy, and the second re-optimization performs combined optimization of multiple strategies; according to the method, on the basis of fully analyzing the energy consumption of the public building, the power loads capable of participating in response are selected, response control strategies of different devices are formed, the response capabilities of different strategies are calculated, the response priority is determined, the decision of the comprehensive response capability of the whole building is made by adopting a double optimization method, and the precision of demand response decision optimization is greatly improved.
Description
Technical Field
The disclosure relates to the field of power demand response control calculation, in particular to a public building power demand response double optimization method based on response priority.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Along with the improvement of living standard of people, the demand on electric energy is larger and larger, the power grid load is frequently innovative, the contradiction between power supply and demand is more and more prominent, the power demand side response is generated by the response on the basis of the development of the power market, the demand side response is to replace the energy source on the supply side with the resources on the user side, the user is guided to change the power utilization mode through price and excitation signals, the pressure of the power grid can be effectively relieved, and the safe operation of the power grid is promoted.
The inventor finds that the public building has the characteristics of large volume, high unit energy consumption, multiple types of power utilization systems, permission of centralized control and the like, and is an ideal resource participating in response of the power demand side. When the power demand response is carried out, the demand response load of the power grid for sending an invitation instruction to the public building is a fixed value, and the public building can adopt various control strategies to omit the value of the response, such as shutting down certain equipment, adjusting the operation parameters of certain equipment and the like. For building users, different response strategies can be implemented to influence the operation of the users to different degrees. How to select a proper strategy combination from the strategies and how to adjust the operation parameters of the equipment so as to meet the requirements of the power grid and reduce the adverse effect on users as much as possible are problems to be solved urgently.
Disclosure of Invention
In order to solve the defects of the prior art, the public building power demand response double optimization method based on the response priority is provided, on the basis of fully analyzing the public building energy consumption, power loads capable of participating in response are selected, response control strategies of different devices are formed, the response capabilities of different strategies are calculated, the response priority is determined, the decision of the comprehensive response capability of the whole building is made by adopting a double optimization method, and the precision of demand response decision optimization is greatly improved.
In order to achieve the purpose, the following technical scheme is adopted in the disclosure:
the first aspect of the disclosure provides a double optimization method for public building power demand response based on response priority.
A public building power demand response double optimization method based on response priority comprises the following processes:
acquiring parameter data of each power load of a public building, and determining the power loads which can participate in demand side response in the public building;
determining a plurality of response strategies according to the obtained power load, calculating the response capability of each strategy, and determining the priority of each response strategy;
and performing double optimization according to the obtained control strategy, response capability and priority, wherein the first re-optimization adjusts the operation parameters of the response strategy, and the second re-optimization performs combined optimization of multiple strategies.
As an optional implementation, the first re-optimization includes the following processes:
starting from the response strategy with the highest priority, obtaining a response capability calculation value of the current response strategy, and setting the operation parameters of the current response strategy to be the maximum value in the adjustable range;
if the calculated value of the comprehensive response capability is larger than the target value of the response capability, the operation parameters of the current response strategy are adjusted down by one grade, then the comprehensive response capability is recalculated and compared with the target value of the response capability until the calculated value of the comprehensive response capability is the same as the target value of the response capability or the difference value is in a preset range.
As an alternative embodiment, the second optimization includes the following processes:
if the comprehensive response capability calculation value is smaller than the response capability target value, the response strategy of the next priority is included in the calculation according to the priority from high to low, and the comprehensive response capability calculation value after the combination of the plurality of response strategies is obtained;
and comparing the obtained comprehensive response capability calculated value with the response capability target value, and repeating the processes of the first optimization and the second optimization until the comprehensive response capability calculated value is the same as the response capability target value or the difference value is in a preset range.
Further, the calculated value of the comprehensive response capability after the combination of the multiple response strategies is the product of the sum of the response capabilities of the strategies and the coefficient of coincidence.
Further, when all the response strategies of all the priorities participate in the second optimization, the calculated value of the comprehensive response capability is still smaller than the target value of the response capability, and then the current calculated value of the comprehensive response capability is output.
Further, when the operation parameter reaches the adjustment lower limit, the current calculated value of the comprehensive response capability is output.
As an optional implementation manner, the response strategy with the minimum work or life influence degree has the highest priority, and the priority ranking of the response strategies is carried out according to the work or life influence degree of the user.
A second aspect of the present disclosure provides a utility power demand response double optimization system based on response priority.
