Power consumption control method of power equipment integrated system based on demand side response model

Abstract

The present invention provides a power consumption control method for an integrated system of power equipment based on a demand-side response model, which relates to the field of power demand response, including: coordinating the acquired historical power consumption data to obtain multiple historical coordination centers, and constructing a dynamic generation model of power consumption data based on the acquired real-time power consumption data of the day and multiple coordination centers; obtaining the historical power demand and power generation power of the charging end, as well as the power generation power, maximum power demand and minimum power demand of the charging end, and optimizing the power demand prediction model according to the fluctuation of the power grid; constructing a demand-side response model based on the dynamic generation model of power consumption data and the power demand prediction model to control the power consumption of the power end to change between the upper and lower limits of the power demand prediction of the charging end, so as to realize power consumption control of the integrated system of power equipment. The present invention can maximize the restriction of peak charging load, avoid waste of resources, and improve the stability of the integrated system.

Description

Technical Field

[0001] The present invention relates to the technical field of electricity demand response, and in particular to an electricity consumption control method of an electricity consumption equipment integration system based on a demand-side response model. Background Art

[0002] With the rapid development of society, the annual peak load of electricity consumption has gradually increased, and grid operators need to invest heavily to expand the capacity of power generation, transmission and distribution systems to meet the peak power needs of users. However, due to the lack of rationality and intelligence in system planning and construction, these peak demands may only last for a few months, a few days or even a few hours a year, resulting in the equipment that has been expanded with a large amount of investment being idle or inefficient for most of the time, resulting in redundant investment and waste of resources, and unable to adapt to the future development needs of the grid.

[0003] At present, technologies such as renewable energy power generation and grid connection, energy storage, microgrid, information and communication, and smart grid are developing rapidly, and power equipment is gradually developing from the traditional pure load form to the intelligent integration of power generation, storage, and power consumption at different scales, including microgrids, charging stations, battery swap stations, and smart buildings. The common feature of these power equipment integration systems is that they are no longer simple power consumption systems, but can simultaneously have the three processes of power generation, storage, and power consumption within the power equipment integration system.

[0004] However, the greater the scale and flexibility of power-consuming equipment, the greater the uncertainty risk of the power grid, and the greater the challenges of its safe and stable operation. Power-consuming equipment has different power demands at different times, and the existing power-consuming equipment integration system does not consider the impact of dynamic power demand behavior and power grid fluctuations, nor does it fully utilize the energy matching relationship between the grid, charging and power-consuming ends. Not only does it cause power supply not meeting demand, waste of power resources, but it also affects the performance of the power-consuming equipment integration system. Summary of the invention

[0005] The present invention provides a power consumption control method of an electric equipment integration system based on a demand-side response model to solve the problems of current electric equipment integration systems, such as insufficient power supply, waste of electric resources, and low performance of the electric equipment integration system. The method comprises:

[0006] Acquire historical electricity consumption data, coordinate the historical electricity consumption data, and obtain multiple historical coordination centers;

[0007] Acquire the real-time electricity consumption data published on the day, and construct a dynamic generation model of the electricity consumption data based on the real-time electricity consumption data and multiple historical coordination centers;

[0008] Obtain the historical power demand and power generation of the charging terminal, as well as the power generation power, maximum power demand and minimum power demand of the charging terminal at the current moment, and build an initial power demand prediction model;

[0009] According to power grid fluctuations, optimizing the initial power demand prediction model to obtain the power demand prediction model;

[0010] Constructing a demand-side response model based on the power consumption data dynamic generation model and the power demand prediction model;

[0011] The power consumption of the power-consuming end is controlled to vary between the predicted upper limit and the predicted lower limit of the power demand at the charging end according to the demand-side response model, thereby realizing power consumption control of the power-consuming equipment integrated system.

[0012] Furthermore, the power consumption data dynamic generation model is constructed based on the real-time power consumption data and multiple coordination centers, including:

[0013] Acquire the real-time electricity consumption data of the day, calculate the change rate of the electricity consumption data before the current moment of the day and the change rate of the coordination center of each coordination center before the current moment of the day;

[0014] Obtaining a distance vector between the power consumption data change rate and the change rate of each coordination center according to the power consumption data change rate and the coordination center change rate;

[0015] According to the distance vector and multiple historical coordination centers, the forecast coordination center for the power consumption data of the day is obtained to obtain the predicted power consumption data; based on the power consumption data at the current moment and the predicted power consumption data at the end moment, a dynamic generation model of the power consumption data at the power consumption end from the current moment to the end moment is obtained.

