CN109243547B - Quantitative evaluation method for demand response potential of air conditioner load group - Google Patents
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Abstract
The invention discloses a quantitative evaluation method for demand response potential of an air conditioner load group, which is used for calculating the demand response potential of the air conditioner load from two angles of demand response quantity and demand response duration, establishing a polymerization model of the air conditioner load group and quantitatively evaluating the demand response potential of the air conditioner load group. The method can accurately and quantitatively evaluate the demand response quantity, the demand response duration and the indoor average temperature change quantity of the air conditioning load group, and provides data support for the control strategy of the air conditioning load participating in the demand response.
Description
Technical Field
The invention relates to a quantitative evaluation method for demand response potential of an air conditioner load group, and belongs to the technical field of evaluation of demand response potential of a power system.
Background
With the development and improvement of the power market, the interest bodies of the power system are diversified step by step, and the demand-side resources are recognized again in the competitive market. Demand response is introduced in power market competition, and the power utilization mode used by a user is changed through price signals or excitation signals, so that the effect of a demand side in the market is increased. Demand responses may provide benefits to multi-party grid operating participants, including: the power utilization cost of a user is reduced, the speed of newly adding installed capacity is slowed down, the power generation and admission capacity of renewable energy sources is increased, the power transmission and distribution cost is reduced, the emergency capacity of a power system is improved, the operation flexibility of the power system is improved, and the like.
The building to which the air conditioning load belongs has heat storage capacity, and the influence of adjusting the running state within several minutes to tens of minutes on the comfort level of a user is small, and the air conditioning load is regarded as an important demand response resource. The demand response potential of the air conditioner load group is influenced by the temperature demand of a user and the external environment, and the indoor temperature change of the air conditioner load is closely related to the operation power of the air conditioner load group, so that the comfort level of the user needs to be considered, the demand response potential of the air conditioner load needs to be evaluated by combining the demand response duration, and particularly, the relation among the demand response quantity of the air conditioner load group, the demand response duration and the indoor average temperature change quantity needs to be evaluated quantitatively.
Disclosure of Invention
The purpose of the invention is as follows: the invention provides a quantitative evaluation method for demand response potential of an air conditioning load group, which quantitatively evaluates the relationship among demand response quantity of the air conditioning load group, demand response duration and indoor average temperature change quantity.
The technical scheme is as follows: the technical scheme adopted by the invention is a quantitative evaluation method for demand response potential of an air conditioner load group, which comprises the following steps:
1) establishing a single air conditioner load operation model according to the historical operation data of the air conditioner load, the indoor and outdoor temperature environment and the air conditioner equipment parameters, and determining the parameters of the single air conditioner load operation model;
2) clustering and grouping the air conditioner loads by using a K-means clustering algorithm according to the geographical positions and the operation parameters of the air conditioner loads;
3) according to the heat energy storage characteristics of the building and a single air conditioner load operation model, each air conditioner load group is equivalent to a polymerization air conditioner, and a polymerization power model of the air conditioner load group is established;
4) calculating the demand response potential of each air conditioning load group according to the weather condition in the demand response process and the demand of the user on the indoor temperature;
5) and calculating the overall demand response potential of all air conditioner loads.
The step 1) establishes an air conditioner load thermodynamic equivalent thermal parameter model by collecting historical operating data parameters of each air conditioner load, and the method specifically comprises the following steps:
wherein i is the air conditioner load serial number; t isi(t) represents the air conditioning load temperature at time t; t iso(t) represents the external ambient temperature at time t; u. ofi(t) is equal to {0,1} and represents the air conditioning load operation state, ui(t) 1 denotes on, ui(t) ═ 0 denotes off; riIs equivalent thermal resistance; ciIs equivalent heat capacity; qiRated power for the air conditioner; etaiThe air conditioner energy efficiency ratio is obtained; delta t is a control step length; e is a natural base constant.
The step 2) further comprises the following steps:
i) according to the geographical position of the air conditioner load, performing first-step grouping on the air conditioner load;
and ii) further clustering and grouping by using a K-means algorithm according to the air conditioner load equivalent thermal resistance, the equivalent thermal capacity and the air conditioner load energy efficiency ratio in each group.
The step 3) comprises the following steps:
i) calculating the equivalent heat capacity C of the aggregated air conditionereq
Wherein, CiThe equivalent heat capacity of the air-conditioning load i, and N is the number of the air-conditioning loads in the aggregation group;
ii) calculating the equivalent thermal resistance R of the polymerization air conditionereq
Wherein R isiEquivalent thermal resistance of air conditioning load i; n is the number of air conditioner loads in the aggregation group;
iii) calculating the aggregated air-conditioning energy efficiency ratio etaeq
Wherein eta isiThe energy efficiency ratio of the air conditioning load i; n is the number of air conditioner loads in the aggregation group;
iv) calculating the average temperature of the polymerization air-conditioning room
Wherein, Tin,iIndoor temperature which is air conditioning load i; t isset,iIs the set temperature of the air conditioning load i; n is the number of air conditioner loads in the aggregation group;
v) establishing an air conditioner load aggregation power model
Wherein P istotal(T) is the air conditioning load aggregate power, To(t) is the external ambient temperature at time t;
vi) aggregating relation model of average temperature and aggregation power in air conditioner room
Wherein R isiEquivalent thermal resistance of air conditioning load i; and N is the number of air conditioning loads in the aggregation group.
The step 4) comprises the following steps:
i) when the aggregate air conditioner load reduction is determined, the relationship between the demand response duration and the indoor temperature change is
Wherein, ttransA demand response duration; preduceReducing the load of the air conditioner for polymerization; t is1，T2The average indoor temperature of the air conditioning unit before and after response is respectively;
ii) aggregating the relationship between the air conditioning load reduction and the indoor temperature variation when the demand response duration is determined as
Wherein:
respectively representing an average indoor temperature of T1，T2The air conditioning load of the time aggregates power.
The step 5) further comprises the following steps:
i) acquiring demand response potentials of all air conditioner aggregation groups;
and ii) carrying out accumulation summation according to the demand response duration and the load reduction of each air conditioner aggregation group to obtain the relation between the whole demand response time and the load reduction of all air conditioner loads.
Has the advantages that: according to the invention, through the air conditioner load aggregate power model and the analysis of the indoor temperature requirement by the user, the relation among the requirement response quantity, the requirement response duration and the indoor average temperature change quantity of the air conditioner load group in different external environments can be evaluated. The method has the following advantages:
(1) the method is applicable to various air conditioner load types, including: fixed frequency air conditioner, frequency conversion air conditioner, central air conditioner, heat pump for regulating indoor temperature of user, etc.
(2) The method considers the diversity of the indoor temperature requirements of the users, is suitable for the indoor temperature requirements of different users, and considers the comfort of different users.
(3) The method can accurately and quantitatively evaluate the demand response quantity, the demand response duration and the indoor average temperature change quantity of the air conditioning load group, and provides data support for the control strategy of the air conditioning load participating in the demand response.
Drawings
FIG. 1 is a circuit model diagram of equivalent thermal parameters of a single air conditioner;
FIG. 2 is a schematic diagram of an equivalent thermal parameter circuit model of an air conditioning load set;
FIG. 3 is a line graph of the actual value of the aggregate power of the air conditioning load and the estimated value thereof in the calculation example;
FIG. 4 is a diagram showing the air conditioner load aggregate power estimation error and its distribution line in the example;
fig. 5 is a three-dimensional graph of the relationship among the demand response amount, the demand response duration, and the indoor average temperature rise amount 3 in the calculation example.
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.
As shown in fig. 1, a single air conditioner is equivalent to a 1-step equivalent thermal parameter model, in which heat storage of a room is equivalent to capacitance, and insulation of a building is equivalent to resistance.
And the air-conditioning load group composed of a plurality of air conditioners is shown in fig. 2, a plurality of air conditioners are aggregated and equivalent to an air-conditioning load model, and the heat storage characteristics and the heat insulation characteristics of the outer walls of a plurality of rooms are respectively equivalent to capacitors and resistors connected in parallel.
