CN114069642B - Temperature control load comprehensive peak shaving method considering user satisfaction - Google Patents
Temperature control load comprehensive peak shaving method considering user satisfaction Download PDFInfo
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- H—ELECTRICITY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
- H02J3/144—Demand-response operation of the power transmission or distribution network
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
- H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
- H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
- H02J2310/58—The condition being electrical
- H02J2310/60—Limiting power consumption in the network or in one section of the network, e.g. load shedding or peak shaving
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
- H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
- H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
- H02J2310/62—The condition being non-electrical, e.g. temperature
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- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/20—End-user application control systems
- Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
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Abstract
The invention provides a temperature control load comprehensive peak shaving method considering user satisfaction, which comprises the following steps: judging whether the temperature control load can respond to the peak shaving requirement of the power grid according to the attribute of each temperature control load in the power system; if the temperature control load can respond to the peak regulation requirement, respectively calculating the current comprehensive satisfaction degree of the user and the predicted adjustable capacity of the temperature control load, taking the ratio of the total satisfaction degree of the user to the predicted adjustable capacity as a peak regulation potential index of the temperature control load, otherwise, assigning the peak regulation potential index to infinity; and determining the response priority of the temperature control load based on the ascending order of the peak regulation potential indexes, and regulating and controlling the temperature control load according to the order of the response priority until the total capacity of the temperature control load reaches the peak regulation requirement of the power grid. According to the temperature control load peak regulation priority determined by the peak regulation potential index provided by the invention, peak regulation is performed by comprehensively considering the peak regulation potential and the user satisfaction, and the user satisfaction and the temperature control load group energy efficiency level of the social temperature control group are improved.
Description
Technical Field
The invention belongs to the field of power grid load peak regulation, and particularly relates to a temperature control load comprehensive peak regulation method considering user satisfaction.
Background
With the gradual increase of the installed capacity of the renewable energy sources in the power system, the peak-valley difference of the power system is further increased, and the counteradjustment of the renewable energy sources is further increased, so that the demand of the power system for peak-to-peak capacity is further increased. In recent years, more and more research and practice projects consider flexible resources on the demand side as important resources for peak shaving of a power grid, temperature control loads are used as flexible resources widely used on the demand side, the flexible resources occupy a considerable proportion in the power grid loads with adjustable peaks, and the temperature control loads occupy one third of the peak of electricity in summer according to statistics. Therefore, the temperature-controlled load is one of the important resources for peak shaving in summer.
In the main research of the temperature control load participating in the peak regulation strategy and method, only the peak regulation potential of the temperature control load on the predicted adjustable capacity is generally considered, for example, in the technical scheme disclosed in application number 2021104758282 and patent name 'aggregate load scheduling method based on demand side load peak regulation potential parameter prediction', the peak regulation potential parameter of the aggregate load is accurately predicted by mining and utilizing the historical air temperature and the user electricity consumption data to obtain a high-precision aggregate load baseline. Because the temperature control load has seasonal and periodic characteristics, the temperature control load is different from other loads in peak shaving, and the control of the temperature control load is limited by the basic requirement of a user on the temperature control load, so that the satisfaction degree of the user is ensured as much as possible in the peak shaving process. The existing method is difficult to directly judge the peak shaving potential level by combining with the user satisfaction degree on the basis of the metering data at the dispatching side, so that the peak shaving effect is poor.
Disclosure of Invention
In order to solve the defects and shortcomings in the prior art, the invention provides a temperature control load comprehensive peak shaving method considering user satisfaction, which comprises the following steps:
judging whether the temperature control load can respond to the peak shaving requirement of the power grid according to the attribute of each temperature control load in the power system;
if the temperature control load can respond to the peak regulation requirement, respectively calculating the current comprehensive satisfaction degree of the user and the predicted adjustable capacity of the temperature control load, taking the ratio of the total satisfaction degree of the user to the predicted adjustable capacity as a peak regulation potential index of the temperature control load, otherwise, assigning the peak regulation potential index to infinity;
and determining the response priority of the temperature control load based on the ascending order of the peak regulation potential indexes, and regulating and controlling the temperature control load according to the order of the response priority until the total capacity of the temperature control load reaches the peak regulation requirement of the power grid.
