CN109872059B - Quantitative evaluation method for demand response dynamic potential of residential air conditioner load group - Google Patents
Quantitative evaluation method for demand response dynamic potential of residential air conditioner load group Download PDFInfo
- Publication number
- CN109872059B CN109872059B CN201910098160.7A CN201910098160A CN109872059B CN 109872059 B CN109872059 B CN 109872059B CN 201910098160 A CN201910098160 A CN 201910098160A CN 109872059 B CN109872059 B CN 109872059B
- Authority
- CN
- China
- Prior art keywords
- conditioning load
- air
- air conditioning
- resident
- 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.)
- Active
Links
Images
Landscapes
- Air Conditioning Control Device (AREA)
Abstract
The invention discloses a quantitative evaluation method for demand response dynamic potential of a resident air conditioning load group, which is characterized by analyzing the dynamic relation between indoor temperature and outdoor temperature and air conditioning power from the response characteristic of a resident air conditioning load aggregation model, fully considering the heat storage characteristic of an air conditioning load, combining the energy conservation principle to obtain an air conditioning load potential model, evaluating whether an air conditioning load response strategy exceeds the air conditioning load regulation and control potential on the basis of the air conditioning load potential model, and providing constraint conditions for the resident air conditioning load demand response strategy.
Description
Technical Field
The invention relates to a quantitative evaluation method for demand response dynamic potential of a residential air conditioning load group, and belongs to the field of evaluation of demand response potential of a power system.
Background
In order to deal with the increasingly severe imbalance problem, demand-side resource management is receiving more and more attention. Because the traditional power generation dispatching potential is promoted to a limited extent, load regulation is gradually one of important means for relieving the power utilization tension. By rearranging the load in time, the load regulation and control can shift peaks and fill valleys, flatten load curves and stabilize new energy fluctuation, and the regulation means of power grid operation and scheduling is enriched.
Indoor heat loads such as air conditioners, heat pumps and the like belong to buildings with heat storage capacity, the influence of adjusting the running state within minutes to tens of minutes on the comfort level of users is small, and meanwhile, the load accounts for a large percentage in urban areas, even exceeds 50%, so that the load is regarded as an important demand response resource. Meanwhile, the load is greatly influenced by the external environment and the comfort level of residents, and the demand response potential of the load is the key that the load regulation and control of the operator of the conductive net are carried out to achieve the purpose of regulation and control without influencing the comfort level of users. In the demand response, the outside temperature can fluctuate indefinitely, and the target power of the response of the air conditioner load response is changed along with time in order to stabilize the load fluctuation, which increases great difficulty for evaluating the response potential.
Disclosure of Invention
The purpose of the invention is as follows: the invention provides a quantitative evaluation method for demand response dynamic potential of a residential air conditioner load group, and provides a foundation for participation of residential air conditioners in load regulation.
The technical scheme is as follows: the invention adopts the technical scheme that the quantitative evaluation method for the demand response dynamic potential of the residential air conditioning load group comprises the following steps:
1) determining the scale of the resident air conditioning load participating in the demand response, and selecting part of the resident air conditioning load as a resident air conditioning load sample group;
2) Calculating an aggregation model of the resident air conditioner load sample group;
3) calculating an aggregation model of all residential air-conditioning load groups;
4) analyzing the air conditioner load demand response characteristic according to the aggregation model of all residential air conditioner load groups;
5) and establishing a potential calculation model of the residential air-conditioning load group.
The aggregation model of the resident air-conditioning load sample group in the step 2) comprises a heat conduction parameter G 1 The calculation formula is as follows:
and heat storage parameter C 1 The method comprises the following steps:
the heat conduction parameter C and the heat storage parameter G of the aggregation model of all the residential air conditioning load groups in the step 3) are as follows:
wherein N, n represents the total resident air conditioning load number and the sample air conditioning load number, respectively.
In the step 4), firstly, an air conditioner load polymerization model is obtained as follows:
wherein T (T) is the average indoor temperature of the resident air-conditioning load at the time T, P (T) is the aggregate power of the resident air-conditioning load group at the time T, and the average indoor temperature T is obtained according to the formula (5) rate The calculation is as follows:
the resident air conditioning load group response characteristic is analyzed based on the formula (6).
