CN115204662A - Method, device, equipment and medium for estimating reducible potential of summer peak load - Google Patents
Method, device, equipment and medium for estimating reducible potential of summer peak load Download PDFInfo
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Abstract
The invention discloses a method, a device, equipment and a medium for estimating reducible potential of summer peak load, which are characterized in that a spring-autumn average typical daily load curve and a summer typical daily load curve are obtained, and the spring-autumn average typical daily load curve is corrected based on the summer typical daily load curve to obtain a target spring-autumn average typical daily load curve, so that the influence of spring-autumn characteristics on temperature control load estimation is reduced; then, based on the target spring-autumn average typical daily load curve, determining peak load in the summer typical daily load curve, wherein the peak load is load in an extremely high-temperature period, so that the period with most obvious influence of high temperature in the summer load is more concerned, and the correlation between the temperature control load and the extremely high temperature is remarkably reflected; and finally, evaluating the reducible potential of the peak load by using an equivalent thermal parameter model of the temperature control load so as to evaluate the reducible temperature control load aiming at the extremely high temperature period, thereby being beneficial to optimizing the planning and operation of the power system.
Description
Technical Field
The invention relates to the technical field of electric power, in particular to a reducible potential evaluation method, device, equipment and medium for summer peak load.
Background
With the continuous refreshing of peak load in summer and the continuous increase of peak-valley difference of the power grid, many areas implement management measures such as peak-valley time-of-use electricity price, two-part electricity price and the like. In order to better improve the demand response management measures for high summer temperatures and further optimize the planning and operation of the power system, it is necessary to evaluate for summer peak loads.
At present, the traditional summer peak load assessment takes a typical daily load curve of two seasons of spring and autumn as a load baseline, and estimates the air conditioner load in the summer typical daily load on the basis of the load baseline. While this approach may reduce the impact of natural load growth rate to some extent, the seasonal nature of different industry production plans is ignored, making the peak load assessment results too idealized. Meanwhile, peak loads in an extreme high temperature period in summer cannot be reflected on load characteristic baselines based on spring and autumn, and the peak loads in the extreme high temperature period are important factors for causing unbalance of power and electricity quantity on a supply and demand side and increasing operation peak-valley difference of a system. It can be seen that a need exists for a method for effectively evaluating peak loads and potential reduction thereof during extreme high temperature periods in summer.
Disclosure of Invention
The invention provides a method, a device, equipment and a medium for estimating reducible potential of summer peak load, which aim to solve the technical problem that the current summer peak load neglects seasonal characteristics of production plans of different industries.
In order to solve the above technical problem, in a first aspect, the present invention provides a reducible potential evaluation method of a summer peak load, including:
acquiring load samples, wherein the load samples comprise a spring and autumn average typical daily load curve and a summer typical daily load curve;
based on the typical daily load curve in summer, correcting the average typical daily load curve in spring and autumn to obtain a target average typical daily load curve in spring and autumn;
determining peak load in a typical daily load curve in summer based on a target spring-autumn average typical daily load curve, wherein the peak load is the load in an extremely high-temperature period;
and evaluating the reducible potential of the peak load by using an equivalent thermal parameter model of the temperature control load.
Preferably, the step of correcting the average typical daily load curve in spring and autumn based on the typical daily load curve in summer to obtain the target average typical daily load curve in spring and autumn comprises:
correcting the average typical daily load curve in spring and autumn according to the typical daily load curve in summer by using a preset normalization formula to obtain a target average typical daily load curve in spring and autumn, wherein the preset normalization formula is as follows:
Preferably, determining the peak load in the summer typical daily load curve based on the target spring-autumn average typical daily load curve comprises:
determining a peak load in a typical daily load curve in summer according to a target spring and autumn average typical daily load curve by using a preset peak load determination strategy, wherein the preset peak load determination strategy has the expression:
L ru,t representing the peak load, L, over a period of time t su,t Representing a typical daily load curve in summer over a period of time t,
represents a target spring and fall average typical daily load curve, and τ represents an extremely high temperature period.