Optionally, the calculating the predicted adjustable capacity of the current comprehensive satisfaction degree and the temperature control load of the user respectively includes:
according to a preset fuzzy rule, a temperature membership function and an electricity price membership function corresponding to the fuzzy rule, calculating the current comprehensive satisfaction degree of a user as follows:
wherein S is the current comprehensive satisfaction of the user, R is the total number of fuzzy rules,for the temperature membership function corresponding to the ith fuzzy rule,/->For the electricity price membership function corresponding to the ith fuzzy rule,/th fuzzy rule>For the ith fuzzy rule, EP represents temperatureThe electricity price of the control load, T, represents the set temperature of the temperature control load.
Optionally, the temperature membership function includes a first membership function μ that is comfortable to temperature perception TCM (T), a second membership function mu for sensing cool temperature TCL (T) and a third membership function μ with respect to temperature perception as hot THT (T)；
The first membership function mu TCM (T) is:
the second membership function mu TCL (T) is:
the third membership function mu THT (T) is:
min represents the lower boundary of the comfort temperature range, max represents the upper boundary of the comfort temperature range, up Min 、Up Max Respectively two intermediate limits in the comfort temperature range, where Min<Up Min <Up Max <Max。
Optionally, the electricity price membership function is a function established for users with different attributes, and each parameter in the electricity price membership function is determined through data driving.
Optionally, the expression of the fuzzy rule is:
wherein f i (EP, T) is an expression of the ith fuzzy rule,all are preset parameters of the ith fuzzy rule.
Optionally, the calculating the predicted adjustable capacity of the current comprehensive satisfaction degree and the temperature control load of the user respectively includes:
acquiring the historical daily load of the temperature control load, selecting the historical daily load of the N days with the lowest external temperature, and calculating the average value to obtain the base load L base The method comprises the following steps:
wherein L is j For the selected historical daily load on the j th day, D is the set of the selected historical daily loads;
calculating each historical daily load L j And the base load L base To obtain the historical prediction adjustable capacity L TCL,j ；
Predicting the adjustable capacity L for history based on a preset time interval TCL,j Sampling and determining the maximum value in the sampled data
Fitting the relation between the external temperature and all the historical daily loads of the temperature control load based on a least square method until a fitting result f (T) and the maximum valueThe square difference of (2) is minimized;
and obtaining the current predicted adjustable capacity of the temperature control load by multiplying the current external temperature substituted into the fitting result by a preset proportionality coefficient.
Optionally, determining the response priority of the temperature control load based on the ascending order of the peak shaving potential indexes, and regulating and controlling the temperature control load according to the order of the response priority until the total capacity of the temperature control load reaches the peak shaving requirement of the power grid, including:
step one: arranging the response priorities of the temperature control loads according to the ascending order of peak shaving potential indexes, wherein the larger the peak shaving potential indexes are, the higher the response priorities of the temperature control loads are;
step two: sequentially sending peak regulation signals to the temperature control loads according to the sequence of response priority from high to low;
step three: when the temperature control load receiving the peak shaving signal finishes the peak shaving response, judging whether the total capacity of the temperature control load reaches the peak shaving requirement of the power grid at the moment;
step four: if the peak regulation requirement is not met, continuously sending a peak regulation signal to the temperature control load in sequence, and repeating the third step until the total capacity of the temperature control load reaches the peak regulation requirement of the power grid.
The technical scheme provided by the invention has the beneficial effects that:
the invention analyzes the user satisfaction degree by using a fuzzy logic method, and gives a more visual index for considering the temperature control load peak regulation potential on the basis. According to the temperature control load peak regulation priority determined by the peak regulation potential index provided by the invention, engineering application requirements can be met, power grid schedulers are helped to evaluate temperature control load energy efficiency rapidly, peak regulation is performed by comprehensively considering peak regulation potential and user satisfaction, and social temperature control group user satisfaction and temperature control load group energy efficiency level are improved.