The potential calculation model of the residential air conditioning load group in the step 5) is as follows:
where M is a constant equal to the initial temperature.
Has the advantages that: aiming at the thermodynamic characteristics of the resident air conditioner load group, the dynamic potential of the air conditioner load demand response power under the outdoor temperature change is quantitatively evaluated based on the comfort level demand of the user on the indoor temperature, the dynamic relation among the aggregate power, the outdoor temperature and the indoor temperature of the resident air conditioner load group is obtained, and a foundation is provided for the resident air conditioner to participate in load regulation and control. Meanwhile, the invention also evaluates the response power of the resident air conditioner load and judges whether the air conditioner load strategy exceeds the requirement of the user on the indoor temperature comfort level.
Drawings
FIG. 1 is a graph of residential air conditioning load group potential characteristics;
fig. 2 is a graph showing the average indoor temperature variation of the residential air conditioning load.
Detailed Description
The present invention is further illustrated by the following figures and specific examples, which are to be understood as illustrative only and not as limiting the scope of the invention, which is to be given the full breadth of the appended claims and any and all equivalent modifications thereof which may occur to those skilled in the art upon reading the present specification.
The method comprises the following steps:
1) and determining the load scale of the resident air conditioners participating in the demand response, and randomly selecting a part of the air conditioners as a sample.
The demand response area is selected, and the total number N of the air conditioning loads of the residents participating in the demand response is determined. In order to reduce the calculation amount, n air conditioning loads are randomly selected as samples, and therefore the aggregation model of all the residential air conditioning loads is evaluated.
2) And calculating an aggregation model of the resident air-conditioning load sample group.
Measuring initial outdoor temperature as T o All sample air conditioning load indoor temperatures are set to T 1 And acquiring sample air conditioner load operating power P 1 And calculating the heat conduction parameter G of the air-conditioning load room according to the data 1 The calculation formula is as follows:
At t trans During the time period, the indoor temperature of the sample air-conditioning load is changed from T 1 The number of batches is adjusted to T 2 And measuring the outdoor temperature T during the period o (t) calculating a sample air conditioning load room heat storage parameter C 1 The method comprises the following steps:
3) and calculating an aggregation model of all residential air conditioning load groups.
Calculating heat conduction parameters C and heat storage parameters G of all residential air conditioning load group aggregation models according to the sample residential air conditioning load model parameters calculated in the step 2), and specifically as follows:
wherein N, n represents the total resident air conditioning load number and the sample air conditioning load number, respectively.
4) And analyzing the air-conditioning load demand response characteristic according to the aggregation model of all the residential air-conditioning load groups.
Utilizing the parameters of the resident air-conditioning load group calculated in the step 3) to obtain an air-conditioning load aggregation model as follows:
wherein, t (t) is the average indoor temperature of the resident air conditioning load at the time t, and p (t) is the aggregated power of the resident air conditioning load group at the time t. Obtaining the average indoor temperature T according to the formula (5) rate The calculation is as follows:
the response characteristic of the residential air conditioning load group is analyzed based on the formula (6), as shown in fig. 1 in detail.
5) And establishing a potential calculation model of the residential air-conditioning load group.
On the basis of the step 4), solving a first order differential equation of a formula (5) and obtaining the following result:
Where M is a constant equal to the initial temperature.
The dynamic change relation among the outdoor temperature, the running power and the average indoor temperature is obtained through the formula (7), the indoor temperature is influenced by the comfort level of residents, and the value interval of the indoor temperature determines the size of the running power, so that the formula (7) is a resident load group demand response potential calculation model.
Examples of the design
120000 residents in a certain area are conditioned. The value ranges of the air-conditioning load parameters of a single resident are assumed as follows (each air-conditioning load parameter is randomly selected in the value range):
the air conditioning load potential calculation model obtained by the method is shown as the following formula:
the residential air conditioning load group potential characteristics are shown in fig. 1.