[0016] Furthermore, constructing the initial power demand prediction model includes:

[0017] Obtain the historical power demand and power generation of the charging terminal to obtain the upper limit of the initial power demand forecast;

[0018] Obtain the power generation power, maximum power demand and minimum power demand of the charging terminal at the current moment, and obtain the upper limit and lower limit of the power demand of the charging terminal at the current moment;

[0019] Taking the minimum power demand as the lower limit of initial power demand prediction;

[0020] According to the initial power demand prediction upper limit, the initial power demand prediction lower limit, the power demand upper limit and the power demand lower limit, an initial power demand prediction model of the charging terminal from the current moment to the end moment is constructed.

[0021] Furthermore, the demand-side response model includes:

[0022] ;

[0023] ;

[0024]

[0025] in, Indicates the dynamic generation model of the power consumption data, which is from the current moment To the end time Electricity consumption data; Indicates that from the current moment To the end time Electricity plan, , Indicates that the power consumption end is The power consumption at the time, ; All represent from the current moment To the end time The power consumption plan coefficient vector, whose elements are all 1 and whose dimension is correspond; represents the coefficient vector; Represents the power demand prediction model, which is from the current moment To the end time Total power demand; , Indicates that the charging terminal is The lower limit of power demand at the moment; , Indicates that the charging terminal is The upper limit of power demand at the moment.

[0026] Furthermore, the power consumption data dynamically generates a model include:

[0027]

[0028] in, Indicates the day Electricity consumption data for a certain period of time; Indicates the forecast coordination centers Represents a distance vector.

[0029] Furthermore, the power demand prediction model includes:

[0030] ;

[0031] in, represents the optimization coefficient based on power grid fluctuations, , represents the power grid fluctuation function, ; Indicates that the charging terminal is The lower limit of power demand at the moment Indicates that the charging terminal is The power generation at the moment; Indicates that the charging terminal is The upper limit of power demand at the moment Indicates the minimum power requirement; represents the upper limit of initial power demand forecast; represents the predicted lower limit of the power demand; represents the predicted upper limit of the power demand.

[0032] Furthermore, the calculation method of the power consumption data change rate and the coordination center change rate includes:

[0033] Get real-time electricity consumption data for the day for: ;in, Indicates the day Electricity usage data for a certain period of time

[0034] The power consumption data change rate The calculation methods include:

[0035]

[0036] in, Indicates The rate of change of electricity consumption data in a time period; Indicates the day Electricity consumption data for a certain period of time;

[0037] The rate of change of the co-ordination center The calculation methods include:

[0038]

[0039] in, Indicates The rate of change of each coordinating center during the time period; Indicates various coordinating centers.

[0040] Furthermore, the distance vector The calculation methods include:

[0041] , ;

[0042] in, Indicates The rate of change of electricity consumption data in a time period; Indicates The rate of change of each coordinating center during the time period.

[0043] Furthermore, at the present moment The power requirement upper limit and the power demand lower limit The calculation methods include:

[0044] ;

[0045] ;

[0046] in, represents the upper limit of the initial power demand forecast, , , , is an undetermined coefficient, which is determined based on the historical power demand and power generation power of the charging terminal; Indicates the maximum power demand at the current moment; Indicates the power generated by the charging terminal at the current moment; Indicates the minimum power requirement at the current moment.

[0047] Furthermore, the overall planning includes:

[0048] Get the historical electricity consumption data for multiple days before today and get the historical electricity consumption data set ; , Indicates Daily electricity consumption data;

[0049] Set the number of coordination centers and the initial value of the coordination center ; , Indicates The coordinating centre for the category;

[0050] Calculate the Euclidean distance from each historical electricity consumption data to the initial value of each coordination center ; The historical electricity consumption data is coordinated according to the Euclidean distance, and the minimum Euclidean distance from the historical electricity consumption data to the initial value of each coordination center is used as the objective function ; Indicates The number of data in a pool;

[0051] The type with the smallest center distance from the historical electricity consumption data to the initial values ​​of each coordination center is taken as the type of the historical electricity consumption data;

[0052] according to The initial values ​​of each coordination center are continuously updated, and the Euclidean distance from the historical power consumption data to the initial values ​​of each coordination center is repeatedly calculated; until the initial values ​​of all coordination centers converge, and multiple converged historical coordination centers are obtained.