Drawings
In order to more clearly illustrate the technical solutions of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a temperature control load comprehensive peak shaving method considering user satisfaction according to an embodiment of the present invention;
fig. 2 is a flow chart of temperature control load control according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein.
It should be understood that, in various embodiments of the present invention, the sequence number of each process does not mean that the execution sequence of each process should be determined by its functions and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
It should be understood that in the present invention, "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements that are expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present invention, "plurality" means two or more. "and/or" is merely an association relationship describing an association object, and means that three relationships may exist, for example, and/or B may mean: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. "comprising A, B and C", "comprising A, B, C" means that all three of A, B, C comprise, "comprising A, B or C" means that one of the three comprises A, B, C, and "comprising A, B and/or C" means that any 1 or any 2 or 3 of the three comprises A, B, C.
It should be understood that in the present invention, "B corresponding to a", "a corresponding to B", or "B corresponding to a" means that B is associated with a, from which B can be determined. Determining B from a does not mean determining B from a alone, but may also determine B from a and/or other information. The matching of A and B is that the similarity of A and B is larger than or equal to a preset threshold value.
As used herein, "if" may be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to detection" depending on the context.
The technical scheme of the invention is described in detail below by specific examples. The following embodiments may be combined with each other, and some embodiments may not be repeated for the same or similar concepts or processes.
Example 1
As shown in fig. 1, this embodiment proposes a temperature-controlled load comprehensive peak shaving method considering user satisfaction, including:
s1: judging whether the temperature control load can respond to the peak shaving requirement of the power grid according to the attribute of each temperature control load in the power system;
s2: if the temperature control load can respond to the peak regulation requirement, respectively calculating the current comprehensive satisfaction degree of the user and the predicted adjustable capacity of the temperature control load, taking the ratio of the total satisfaction degree of the user to the predicted adjustable capacity as a peak regulation potential index of the temperature control load, otherwise, assigning the peak regulation potential index to infinity;
s3: and determining the response priority of the temperature control load based on the ascending order of the peak regulation potential indexes, and regulating and controlling the temperature control load according to the order of the response priority until the total capacity of the temperature control load reaches the peak regulation requirement of the power grid.
In this embodiment, parameters of a temperature control load in the power system and an external temperature are predetermined, and then it is determined whether the temperature control load can participate in peak shaving, if the temperature control load is a load which is important for normal maintenance of production and life and is not adjustable, the temperature control load will not participate in the following analysis and peak shaving processes.
For the residual temperature control load capable of participating in peak shaving, the embodiment constructs a peak shaving potential index according to the overall satisfaction degree of the user and the predicted adjustable capacity of the temperature control loadThe peak regulation is carried out on the temperature control load according to the peak regulation potential index, so that the highest social comprehensive satisfaction can be achieved, and the temperature control load is facilitated to participate in the development of peak regulation.
In this embodiment, the method for calculating the overall user satisfaction S is as follows:
wherein S is the current comprehensive satisfaction of the user, R is the total number of fuzzy rules,for the temperature membership function corresponding to the ith fuzzy rule,/->For the electricity price membership function corresponding to the ith fuzzy rule,/th fuzzy rule>For the ith fuzzy rule, EP represents the electricity price of the temperature-controlled load, and T represents the set temperature of the temperature-controlled load.
In this embodiment, the expression of the fuzzy rule is:
wherein f i (EP, T) is an expression of the ith fuzzy rule,all are preset parameters of the ith fuzzy rule.
The meaning of the fuzzy rule expression is that when the electricity price is EP and the set temperature is T, the satisfaction degree of the user to the temperature control load can be determined according to the historical operation condition of the temperature control load, for example, the historical electricity price, the historical set temperature and the corresponding user complaint condition of the temperature control load, and the preset parameters in the fuzzy rule expression are determined, so that a fuzzy rule library covering the corresponding conditions of the user satisfaction degree, the electricity price and the set temperature is constructed.