A dual response priority-based utility power demand response optimization system, comprising:
a data acquisition module configured to: acquiring parameter data of each power load of a public building, and determining the power loads which can participate in demand side response in the public building;
a response policy calculation module configured to: determining a plurality of response strategies according to the obtained power load, calculating the response capability of each strategy, and determining the priority of each response strategy;
a dual optimization module configured to: and performing double optimization according to the obtained control strategy, response capability and priority, wherein the first re-optimization adjusts the operation parameters of the response strategy, and the second re-optimization performs combined optimization of multiple strategies.
A third aspect of the present disclosure provides a computer-readable storage medium having stored thereon a program which, when executed by a processor, implements the steps in the response priority based doubly optimized method for utility power demand response as described in the first aspect of the present disclosure.
A fourth aspect of the present disclosure provides an electronic device, comprising a memory, a processor, and a program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of the response priority based dual optimization method for power demand response of public buildings according to the first aspect of the present disclosure.
Compared with the prior art, the beneficial effect of this disclosure is:
1. the method, system, medium or electronic device of the present disclosure can find the final comprehensive response capability P of the public building through the adjustment of the operation parameters (first optimization) and the combination of various strategies (second optimization)i.sumThe value is most matched with the target load required by the power grid, and is also the optimal combination of multiple response strategies of the building, because the response willingness of the user and the influence on the comfort of the user are considered in the method, the influence caused by the work or life of the user can be reduced to the minimum while the requirement of the power grid is met.
2. According to the method, the system, the medium or the electronic equipment, on the basis of fully analyzing the energy consumption of the public building, the power loads capable of participating in response are selected, response control strategies of different equipment are formed, the response capabilities of different strategies are calculated, the response priority is determined, the decision of the comprehensive response capability of the whole building is made by adopting a double optimization method, and the precision of demand response decision optimization is greatly improved.
Advantages of additional aspects of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosure.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
Fig. 1 is a schematic flow chart of a double optimization decision method for comprehensive response capability provided in embodiment 1 of the present disclosure.
Detailed Description
The present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
Example 1:
as shown in fig. 1, embodiment 1 of the present disclosure provides a method for doubly optimizing a demand response of a utility building based on response priority, including the following processes:
acquiring parameter data of each power load of a public building, and determining the power loads which can participate in demand side response in the public building;
determining a plurality of response strategies according to the obtained power load, calculating the response capability of each strategy, and determining the priority of each response strategy;
and performing double optimization according to the obtained control strategy, response capability and priority, wherein the first re-optimization adjusts the operation parameters of the response strategy, and the second re-optimization performs combined optimization of multiple strategies.
Specifically, the method comprises the following steps:
s1: determining electrical loads in a public building that can participate in demand side responses
In order to facilitate demand side response management, firstly, the operation characteristics of the load of a target building need to be analyzed, and electric equipment with larger controllable capacity and smaller influence on the work and life of a user when the electric equipment operates in a response mode is taken as an object participating in demand response.
The following is set forth in connection with a particular application scenario. Table 1 shows the loads in a public building that can participate in demand response after analysis.
First, air conditioning in public buildings and power loads in non-critical locations, such as: electric water heaters and laundry room equipment are participants with ideal demand response;
secondly, the illumination of part garage and passageway illumination also can participate in regulation and control, replaces work with emergency lighting this moment, can not produce too big influence to the user.
For fire fighting equipment, data centers and other important equipment in public buildings, reducing load output can seriously affect the work of users and even bring potential safety hazard to the buildings, so that the load is not advocated to participate in demand response.
Table 1: load meter for response of demands participated by certain public building
S2: forming response control strategies
The control mode of the demand response can be divided into rigid and flexible modes. The rigidity control is to directly shut down all or part of the equipment; the flexibility adjustment is to adjust the output of the equipment by changing the operation parameters or operation modes of single or multiple equipment. For public buildings, the control mode of lighting load and power load basically adopts rigid control; compared with the prior art, the air conditioning system has the advantages of large equipment quantity, multiple parameters, various regulation and control means, applicability to rigid and flexible regulation and control modes, and capability of finally forming various demand response strategies. The main demand response strategy of the air conditioning system is shown in table 2.