[0053] In general, the present invention provides a method for controlling power consumption of an integrated power consumption system based on a demand-side response model. The technical solution conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0054] First, the present invention establishes a demand-side response model based on the dynamic generation model of power consumption data and the power demand prediction model, which not only matches the supply and demand between the power grid, the charging end and the power consumption end, but also takes into account that the demand behavior is dynamically changing, so that the power storage capacity of the charging end and the power grid end always changes with the change of the power consumption of the power consumption end, that is, the power consumption of the power consumption end is controlled to change between the upper limit of the power demand prediction and the lower limit of the power demand prediction at the charging end in different time periods as the power consumption data changes. In the peak period of power consumption, the power consumption of all power consumption equipment at the power consumption end is allocated according to the minimum power; in the low peak period of power consumption, the power consumption of all power consumption equipment at the power consumption end is allocated according to the maximum power, so that the demand of each power consumption equipment can be met regardless of the peak period of power consumption or the low peak period of power consumption. It not only reduces the waste of resources, but also reduces the adverse effects on the power grid end, meets the power consumption demand of the power consumption end, improves the stability of the power consumption equipment integration system to a certain extent, and provides a strong guarantee for the reliable operation of the power consumption equipment integration system.

[0055] Second, the present invention takes into account the impact of grid fluctuations at the distribution network end, and proposes a power demand prediction model obtained by optimizing the grid fluctuation function, which provides more comprehensive power demand information for the demand-side response model. It not only effectively limits the peak charging load, but also improves the linkage between the charging end and the grid end, and also provides a strong guarantee for the reliable operation of the integrated system of power equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0057] Figure 1 It is a schematic diagram of a power consumption control method of a power consumption equipment integration system based on a demand-side response model provided by the present invention;

[0058] Figure 2It is a schematic diagram of a method for dynamically generating a model for electricity consumption data provided by the present invention;

[0059] Figure 3 It is a schematic diagram of a method of an initial power demand prediction model provided by the present invention;

[0060] Figure 4 A schematic diagram of the charging start time, charging end time and initial SOC of an electric vehicle provided by the present invention;

[0061] Figure 5 It is a schematic diagram of simulation results of the power consumption control method in the prior art;

[0062] Figure 6 It is a schematic diagram of simulation results of a power consumption control method of an electric equipment integration system based on a demand-side response model provided by the present invention. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings and embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0064] It should be noted that, in the description of the embodiments of the present invention, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a method or step including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such method or step. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the method or step including the element.

[0065] The present invention provides a power consumption control method of an electric device integration system based on a demand-side response model. The electric device integration system includes a charging end and a power consumption end.

[0066] It should be noted that the charging end can be photovoltaic charging equipment, charging piles, etc.; the power consumption end can be electric vehicles, commercial power consumption equipment, etc.

[0067] The present invention realizes power consumption control in the power equipment integration system based on the demand-side response target of power consumption data. Since the factors affecting the power consumption control of the power equipment integration system mainly include the power consumption data information of the power consumption end, the power demand information and power generation information of the charging end, and the power demand information of the power grid end, the demand-side response target of the power equipment integration system can be expressed as:

[0068] ;

[0069]

[0070] in, Indicates the day Electricity consumption data for a certain period of time; Indicates the number of time periods per day; Indicates the day Electricity demand data for a time period; Indicates the power demand for the day; Indicates the day The amount of power generated during a time period.

[0071] In order to realize the power consumption control of the power equipment integrated system, the method proposed in the present invention is as follows Figure 1 As shown, specifically including:

[0072] S101: Acquire historical electricity consumption data, coordinate the historical electricity consumption data, and obtain multiple historical coordination centers.

[0073] It should be noted that historical electricity consumption data refers to historical electricity consumption data of multiple days before the current day; and electricity consumption data refers to the electricity demand of the electricity user, which can be electricity consumption, electricity power, electricity voltage, electricity current, etc., preferably electricity power.

[0074] As an embodiment of the present invention, the overall planning method includes:

[0075] Get the historical electricity consumption data for multiple days before today and get the historical electricity consumption data set ; , Indicates Daily electricity consumption data;

[0076] Set the number of coordination centers and the initial value of the coordination center ; , Indicates The coordinating centre for the category;

[0077] Calculate the Euclidean distance from each historical electricity consumption data to the initial value of each coordination center ; The historical electricity consumption data is coordinated according to the Euclidean distance, and the minimum Euclidean distance from the historical electricity consumption data to the initial value of each coordination center is used as the objective function ; Indicates The number of data in a pool;

[0078] The type with the smallest center distance from the historical electricity consumption data to the initial values ​​of each coordination center is taken as the type of the historical electricity consumption data;

[0079] according to The initial values ​​of each coordination center are continuously updated, and the Euclidean distance from the historical electricity consumption data to the initial values ​​of each coordination center is repeatedly calculated; until the initial values ​​of all coordination centers converge, and multiple historical coordination centers after convergence are obtained.