By applying the method of the present invention, it can be determined whether the response potential is exceeded or not for the response power of the specific residential air conditioning load group, as shown in fig. 2. It can be seen from fig. 2 that as the aggregate power of the air conditioning groups is decreased, the indoor temperature of the air conditioner is increased to 25.5 ℃ at most, and does not exceed the comfort level range of the indoor temperature of the user (in this example, the comfort level range of the indoor temperature is defined as 23,27 ℃).
Claims (1)
1. A quantitative evaluation method for demand response dynamic potential of a residential air conditioning load group is characterized by comprising the following steps:
1) Determining the scale of the resident air-conditioning load participating in the demand response, and selecting part of the resident air-conditioning load as a resident air-conditioning load sample group;
2) calculating an aggregation model of the resident air-conditioning load sample group, wherein the aggregation model comprises a heat conduction parameter G 1 The calculation formula is as follows:
and heat storage parameter C 1 The method comprises the following steps:
wherein, P 1 Represents the collected sample air conditioner load operating power, T o Denotes the initial outdoor temperature, T 1 Represents the indoor temperature of all sample air conditioning loads at t trans During the time period, the indoor temperature of the sample air-conditioning load is changed from T 1 The number of batches is adjusted to T 2 ，T o (t) represents the outdoor temperature during this time;
3) calculating an aggregation model of all residential air conditioning load groups, wherein the heat conduction parameter C and the heat storage parameter G of the aggregation model of all residential air conditioning load groups are as follows:
n, n represents the total resident air conditioning load and the sample air conditioning load respectively;
4) analyzing the air-conditioning load demand response characteristic according to the aggregation model of all residential air-conditioning load groups, and firstly obtaining the air-conditioning load aggregation model as follows:
wherein T (T) is the average indoor temperature of the resident air-conditioning load at the time T, P (T) is the aggregate power of the resident air-conditioning load group at the time T, and the average indoor temperature T is obtained according to the formula (5) rate The calculation is as follows:
analyzing response characteristics of the resident air conditioning load group based on the formula (6);
5) establishing a potential calculation model of the residential air conditioner load group, wherein the potential calculation model of the residential air conditioner load group is as follows:
where M is a constant equal to the initial temperature.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910098160.7A CN109872059B (en) | 2019-01-31 | 2019-01-31 | Quantitative evaluation method for demand response dynamic potential of residential air conditioner load group |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910098160.7A CN109872059B (en) | 2019-01-31 | 2019-01-31 | Quantitative evaluation method for demand response dynamic potential of residential air conditioner load group |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109872059A CN109872059A (en) | 2019-06-11 |
| CN109872059B true CN109872059B (en) | 2022-07-29 |
Family
ID=66918393
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910098160.7A Active CN109872059B (en) | 2019-01-31 | 2019-01-31 | Quantitative evaluation method for demand response dynamic potential of residential air conditioner load group |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109872059B (en) |
Families Citing this family (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111102644A (en) * | 2019-12-10 | 2020-05-05 | 中国电力科学研究院有限公司 | Method and system for determining potential of heat accumulating type electric heating participating in power grid peak shaving |
| CN111555274B (en) * | 2020-05-08 | 2022-06-03 | 燕山大学 | Dynamic assessment method for air conditioner load demand response capability |
| CN113420413B (en) * | 2021-05-27 | 2023-03-28 | 国网上海市电力公司电力科学研究院 | Flexible load adjustability quantification method and system based on load plasticity |
| CN114719408A (en) * | 2022-03-29 | 2022-07-08 | 湖北合合能源科技发展有限公司 | Method for adjusting central air-conditioning system by using meteorological data |
| CN115330280B (en) * | 2022-10-14 | 2023-02-21 | 国网山东省电力公司营销服务中心(计量中心) | Method and system for evaluating adjustable potential of air conditioner load demand response in aggregated load |
| CN116136978B (en) * | 2023-04-14 | 2023-07-11 | 国网江苏省电力有限公司南通供电分公司 | Method and system for evaluating load aggregation demand response potential of massive small residents |