Preferably, the reducible potential of the peak load is evaluated by using an equivalent thermal parameter model of the temperature controlled load, comprising:
determining the temperature of the air conditioner according to the equivalent thermal parameters of the air conditioner by using an equivalent thermal parameter model;
based on the peak load and the air-conditioning temperature, a reducible temperature control load in the target area is calculated.
Preferably, the equivalent thermal parameter model comprises a refrigeration state submodel and a controlled state submodel, and the refrigeration state submodel has the expression:
the expression of the controlled state submodel is:
wherein, theta i,t Indicating the temperature, theta, of the ith air conditioner at time t α Is the ambient temperature, P, of the target area i Rated power of air conditioner for ith air conditioner i Setting the coefficient of air conditioning efficiency of the ith air conditioner, wherein delta t is a time interval; r is i And C i Are all equivalent thermal parameters, R, of the ith air conditioner i Denotes the thermal resistance parameter, C i Representing a thermal capacitance parameter.
Preferably, the calculating of the reducible temperature controlled load in the target area based on the peak load and the air conditioning temperature includes:
calculating reducible temperature control load in the target area according to peak load and air conditioner temperature by using a reducible temperature control load calculation formula, wherein the reducible temperature control load calculation formula is as follows:
δ＝θ max -θ min ；
wherein, P cut Indicating the maximum reducible temperature control load, L ru Representing peak load, theta α Delta represents a space temperature interval theta for the ambient temperature of the target area max Indicates the maximum air-conditioning temperature, theta, accepted by the user in the target area min The minimum air-conditioning temperature accepted by a user in the target area is represented, R represents an average air-conditioning thermal resistance parameter in the target area, P represents an average air-conditioning rated power in the target area, and eta is an average air-conditioning efficiency coefficient in the target area.
In a second aspect, the present invention further provides a potential evaluation device for summer peak load reduction, including:
the acquisition module is used for acquiring load samples, and the load samples comprise a spring and autumn average typical daily load curve and a summer typical daily load curve;
the correcting module is used for correcting the average typical daily load curve in spring and autumn based on the typical daily load curve in summer to obtain a target average typical daily load curve in spring and autumn;
the determining module is used for determining peak load in the typical daily load curve in summer based on the target average typical daily load curve in spring and autumn, and the peak load is load in an extremely high temperature period;
and the evaluation module is used for evaluating the reducible potential of the peak load by utilizing the equivalent thermal parameter model of the temperature control load.
Preferably, the evaluation module comprises:
the determining unit is used for determining the temperature of the air conditioner according to the equivalent thermal parameters of the air conditioner by using the equivalent thermal parameter model;
and the calculating unit is used for calculating reducible temperature control load in the target area based on the peak load and the air conditioning temperature.
In a third aspect, the present invention also provides a computer device comprising a processor and a memory for storing a computer program which, when executed by the processor, implements a method of reducible potential assessment of summer peak loads as in the first aspect.
In a fourth aspect, the present invention also provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the reducible potential evaluation method of summer peak load according to the first aspect.
Compared with the prior art, the invention has the following beneficial effects:
according to the method, the average typical daily load curve in spring and autumn and the average typical daily load curve in summer are obtained, and the average typical daily load curve in spring and autumn is corrected based on the typical daily load curve in summer to obtain a target average typical daily load curve in spring and autumn, so that the influence of the characteristics of spring and autumn on temperature control load evaluation is reduced, and the load characteristic in summer is highlighted; determining peak load in the typical daily load curve in summer based on the target average typical daily load curve in spring and autumn, wherein the peak load is load in an extremely high temperature period, so that the most obvious period influenced by high temperature in the summer load is concerned, and the correlation between the temperature control load and the extremely high temperature is reflected; and finally, evaluating the reducible potential of the peak load by using an equivalent thermal parameter model of the temperature control load so as to evaluate the reducible temperature control load aiming at the extremely high temperature period, thereby being beneficial to making a more reasonable user demand response strategy and optimizing the planning and operation of the power system.