In this embodiment, the temperature membership function includes a first membership function μ that is comfortable to temperature perception TCM (T), a second membership function mu for sensing cool temperature TCL (T) and a third membership function μ with respect to temperature perception as hot THT (T) it follows that, in the calculation formula of the user overall satisfaction S, for each fuzzy rule, itAll compriseThree kinds.
The first membership function mu TCM (T) is:
the second membership function mu TCL (T) is:
the third membership function mu THT (T) is:
min represents comfortThe lower limit of the temperature range, max, represents the upper limit of the comfort temperature range, up Min 、Up Max Respectively two intermediate limits in the comfort temperature range, where Min<Up Min <Up Max <Max。
Through the three membership functions, the embodiment can obtain membership of the same temperature control load in comfort, coolness and heat user perception respectively, so that satisfaction of users on set temperature is quantized more accurately.
The electricity price membership function is a function established for users with different attributes, and various parameters in the electricity price membership function are determined through data driving. The data driving is to organize the data to form information by collecting massive data, integrate and refine related information, and form an automatic decision model through training and fitting on the basis of the data. Because users with different attributes have different satisfaction degrees on electricity prices, the embodiment is based on a data driving technology, collects a difference adjustment questionnaire of the electricity prices of users with various different attributes such as merchants, residents, enterprises and factories, and performs data modeling after information in the difference adjustment questionnaire is refined and integrated, and the built model is the membership function of the electricity prices.
In this embodiment, the method for calculating the overall user satisfaction P is as follows:
acquiring the historical daily load of the temperature control load, selecting the historical daily load of the N days with the lowest external temperature, and calculating the average value to obtain the base load L base The method comprises the following steps:
wherein L is j For the selected historical daily load on the j th day, D is the set of the selected historical daily loads;
calculating each historical daily load L j And the base load L base To obtain the historical prediction adjustable capacity L TCL,j 。
The basic load is free from the change of the external temperatureAnd therefore not within the analysis of the temperature-controlled load peak shaving potential. The present embodiment uses the daily load L per history j And the base load L base The historical prediction adjustable capacity of the temperature control load of the user can be obtained and used as training data of subsequent fitting.
In actual power systems, the peak shaver demand of the power system is often during peak load periods. In a summer high temperature environment, the load peak value is often caused by the increase of the temperature control load, and when the peak adjustment is needed, the operation power of the temperature control load is at a higher level. Therefore, in order to estimate the response capacity provided by the temperature-controlled load during peak shaving, the embodiment can obtain the relationship between the maximum power of the temperature-controlled load and the ambient temperature through function fitting according to the different days of the historical data, which specifically includes:
predicting the adjustable capacity L for history based on a preset time interval TCL,j Sampling and determining the maximum value in the sampled dataFitting the relation between the external temperature and all the historical daily loads of the temperature control load based on a least square method until the fitting result f (T) and the maximum value +.>The square difference of (2) is minimized, i.e.)>Reach a minimum value, T j Regarding the external temperature at the j th day, regarding the fitted f (T) at the moment as the relation between the external temperature and the temperature control load prediction adjustable capacity; after obtaining the current external temperature and substituting the fitting result, multiplying the current external temperature by a preset proportionality coefficient gamma to obtain the current predicted adjustable capacity P of the temperature control load, namely:
P＝γf(T)。
in this example, the historical predicted tunable capacity L is every 15 minutes TCL,j Sampling once to obtain a plurality of sampled data L TCL,j,t T tableIndicating the sampling time and further determining
And substituting the predicted temperature of the weather forecast of the future date into P=γf (T), so as to obtain the predicted adjustable capacity of the temperature control load, and comprehensively evaluating the peak shaving potential of the temperature control load by combining the overall satisfaction degree of the user.
In this embodiment, determining the response priority of the temperature control load based on the ascending order of the peak shaving potential indexes, and regulating and controlling the temperature control load according to the order of the response priority until the total capacity of the temperature control load reaches the peak shaving requirement of the power grid, where the method includes:
step one: arranging the response priorities of the temperature control loads according to the ascending order of peak shaving potential indexes, wherein the larger the peak shaving potential indexes are, the higher the response priorities of the temperature control loads are;
step two: sequentially sending peak regulation signals to the temperature control loads according to the sequence of response priority from high to low;
step three: when the temperature control load receiving the peak shaving signal finishes the peak shaving response, judging whether the total capacity of the temperature control load reaches the peak shaving requirement of the power grid at the moment;
step four: if the peak regulation requirement is not met, continuously sending a peak regulation signal to the temperature control load in sequence, and repeating the third step until the total capacity of the temperature control load reaches the peak regulation requirement of the power grid.