| CN116151032B (en) * | 2023-04-17 | 2023-07-14 | 湖南大学 | Residential building dynamic load flexible potential calculation method, device, equipment and medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105138847A (en) * | 2015-09-01 | 2015-12-09 | 东南大学 | Method for electricity-saving potential estimation on load involved demand response of variable-frequency air conditioners |
| CN107591801A (en) * | 2017-09-15 | 2018-01-16 | 东南大学 | A kind of load participates in the polymerization potential appraisal procedure of demand response |
| CN109243547A (en) * | 2018-07-09 | 2019-01-18 | 河海大学 | A kind of air conditioner load group demand response potentiality quantitative evaluating method |
-
2019
- 2019-01-31 CN CN201910098160.7A patent/CN109872059B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105138847A (en) * | 2015-09-01 | 2015-12-09 | 东南大学 | Method for electricity-saving potential estimation on load involved demand response of variable-frequency air conditioners |
| CN107591801A (en) * | 2017-09-15 | 2018-01-16 | 东南大学 | A kind of load participates in the polymerization potential appraisal procedure of demand response |
| CN109243547A (en) * | 2018-07-09 | 2019-01-18 | 河海大学 | A kind of air conditioner load group demand response potentiality quantitative evaluating method |
Non-Patent Citations (4)
| Title |
|---|
| 中央空调负荷直接控制策略及其可调度潜力评估;朱宇超等;《电力自动化设备》;20180504(第05期);全文 * |
| 基于SOM需求响应潜力的居民用户优化聚合模型;孙彦萍等;《电力建设》;20170701(第07期);全文 * |
| 基于模糊优化集对分析理论的需求响应潜力评估;刘国辉等;《电力需求侧管理》;20181120(第06期);全文 * |
| 朱宇超等.中央空调负荷直接控制策略及其可调度潜力评估.《电力自动化设备》.2018,(第05期), * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109872059A (en) | 2019-06-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109872059B (en) | Quantitative evaluation method for demand response dynamic potential of residential air conditioner load group | |
| CN109243547B (en) | Quantitative evaluation method for demand response potential of air conditioner load group | |
| Cai et al. | Laboratory-based assessment of HVAC equipment for power grid frequency regulation: Methods, regulation performance, economics, indoor comfort and energy efficiency | |
| Wu et al. | Residential air-conditioner usage in China and efficiency standardization | |
| CN108036468B (en) | Aggregation control method for air conditioning system | |
| CN109827310B (en) | Resident air conditioner load cluster die type establishing method | |
| Qian et al. | Power consumption and energy efficiency of VRF system based on large scale monitoring virtual sensors | |
| Cheng et al. | Automated laboratory load-based testing and performance rating of residential cooling equipment | |
| CN105790286A (en) | Strategy for central air conditioner load aggregation and participation in distributed power supply output stabilization | |
| CN114186393A (en) | Variable frequency air conditioner cluster response capability assessment method and system | |
| CN115882463A (en) | Commercial building air conditioner load schedulable potential evaluation method | |
| CN111737857A (en) | Heating ventilation air-conditioning cluster coordination control method based on interaction capacity curve | |
| Ma et al. | Heat-pump control design performance evaluation using load-based testing | |
| CN113610330B (en) | User experience-based user-side flexible resource energy utilization behavior optimization method | |
| Hisham et al. | Statistical analysis of air-conditioning and total load diversity in typical residential buildings | |
| Xu et al. | Peak shaving potential analysis of distributed load virtual power plants | |
| Wang et al. | Aggregated large-scale air-conditioning load: Modeling and response capability evaluation of virtual generator units | |
| CN111681133B (en) | Method and device for processing electric load information | |
| CN115330280B (en) | Method and system for evaluating adjustable potential of air conditioner load demand response in aggregated load | |
| CN111859242A (en) | Household power energy efficiency optimization method and system | |
| CN114936529A (en) | Temperature control load group aggregation model, modeling method and temperature control load group adjustable potential evaluation method | |
| CN115271168A (en) | Method and device for predicting response potential of electrical load and storage medium | |
| Teo et al. | Energy management controls for chiller system: A review | |
| Naderi et al. | Clustering based analysis of residential duck curve mitigation through solar pre-cooling: A case study of Australian housing stock | |
| Cai et al. | Laboratory demonstration of variable-speed packaged air-conditioning units for provision of frequency regulation services |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |