Multi-temporal-spatial-scale air conditioner load demand response potential quantitative evaluation method and device, electronic equipment and storage medium

Abstract

The invention relates to a multi-temporal-spatial-scale air conditioner load demand response potential quantitative evaluation method and device, electronic equipment and a storage medium, and the method comprises the steps: classifying each building and an air conditioning system according to the building data of each building in a target area and a preset classification standard, and building a reference simulation model; a plurality of air conditioner load models are determined, and baseline working conditions before demand response of all the air conditioner load models are determined; carrying out analogue simulation on the air conditioner load models corresponding to various building air conditioner loads, and obtaining a demand response potential probability density curve by utilizing each simulation result and the corresponding baseline working condition; obtaining a total demand response potential probability density curve by using the demand response potential probability density curves of the various building air conditioner loads; according to the total demand response potential probability density curve, the demand response potential of the target area under different confidence degrees is determined, quantitative evaluation of the air conditioner load adjusting potential under multiple spatial and temporal scales can be achieved, and the method has the advantages of being high in precision and generalization.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of electric power, in particular to a multi-time-space scale air conditioner load demand response potential evaluation method and device, electronic equipment and storage medium. BACKGROUND

[0002] Under the background of extreme climate change, the seasonal and time-periodic peak load in the power system of China is increasing, among which the air conditioner cooling load accounts for 30%-50% of the summer peak load, which should be focused on. At this time, it is difficult to support the green and low-carbon development of the power grid by relying only on the expansion and adjustment of the power supply side. Therefore, how to scientifically and quantitatively analyze the demand response (DR) potential of urban air conditioner load is of great significance to realize "peak clipping and valley filling", improve the flexibility of the power system, and ensure the safe and stable operation of the power grid.

[0003] The existing urban air conditioner load modeling methods are mainly divided into two categories: one is a top-down modeling method, which only focuses on the correlation between energy use and macroeconomic variables, and is suitable for macro policy making. The other is a bottom-up modeling method, which starts from the characteristics of a single building and aggregates the characteristics to the regional scale, and is more suitable for dynamic energy consumption analysis.

[0004] Therefore, a bottom-up modeling method should be used in the demand response scenario. This kind of method is divided into two types: one is a data-driven modeling method, which studies the problem of unclear regulation mechanism, and it is difficult to deeply reveal the influence of factors such as building ontology, equipment characteristics, and power grid regulation mode on the flexibility potential of air conditioner load, which leads to lack of basis support when the model is applied to the real power grid regulation process, insufficient precision and difficulty in generalization to the city scale.

[0005] The other is to model the air conditioner load through physical simulation, which can accurately represent the dynamic thermal characteristics of the building air conditioning system and support the scientificity of demand response potential evaluation. However, the traditional air conditioner load physical modeling method only focuses on a single building, the model is not representative enough, the modeling information is complex, and the random factors are not considered, which makes it difficult to adapt to the modeling process of urban air conditioner load. At the same time, the existing urban air conditioner load modeling only quantifies the energy consumption level, and does not involve the dynamic demand response potential of air conditioner system participating in power grid regulation. SUMMARY

[0006] According to an aspect of the present disclosure, a method for evaluating the demand response potential of air conditioner load is provided, the method comprising:

[0007] According to the building data of each building in the target area and the preset classification standard, each building and air conditioning system is classified, and a baseline simulation model of each type of building air conditioner load is established;

[0008] According to the target parameter combination, each reference simulation model is processed to obtain a plurality of air conditioner load models corresponding to each reference simulation model, each air conditioner load model is simulated to determine a baseline working condition of each air conditioner load model before demand response, wherein the target parameter combination includes a plurality of parameters affecting the demand response potential of the air conditioner load.

[0009] According to at least one preset demand response strategy, the air conditioner load models corresponding to the air conditioner loads of each type of building are simulated, and the demand response potential probability density curve of the air conditioner loads of each type of building is obtained by using each simulation result and the corresponding baseline working condition.

[0010] The demand response potential probability density curve of each type of building air conditioner load is used to obtain the total demand response potential probability density curve of each type of building air conditioner load in the target area.

[0011] According to the total demand response potential probability density curve, the demand response potential of the target area under different confidence levels is determined.

[0012] In a possible implementation, according to the target parameter combination, each reference simulation model is processed to obtain a plurality of air conditioner load models corresponding to each reference simulation model, including:

[0013] Each parameter value in the target parameter combination is sampled K times to obtain K sample parameter value combinations, wherein the sample parameter value combination includes a plurality of sample parameter values, the sample parameter value is subject to a preset distribution range of the corresponding parameter, and K is a positive integer.

[0014] Each sample parameter value in the sample parameter value combination is loaded into the corresponding reference simulation model to obtain the corresponding air conditioner load model.

[0015] In a possible implementation, the target parameter combination includes at least one of the following: personnel density, air conditioning system energy efficiency, indoor thermal mass, personnel work and rest period, air conditioning set temperature, fresh air volume, and personnel in-room rate.

[0016] The sampling method includes any one of the following: Latin hypercube sampling, Sobol sampling, random sampling and its improved variants, and E-Fast sampling.

[0017] In a possible implementation, the simulation of each air conditioner load model includes:

[0018] The meteorological file of the region where the target area is located in a preset time period is obtained, and the meteorological file includes meteorological data.

[0019] Each air conditioner load model is simulated by using the meteorological file.

[0020] In a possible implementation, according to at least one preset demand response strategy, air conditioning load models corresponding to various types of building air conditioning loads are simulated, and a demand response potential probability density curve of the corresponding type of building air conditioning load is obtained by using each simulation result and a corresponding baseline working condition, including:

[0021] According to at least one preset demand response strategy and a meteorological file of a region where the target area is located in a preset period, air conditioning load models corresponding to various types of building air conditioning loads are simulated to obtain a response working condition under the preset demand response strategy, and the meteorological file includes meteorological data.

[0022] A response potential evaluation index is determined according to the response working condition and the baseline working condition, and the response potential evaluation index includes at least one of an average power reduction, a peak power reduction in a demand response period, a cumulative energy consumption reduction in a demand response period, a 15-minute average indoor temperature offset, and a maximum indoor temperature offset in a demand response period.

[0023] A demand response potential probability density curve of each type of building air conditioning load is obtained according to the response potential evaluation index.

[0024] In a possible implementation, the preset demand response strategy includes an adjustment strategy and an adjustment period, and the adjustment strategy includes at least one of increasing or decreasing an indoor air conditioning temperature setting value, increasing or decreasing a supply air temperature, increasing or decreasing a supply air pressure, pre-cooling or pre-heating, increasing a water supply temperature of a water chiller, and shutting down a chiller.

[0025] In a possible implementation, the total demand response potential probability density curve of the various types of building air conditioning loads in the target area is obtained by using the demand response potential probability density curve of each type of building air conditioning load, including:

[0026] The demand response potential probability density curves of the various types of building air conditioning loads are aggregated to obtain the total demand response potential probability density curve of the various types of building air conditioning loads in the target area; or

[0027] The demand response potential probability density curves of the various types of building air conditioning loads are respectively sampled multiple times to obtain multiple sampling sample data, and the total demand response potential probability density curve of the various types of building air conditioning loads in the target area is obtained by fitting the multiple sampling sample data.

[0028] In a possible implementation, the demand response potential under different confidence levels of the target area is determined according to the total demand response potential probability density curve, including:

[0029] An accumulated probability distribution of the demand response potential is determined according to the total demand response potential probability density curve.

[0030] determining a range of the demand response potential under the confidence level according to the cumulative probability distribution of the response potential.

[0031] According to an aspect of the present disclosure, there is provided an air conditioner load demand response potential quantification evaluation device, comprising:

[0032] a classification modeling module configured to classify each building and air conditioner system in a target area according to building data of each building and a preset classification standard, and establish a benchmark simulation model of air conditioner load of each type of building;

[0033] a processing simulation module configured to process each benchmark simulation model according to a target parameter combination to obtain a plurality of air conditioner load models corresponding to each benchmark simulation model, simulate each air conditioner load model, and determine a baseline working condition of each air conditioner load model before demand response, wherein the target parameter combination comprises a plurality of parameters affecting the demand response potential of air conditioner load;

[0034] a first determining module configured to simulate each air conditioner load model corresponding to air conditioner load of each type of building according to at least one preset demand response strategy, and obtain a demand response potential probability density curve of air conditioner load of each type of building by using each simulation result and the corresponding baseline working condition;

[0035] a second determining module configured to obtain a total demand response potential probability density curve of air conditioner load of each type of building in the target area by using the demand response potential probability density curves of air conditioner load of each type of building;

[0036] a third determining module configured to determine the demand response potential of the target area under different confidence levels according to the total demand response potential probability density curve.