In this embodiment, peak shaving signals are sent to the temperature control loads according to the order of priority from high to low, peak shaving response is firstly performed from the temperature control load with the highest priority, and it should be noted that if there are multiple temperature control loads with the highest priority, as shown in fig. 2, peak shaving required capacity Δp calculated by the dispatching center is firstly obtained when peak shaving starts in the same priority, peak shaving signals are sent to the i-th temperature control load from i=1, after the i-th temperature control load responds to the peak shaving signals, whether the total capacity of all the temperature control loads is larger than or equal to Δp at the moment is judged, if yes, peak shaving is finished, otherwise, i=i+1 continues to circulate the above process. If the peak regulation of all the temperature control loads with the highest priority is finished and the total capacity is not more than or equal to delta P, the process is circulated to the temperature control load with the next priority until the requirement is met or all the temperature control loads participate in the peak regulation response and then stop.
So far, the embodiment determines the sequence of the temperature control loads participating in peak regulation according to the peak regulation potential index EERCS, so that the temperature control load with better peak regulation effect can participate preferentially, and the efficiency and effect of peak regulation of the temperature control load are improved on the premise of comprehensive user satisfaction.
The various numbers in the above embodiments are for illustration only and do not represent the order of assembly or use of the various components.
The foregoing is illustrative of the present invention and is not to be construed as limiting thereof, but rather, the present invention is to be construed as limited to the appended claims.
Claims (5)
1. The temperature control load comprehensive peak regulation method considering the user satisfaction is characterized by comprising the following steps of:
judging whether the temperature control load can respond to the peak shaving requirement of the power grid according to the attribute of each temperature control load in the power system;
if the temperature control load can respond to the peak regulation requirement, respectively calculating the current comprehensive satisfaction degree of the user and the predicted adjustable capacity of the temperature control load, taking the ratio of the total satisfaction degree of the user to the predicted adjustable capacity as a peak regulation potential index of the temperature control load, otherwise, assigning the peak regulation potential index to infinity;
determining the response priority of the temperature control load based on the ascending order of the peak regulation potential indexes, and regulating and controlling the temperature control load according to the order of the response priority until the total capacity of the temperature control load reaches the peak regulation requirement of the power grid;
the method for respectively calculating the current comprehensive satisfaction degree of the user and the predicted adjustable capacity of the temperature control load comprises the following steps:
according to a preset fuzzy rule, a temperature membership function and an electricity price membership function corresponding to the fuzzy rule, calculating the current comprehensive satisfaction degree of a user as follows:
wherein S is the current comprehensive satisfaction of the user, R is the total number of fuzzy rules,for the temperature membership function corresponding to the ith fuzzy rule,/->For the electricity price membership function corresponding to the ith fuzzy rule,/th fuzzy rule>For the ith fuzzy rule, EP represents the electricity price of the temperature control load, and T represents the set temperature of the temperature control load;
the temperature membership function comprises a first membership function mu which is comfortable to the temperature perception TCM (T), a second membership function mu for sensing cool temperature TCL (T) and a third membership function μ with respect to temperature perception as hot THT (T)；
The first membership function mu TCM (T) is:
the second membership function mu TCL (T) is:
the third membership function mu THT (T) is:
min represents the lower boundary of the comfort temperature range, max represents the upper boundary of the comfort temperature range, up Min 、Up Max Respectively two intermediate limits in the comfort temperature range, where Min<Up Min <Up Max <Max。
2. The method for comprehensively regulating peak load according to claim 1, wherein the electricity price membership function is a function established for users with different attributes, and each parameter in the electricity price membership function is determined through data driving.
3. The method for comprehensively peak shaving a temperature-controlled load in consideration of user satisfaction according to claim 1, wherein the expression of the fuzzy rule is:
wherein f i (EP, T) is an expression of the ith fuzzy rule,all are preset parameters of the ith fuzzy rule.
4. The method for comprehensively peak shaving a temperature-controlled load in consideration of user satisfaction according to claim 1, wherein the calculating of the predicted adjustable capacities of the current comprehensive satisfaction and the temperature-controlled load of the user, respectively, comprises:
acquiring the historical daily load of the temperature control load, selecting the historical daily load of the N days with the lowest external temperature, and calculating the average value to obtain the base load L base The method comprises the following steps:
wherein L is j For the selected historical daily load on the j th day, D is the set of the selected historical daily loads;
calculating each historical daily load L j And the base load L base To obtain the historical prediction adjustable capacity L TCL,j ；
Predicting the adjustable capacity L for history based on a preset time interval TCL,j Sampling and determining the maximum value in the sampled data
Fitting the relation between the external temperature and all the historical daily loads of the temperature control load based on a least square method until a fitting result f (T) and the maximum valueThe square difference of (2) is minimized;
and obtaining the current predicted adjustable capacity of the temperature control load by multiplying the current external temperature substituted into the fitting result by a preset proportionality coefficient.
5. The comprehensive peak shaving method of temperature control load considering user satisfaction according to claim 1, wherein the determining the response priority of the temperature control load based on the ascending order of peak shaving potential indexes, and adjusting the temperature control load according to the order of the response priority until the total capacity of the temperature control load reaches the peak shaving requirement of the power grid, comprises:
step one: arranging the response priorities of the temperature control loads according to the ascending order of peak shaving potential indexes, wherein the larger the peak shaving potential indexes are, the higher the response priorities of the temperature control loads are;
step two: sequentially sending peak regulation signals to the temperature control loads according to the sequence of response priority from high to low;
step three: when the temperature control load receiving the peak shaving signal finishes the peak shaving response, judging whether the total capacity of the temperature control load reaches the peak shaving requirement of the power grid at the moment;
step four: if the peak regulation requirement is not met, continuously sending a peak regulation signal to the temperature control load in sequence, and repeating the third step until the total capacity of the temperature control load reaches the peak regulation requirement of the power grid.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4023043A (en) * | 1974-08-16 | 1977-05-10 | Megatherm Corporation | Computerized peak-shaving system for alleviating electric utility peak loads |
| CN111244978A (en) * | 2020-01-19 | 2020-06-05 | 国网冀北电力有限公司电力科学研究院 | Low-voltage distribution network three-phase balancing method based on single-phase user power characteristics |
| CN112633571A (en) * | 2020-12-18 | 2021-04-09 | 浙江大学 | LSTM-based ultrashort-term load prediction method under source network load interaction environment |
| CN113205252A (en) * | 2021-04-29 | 2021-08-03 | 国网浙江省电力有限公司杭州供电公司 | Aggregated load scheduling method based on demand side load peak regulation potential parameter prediction |
Family Cites Families (1)
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4023043A (en) * | 1974-08-16 | 1977-05-10 | Megatherm Corporation | Computerized peak-shaving system for alleviating electric utility peak loads |
| CN111244978A (en) * | 2020-01-19 | 2020-06-05 | 国网冀北电力有限公司电力科学研究院 | Low-voltage distribution network three-phase balancing method based on single-phase user power characteristics |
| CN112633571A (en) * | 2020-12-18 | 2021-04-09 | 浙江大学 | LSTM-based ultrashort-term load prediction method under source network load interaction environment |
| CN113205252A (en) * | 2021-04-29 | 2021-08-03 | 国网浙江省电力有限公司杭州供电公司 | Aggregated load scheduling method based on demand side load peak regulation potential parameter prediction |
Non-Patent Citations (1)
| Title |
|---|
| 基于模糊融合的多元温控负荷群调控及风电消纳策略;龚逊东;薛溟枫;毛晓波;潘湧涛;;电力需求侧管理(第04期);全文 * |
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