Table 2: demand response policy table for air conditioning system
Taking a public building as an example, the total building area of the building is 197140 square meters, the building height is about 110 meters, and the building has 30 floors on the ground and 3 floors on the ground. The building is mainly used for office work, the central air-conditioning unit is a water-cooled water chilling unit, and the tail end of the central air-conditioning unit is provided with a fresh air unit and a fan coil. After the analysis of step S1, it is first determined that the devices participating in the response are: an air conditioning system, channel lighting and a floor electric heating water dispenser, and a power grid peak shaving response strategy corresponding to the equipment is formed, as shown in table 3.
Table 3: certain building demand response policy table
S3: calculating the response capability of each policy
The responsiveness calculation refers to calculating the magnitude of the actual load that can be reduced by the user in comparison with the baseline load in the case of demand response. The overall response capacity of the entire building varies according to different combinations of various regulatory strategies. The response capability of the flexible control strategy of the air conditioning system is relatively complex to calculate and can be obtained by adopting a simulation method of building energy consumption simulation software.
The currently used building energy consumption simulation software mainly comprises: DOE2, EnergyPlus, TRNSYS, DEST, etc. In the software, firstly, simulation models of a building envelope, a heating ventilation air-conditioning system, other environment control systems and the like are established, and then the dynamic energy consumption of the air-conditioning system and the change condition of the building environment are obtained by simulating various control modes.
S4: determining response priority
In order to facilitate later-stage comprehensive optimization, the priority of each response strategy needs to be determined. The priority starts with 1, 1 is the highest, and then increments by one, and does not repeat with the priorities of other policies. And responding to execute the strategy from high to low according to the priority.
The basic principle of determining the priority is to have a higher priority to a policy that has less influence on the work or life of the user. The influence of the air conditioning load on the user is mainly reflected in the influence of the indoor temperature change on the comfort level of the human body. To quantify this effect, a comfort evaluation factor D is introduced herecomfAs shown in formula (1):
where n denotes the total number of rooms with air conditioning terminals in a building, deltaiDetermined by equation (2):
in the formula (2) < delta >iIs the comfort deviation factor, theta, of a single roomiIs the actual temperature in the room, thetasetThe set value of the indoor temperature is also the expected value of the indoor temperature of the user in each room. Considering that the small fluctuation of the indoor temperature within the set value range does not affect the comfort of human body, the fluctuation deviation Delta theta on the temperature is introduced1And lower fluctuation deviation Δ θ2Their values can be set by the user himself. When the room temperature fluctuates within the deviation range, δiIs 0. When indoor temperature thetaiGreater than thetaset+Δθ1When the indoor temperature is too high, the comfort of the user is reduced, deltaiIncreasing; when the indoor temperature is lower than thetaset-Δθ2When the temperature in the room is low, the comfort of the user is also reduced, δiIs also increasedIs large. As can be seen from the formula (1), the comfort evaluation factor DcomfThe smaller, the higher the comfort of the user.
Each response strategy of the corresponding air conditioning system can obtain the response capability of the system by adjusting control parameters through building energy consumption simulation software and simulate the change of indoor temperature after response is executed, then comfort evaluation factors of each response strategy are determined by the formulas (1) and (2), the smaller the factor is, the smaller the influence on users is, and higher demand response priority can be given. For example, the priorities of different response policies of a building after analysis are shown in table 3.
S5: double optimization decision method for comprehensive response capability of public building
After receiving a demand side response offer of the power grid, the public building needs to reduce or increase a certain amount of electric load within a specified time according to a response instruction of the power grid side, wherein the load is a response target value Pgoal. This value may not be possible by executing only one strategy, requiring a combination of the results of multiple strategies to be considered.
The embodiment provides a double optimization decision method to obtain the optimal strategy combination meeting the power grid requirements, wherein the first re-optimization of the method is the adjustment of operation parameters of a specific response strategy; for example, strategy 5 in table 3, when the peak shaving response of the power grid is performed, the electricity consumption of the air conditioning unit is reduced by increasing the outlet water temperature of the chilled water of the central air conditioning system, and the adjustable range of the outlet water temperature is 5 ℃; if the current outlet water temperature is 7 deg.C, the maximum adjustable upper limit is 12 deg.C. The second is the combination optimization of a plurality of strategies, such as the reasonable combination of the 1 st to 8 th strategies in the table 3. The flow chart of the double optimization decision method is shown in fig. 1, and the specific process is as follows:
s5.1: based on the response capabilities of the various policies calculated in S3, the response policy with the highest priority is first calculated, i is 1, i is the priority, and the maximum value is n. Considering the operation parameter j according to the maximum value m in the adjustable range; for example, strategy 5 in table 3, calculated as the upper limit of the adjustable range of the effluent temperature, 12 ℃. At this time, the response capability of the priority policy is obtainedIs Pi.jAnd using the value as the comprehensive response capability Pi.sumThe initial value of (c).