[0037] According to an aspect of the present disclosure, there is provided an electronic device, comprising:

[0038] a processor;

[0039] a memory for storing processor-executable instructions;

[0040] wherein the processor is configured to invoke the instructions stored in the memory to perform the method.

[0041] According to an aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon computer program instructions, which, when executed by a processor, implement the method.

[0042] The embodiment of the present disclosure classifies each building and air conditioning system according to the building data of each building in the target area and the preset classification standard, and establishes a benchmark simulation model of each type of building air conditioning load, which can realize accurate characterization of the dynamic adjustment characteristics of different types of air conditioning loads, ensure the evaluation accuracy, process each benchmark simulation model according to the target parameter combination, obtain a plurality of air conditioning load models corresponding to each benchmark simulation model, which can realize comprehensive consideration of user multi-source uncertainty factors, improve the generalization ability of the model, obtain the demand response potential probability density curve of the corresponding type of building air conditioning load by using each simulation result and the corresponding baseline working condition, obtain the total demand response potential probability density curve of each type of building air conditioning load in the target area by using the demand response potential probability density curve of each type of building air conditioning load, which can reflect multi-source user uncertainty, realize accurate quantification of multi-time scale demand response potential of regional level or even city level air conditioning load cluster, thereby realizing quantitative evaluation of air conditioning load adjustment potential under multi-time and space scale, and having the advantages of high precision and strong generalization.

[0043] It should be understood that the above general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. Other features and aspects of the present disclosure will become apparent from the detailed description of exemplary embodiments below, with reference to the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0044] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the present disclosure and, together with the specification, serve to explain the technical solutions of the present disclosure.

[0045] Figure 1 A flowchart of an air conditioning load demand response potential quantitative evaluation method according to an embodiment of the present disclosure is shown.

[0046] Figure 2 A possible implementation flowchart of step S13 in the method according to an embodiment of the present disclosure is shown.

[0047] Figure 3 A schematic diagram of a unit square meter demand response potential probability density curve is shown.

[0048] Figure 4 A block diagram of an air conditioning load demand response potential quantitative evaluation device according to an embodiment of the present disclosure is shown.

[0049] Figure 5 A block diagram of an electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0050] Various exemplary embodiments, features, and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numbers in different drawings represent the same or similar elements. Although various aspects of embodiments are illustrated in the drawings, the drawings are not necessarily drawn to scale unless specifically noted.

[0051] In the description of the present disclosure, it needs to be understood that the terms "length", "width", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the purpose of facilitating the description of the present disclosure and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present disclosure.

[0052] In addition, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present disclosure, the meaning of "multiple" is two or more, unless otherwise explicitly specified and limited.

[0053] In the present disclosure, unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connection", "fixing" and the like should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or it can be integrated; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the internal communication of two elements or the interaction relationship between two elements. For those skilled in the art, the specific meaning of the above terms in the present disclosure can be understood according to the specific circumstances.

[0054] The term "exemplary" herein means "serving as an example, an implementation, or an illustration". Any embodiment described herein as "exemplary" is not necessarily to be construed as being superior to or better than other embodiments.

[0055] The term "and / or" herein is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone. In addition, the term "at least one" herein means any one of the plurality or any combination of at least two of the plurality, for example, including at least one of A, B and C can mean including any one or more elements selected from the set consisting of A, B and C.

[0056] In addition, for a better illustration of the present disclosure, numerous specific details are given in the following detailed description. Those skilled in the art will understand that the present disclosure can be practiced without certain specific details. In some instances, well-known methods, means, elements and circuits have not been described in detail in order to emphasize the principles of the present disclosure.

[0057] Please refer to Figure 1 , Figure 1 A flow chart of an air conditioning load demand response potential evaluation method according to an embodiment of the present disclosure is shown.

[0058] As Figure 1 shown, the method comprises:

[0059] Step S11, according to the building data of each building in the target area and the preset classification standard, classifying each building and air conditioning system, and establishing a benchmark simulation model of each type of building air conditioning load;

[0060] Step S12, processing each benchmark simulation model according to a target parameter combination to obtain a plurality of air conditioning load models corresponding to each benchmark simulation model, simulating each air conditioning load model, and determining the baseline working condition of each air conditioning load model before demand response, wherein the target parameter combination includes a plurality of parameters affecting the demand response potential of air conditioning load;

[0061] Step S13, simulating each air conditioning load model corresponding to each type of building air conditioning load according to at least one preset demand response strategy, and obtaining the demand response potential probability density curve of the corresponding type of building air conditioning load using each simulation result and the corresponding baseline working condition;

[0062] Step S14, using the demand response potential probability density curve of each type of building air conditioning load to obtain the total demand response potential probability density curve of each type of building air conditioning load in the target area;

[0063] Step S15, determining the demand response potential of the target area under different confidence levels according to the total demand response potential probability density curve.

[0064] The demand response in the embodiments of the present disclosure can represent the behavior of the demand side or end consumer changing its short-term power consumption mode (consumption time or consumption level) and long-term power consumption mode in response to market-based price signals, incentives, or direct instructions from system operators. The baseline working condition can include working condition parameters related to the air conditioning system in the building when the demand response is not performed, for example, can include baseline load, indoor temperature, indoor humidity, indoor air conditioning temperature set value, etc., wherein the baseline load can be understood as the theoretical air conditioning load obtained according to the target parameter, and the baseline load can represent the calculated air conditioning load under the assumption that the user does not participate in the demand response during the demand response event. As an example, the demand response can be understood as the influence of the actual use behavior of the user on the baseline load (or other baseline working condition), such as increasing or decreasing the baseline load. The load of the air conditioner can be represented by power parameters such as megawatts.

[0065] The demand response potential in the embodiments of the present disclosure can refer to the ability to reduce / increase the load (or other baseline working condition) based on the baseline load (or other baseline working condition) when the user participates in the demand response, or the demand response potential can refer to the temperature offset used to measure the impact of the demand response on the user's comfort, that is, the measurement method of the demand response potential is various, and the embodiments of the present disclosure do not limit it. In other words, the demand response potential reflects how much influence the actual use behavior of the user will bring to the baseline working condition. Here, the baseline load can refer to the load that the user should consume if he does not participate in the demand response, and accordingly, the unit of the demand response potential can be power units, such as megawatts MW.

[0066] The embodiments of the present disclosure classify each building and air conditioning system according to the building data of each building in the target area and the preset classification standard, establish baseline simulation models of air conditioning loads of each type of building, can realize accurate characterization of dynamic adjustment characteristics of different types of air conditioning loads, ensure evaluation accuracy, process each baseline simulation model according to the target parameter combination, obtain a plurality of air conditioning load models corresponding to each baseline simulation model, can comprehensively consider multi-source uncertainty factors of users, improve the generalization ability of the model, obtain the demand response potential probability density curve of the air conditioning load of the corresponding type of building by using each simulation result and the corresponding baseline working condition, obtain the total demand response potential probability density curve of the air conditioning load of each type of building in the target area by using the demand response potential probability density curves of air conditioning loads of each type of building, can reflect multi-source user uncertainty, realize accurate quantification of multi-time scale demand response potential of air conditioning load clusters at the regional level or even the city level, thereby realizing quantification evaluation of air conditioning load adjustment potential under multi-time and space scales, and having the advantages of high precision and strong generalization.

[0067] It can be seen that the air conditioner load demand response potential evaluation method of the embodiments of the present disclosure has the following advantages: on the one hand, based on the air conditioner load physical simulation modeling method, the dynamic adjustment characteristics of different types of air conditioner loads are accurately characterized, and the evaluation accuracy is guaranteed. On the other hand, by sampling and simulating the key influence parameters (such as equipment energy efficiency, indoor thermal mass, personnel work and rest, etc.) of the air conditioner load demand response potential, the generalization ability of the model is improved. In addition, the "interval-probability" (response potential probability density curve) evaluation method is introduced to scientifically reflect the multi-source user uncertainty in the form of "different confidence corresponding to different demand response potential", and to realize the scientific and accurate quantification of the multi-time scale demand response potential of the regional level or even the city level air conditioner load cluster.