Drawings
FIG. 1 is a schematic flow chart illustrating a method for estimating potential for reducing summer peak load according to an embodiment of the present invention;
FIG. 2 is a schematic illustration of a typical daily load curve before modification according to an embodiment of the present invention;
FIG. 3 is a schematic illustration of a modified typical daily load curve according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a potential estimating apparatus for summer peak load reduction according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a schematic flow chart illustrating a method for estimating a reducible potential of a summer peak load according to an embodiment of the present invention. The summer peak load reducible potential evaluation method can be applied to computer equipment, and the computer equipment comprises equipment such as a smart phone, a notebook computer, a tablet computer, a desktop computer, a physical server and a cloud server. As shown in fig. 1, the method for estimating the reducible potential of the summer peak load of the present embodiment includes steps S101 to S104, which are detailed as follows:
step S101, obtaining load samples, wherein the load samples comprise a spring and autumn average typical daily load curve and a summer typical daily load curve.
In this step, according to weather conditions such as air temperature and humidity, reasonable load data is selected to determine a spring-autumn (including spring and autumn) average typical daily load curve and a summer typical daily load curve. Optionally, load data in a target area within a period of time is obtained, and the load data is subjected to preliminary processing: comprehensively considering temperature and humidity according to preset evaluation requirementsAnd selecting the required load data according to meteorological information such as the degree and social factors such as working days, festivals and holidays. Illustratively, a sunny day of April and October and a normal working day may be selected as the spring and autumn typical day load sample L spr Selecting sunny days of July and August and normal working days as a summer typical daily load sample L su . Alternatively, samples that are apparently abnormal due to force-inefficacy factors or the like are rejected.
And S102, correcting the average typical daily load curve in spring and autumn based on the typical daily load curve in summer to obtain a target average typical daily load curve in spring and autumn.
In this step, the average typical daily load in spring and autumn as in the background art can reduce the estimation result of the temperature control load along with the natural load increase of economic development, but the mode does not consider the influence of the seasonal characteristics of different industries on the load demand. Therefore, the seasonal characteristic is abstracted into a typical daily load curve shape, the average typical daily load curve in spring and autumn is converted into a typical daily load curve in summer by a correction mode, the occurrence range of the peak load represented as the typical daily load is highlighted, and the influence of the peak load on the absolute value is correspondingly weakened, so that the influence of the seasonal characteristic of the user production plan on the temperature control load evaluation is reduced.
Alternatively, in order to reduce the seasonal characteristics that the user production plan itself has, a normalization method is adopted to reduce the average typical daily load curve in spring and autumn to the typical daily load curve in summer.
In some embodiments, the spring-autumn average typical daily load curve is modified according to the summer typical daily load curve by using a preset normalization formula, so as to obtain a target spring-autumn average typical daily load curve, where the preset normalization formula is:
In this embodiment, after the normalization, the average spring-autumn typical day total load demand and the summer typical day total load demand will be the same, and thus a portion of the temperature controlled load due to the increase in the average air temperature will be lost. However, the present embodiment emphasizes that the electricity demand of the user represents different characteristics in different seasons, i.e. different shapes of load curves. In other words, after the normalization correction, the difference of the absolute values of the typical daily load curves of the two seasons will be weakened because the total load is the same, however, the normalization will not change the shape and other statistical characteristics of the daily load curves (such as the difference between the peak and valley of the load curve and the occurrence position of the peak load), so the normalized typical daily load curves will further highlight the characteristics of the typical daily load curves of the different seasons.
Step S103, determining a peak load in the summer typical daily load curve based on the target spring-autumn average typical daily load curve, wherein the peak load is a load in an extremely high-temperature period.
In the step, the corrected average daily load curve of spring and autumn is used as a load baseline, and the time interval in the typical daily load curve of summer, which is obviously influenced by the extreme high temperature, can be determined and used as the peak load time interval.
In some embodiments, a preset peak load determination strategy is used to determine a peak load in the summer typical daily load curve according to the target spring-autumn average typical daily load curve, and an expression of the preset peak load determination strategy is as follows:
L ru,t representing said peak load, L, over a time period t su,t Indicating the summer season typical over a period of time tThe daily load curve of the load is as follows,
represents the target spring and fall average typical daily load curve, and τ represents an extremely high temperature period.
In the present embodiment, the peak load in the extremely high temperature period in summer is the summer load minus the corrected spring load. According to the method, a time interval which is most obviously influenced by high-temperature weather is selected as a peak load time interval, the temperature control load in the time interval is focused, and in fact, the power and electricity imbalance is most likely to occur in the time interval and have larger influence on a power system, so that the temperature control load in the time interval is evaluated, and a user side demand response scheme can be provided more specifically.