S5.2: will Pi.sumAnd a target value PgoalMaking a comparison if Pi.sumIf the value is larger than the target value, the parameter adjustment value is over-large, the operation parameter j of the strategy i is adjusted downwards by one grade, j is equal to j-1, and then P is recalculatedi.jAnd then the comparison is performed. This is the first re-optimization, and the adjustable range is adjusted from top to bottom for the operating parameters of a single strategy until the lower limit of the adjustable parameter range is reached.
S5.3: if P isi.sumIf the response capacity is less than the target value, the response capacity cannot meet the requirement of the power grid only by a single strategy, second optimization is needed, namely, a response strategy with the next priority is included, i is i +1, and the comprehensive response capacity P of the building is obtained according to the following formulai.sum. When multiple strategies are implemented simultaneously, the total response capability of the system is not simply the response capability superposition of each strategy, so the simultaneous coefficient k is consideredΣTo quantify this effect, is obtained from equation (3).
Pl.j: the response capability of the different policies resulting from S3.
S5.4: then P is puti.sumAnd a target value PgoalComparing, repeating the process of S5.2 and S5.3 until Pi.sumThe target value is reached or just exceeded and the decision process ends.
Example 2:
the embodiment 2 of the present disclosure provides a public building power demand response dual optimization system based on response priority, including:
a data acquisition module configured to: acquiring parameter data of each power load of a public building, and determining the power loads which can participate in demand side response in the public building;
a response policy calculation module configured to: determining a plurality of response strategies according to the obtained power load, calculating the response capability of each strategy, and determining the priority of each response strategy;
a dual optimization module configured to: and performing double optimization according to the obtained control strategy, response capability and priority, wherein the first re-optimization adjusts the operation parameters of the response strategy, and the second re-optimization performs combined optimization of multiple strategies.
The working method of the system is the same as the double optimization method of the public building power demand response based on the response priority provided in embodiment 1, and details are not repeated here.
Example 3:
the embodiment 3 of the present disclosure provides a computer-readable storage medium, on which a program is stored, which when executed by a processor, implements the steps in the response priority based dual optimization method for the demand response of utility power in public buildings according to the embodiment 1 of the present disclosure.
Example 4:
the embodiment 4 of the present disclosure provides an electronic device, which includes a memory, a processor, and a program stored in the memory and executable on the processor, and the processor executes the program to implement the steps in the method for doubly optimizing the response priority based power demand response of the public building according to embodiment 1 of the present disclosure.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (10)
1. A public building power demand response double optimization method based on response priority is characterized in that: the method comprises the following steps:
acquiring parameter data of each power load of a public building, and determining the power loads which can participate in demand side response in the public building;
determining a plurality of response strategies according to the obtained power load, calculating the response capability of each strategy, and determining the priority of each response strategy;
and performing double optimization according to the obtained control strategy, response capability and priority, wherein the first re-optimization adjusts the operation parameters of the response strategy, and the second re-optimization performs combined optimization of multiple strategies.
2. The response priority based dual optimization method for utility power demand response of claim 1, wherein:
a first re-optimization comprising the following processes:
starting from the response strategy with the highest priority, obtaining a response capability calculation value of the current response strategy, and setting the operation parameters of the current response strategy to be the maximum value in the adjustable range;
if the calculated value of the comprehensive response capability is larger than the target value of the response capability, the operation parameters of the current response strategy are adjusted down by one grade, then the comprehensive response capability is recalculated and compared with the target value of the response capability until the calculated value of the comprehensive response capability is the same as the target value of the response capability or the difference value is in a preset range.
3. A method for response priority based dual optimization of utility power demand response as claimed in claim 1 or 2 wherein:
and a second optimization, comprising the following processes:
if the comprehensive response capability calculation value is smaller than the response capability target value, the response strategy of the next priority is included in the calculation according to the priority from high to low, and the comprehensive response capability calculation value after the combination of the plurality of response strategies is obtained;
and comparing the obtained comprehensive response capability calculated value with the response capability target value, and repeating the processes of the first optimization and the second optimization until the comprehensive response capability calculated value is the same as the response capability target value or the difference value is in a preset range.