[0068] The execution subject of the method is not limited in the embodiments of the present disclosure, and in some possible implementation manners, the method of the embodiments of the present disclosure can be executed by a terminal device or a server or other processing device. The terminal device can be a user equipment (User Equipment, UE), a mobile device, a user terminal, a terminal, a handheld device, a computing device or a vehicle-mounted device, etc. For example, the terminal can be a mobile phone, a tablet computer, a notebook computer, a palm computer, a mobile Internet device (Mobile Internet device, MID), a wearable device, a virtual reality (Virtual Reality, VR) device, an augmented reality (Augmented reality, AR) device, a wireless terminal in industrial control, a wireless terminal in self-driving, a wireless terminal in remote medical surgery, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, a wireless terminal in Internet of Vehicles, etc. For example, the server can be a local server or a cloud server.

[0069] In some possible implementations, the method can be implemented by a processing component invoking computer-readable instructions stored in a memory. In one example, the processing component includes, but is not limited to, a single processor, or a discrete component, or a combination of the processor and the discrete component. The processor can include a controller having a function of executing instructions in an electronic device, and can be implemented in any appropriate manner, for example, by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic elements. Inside the processor, the executable instructions can be executed by hardware circuits such as logic gates, switches, application specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers.

[0070] The embodiments of the present disclosure do not limit the specific implementation of each step, and a person skilled in the art can implement it according to the actual situation and needs by using appropriate technical means.

[0071] The embodiments of the present disclosure do not limit the size of the target area, which can be a city, a certain area in the city, etc., and a person skilled in the art can set it according to the actual situation and needs.

[0072] The embodiments of the present disclosure do not limit the specific information included in the building data, and a person skilled in the art can set it according to the actual situation and needs, for example, the building data can include building type, building age, building area, building height, building layer, etc., the air conditioning system form can include cold and heat source type, air conditioning terminal type, circulating water system type, etc. Of course, the building data and the acquisition method of the air conditioning system type can include multiple types, and the embodiments of the present disclosure do not limit this, which will be exemplarily introduced below. Exemplarily, the reference simulation model established after classification in step S11 can include the air conditioning load simulation model of the whole building, and can also include the air conditioning load simulation model of each type of air conditioning classified in the building, for example, according to the form of the air conditioning system (cold and heat source type, air conditioning terminal type, circulating water system type, etc.), the air conditioning load simulation model corresponding to the air conditioning system is established, such as cold and heat source air conditioning load simulation model, air conditioning terminal air conditioning load simulation model, circulating water system air conditioning load simulation model, etc. Of course, the embodiments of the present disclosure do not limit the specific form of the reference simulation model. For example, if the same type of building contains multiple types of air conditioning systems, the reference simulation model of the same type of building can contain simulation sub-models corresponding to different types of air conditioning systems.

[0073] For example, the geographic position, the outer contour, the building height, the building layer number and the building POI (Point of Interest) information of each building in the target area can be obtained by obtaining the city-scale geographic information system (GIS) map data in the official database of various maps (such as Gaode map, Baidu map, Google map and the like).

[0074] The specific implementation of the building data obtained according to the data obtained from the map database is not limited in the embodiments of the present disclosure, and a person skilled in the art can implement it according to the actual situation and needs, for example, the data obtained from the map database can be input into a trained machine learning model, and the building data of each building can be output by using the machine learning model. Of course, the specific implementation and training method of the machine learning model are not limited in the embodiments of the present disclosure, and a person skilled in the art can implement it according to the actual situation and needs.

[0075] For example, if the outer contour, the building layer number and the area of each layer of the building are collected, the building area can be calculated by using the building outer contour, and the total building area can be calculated according to the building layer number multiplied by the area of each layer.

[0076] The specific content of the preset classification standard is not limited in the embodiments of the present disclosure, and a person skilled in the art can set it according to the actual situation and needs, for example, the preset classification standard can include the building type, the building age, the air conditioning and refrigeration mode in the building, the building area and the like, and of course, the release year of the energy saving standard, the power consumption design standard (such as “Public Building Energy Consumption Design Standard” and “Residential Building Energy Saving Design Standard”) and the like in the building field can also be combined for classification.

[0077] Exemplarily, the building construction year can be determined by comparing GIS map data of different years; the building type can be determined according to POI information, and of course, the specific classification manner of the building type is not limited in the embodiments of the present disclosure. Exemplarily, the building type can be divided according to the building design standard, and in the current embodiment, the building type can be divided into any combination of the following types: small office building, large office building, large hotel, small hotel, shopping mall building, medical and health building, comprehensive building, education building, cultural building, sports building, residential building and other building.

[0078] The specific implementation manner of establishing the benchmark simulation model of various building air conditioning loads is not limited in the embodiments of the present disclosure, and the person skilled in the art can establish the benchmark simulation model according to the actual situation and needs by using related technologies.

[0079] Exemplarily, when establishing the benchmark simulation model, each building can be further classified according to the building type and construction year data, and the air conditioning users can be classified in detail according to the publication year of the Public Building Energy Consumption Design Standard and the Residential Building Energy Saving Design Standard.

[0080] For example, the public building can be divided into three years: built before 2005, built in 2006-2014 and built after 2015. The residential building is divided according to the building thermal design zoning, for example, the hot summer and cold winter region is divided into: built before 2001, built in 2002-2009 and built after 2010; the cold region is divided into: built before 2010, built in 2011-2017 and built after 2018. The difference between different construction years is mainly in the performance of the envelope structure and the energy efficiency range of the air conditioning system.

[0081] For example, the statistical analysis of the real building construction year and the air conditioning system type can also be performed, and finally the modeling of multiple types of air conditioning loads can be supported.

[0082] For example, when the statistical data cannot be obtained, the air conditioning system type can be reasonably divided according to the building type and building area, for example, the large office building generally adopts the water-cooled central air conditioning, and the residential user mostly adopts the split air conditioning system.

[0083] After obtaining the data support, a building energy consumption simulation software is used to build a benchmark simulation model of air conditioning load of each type of user. The benchmark simulation model reflects the operation status of the air conditioner in the building, which includes the relationship between the operation parameters (such as refrigeration power, heating power, energy consumption of various air conditioning equipment, etc.) of the air conditioning system in the building and the environmental parameters (such as temperature, etc.). Based on the air conditioning operation power, air conditioning system energy efficiency and other parameters, the operation status of the building can be simulated. During the simulation process of the benchmark simulation model, based on the environmental parameters, the overall operation result of the air conditioner in the building under the environment can be simulated. The lighting power density, equipment power density and other parameters can all use the recommended values of the design standards in the corresponding year.

[0084] The specific parameter type in the target parameter combination is not limited in the embodiments of the present disclosure, and a person skilled in the art can set it according to the actual situation and needs. In one possible implementation, the target parameter combination includes at least one of the following: personnel density, air conditioning system energy efficiency, indoor thermal mass (including indoor unit area thermal mass, etc.), personnel work and rest period, air conditioning set temperature, fresh air volume, and personnel in-room rate. The parameters in the target parameter combination can be the parameters possessed by the benchmark simulation model, and the specific values of the target parameter combination can be brought into or assigned to the corresponding parameters in the benchmark simulation model.

[0085] The specific implementation of step S12 of processing each benchmark simulation model according to the target parameter combination to obtain a plurality of air conditioning load models corresponding to each benchmark simulation model is not limited in the embodiments of the present disclosure, and a person skilled in the art can implement it according to the actual situation and needs. For example, in one possible implementation, step S12 of processing each benchmark simulation model according to the target parameter combination to obtain a plurality of air conditioning load models corresponding to each benchmark simulation model can include:

[0086] The parameter values of each parameter in the target parameter combination are sampled K times to obtain K sample parameter value combinations, wherein the sample parameter value combination includes a plurality of sample parameter values, the sample parameter values are subject to a preset distribution range of the corresponding parameter, and K is a positive integer;

[0087] Each sample parameter value in the sample parameter value combination is loaded into the corresponding benchmark simulation model to obtain the corresponding air conditioning load model.

[0088] The target parameter combination includes a plurality of parameters affecting the demand response potential of the air conditioning load. By sampling the plurality of parameters affecting the demand response potential of the air conditioning load K times and loading each sample parameter value in the sample parameter value combination into the corresponding benchmark simulation model, a plurality of air conditioning load models can be constructed to comprehensively represent the multi-source uncertainty.

[0089] For example, there are M types of buildings, each type of building corresponds to a benchmark simulation model, K target parameter combinations can be obtained by sampling, and for the benchmark simulation model of each building, the K target parameter combinations are respectively brought into the benchmark simulation model, and K air conditioning load models corresponding to the building can be obtained, and the running parameters of the K air conditioning load models under the baseline working condition, for example, air conditioning power, air conditioning temperature setting value, indoor temperature and humidity, etc.

[0090] The sampling method is not limited in the embodiments of the present disclosure, and can be set according to actual conditions and needs by those skilled in the art, for example, in a possible implementation, the sampling method includes any one of the following: Carting hypercube sampling, Sobol sampling, random sampling and its improved variants (for example, random sampling based on Copula function and the like), E-Fast sampling, etc.

[0091] Exemplarily, the sampling can refer to randomly sampling the parameters in the statistical distribution range to which the parameters conform. The loading can also be referred to as simulation, which can refer to assigning (or modifying) the parameters obtained by sampling to the parameters of the corresponding benchmark simulation model, for example, the sampling result of the personnel density is 0.11 person / m 2 , and the personnel density in the benchmark simulation model is modified to 0.11 person / m 2 , and the simulation can be performed by using relevant building energy consumption simulation software, for example, EnergyPlus, DesignBuilder, etc.

[0092] Exemplarily, each air conditioning load model is obtained by sampling and modifying the key parameters of a type of benchmark simulation model, and K models can be obtained by K times of sampling. For example, the personnel density in the benchmark simulation model of an office building is 0.1 person / m 2 , the first sampling obtains 0.11 person / m 2 , the second sampling obtains 0.12 person / m 2 , and the personnel density of the first model is 0.11 person / m 2 , and the personnel density of the second model is 0.12 person / m 2 .

[0093] It should be understood that different buildings can have different target parameter combinations, that is, the types of parameters affecting the air conditioning load demand response potential can be different for different buildings.

[0094] Exemplarily, K times of sampling are performed on the parameter values of each parameter in the target parameter combination, and each sampling can refer to sampling each parameter combination in the target parameter once, so that K times of sampling can obtain K sampling parameter value combinations.

[0095] The number of times of sampling simulation depends on the number of users in the specific air conditioning load cluster, and thus the embodiments of the present disclosure are suitable for both regional and city-level air conditioning load cluster modeling. The more the number of times of sampling simulation, the more accurate the demand response potential obtained after simulation, but at the same time, the calculation amount will increase, and thus a person skilled in the art can reasonably design the number of times of sampling simulation based on modeling accuracy and calculation cost considerations, for example, K can be greater than or equal to 100 times. The value range of the parameters set in the model is based on the reference literature and the relevant national design standard. Since there are many types of buildings, for example, large office buildings built before 2005, the specific parameter value range in the embodiments is shown in Table 1:

[0096] Table 1

[0097]

[0098] The key parameters of other building types are shown in Table 2, wherein for residential buildings, other buildings using split air conditioning systems, the parameter of fresh air volume is not included, and the rest are the same as large office buildings.

[0099] Table 2

[0100]

[0101] For the personnel work and rest parameters affected by strong randomness, a time-division personnel occupancy rate is constructed, which is based on literature research and relevant design specifications or survey statistics. The parameter value range of the personnel work and rest of the office building in the embodiments is shown in Table 3:

[0102] Table 3

[0103]

[0104]

[0105] The personnel occupancy rate of other building types is shown in Table 4, which needs to include the personnel occupancy rate from 0:00 to 23:00, and the rest are the same as large office buildings. It needs to be emphasized that the parameter value range of the embodiments does not mean that the personnel occupancy rate of the office building must be subject to the distribution and range of the above table. The actual data can be flexibly replaced according to the survey data or research needs, and a Markov chain or other random process can be used to generate the personnel work and rest table.

[0106] Table 4

[0107]

[0108] In the embodiment, the probability distribution is mainly based on reference research and relevant design specifications or standards. When the standard has a clear recommended value, a normal distribution is used. When the recommended value is an interval, a triangular distribution or a uniform distribution is used. The maximum value is also selected based on the research results. In all embodiments, the optimal case is the research statistics to match the real situation of the city. If statistical data cannot be obtained, it can be reasonably selected based on literature and relevant design standards. Especially for personnel work parameters, the influence of human behavior needs to be considered, such as office buildings that need to consider events such as work, lunch break, and work. It should be understood that due to the high cost of large-scale basic information research, the current personnel density, fresh air volume, and cold machine COP data are related to specifications or standards, but this does not conflict with the real research data. The most ideal case is to obtain real research data, and using specifications or standards is a suboptimal choice considering economy.

[0109] Of course, the embodiment of the present disclosure does not limit the specific method of obtaining the baseline operating condition of various types of building air conditioning systems without demand response in step S12. Those skilled in the art can use related technologies according to actual conditions and needs.

[0110] For example, in one possible implementation, step S12 simulates each air conditioning load model, which can include:

[0111] Obtain the weather file of the region where the target area is located in a preset time period, and the weather file includes weather data;

[0112] Simulate each air conditioning load model using the weather file.

[0113] The embodiment of the present disclosure does not limit the specific form and acquisition method of the weather file. Those skilled in the art can use related technologies according to actual conditions and needs. The weather data can include temperature, humidity, wind size, etc. The embodiment of the present disclosure can realize the running simulation of the air conditioning load model in the corresponding environment through the weather file, so that the air conditioning running condition of each building in the target area is close to the running condition in the actual environment, and the accuracy of the simulation is improved.

[0114] For example, simulate the air conditioning load model that has been built. The weather file used in the simulation can be selected according to the research content. The optimal case is to use the measured weather data, and the time span can include the entire refrigeration season and winter.

[0115] After obtaining the weather data from the weather file, each air conditioning load model is simulated under the weather data, so as to obtain the baseline operating condition of each air conditioning load model before demand response.

[0116] The specific implementation of the demand response potential probability density curve of the corresponding building air conditioning load obtained by simulating the air conditioning load model corresponding to each type of building air conditioning load according to at least one preset demand response strategy in step S13 and using each simulation result and the corresponding baseline working condition is not limited, and a person skilled in the art can implement it according to actual conditions and needs. It should be understood that the simulation of the air conditioning load model corresponding to each type of building air conditioning load according to at least one preset demand response strategy can obtain each simulation result under the corresponding working condition.

[0117] Please refer to Figure 2 , Figure 2 A possible implementation flowchart of step S13 in the method according to the embodiments of the present disclosure is shown.

[0118] For example, in a possible implementation, as shown in Figure 2 , step S13 simulates the air conditioning load model corresponding to each type of building air conditioning load according to at least one preset demand response strategy, and obtains the demand response potential probability density curve of the corresponding building air conditioning load by using each simulation result and the corresponding baseline working condition, which can include:

[0119] Step S131, simulate the air conditioning load model corresponding to each type of building air conditioning load according to at least one preset demand response strategy and a meteorological file of a region where the target area is located in a preset time period, to obtain a response working condition under the preset demand response strategy, and the meteorological file includes meteorological data; wherein the simulation result can include the response working condition under the preset demand response strategy, and the response working condition can include the working condition parameters related to the air conditioning system in the building when the demand response is performed, for example, can include air conditioning load, indoor temperature, indoor humidity, indoor air conditioning temperature set value, etc.

[0120] Step S132, determine a response potential evaluation index according to the response working condition and the baseline working condition, the response potential evaluation index includes at least one of the average power reduction (which can represent the average reduction of air conditioning power per 15 minutes compared with the baseline load when the DR is implemented), the peak power reduction in the demand response period (which can represent the maximum reduction of air conditioning power compared with the baseline load in the DR period), the cumulative energy consumption reduction in the demand response period (which can represent the cumulative air conditioning power reduction compared with the baseline load in the DR period), the average indoor temperature offset per 15 minutes (which can represent the average indoor temperature offset per 15 minutes in the response working condition compared with the indoor temperature in the baseline working condition in the DR period), the maximum indoor temperature offset in the demand response period (which can represent the maximum indoor temperature offset in the response working condition compared with the indoor temperature in the baseline working condition in the DR period), etc.

[0121] Step S133, obtaining the demand response potential probability density curve of each type of building air conditioning load in the target area according to the response potential evaluation index;

[0122] In this way, the disclosed embodiments can quickly and accurately obtain the actual demand response potential probability density curve of each type of building air conditioning load in the target area, and aggregate the demand response potential probability density curves of each type of building air conditioning load in the same type of building air conditioning load to obtain the demand response potential probability density curve of the type of building air conditioning load.