And step S104, evaluating the reducible potential of the peak load by using an equivalent thermal parameter model of the temperature control load.
In this step, the temperature control load includes a cooling state and a controlled state, and the corresponding equivalent thermal parameter model is a cooling state submodel and a controlled state submodel. Optionally, the cooling state submodel expression is:
the expression of the controlled state submodel is as follows:
wherein, theta i,t Denotes the temperature of the ith air conditioner at time t, theta α The environmental temperature of the target area is considered to be maintained constant in the target area for a certain period of time, P i Rated power of air conditioner for ith air conditioner i Setting the coefficient of air conditioning efficiency of the ith air conditioner, wherein delta t is a time interval; r i And C i Are all equivalent thermal parameters, R, of the ith air conditioner i Represents a thermal resistance parameter (DEG C/kW), C i Represents a thermal capacitance parameter (kWh/. Degree.C.).
In some embodiments, the evaluating the reducible potential of the spike load using the equivalent thermal parameter model of the temperature controlled load comprises:
determining the temperature of the air conditioner according to the equivalent thermal parameters of the air conditioner by using the equivalent thermal parameter model;
based on the peak load and the air conditioning temperature, a reducible temperature control load within a target area is calculated.
In this embodiment, under the incentive demand response item, if the temperature is within the user acceptable range, the power grid company or the user aggregator can control the temperature control load, so that the air conditioning load that can be reduced in the area is determined according to the air conditioning temperature that can be acceptable to the user in the target area.
Optionally, a reducible temperature control load in the target area is calculated according to the peak load and the air-conditioning temperature by using a reducible temperature control load calculation formula:
δ＝θ max -θ min ；
wherein, P cut Indicating the maximum curtailable temperature-controlled load, L ru Representing said peak load, θ α Is the ambient temperature of the target area, delta represents the space temperature interval, theta max Indicates the maximum air-conditioning temperature, theta, accepted by the user in the target area min The minimum air-conditioning temperature accepted by a user in the target area is represented, R represents an average air-conditioning thermal resistance parameter in the target area, P represents an average air-conditioning rated power in the target area, and eta is an average air-conditioning efficiency coefficient in the target area.
The air conditioning load, the ambient temperature, and the air conditioning set temperature that can be reducedThe temperature interval acceptable by the user, and the like. In fact, the number of users is huge, the air conditioning load composition is also very complicated, and each parameter is different, thereby representing heterogeneous load composition. The temperature control loads can be aggregated by selecting a plurality of limited representative users and corresponding parameters thereof as homogeneous load composition according to the number of the representative parameters, the load requirements and the like, and finally the evaluation result of the reducible temperature control loads of the whole user can be obtained
Wherein eta is i Represents the air-conditioning efficiency coefficient, P, of the ith user cut,i Indicating the reducible temperature control load of the ith user.
By way of example and not limitation, by taking the load data of 24h as an example, the date which is clear in 4 months and 10 months and the highest air temperature is not higher than 28 ℃ is taken as a sample, and the average daily load is calculated as a typical daily load curve in spring and autumn. And selecting days with clear days in 7 months and 8 months and the highest temperature higher than 35 ℃ as samples, and calculating the average daily load of the days as a typical daily load curve in summer.
As shown in fig. 2, the spring-autumn typical daily load curve B and the summer typical daily load curve a have different seasonal characteristics, i.e., different shapes, in addition to absolute values. Specifically, the double peak of the spring and autumn typical day load occurs in the middle of 11:00-12: 00. night 19:00, whereas the peak of typical daily load in summer occurs in the middle of the day 12: 00. in the afternoon, 15: about 00 and at night 22:00-23:00. due to the fact that temperature rises in summer, the demand of users on load is increased, and the absolute numerical value of typical daily load in different seasons is different in height. However, the seasonal nature of the curve is also related to the seasonal nature of the user's own production and life plans and electricity demand. Therefore, it is necessary to conduct research to find the peak load period most significantly affected by high temperature based on the seasonal characteristics of typical daily loads.
The typical daily load in spring and autumn is converted into summer by normalization, and the time period with the most obvious influence of the extreme high temperature in summer and the corresponding peak load are obtained through the corrected typical daily load, such as 13 in fig. 3:00-17:00, etc. After the peak time period and the peak load are determined, corresponding evaluation results can be obtained according to a common reducible temperature control load evaluation method and serve as important reference bases formulated by demand response projects. Take 35 degree centigrade ambient temperature, 22 degree centigrade air-conditioning setting temperature, 19-25 degree centigrade temperature as examples. In actual implementation, the corresponding result can be obtained by considering the actual composition of different user parameters, which is specifically shown in the following table.
It should be noted that, on the basis that the original average spring and autumn typical daily load is taken as a load baseline, the invention considers that the production life of users has certain seasonal characteristics besides the temperature control load, so that the load requirements of spring and autumn and summer are different. In order to reduce the influence of the factor on temperature control load evaluation, a normalization mathematical method is adopted, and summer peak load moments are highlighted. Specifically, the average typical daily load in spring and autumn is converted to summer, the corrected average typical daily load in spring and autumn is used as a load reference, a period in summer in which the influence of air temperature is most significant is obtained, and the temperature-controlled load can be reduced according to the period. Compared with the existing method, the method disclosed by the invention focuses more on the time period with the most obvious influence of high temperature in summer load, remarkably reflects the correlation between the temperature control load and the extreme high temperature, can reduce the evaluation of the temperature control load, is beneficial to making a more reasonable user demand response strategy, and has obvious advantages.
In order to implement the reducible potential evaluation method of summer peak load corresponding to the above method embodiment, corresponding functions and technical effects are realized. Referring to fig. 4, fig. 4 is a block diagram illustrating a potential evaluation apparatus for summer peak load reduction according to an embodiment of the present invention. For convenience of explanation, only the part related to the embodiment is shown, and the reducible potential evaluation device of the summer peak load provided by the embodiment of the present invention includes:
an obtaining module 401, configured to obtain load samples, where the load samples include a spring and autumn average typical daily load curve and a summer typical daily load curve;
a correcting module 402, configured to correct the spring-autumn average typical daily load curve based on the summer typical daily load curve to obtain a target spring-autumn average typical daily load curve;
a determining module 403, configured to determine a peak load in the summer typical daily load curve based on the target spring-autumn average typical daily load curve, where the peak load is a load in an extremely high temperature period;
an evaluation module 404 for evaluating the curtailable potential of the spike load using an equivalent thermal parameter model of the temperature controlled load.
In some embodiments, the modification module 402 is specifically configured to:
correcting the average typical daily load curve in spring and autumn according to the typical daily load curve in summer by using a preset normalization formula to obtain a target average typical daily load curve in spring and autumn, wherein the preset normalization formula is as follows:
In some embodiments, the determining module 403 is specifically configured to:
determining the peak load in the typical daily load curve in summer according to the target average typical daily load curve in spring and autumn by using a preset peak load determination strategy, wherein the preset peak load determination strategy has an expression as follows:
L ru,t representing said peak load, L, over a time period t su,t Representing a typical daily load curve in summer over a period of time t,
represents the target spring and fall average typical daily load curve, and τ represents an extremely high temperature period.
In some embodiments, the evaluation module 404 includes:
the determining unit is used for determining the temperature of the air conditioner according to the equivalent thermal parameters of the air conditioner by using the equivalent thermal parameter model;
a calculation unit for calculating a reducible temperature control load in a target area based on the peak load and the air-conditioning temperature.
In some embodiments, the equivalent thermal parameter model includes a cooling state sub-model and a controlled state sub-model, the cooling state sub-model expression being:
the expression of the controlled state submodel is as follows:
wherein, theta i,t Indicating the temperature, theta, of the ith air conditioner at time t α Is the ambient temperature, P, of the target area i Rated power of air conditioner for ith air conditioner i The coefficient of air conditioning efficiency of the ith air conditioner is, and delta t is a time interval; r i And C i Are all equivalent thermal parameters, R, of the ith air conditioner i Representing a thermal resistance parameter, C i Representing a thermal capacitance parameter.
In some embodiments, the computing unit is specifically configured to:
calculating reducible temperature control load in a target area according to the peak load and the air-conditioning temperature by using a reducible temperature control load calculation formula, wherein the reducible temperature control load calculation formula is as follows:
δ＝θ max -θ min ；
wherein, P cut Indicating the maximum curtailable temperature-controlled load, L ru Representing said peak load, θ α Is the ambient temperature of the target area, delta represents the space temperature interval, theta max Represents the maximum air conditioning temperature, θ, accepted by the user in the target area min The minimum air-conditioning temperature accepted by a user in the target area is represented, R represents an average air-conditioning thermal resistance parameter in the target area, P represents an average air-conditioning rated power in the target area, and eta is an average air-conditioning efficiency coefficient in the target area.
The reducible potential evaluation device of summer peak load may implement the reducible potential evaluation method of summer peak load of the above method embodiment. The alternatives in the above-described method embodiments are also applicable to this embodiment and will not be described in detail here. The rest of the embodiments of the present invention may refer to the contents of the above method embodiments, and in this embodiment, details are not repeated.
Fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present invention. As shown in fig. 5, the computer device 5 of this embodiment includes: at least one processor 50 (only one shown in fig. 5), a memory 51, and a computer program 52 stored in the memory 51 and executable on the at least one processor 50, the processor 50 implementing the steps of any of the above-described method embodiments when executing the computer program 52.
The computer device 5 may be a computing device such as a smart phone, a tablet computer, a desktop computer, and a cloud server. The computer device may include, but is not limited to, a processor 50, a memory 51. Those skilled in the art will appreciate that fig. 5 is merely an example of the computer device 5, and does not constitute a limitation of the computer device 5, and may include more or less components than those shown, or may combine some components, or different components, and may further include input and output devices, network access devices, and the like, for example.
The Processor 50 may be a Central Processing Unit (CPU), and the Processor 50 may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 51 may in some embodiments be an internal storage unit of the computer device 5, such as a hard disk or a memory of the computer device 5. The memory 51 may also be an external storage device of the computer device 5 in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the computer device 5. Further, the memory 51 may also include both an internal storage unit and an external storage device of the computer device 5. The memory 51 is used for storing an operating system, an application program, a BootLoader (BootLoader), data, and other programs, such as program codes of the computer program. The memory 51 may also be used to temporarily store data that has been output or is to be output.
In addition, an embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps in any of the method embodiments described above.
Embodiments of the present invention provide a computer program product, which when running on a computer device, enables the computer device to implement the steps in the above method embodiments when executed.
In the several embodiments provided in the present invention, it should be understood that each block in the flowchart or block diagrams may represent a module, a program segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-mentioned embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, and it should be understood that the above-mentioned embodiments are only examples of the present invention and are not intended to limit the scope of the present invention. It should be understood that any modifications, equivalents, improvements and the like, which come within the spirit and principle of the invention, may occur to those skilled in the art and are intended to be included within the scope of the invention.
Claims (10)
1. A reducible potential evaluation method of summer peak load is characterized by comprising the following steps:
acquiring load samples, wherein the load samples comprise a spring and autumn average typical daily load curve and a summer typical daily load curve;
correcting the average typical daily load curve in spring and autumn based on the typical daily load curve in summer to obtain a target average typical daily load curve in spring and autumn;
determining a peak load in the typical daily load curve in summer based on the target average typical daily load curve in spring and autumn, wherein the peak load is a load in an extremely high temperature period;
and evaluating the reducible potential of the peak load by using an equivalent thermal parameter model of the temperature control load.
2. The method for estimating reducible potential of a summer peak load according to claim 1, wherein the step of modifying the spring-autumn average typical daily load curve based on the summer typical daily load curve to obtain a target spring-autumn average typical daily load curve comprises:
correcting the average typical daily load curve in spring and autumn according to the typical daily load curve in summer by using a preset normalization formula to obtain a target average typical daily load curve in spring and autumn, wherein the preset normalization formula is as follows:
3. The summer peak load reducible potential assessment method according to claim 1, wherein said determining a peak load in said summer typical daily load curve based on said target spring-autumn average typical daily load curve, comprises:
determining a peak load in the typical daily load curve in summer according to the target average typical daily load curve in spring and autumn by using a preset peak load determination strategy, wherein the preset peak load determination strategy has an expression as follows:
L ru，t representing said peak load, L, over a time period t su，t Representing a typical daily load curve in summer over a period of time t,
represents the target spring and fall average typical daily load curve, and τ represents an extremely high temperature period.