The embodiment comprises the following steps:
1) and establishing a single air conditioner load operation model according to the historical air conditioner load operation data, the indoor and outdoor temperature environment and the air conditioner equipment parameters, and determining the single air conditioner load operation model parameters.
The method comprises the following steps of establishing an air conditioner load thermodynamic equivalent thermal parameter model by collecting historical operating data parameters of each air conditioner load, wherein the method specifically comprises the following steps:
wherein i is the air conditioner load serial number; t isi(t) represents the air conditioning load temperature at time t; t iso(t) represents the external ambient temperature at time t; u. ofi(t) is equal to {0,1} and represents the air conditioning load operation state, ui(t) 1 denotes on, ui(t) ═ 0 denotes off; riIs equivalent thermal resistance; ciIs equivalent heat capacity; qiRated power for the air conditioner; etaiThe air conditioner energy efficiency ratio is obtained; delta t is a control step length; e is a natural base constant.
2) According to the geographical position and the operation parameters of the air conditioner load, clustering and grouping the air conditioner load by using a K-means clustering algorithm, which specifically comprises the following steps:
i) and performing first-step grouping on the air conditioning loads according to the geographical positions of the air conditioning loads.
And ii) further clustering and grouping by using a K-means algorithm according to the air conditioner load equivalent thermal resistance, the equivalent thermal capacity and the air conditioner load energy efficiency ratio in each group.
3) According to the heat energy storage characteristics of the building and a single air conditioner load operation model, each air conditioner load group is equivalent to a polymerization air conditioner, and a polymerization model of the air conditioner load group is established, wherein the method specifically comprises the following steps:
i) calculating the equivalent heat capacity C of the aggregated air conditionereq
Wherein, CiIs the equivalent heat capacity of the air-conditioning load i; and N is the number of air conditioning loads in the aggregation group.
ii) calculating the equivalent thermal resistance R of the polymerization air conditionereq
Wherein R isiEquivalent thermal resistance of air conditioning load i; and N is the number of air conditioning loads in the aggregation group.
iii) calculating the aggregated air-conditioning energy efficiency ratio etaeq
Wherein eta isiThe energy efficiency ratio of the air conditioning load i; and N is the number of air conditioning loads in the aggregation group.
iv) calculating the average temperature of the polymerization air-conditioning room
Wherein, Tin,iIndoor temperature which is air conditioning load i; t isset,iIs the set temperature of the air conditioning load i; and N is the number of air conditioning loads in the aggregation group.
v) establishing an air conditioner load aggregation power model
Wherein P istotal(T) is the air conditioning load aggregate power, To(t) is the external ambient temperature at time t,
vi) aggregating relation model of average temperature and aggregation power in air conditioner room
Wherein R isiEquivalent thermal resistance of air conditioning load i; and N is the number of air conditioning loads in the aggregation group.
4) According to the weather condition in the demand response process and the demand of the user for the indoor temperature, calculating the demand response potential of each air conditioning load group, and specifically comprising the following steps:
i) when the aggregate air conditioner load reduction is determined, the relationship between the demand response duration and the indoor temperature change is
Wherein, ttransA demand response duration; preduceReducing the load of the air conditioner for polymerization; t is1，T2The average indoor temperature of the air conditioning set before and after response is respectively.
ii) aggregating the relationship between the air conditioning load reduction and the indoor temperature variation when the demand response duration is determined as
Wherein:
respectively representing an average indoor temperature of T1，T2The air conditioning load of the time aggregates power.
5) Calculating the whole demand response potential of all air conditioner loads, specifically:
i) and acquiring the demand response potential of all air conditioner aggregation groups.
And ii) carrying out accumulation summation according to the demand response duration and the load reduction of each air conditioner aggregation group to obtain the relation between the whole demand response time and the load reduction of all air conditioner loads.
Calculation example:
and 10000 air-conditioning loads which can participate in demand response and are owned in a certain area are used for potential quantitative evaluation.
Firstly, the value ranges of specific parameters calculated through the historical operating data of the air conditioner load are as follows:
and then, according to the geographical position and the operation parameters of the air conditioner load, clustering and grouping the air conditioner load by using a K-means clustering algorithm.