[0123] The demand response strategy represents various air conditioning use demands or air conditioning operation modes that the user can propose, as well as use time periods. The disclosed embodiments do not limit the specific settings of the preset demand response strategy, and in one possible implementation, the preset demand response strategy can include an adjustment strategy and an adjustment time period, etc. The adjustment strategy can include at least one of raising or lowering the indoor air conditioning temperature set value, raising or lowering the supply air temperature, raising or lowering the supply air pressure, pre-cooling or pre-heating, raising the water supply temperature of the chiller, shutting down the chiller, etc. The adjustment time period can include at least one of the peak power supply period of the power grid, the peak electricity price period, the demand response period issued by the power grid, etc. The disclosed embodiments can simulate the demand response of the user through the preset demand response strategy, thereby simulating the case of the air conditioning load model when the user demand response is introduced, and obtaining the response working condition of the building air conditioning load under the preset demand response strategy. For example, corresponding to the preset user operation mode, the corresponding parameters in the air conditioning load model can be adjusted (such as adjusting the temperature, changing the air conditioning operation mode, turning on part of the air conditioning or turning off part of the air conditioning, etc.). The disclosed embodiments can realize the running simulation of the air conditioning load model in the corresponding environment through the meteorological file, so that the air conditioning operation of each building in the target area is close to the actual running condition under the actual environment, and the accuracy of the simulation is improved.

[0124] Of course, the preset demand response strategy can be set according to the actual regulation and control demand of the power grid, and is not limited to the foregoing examples.

[0125] Exemplarily, after setting the preset demand response strategy, the embodiment of the present disclosure can simulate the air conditioning load model corresponding to each type of building air conditioning load according to the preset demand response strategy and the weather file of the region where the target area is located in the preset period, to obtain the response working condition under the preset demand response strategy (step S131). The difference between step S13 and step S12 is that step S13 adds a preset demand response strategy, and other contents please refer to the previous introduction, which will not be repeated here. Each air conditioning load model can correspond to at least one preset demand response strategy, and the preset demand response strategies corresponding to each air conditioning load model can not be completely the same. The preset demand response strategy for the air conditioning load model of a type of building can be the same. For an air conditioning load model, a simulation result can be obtained based on each type of preset demand response strategy, and the simulation result corresponds to the baseline working condition obtained by the air conditioning load model.

[0126] The embodiment of the present disclosure does not limit the specific implementation of step S132 for determining the response potential evaluation index according to the response working condition and the baseline working condition, and does not limit the type of response potential evaluation index. The person skilled in the art can set it according to the actual situation and needs.

[0127] Next, take the evaluation indexes of demand response period peak power reduction (PPR) and cumulative energy reduction (CER) as examples for introduction.

[0128] The calculation method of CER and PPR is shown in formula (1) and formula (2)

[0129]

[0130] Where t0 represents the start time of demand response, tDR represents the end time of demand response, T DR represents the entire demand response period, represents the baseline load at time t (obtained in step S12), represents the air conditioning load at time t (obtained in step S131).

[0131] The demand response potential can be represented by one or more of the above-mentioned response potential evaluation indexes, for example, the cumulative energy reduction CER in the demand response period.

[0132] Exemplarily, step S133 obtains the demand response potential probability density curve of the air conditioning load of each type of building in the target area according to the response potential evaluation index, which can include, for example:

[0133] Step S1331, after calculating the response potential evaluation index of each building air conditioning load according to the definition manner of the aforementioned response potential evaluation index, fitting is performed on each response potential evaluation index to obtain the theoretical demand response potential probability density curve of each type of building air conditioning load. It should be noted that the demand response potential results obtained before are all obtained on the basis of the benchmark simulation model, and the benchmark simulation model is derived from the theoretical model library or the representative model extracted in the target area, so the theoretical demand response potential probability density curve of each type of building air conditioning load is obtained in step S1331. It should be understood that the specific fitting manner is not limited in the embodiments of the present disclosure, and a person skilled in the art can implement it according to actual conditions and needs by using related technologies, for example, the fitting manner can be: kernel function probability density estimation method, frequency-based probability density fitting, the former is to regard the CER of each air conditioning load model as a normal distribution kernel function, and then estimate the CER probability density curve of the single air conditioning load model, for details, please refer to the explanation of related technologies. The latter is the simplest fitting method, that is, "frequency / total number=probability", for example, in 1000 models, CER=5W / m2 appears 300 times, so the probability of CER=5W / m2 is 30%.

[0134] Step S1332, the actual parameters of each specific building in the target area are used to correct the theoretical demand response potential probability density curve of the type of building, to obtain the demand response potential probability density curve of each specific building; for example, taking the demand response potential probability density curve of the single type of building air conditioning load as a benchmark, the building area of each actual building in the target area is scaled (the vertical axis (probability density) and the horizontal axis (independent variable) of the probability density function are transformed) to obtain the demand response potential probability density curve of each building air conditioning load.

[0135] Step S1333, the demand response potential probability density curves of each specific building are aggregated to obtain the demand response potential probability density curve of the type of building air conditioning load, and in this way, the demand response potential probability density curves of each type of building air conditioning load are obtained.

[0136] Of course, the above specific and exemplary description of step S133 for obtaining the demand response potential probability density curve of each type of building air conditioning load according to the response potential evaluation index is exemplary, and should not be regarded as a limitation of the embodiments of the present disclosure. A person skilled in the art can implement this step according to actual conditions and needs by using related technologies.

[0137] After obtaining the demand response potential evaluation index for each air conditioning user, this embodiment of the disclosure can obtain the demand response potential probability density curve of a single type of building's air conditioning load based on the evaluation index (step S133). This avoids a large amount of repetitive modeling work and comprehensively represents the demand response potential of a single type of air conditioning system user in the form of a probability density curve. Step S133 can obtain a corresponding demand response potential probability density curve for each type of building.

[0138] Please see Figure 3 , Figure 3 A schematic diagram of the probability density curve of demand response potential per unit square meter is shown.

[0139] For example, such as Figure 3 The probability density curve for demand response potential per unit square meter shown has the horizontal axis representing the demand response potential per unit square meter and the vertical axis representing the probability density. The area enclosed by the curve represents the probability of taking the demand response potential within the current range.

[0140] For example, after obtaining the demand response potential probability density curves of the air conditioning load of the same type of buildings, the demand response potential probability density curves of each building's air conditioning load can be aggregated based on the building area of ​​each actual building in the target area (by transforming the vertical axis (probability density) and the horizontal axis (independent variable) of the probability density function) to obtain the demand response potential probability density curves of the air conditioning load of that type of building. In addition to using the aggregation method, the demand response potential probability density curves of the air conditioning load of that type of building can also be obtained by sampling fitting. This disclosure does not limit the specific method used.

[0141] The following is an example.

[0142] For example, the research object includes n large office buildings built after 2015. These n buildings belong to one category. The demand response potential per unit square meter for each building is Xi (i is a positive integer of 1, 2, ... n), which all follow the fitted demand response potential probability density curve f(X) and are independent of each other. The building area is Ai, i is the building number, and n is the total number of buildings. Then the total demand response potential of this type of building is Y, and the calculation method is shown in formula (3).

[0143] Y = X1 × A1 + X2 × A2 + …… Xn × An (Formula 3)

[0144] In this article, "×" represents a multiplication operation.

[0145] For example, if the sample size n is large, Y can be approximated as following a normal distribution.

[0146] The expectation and variance of the unit square meter demand response potential Xi are calculated. Since Xi of the same type of building obeys the same distribution f(X) after fitting, the expectation and variance of Xi are E[X] and Var[X] respectively, and the expectation and variance of the total demand response potential Y of the type of building are calculated according to formula (4) and formula (5), and the final total demand response potential probability density function is shown in formula (6).

[0147] E[Y] = A1 x E[X] + A2 x E[X] + …… An x E[X] Formula (4)

[0148] Var[Y] = (A1) 2 x Var[X] + (A2) 2 x Var[X] + …… (An) 2 x Var[X] Formula (5)

[0150]

[0151] Wherein, fY(y) represents the probability density function after clustering for a type of building, and y represents the total demand response potential of the type of building.

[0152] For example, if the sample size n is small, it is difficult to apply the central limit theorem, or the accuracy requirement of the final probability density curve is very high, then the method including but not limited to area weighted aggregation can be used.

[0153] For example, the steps of the weighted aggregation method can include:

[0154] First, the demand response potential Xi x Ai of each building can be transformed into Zi = Xi x Ai, and the probability density function f Zi (z) of each Zi is calculated according to formula (8)

[0155]

[0156] The probability density function f Y (y) of the total demand response potential of the type of building is calculated according to formula (9A), and the convolution operation of formula (9A) is shown in formula (9B).

[0157]

[0158]

[0159] Wherein, “*” represents convolution operation.

[0160] In addition, the sampling fitting method can also be used to achieve the method, and the sampling fitting method includes the following steps, for example: sampling f(X) multiple times to generate a large number of samples, each sample can be allocated to any Xi, and finally, the area is aggregated to calculate the method, which is the same as formula (3). One Y value represents the total demand response potential under one demand response event, multiple Y values are calculated, the probability density curve of Y is fitted, and the total demand response potential probability density curve of the building is obtained.