4. The summer peak load reducible potential assessment method according to claim 1, wherein said assessing the peak load reducible potential using an equivalent thermal parameter model of a temperature controlled load comprises:
determining the temperature of the air conditioner according to the equivalent thermal parameters of the air conditioner by using the equivalent thermal parameter model;
based on the peak load and the air conditioning temperature, a reducible temperature control load within a target area is calculated.
5. The summer peak load reducible potential assessment method of claim 4, wherein said equivalent thermal parameter model comprises a cooling state sub-model and a controlled state sub-model, said cooling state sub-model expression being:
the expression of the controlled state submodel is as follows:
wherein, theta i，t Indicating the temperature, theta, of the ith air conditioner at time t α Is the ambient temperature, P, of the target area i Rated power of air conditioner for ith air conditioner i The coefficient of air conditioning efficiency of the ith air conditioner is, and delta t is a time interval; r is i And C i Are all equivalent thermal parameters, R, of the ith air conditioner i Denotes the thermal resistance parameter, C i Representing a thermal capacitance parameter.
6. The summer peak load reducible potential evaluation method according to claim 4, wherein the calculating reducible temperature control loads in a target area based on the peak load and the air conditioning temperature comprises:
calculating reducible temperature control load in a target area according to the peak load and the air-conditioning temperature by using a reducible temperature control load calculation formula, wherein the reducible temperature control load calculation formula is as follows:
δ＝θ max -θ min ；
wherein, P cut Indicating the maximum reducible temperature control load, L ru Representing said peak load, θ α Delta represents a space temperature interval theta for the ambient temperature of the target area max Indicates the maximum air-conditioning temperature, theta, accepted by the user in the target area min The minimum air-conditioning temperature accepted by a user in the target area is represented, R represents an average air-conditioning thermal resistance parameter in the target area, P represents an average air-conditioning rated power in the target area, and eta is an average air-conditioning efficiency coefficient in the target area.
7. A potential reducible evaluation device for a summer peak load, comprising:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring load samples, and the load samples comprise a spring and autumn average typical daily load curve and a summer typical daily load curve;
the correcting module is used for correcting the spring and autumn average typical daily load curve based on the summer typical daily load curve to obtain a target spring and autumn average typical daily load curve;
a determining module, configured to determine a peak load in the typical daily load curve in summer based on the target average typical daily load curve in spring and autumn, where the peak load is a load in an extremely high temperature period;
and the evaluation module is used for evaluating the reducible potential of the peak load by utilizing an equivalent thermal parameter model of the temperature control load.
8. The summer peak load reducible potential evaluation apparatus of claim 7, wherein the evaluation module comprises:
the determining unit is used for determining the temperature of the air conditioner according to the equivalent thermal parameters of the air conditioner by using the equivalent thermal parameter model;
a calculation unit for calculating a reducible temperature control load in a target area based on the peak load and the air-conditioning temperature.
9. A computer arrangement comprising a processor and a memory for storing a computer program which, when executed by the processor, implements a method of reducible potential assessment of summer peak loads as claimed in any one of claims 1 to 6.
10. A computer-readable storage medium, characterized in that it stores a computer program which, when being executed by a processor, implements a method of reducible potential evaluation of summer peak load as claimed in any one of claims 1 to 6.
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| CN115411730A (en) * | 2022-10-31 | 2022-11-29 | 国网浙江省电力有限公司金华供电公司 | Air conditioner load multi-period adjustable potential evaluation method and related device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115411730A (en) * | 2022-10-31 | 2022-11-29 | 国网浙江省电力有限公司金华供电公司 | Air conditioner load multi-period adjustable potential evaluation method and related device |
| CN115411730B (en) * | 2022-10-31 | 2023-01-31 | 国网浙江省电力有限公司金华供电公司 | Air conditioner load multi-period adjustable potential evaluation method and related device |
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