And then, according to the heat energy storage characteristics of the building and a single air-conditioning load operation model, each air-conditioning load group is equivalent to a polymerization air conditioner, and a polymerization power model of the air-conditioning load group is established. The aggregate power estimate is shown in fig. 3, with the error in the estimate shown in fig. 4.
And then, according to the heat energy storage characteristics of the building and the single air-conditioning load operation model, each air-conditioning load group is equivalent to an aggregated air conditioner, and an aggregated power model of the air-conditioning load group is established.
And finally, calculating the whole demand response potential of all air conditioner loads. The air conditioning load demand response potential calculated by the method is shown in fig. 5.
Claims (5)
1. A quantitative evaluation method for demand response potential of an air conditioning load group is characterized by comprising the following steps:
1) establishing a single air conditioner load operation model according to the historical operation data of the air conditioner load, the indoor and outdoor temperature environment and the air conditioner equipment parameters, and determining the parameters of the single air conditioner load operation model;
2) clustering and grouping the air conditioner loads by using a K-means clustering algorithm according to the geographical positions and the operation parameters of the air conditioner loads;
3) according to the heat energy storage characteristics of the building and a single air conditioner load operation model, each air conditioner load group is equivalent to a polymerization air conditioner, and a polymerization power model of the air conditioner load group is established;
4) according to the weather condition in the demand response process and the demand of the user on the indoor temperature, calculating the demand response potential of each air conditioning load group, and the method comprises the following steps:
i) when the aggregate air conditioner load reduction is determined, the relationship between the demand response duration and the indoor temperature change is
Wherein, ttransA demand response duration; preduceReducing the load of the air conditioner for polymerization; t is1，T2The average indoor temperature of the air conditioning unit before and after response is respectively; reqTo aggregate the equivalent thermal resistance of air conditioners, CeqTo aggregate the equivalent heat capacity, η, of the air conditionereqThe energy efficiency ratio of the air conditioner is aggregated;
ii) aggregating the relationship between the air conditioning load reduction and the indoor temperature variation when the demand response duration is determined as
Wherein:
respectively representing an average indoor temperature of T1，T2The aggregate power of the air conditioning load; t isoIs the temperature of the external environment, and is,
5) and calculating the overall demand response potential of all air conditioner loads.
2. The quantitative evaluation method for demand response potential of air conditioning load group according to claim 1, wherein the step 1) is to establish a thermodynamic equivalent thermal parameter model of the air conditioning load by collecting historical operating data parameters of each air conditioning load, and the thermodynamic equivalent thermal parameter model is as follows:
wherein i is the air conditioner load serial number; t isi(t) represents the air conditioning load temperature at time t; t iso(t) represents the external ambient temperature at time t; u. ofi(t) is equal to {0,1} and represents the air conditioning load operation state, ui(t) 1 denotes on, ui(t) ═ 0 denotes off; riIs equivalent thermal resistance; ciIs equivalent heat capacity; qiRated power for the air conditioner; etaiThe air conditioner energy efficiency ratio is obtained; delta t is a control step length; e is a natural base constant.
3. The quantitative evaluation method for the demand response potential of the air conditioning load group according to claim 1, wherein the step 2) further comprises the following steps:
i) according to the geographical position of the air conditioner load, performing first-step grouping on the air conditioner load;
and ii) further clustering and grouping by using a K-means algorithm according to the air conditioner load equivalent thermal resistance, the equivalent thermal capacity and the air conditioner load energy efficiency ratio in each group.
4. The quantitative evaluation method for the demand response potential of the air conditioning load group according to claim 1, wherein the step 3) comprises:
i) calculating the equivalent heat capacity C of the aggregated air conditionereq
Wherein, CiThe equivalent heat capacity of the air-conditioning load i, and N is the number of the air-conditioning loads in the aggregation group;
ii) calculating the equivalent thermal resistance R of the polymerization air conditionereq
Wherein R isiEquivalent thermal resistance of air conditioning load i; n is the number of air conditioner loads in the aggregation group;
iii) calculating the aggregated air-conditioning energy efficiency ratio etaeq
Wherein eta isiThe energy efficiency ratio of the air conditioning load i; n is the number of air conditioner loads in the aggregation group;
iv) calculating the average temperature of the polymerization air-conditioning room
Wherein, Tin,iIndoor temperature which is air conditioning load i; t isset,iIs the set temperature of the air conditioning load i; n is the number of air conditioner loads in the aggregation group;
v) establishing an air conditioner load aggregation power model
Wherein P istotal(T) is the air conditioning load aggregate power, To(t) is the external ambient temperature at time t;
vi) aggregating relation model of average temperature and aggregation power in air conditioner room
Wherein R isiEquivalent thermal resistance of air conditioning load i; and N is the number of air conditioning loads in the aggregation group.
5. The quantitative evaluation method for the demand response potential of the air conditioning load group according to claim 1, wherein the step 5) further comprises:
i) acquiring demand response potentials of all air conditioner aggregation groups;
and ii) carrying out accumulation summation according to the demand response duration and the load reduction of each air conditioner aggregation group to obtain the relation between the whole demand response time and the load reduction of all air conditioner loads.
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| CN109827310B (en) * | 2019-01-31 | 2020-12-11 | 河海大学 | Resident air conditioner load cluster die type establishing method |
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| CN113701232B (en) * | 2021-08-26 | 2023-01-17 | 浙江大学常州工业技术研究院 | Heat supply system building-level regulation and control method and system based on temperature diversity analysis |
| 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 |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105042800A (en) * | 2015-09-01 | 2015-11-11 | 东南大学 | Variable-frequency air conditioner load modeling and operation controlling method based on demand responses |
| 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 |
| CN107101322A (en) * | 2017-04-13 | 2017-08-29 | 东南大学 | The convertible frequency air-conditioner group potential evaluation method of unified maximum reduction plans duration |
| CN107545364A (en) * | 2017-08-25 | 2018-01-05 | 合肥工业大学 | The user response policy optimization method of stimulable type electricity needs response |
| 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 |
| CN108151242A (en) * | 2017-12-21 | 2018-06-12 | 天津大学 | A kind of central air-conditioner control method towards cluster demand response |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8090477B1 (en) * | 2010-08-20 | 2012-01-03 | Ecofactor, Inc. | System and method for optimizing use of plug-in air conditioners and portable heaters |
| US20150370272A1 (en) * | 2014-06-23 | 2015-12-24 | Google Inc. | Intelligent configuration of a smart environment based on arrival time |
-
2018
- 2018-07-09 CN CN201810743764.8A patent/CN109243547B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105042800A (en) * | 2015-09-01 | 2015-11-11 | 东南大学 | Variable-frequency air conditioner load modeling and operation controlling method based on demand responses |
| 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 |
| CN107101322A (en) * | 2017-04-13 | 2017-08-29 | 东南大学 | The convertible frequency air-conditioner group potential evaluation method of unified maximum reduction plans duration |
| CN107545364A (en) * | 2017-08-25 | 2018-01-05 | 合肥工业大学 | The user response policy optimization method of stimulable type electricity needs response |
| 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 |
| CN108151242A (en) * | 2017-12-21 | 2018-06-12 | 天津大学 | A kind of central air-conditioner control method towards cluster demand response |
Non-Patent Citations (4)
| Title |
|---|
| Bi-level Optimization Method of Air-conditioning System Based on Office Building Energy Storage Characteristics;Qingze WANG.et;《IOP Conference Series:Materials Science and Engineering》;20171231;第199卷;1-9页 * |
| 基于需求侧响应的空调负荷虚拟储能模型研究;王怡岚等;《电网技术》;20170228;第41卷(第2期);第394-401页 * |
| 空调负荷聚合建模及其在电力系统中的应用;孙玲玲;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20180415(第4期);第C042-887页 * |
| 考虑状态差异性聚类的空调负荷直接负荷控制动态优化方法;徐青山等;《电力系统自动化》;20160725;第40卷(第14期);第33-42页 * |
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