[0161] It should be understood that the above description of the calculation method of the demand response potential probability density curve of the air conditioning load of the building is exemplary and should not be considered as a limitation of the embodiments of the present disclosure. Those skilled in the art can use other methods according to actual conditions and needs, and the specific implementation of the above method can refer to related technologies, which will not be described here.

[0162] The embodiments of the present disclosure do not limit the specific implementation of the step S14 using the demand response potential probability density curve of the air conditioning load of each type of building to obtain the total demand response potential probability density curve of the air conditioning load of each type of building in the target area, and those skilled in the art can use appropriate technical means to achieve it according to actual conditions and needs. Among them, all types of buildings in the target area finally obtain a total demand response potential probability density curve. For example, in one possible implementation, the step S14 using the demand response potential probability density curve of the air conditioning load of each type of building to obtain the total demand response potential probability density curve of the air conditioning load of each type of building in the target area can include:

[0163] The demand response potential probability density curves of the air conditioning loads of each type of building are aggregated to calculate the total demand response potential probability density curve of the air conditioning loads of each type of building in the target area; or

[0164] The demand response potential probability density curves of the air conditioning loads of each type of building are respectively sampled multiple times to obtain multiple sampling sample data; and the multiple sampling sample data are fitted to obtain the total demand response potential probability density curve of the air conditioning loads of each type of building in the target area.

[0165] For example, the specific implementation of the aggregation calculation and the sampling fitting is not limited in the embodiments of the present disclosure, and those skilled in the art can use related technologies to achieve it according to actual conditions and needs.

[0166] For example, the demand response potential probability density curves of the air conditioning loads of each type of building are aggregated to calculate, which can include: the demand response potential probability density curves of the air conditioning loads of each type of building obtained by the step S14 are calculated by using the convolution method or the method for calculating the joint probability density in related technologies to obtain the total demand response potential probability density curve of the air conditioning loads of each type of building in the target area.

[0167] Exemplarily, the sampling fitting of the demand response potential probability density curves of various types of building air conditioning loads can include sampling each of the demand response potential probability density curves of various types of building air conditioning loads multiple times to obtain multiple sampling sample data; fitting the multiple sampling sample data to obtain the total demand response potential probability density curve of various types of building air conditioning loads in the target region, for example, the demand response potential probability density curve 1, the demand response potential probability density curve 2, and the like can be sampled multiple times to generate a large number of samples, and finally the total demand response potential probability density curve (i.e., the total demand response potential probability density function f(x)) of the target region is fitted.

[0168] Exemplarily, the fitting methods include but are not limited to: frequency-based probability density curve fitting, kernel density function probability distribution estimation method. These two types of methods have been developed relatively maturely, and therefore are not described in detail.

[0169] The specific implementation of step S15 of determining the demand response potential of the target region under different confidence levels according to the total demand response potential probability density curve is not limited in the embodiments of the present disclosure, and a person skilled in the art can implement it according to actual conditions and needs, exemplarily, in a possible implementation, step S15 of determining the demand response potential of the target region under different confidence levels according to the total demand response potential probability density curve can include:

[0170] determining the cumulative probability distribution of the demand response potential according to the total demand response potential probability density curve;

[0171] determining the range of the demand response potential under the confidence level according to the cumulative probability distribution of the response potential.

[0172] The implementation of determining the cumulative probability distribution of the demand response potential according to the total demand response potential probability density curve and determining the range of the demand response potential under the confidence level according to the cumulative probability distribution of the response potential is exemplarily described below, and it should be understood that the following introduction should not be regarded as a limitation of the embodiments of the present disclosure. The following is a confidence interval calculation method and steps under a fixed confidence level:

[0173] 1. According to the total demand response potential probability density function f(x) of the air conditioning load cluster in multiple spatiotemporal scales (corresponding to the overall situation of multiple types of buildings in the target region) obtained in step S14, the cumulative probability distribution function F(x) is calculated, and the calculation formula is shown in formula 10:

[0174]

[0175] 2. According to formulas 11 and 12, the lower limit L and the upper limit U of the confidence interval are obtained:

[0176]

[0177] where interp (parameters A, formula B, parameter C) represents an interpolation function, parameter A is used to define the positions of points in formula B, parameter B represents the values at these points, and parameter C is the position of a point at which interpolation is to be calculated, and the value (L, U) obtained by interp (parameters A, formula B, parameter C) represents the interpolation result. Of course, the interpolation function can include various interpolation functions such as nearest neighbor interpolation, linear interpolation, piecewise linear interpolation, and the like, and the embodiments of the present disclosure are not limited in this regard.

[0178] 3. Finally, the demand response potential of the region under the confidence level a can be [U, L].

[0179] The present disclosure aims to obtain a demand response potential total probability density curve of a current air conditioning load cluster, and the spatial scale (target region) includes but is not limited to the following: park level, region level, city level, and the like. Finally, the air conditioning load adjustment potential interval under different confidence levels is calculated, and the interval can be optimized, including but not limited to the following: the narrowest interval under the same confidence level, the interval median is the highest point of the probability density, the interval under the corresponding confidence level, and the like. The optimal selection standard is based on the regulation and control demand of the power grid, and the specific calculation formula is based on the selection basis of the optimal interval. After obtaining the region-level demand response potential probability density curve, the interval needs to be calculated in the subsequent process, for example: under the confidence level of 80%, the demand response potential range of the region is X1-X2 MW (megawatt); under the confidence level of 60%, the demand response potential range of the region is X3-X4 MW.

[0180] Of course, the selection of the confidence level is determined based on the research demand or precision requirement of the present disclosure, and the embodiments of the present disclosure are not limited in this regard.

[0181] It is worth noting that although there are methods for determining the demand response potential of air conditioning load in the related art, the related art usually has the following defects:

[0182] 1) Although the related art can construct a simple air conditioning load mechanism model, it is difficult to deeply characterize the influence of the building body, the characteristics of the air conditioning equipment, and the change of the power grid regulation and control demand on the demand response potential of the air conditioning load, and the model precision is limited. In a large spatial scale air conditioning load cluster such as a region level or a city level, important model parameters such as room temperature and building envelope thermal inertia are difficult to collect on a large scale, which leads to difficulties in modeling work or a large deviation between the model and the actual situation.

[0183] 2) Related technologies generally only use a single model to generalize to an air conditioning load cluster, and do not solve the problem of insufficient model representation. In multi-temporal and spatial scale air conditioning load cluster modeling, the air conditioning system energy efficiency, indoor thermal mass, personnel work and rest, and other parameters of different buildings are significantly different, and the key parameters have a greater impact on the response potential of air conditioning load demand. When the influence of uncertain factors is not considered, it will be difficult to accurately quantify the demand response potential of the air conditioning load cluster, resulting in evaluation results that cannot effectively support real grid regulation.

[0184] It can be seen from the introduction that the air conditioning load demand response potential quantitative evaluation of the embodiments of the present disclosure can solve the following problems:

[0185] 1) Multi-space scale large-scale building information identification: building information such as building area and building type is extracted from GIS map data to realize low-cost large-scale building information collection;

[0186] 2) City-level air conditioning load modeling considering multi-source uncertainty: air conditioning users are classified according to building type, and a corresponding type of air conditioning load benchmark model is constructed; at the same time, a random distribution sampling method is used to sample key factors affecting the demand response potential of air conditioning load, such as system energy efficiency, indoor thermal mass, personnel work and rest, and set temperature, to construct a city-level air conditioning load cluster, and realize comprehensive consideration of multi-source uncertainty factors of users;

[0187] 3) City-level air conditioning load regulation potential quantitative evaluation method: according to different grid regulation requirements, the demand response strategy of the air conditioning load is adjusted, the demand response potential interval of the city-level air conditioning load cluster under multiple regulation strategies is obtained, and the "interval-probability" evaluation method is introduced to obtain the city-level air conditioning load regulation potential interval under the corresponding confidence.

[0188] It can be seen that the embodiments of the present disclosure can solve the problems existing in related technologies.

[0189] It can be understood that the above-mentioned various method embodiments mentioned in the present disclosure can be combined with each other to form combined embodiments without deviating from the principle logic. Limited by the length, the present disclosure will not be repeated. Those skilled in the art can understand that in the above-mentioned method of the specific implementation manner, the specific execution order of each step should be determined according to its function and possible internal logic.

[0190] Please refer to Figure 4 , Figure 4 A block diagram of an air conditioning load demand response potential quantitative evaluation device according to an embodiment of the present disclosure is shown.