4. A method for dual optimization of utility power demand response based on response priority as claimed in claim 3 wherein:
the calculation value of the comprehensive response capability after the combination of the plurality of response strategies is the product of the summation of the response capabilities of the strategies and the simultaneous coefficient.
5. A method for dual optimization of utility power demand response based on response priority as claimed in claim 3 wherein:
and when all the response strategies of all the priorities participate in the second optimization, the comprehensive response capability calculated value is still smaller than the response capability target value, and the current comprehensive response capability calculated value is output.
6. The response priority based dual optimization method for utility power demand response of claim 2, wherein:
and outputting the current calculated value of the comprehensive response capability when the operation parameters reach the lower adjustment limit.
7. The response priority based dual optimization method for utility power demand response of claim 1, wherein:
and the response strategy with the minimum work or life influence degree of the user has the highest priority, and the priority of the response strategies is sorted according to the work or life influence degree of the user.
8. A public building power demand response dual optimization system based on response priority is characterized in that: the method comprises the following steps:
a data acquisition module configured to: acquiring parameter data of each power load of a public building, and determining the power loads which can participate in demand side response in the public building;
a response policy calculation module configured to: determining a plurality of response strategies according to the obtained power load, calculating the response capability of each strategy, and determining the priority of each response strategy;
a dual optimization module configured to: and performing double optimization according to the obtained control strategy, response capability and priority, wherein the first re-optimization adjusts the operation parameters of the response strategy, and the second re-optimization performs combined optimization of multiple strategies.
9. A computer-readable storage medium on which a program is stored, the program, when executed by a processor, implementing the steps in the response priority based doubly optimized method of utility power demand response as claimed in any of claims 1-7.
10. An electronic device comprising a memory, a processor, and a program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps in the response priority based doubly optimized method for utility power demand response as claimed in any of claims 1-7.
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| PCT/CN2021/088183 WO2022198734A1 (en) | 2021-03-23 | 2021-04-19 | Response priority-based dual optimization method for public building power demand response |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114279063A (en) * | 2021-12-30 | 2022-04-05 | 广东美的制冷设备有限公司 | Air conditioner demand response method, device and equipment, air conditioner and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103593507A (en) * | 2013-10-17 | 2014-02-19 | 广东电网公司茂名供电局 | Automatic intelligent central deloading method for power grids |
| CN107341562A (en) * | 2017-05-25 | 2017-11-10 | 杭州赫智电子科技有限公司 | A kind of building energy management system and energy management method for merging demand response |
| CN108599177A (en) * | 2018-05-23 | 2018-09-28 | 云南电网有限责任公司 | A kind of automatic demand response strategy of resident based on dynamic priority |
| CN109698506A (en) * | 2018-12-26 | 2019-04-30 | 江苏方天电力技术有限公司 | A kind of interior of building flexible load interrupt response management method |
| CN110994602A (en) * | 2019-12-10 | 2020-04-10 | 深圳供电局有限公司 | Power grid dispatching demand response system and method |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7340325B2 (en) * | 2005-08-03 | 2008-03-04 | Texas Instruments Incorporated | Priority powerdown system and method for power distribution systems |
| CN105703355B (en) * | 2015-12-10 | 2020-07-17 | 中国电力科学研究院 | Diversity load grading self-discipline collaborative demand response method |
-
2021
- 2021-03-23 CN CN202110308845.7A patent/CN112926797A/en active Pending
- 2021-04-19 WO PCT/CN2021/088183 patent/WO2022198734A1/en active Application Filing
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103593507A (en) * | 2013-10-17 | 2014-02-19 | 广东电网公司茂名供电局 | Automatic intelligent central deloading method for power grids |
| CN107341562A (en) * | 2017-05-25 | 2017-11-10 | 杭州赫智电子科技有限公司 | A kind of building energy management system and energy management method for merging demand response |
| CN108599177A (en) * | 2018-05-23 | 2018-09-28 | 云南电网有限责任公司 | A kind of automatic demand response strategy of resident based on dynamic priority |
| CN109698506A (en) * | 2018-12-26 | 2019-04-30 | 江苏方天电力技术有限公司 | A kind of interior of building flexible load interrupt response management method |
| CN110994602A (en) * | 2019-12-10 | 2020-04-10 | 深圳供电局有限公司 | Power grid dispatching demand response system and method |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114279063A (en) * | 2021-12-30 | 2022-04-05 | 广东美的制冷设备有限公司 | Air conditioner demand response method, device and equipment, air conditioner and storage medium |
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