[0191] As Figure 4 shown, the device includes:

[0192] The classification modeling module 10 is configured to classify each building and air conditioning system in the target region according to building data of each building in the target region and a preset classification standard, and establish a benchmark simulation model of air conditioning load of each type of building.

[0193] The processing simulation module 20 is configured to process each benchmark simulation model according to a target parameter combination to obtain a plurality of air conditioning load models corresponding to each benchmark simulation model, simulate each air conditioning load model, and determine a baseline operating condition of each air conditioning load model before demand response, wherein the target parameter combination includes a plurality of parameters affecting demand response potential of the air conditioning load.

[0194] The first determination module 30 is configured to simulate each air conditioning load model corresponding to each type of building air conditioning load according to at least one preset demand response strategy, and obtain a demand response potential probability density curve of the corresponding type of building air conditioning load by using each simulation result and the corresponding baseline operating condition.

[0195] The second determination module 40 is configured to obtain a total demand response potential probability density curve of each type of building air conditioning load in the target region by using the demand response potential probability density curves of each type of building air conditioning load.

[0196] The third determination module 50 is configured to determine demand response potential of the target region under different confidence levels according to the total demand response potential probability density curve.

[0197] According to the building data of each building in the target region and the preset classification standard, the classification modeling module 10 classifies each building and air conditioning system, and establishes a benchmark simulation model of air conditioning load of each type of building. The dynamic adjustment characteristics of different types of air conditioning loads can be accurately characterized, and the evaluation accuracy is ensured. Each benchmark simulation model is processed according to a target parameter combination to obtain a plurality of air conditioning load models corresponding to each benchmark simulation model. The comprehensive consideration of multiple source user uncertainties is realized, the generalization ability of the model is improved, the demand response potential probability density curve of the corresponding type of building air conditioning load is obtained by using each simulation result and the corresponding baseline operating condition, the total demand response potential probability density curve of each type of building air conditioning load in the target region is obtained by using the demand response potential probability density curves of each type of building air conditioning load, the multiple source user uncertainties can be reflected, the precise quantification of the multi-time scale demand response potential of the regional level or even the city level air conditioning load cluster is realized, and the quantitative evaluation of the air conditioning load adjustment potential under the multi-time and space scale is realized. The advantages of high precision and strong generalization are considered.

[0198] In a possible implementation, processing each benchmark simulation model according to a target parameter combination to obtain a plurality of air conditioning load models corresponding to each benchmark simulation model includes:

[0199] K times of sampling are performed on parameter values of each parameter in the target parameter combination to obtain K sample parameter value combinations, wherein the sample parameter value combination includes a plurality of sample parameter values, the sample parameter values are subject to a preset distribution range of the corresponding parameter, and K is a positive integer;

[0200] Each sample parameter value in the sample parameter value combination is loaded into a corresponding baseline simulation model to obtain a corresponding air conditioner load model.

[0201] In a possible implementation, the target parameter combination includes at least one of the following: a person density, an air conditioner system energy efficiency, an indoor thermal mass, a person work and rest period, an air conditioner set temperature, a fresh air volume, and a person in-room rate,

[0202] The sampling method includes any one of the following: a Cartesian hypercube sampling, a Sobol sampling, a random sampling and an improved form thereof, and an E-Fast sampling.

[0203] In a possible implementation, the simulation and modeling of each air conditioner load model includes the following:

[0204] A meteorological file of a region where the target area is located in a preset time period is obtained, and the meteorological file includes meteorological data.

[0205] The simulation and modeling of each air conditioner load model are performed by using the meteorological file.

[0206] In a possible implementation, the simulation and modeling of air conditioner load models corresponding to air conditioner loads of various types of buildings are performed according to at least one preset demand response strategy, and a demand response potential probability density curve of air conditioner loads of each type of building is obtained by using each simulation result and a corresponding baseline working condition, including the following:

[0207] The simulation and modeling of air conditioner load models corresponding to air conditioner loads of various types of buildings are performed according to at least one preset demand response strategy and a meteorological file of a region where the target area is located in a preset time period, and a response working condition under the preset demand response strategy is obtained, wherein the meteorological file includes meteorological data.

[0208] A response potential evaluation index is determined according to the response working condition and the baseline working condition, and the response potential evaluation index includes at least one of the following: an average power reduction, a peak power reduction in a demand response period, a cumulative energy consumption reduction in the demand response period, a 15-minute average indoor temperature offset, and a maximum indoor temperature offset in the demand response period.

[0209] A demand response potential probability density curve of air conditioner loads of each type of building is obtained according to the response potential evaluation index.

[0210] In a possible implementation, the preset demand response strategy includes an adjustment strategy and an adjustment time period, and the adjustment strategy includes at least one of the following: an increase in a set temperature of an air conditioner, a decrease in a set temperature of an air conditioner, an increase in a fresh air volume, a decrease in a fresh air volume, an increase in a set temperature of an air conditioner in a demand response period, a decrease in a set temperature of an air conditioner in the demand response period, an increase in a fresh air volume in the demand response period, and a decrease in the fresh air volume in the demand response period.

[0211] or at least one of reducing indoor air conditioning temperature set value, increasing or reducing air supply temperature, increasing or reducing air supply pressure, pre-cooling or pre-heating, increasing water supply temperature of chiller unit, shutting down chiller,

[0212] The adjustment period includes at least one of a power supply peak period, a peak period, a peak electricity price period, and a demand response period published by a power grid.

[0213] In a possible implementation, the total demand response potential probability density curve of the air conditioning loads of various types in the target area is obtained by using the demand response potential probability density curves of the air conditioning loads of various types, and includes:

[0214] The demand response potential probability density curves of the air conditioning loads of various types are aggregated to obtain the total demand response potential probability density curve of the air conditioning loads of various types in the target area; or

[0215] The demand response potential probability density curves of the air conditioning loads of various types are respectively sampled multiple times to obtain multiple sampling sample data, and the total demand response potential probability density curve of the air conditioning loads of various types in the target area is obtained by fitting the multiple sampling sample data.

[0216] In a possible implementation, the demand response potential of the target area under different confidence levels is determined according to the total demand response potential probability density curve, and includes:

[0217] The cumulative probability distribution of the demand response potential is determined according to the total demand response potential probability density curve.

[0218] The range of the demand response potential under the confidence level is determined according to the cumulative probability distribution of the demand response potential.

[0219] In some embodiments, the apparatus provided by the embodiments of the present disclosure has functions or includes modules that can be used to perform the methods described in the above method embodiments, and the specific implementation can refer to the description of the above method embodiments. For brevity, it will not be repeated here.

[0220] The present application has the following advantages and effects compared with the prior art:

[0221] In terms of technology:

[0222] The traditional air conditioning load modeling method is difficult to simultaneously consider high precision and strong generalization ability, and the present application remedies the deficiency. By constructing a multi-type air conditioning load physical simulation model and using a key parameter random distribution sampling simulation mode, the present application realizes comprehensive representation of user air conditioning load multi-source uncertainty and scientifically reflects the influence law of uncertain factors on air conditioning load demand response potential.

[0223] The interval-probability evaluation method is introduced, the traditional single-point potential evaluation result is replaced by the probability curve, and finally the mathematical statistical method is used for superposition and aggregation, which technically solves the scientific aggregation of air conditioning load expansion from single building to air conditioning load cluster, so as to realize the scientific quantitative evaluation of air conditioning load regulation potential under multiple time and space scales.

[0224] For application level:

[0225] The time and space scale limitation of the traditional air conditioning load modeling method is broken through; the output model can evaluate the demand response potential and the corresponding regulation scheme according to the real regulation and control demand of the power grid side, establishes the flexible regulation link between the power grid side and the air conditioning user side, can effectively support the intelligent management and control of the power grid side to the air conditioning load cluster, and promotes the green and low-carbon development and safe and stable operation of the power grid.

[0226] The embodiment of the disclosure also provides a computer readable storage medium, which stores computer program instructions, and the computer program instructions are executed by a processor to realize the above method. The computer readable storage medium can be a non-volatile computer readable storage medium.

[0227] The embodiment of the disclosure also provides an electronic device, including: a processor; a memory for storing processor executable instructions; wherein the processor is configured to call the instructions stored in the memory to execute the above method.

[0228] The embodiment of the disclosure also provides a computer program product, including computer readable code or non-volatile computer readable storage medium carrying computer readable code, when the computer readable code runs in the processor of the electronic device, the processor in the electronic device executes the above method.

[0229] The electronic device can be provided as a terminal, a server or other forms of devices.

[0230] Please refer to Figure 5 , Figure 5 A block diagram of an electronic device according to an embodiment of the disclosure is shown.

[0231] For example, the electronic device 1900 can be provided as a server. Referring to Figure 5 , the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932, for storing instructions executable by the processing component 1922, such as an application program. The application program stored in the memory 1932 can include one or more than one module each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute the instructions to perform the above method.

[0232] The electronic device 1900 can further include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output (I / O) interface 1958. The electronic device 1900 can operate based on an operating system stored in the memory 1932, such as Microsoft Windows Server operating system (Windows Server TM ), Apple's graphical user interface-based operating system (Mac OSX TM ), multi-user multi-processing computer operating system (Unix TM ), free and open-source Unix-like operating system (Linux TM ), open-source Unix-like operating system (FreeBSD TM ), or the like.

[0233] In an exemplary embodiment, there is also provided a non-transitory computer readable storage medium, such as the memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to perform the above-described method.

[0234] The present disclosure can be a system, a method, and / or a computer program product. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure.

[0235] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or punched tape, a

[0236] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0237] Computer readable program instructions for carrying out operations of the present disclosure can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computing / processing device, partly on the user's computing / processing device, as a stand-alone software package, partly on the user's computing / processing device and partly on a remote computing / processing device or entirely on the remote computing / processing device or server. In the latter scenario, the remote computing / processing device can be connected to the user's computing / processing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computing / processing device, for example, through the Internet using an Internet Service Provider. In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0238] The computer readable program instructions can also be loaded onto a computing / processing device, other programmable data processing apparatus, or other device to cause a series of operations to be performed on the computing / processing device, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computing / processing device, other programmable apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0239] These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can include a non-transitory computer readable storage medium that can be a computer readable storage medium having no data, programs, program modules, and / or computer readable program instructions presently, and / or not yet, stored thereon. The instructions can be stored in the computer readable storage medium at the time of manufacturing of the computer readable media, or can be loaded onto a computer, other programmable data processing apparatus, or other device from a computer readable storage medium at a later time so that the instructions stored thereon implement one or more embodiments of the described techniques. The computer readable storage medium can include, but is not limited to, one or more types of tangible memory devices including volatile and / or non-volatile memory devices.

[0240] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0241] The flow diagrams and the block diagrams in the drawings are presented to illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flow diagrams and the block diagrams can represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical functions (‘instructions’). In some alternative implementations, the functions noted in the blocks can 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. It will also be noted that each block of the block diagrams and / or flow diagrams, and combinations thereof, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

[0242] The computer program product can be embodied by a hardware, software or a combination thereof. In an optional embodiment, the computer program product is embodied as a computer storage medium, and in another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (SDK) or the like.

[0243] Having described above several embodiments of the disclosure, any modifications and variations that fall within the scope of the described embodiments are also intended to be within the scope of the disclosure. As will be apparent to those skilled in the art, some modifications and variations to the embodiments described above can be practiced while staying within the scope and spirit of the described embodiments. The foregoing description of the described embodiments has been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the described embodiments to the precise form disclosed. Many modifications and variations are possible in light of the above teachings. It is intended that the disclosed embodiments be limited only by the claims.

Claims

1. A method for quantitatively assessing the demand response potential of air conditioning loads, characterized in that, The method includes: Based on the building data and preset classification standards of each building in the target area, the buildings and air conditioning systems are classified, and a benchmark simulation model of the air conditioning load of each type of building is established. The target parameter combination is processed to obtain multiple air conditioning load models corresponding to each target simulation model. The air conditioning load models are simulated to determine the baseline operating conditions of each air conditioning load model before demand response. The target parameter combination includes multiple parameters that affect the demand response potential of air conditioning load. Based on at least one preset demand response strategy, the air conditioning load model corresponding to the air conditioning load of various types of buildings is simulated, and the probability density curve of the demand response potential of the corresponding type of building air conditioning load is obtained by using the simulation results and the corresponding baseline conditions. By utilizing the probability density curves of demand response potential for various types of building air conditioning loads, the total probability density curve of demand response potential for various types of building air conditioning loads in the target area is obtained. The demand response potential of the target area at different confidence levels is determined based on the probability density curve of the total demand response potential.

2. The method according to claim 1, characterized in that, Based on the combination of target parameters, each benchmark simulation model is processed to obtain multiple air conditioning load models corresponding to each benchmark simulation model, including: The parameter values ​​of each parameter in the target parameter combination are sampled K times to obtain K sample parameter value combinations. The sample parameter value combinations include multiple sample parameter values, and the sample parameter values ​​follow a preset distribution range of the corresponding parameters. K is a positive integer. Each sampling parameter value in the combination of sampling parameter values ​​is loaded into the corresponding benchmark simulation model to obtain the corresponding air conditioning load model.

3. The method according to claim 2, characterized in that, The target parameter combination includes at least one of the following: personnel density, air conditioning system energy efficiency, indoor heat quality, personnel work and rest periods, air conditioning set temperature, fresh air volume, and personnel occupancy rate. Sampling methods include any one of the following: Karting hypercube sampling, Sobol sampling, random sampling and its improved variations, and E-Fast sampling.

4. The method according to claim 2, characterized in that, The simulation of each of the aforementioned air conditioning load models includes: Obtain meteorological files for the target area during a preset time period, the meteorological files including meteorological data; The meteorological files are used to simulate the various air conditioning load models.

5. The method according to claim 1, characterized in that, Based on at least one preset demand response strategy, simulations are performed on the air conditioning load models corresponding to various types of building air conditioning loads. The demand response potential probability density curves for the corresponding types of building air conditioning loads are obtained using the simulation results and corresponding baseline conditions, including: Based on at least one preset demand response strategy and the meteorological file of the target area during a preset time period, the air conditioning load model corresponding to the air conditioning load of various buildings is simulated to obtain the response conditions under the preset demand response strategy. The meteorological file includes meteorological data. The response potential evaluation index is determined based on the response conditions and the baseline conditions. The response potential evaluation index includes at least one of the following: average power reduction, peak power reduction during the demand response period, cumulative energy consumption reduction during the demand response period, average indoor temperature deviation over 15 minutes, and maximum indoor temperature deviation during the demand response period. Based on the aforementioned response potential evaluation index, the probability density curves of demand response potential for various building air conditioning loads are obtained. The preset demand response strategy includes adjustment strategies and adjustment periods. The adjustment strategies include at least one of the following: increasing or decreasing the indoor air conditioning temperature setpoint, increasing or decreasing the supply air temperature, increasing or decreasing the supply air pressure, pre-cooling or preheating, increasing the chiller supply water temperature, and shutting down the chiller. The adjustment period includes at least one of the following: peak power supply period, peak period, peak electricity price period, and demand response period published by the power grid.

6. The method according to claim 1, characterized in that, The method of obtaining the total demand response potential probability density curve of various building air conditioning loads in the target area using the demand response potential probability density curves of various building air conditioning loads includes: The probability density curves of demand response potential for various types of building air conditioning loads are aggregated to obtain the total probability density curve of demand response potential for various types of building air conditioning loads in the target area; or Multiple samplings were performed on the demand response potential probability density curves of air conditioning loads for various types of buildings to obtain multiple sample data; the multiple sample data were then fitted to obtain the total demand response potential probability density curves of air conditioning loads for various types of buildings in the target area.

7. The method according to claim 1, characterized in that, Determining the demand response potential of the target area at different confidence levels based on the probability density curve of the total demand response potential includes: The cumulative probability distribution of the demand response potential is determined based on the total demand response potential probability density curve. The range of the demand response potential under the given confidence level is determined based on the cumulative probability distribution of the response potential.

8. A device for quantitatively assessing the potential of air conditioning load demand response, characterized in that, The device includes: The classification modeling module is used to classify each building and its air conditioning system based on the building data and preset classification standards of each building in the target area, and to establish a benchmark simulation model of the air conditioning load of each type of building. The processing simulation module is used to process each benchmark simulation model according to the target parameter combination to obtain multiple air conditioning load models corresponding to each benchmark simulation model, perform simulation on each of the air conditioning load models, and determine the baseline operating conditions of each of the air conditioning load models before demand response. The target parameter combination includes multiple parameters that affect the air conditioning load demand response potential. The first determination module is used to simulate the air conditioning load model corresponding to various types of building air conditioning loads according to at least one preset demand response strategy, and obtain the demand response potential probability density curve of the corresponding type of building air conditioning load using the simulation results and the corresponding baseline conditions. The second determining module is used to obtain the total demand response potential probability density curve of the air conditioning load of various types of buildings in the target area by utilizing the demand response potential probability density curve of the air conditioning load of various types of buildings. The third determining module is used to determine the demand response potential of the target area at different confidence levels based on the probability density curve of the total demand response potential.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 7.

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