[0080] S102: Obtain the real-time electricity consumption data published on the day, and build a dynamic electricity consumption data generation model based on the real-time electricity consumption data and multiple historical coordination centers.

[0081] Preferably, a dynamic generation model of electricity consumption data is constructed based on real-time electricity consumption data and multiple historical coordination centers, such as Figure 2 As shown, including:

[0082] S201: Acquire the real-time electricity consumption data of the day, calculate the change rate of the electricity consumption data before the current moment of the day and the change rate of the coordination center of each coordination center before the current moment of the day.

[0083] As an example, first obtain the real-time electricity consumption data of the day for: ;in, Indicates the day Electricity consumption data for a certain period of time, Indicates the number of time periods per day. Then calculate the rate of change of power consumption data and the rate of change of the coordinating center .

[0084] More specifically, the rate of change of electricity consumption data The calculation methods include:

[0085]

[0086] in, Indicates The rate of change of electricity consumption data in a time period; Indicates the day Electricity usage data for a period of time.

[0087] Coordination center change rate The calculation methods include:

[0088]

[0089] in, Indicates The rate of change of each coordinating center during the time period; Indicates various coordinating centers.

[0090] S202: Obtain the distance vector between the power consumption data change rate and the change rate of each coordination center according to the power consumption data change rate and the coordination center change rate.

[0091] As a specific embodiment, the distance vector The calculation methods include:

[0092] , ;

[0093] in, Indicates The rate of change of electricity consumption data in a time period; Indicates The rate of change of each coordinating center during the time period.

[0094] S203: Obtain the forecast coordination center of the electricity consumption data of the day according to the distance vector and multiple historical coordination centers to obtain the forecasted electricity consumption data; obtain a dynamic generation model of the electricity consumption data from the current moment to the end moment based on the electricity consumption data at the current moment and the forecasted electricity consumption data at the end moment.

[0095] It should be noted that, based on the distance vector and multiple historical coordination centers, the one with the most similar change rate of the electricity consumption data published on the day is selected as the coordination center to which the electricity consumption data of the day belongs; then the data of the coordination center at the future time is used as the predicted electricity consumption data at the end of the day, and combined with the electricity consumption data at the current time, the predicted electricity consumption data from the current time is obtained. By the end of the day The rate of change of electricity consumption data, that is, the dynamic generation model of electricity consumption data, can be:

[0096] ;

[0097] in, Indicates the day Electricity consumption data for a certain period of time; Indicates each forecast coordination center; Represents a distance vector.

[0098] S103: Obtain the historical power demand and power generation of the charging terminal, as well as the power generation power, maximum power demand and minimum power demand of the charging terminal at the current moment, and construct an initial power demand prediction model.

[0099] Preferably, an initial power demand prediction model is constructed, such as Figure 3 As shown, including:

[0100] S301: Obtain the historical power demand and power generation power of the charging terminal to obtain the upper limit of the initial power demand forecast.

[0101] Specifically, the charging end is a photovoltaic charging device. According to the output characteristics of the photovoltaic charging device, that is, the historical power demand and power generation, the upper limit of the initial power demand forecast of the photovoltaic charging device is obtained. , the specific calculation method can be:

[0102] ;

[0103] in, , , It is an undetermined coefficient, which is determined based on the historical power demand and power generation power of the photovoltaic charging equipment.

[0104] S302: Obtain the power generation power, maximum power demand and minimum power demand of the charging terminal at the current moment, and obtain the upper limit and lower limit of the power demand of the charging terminal at the current moment.

[0105] As a specific embodiment of the present invention, the charging end is a photovoltaic charging device. Since the power demand of the photovoltaic charging device is the power demand of the electric vehicle, the power demand of the photovoltaic charging device is variable. Therefore, the calculation of the photovoltaic charging device on the first day of the day Maximum power demand during the time period and minimum power requirements , specifically:

[0106] ;

[0107] ;

[0108] in, Indicates The number of electric vehicles in the PV charging facility during the time period; Indicates the maximum charging power of an electric vehicle; Indicates the duration of each time period; Indicates the rated capacity of the electric vehicle; represents the target SOC of the electric vehicle; represents the initial SOC of the electric vehicle; Indicates the time when the electric vehicle completes charging and leaves; Indicates the time when the electric vehicle starts charging.

[0109] Combined with photovoltaic charging equipment on the day Maximum power demand during the time period , minimum power requirement As well as the photovoltaic power generation power at the current moment, the power demand upper limit of the initial power demand prediction model of the photovoltaic charging equipment at the current moment n is obtained And the power demand lower limit , specifically:

[0110] ;

[0111] ;

[0112] in, Indicates the maximum power demand at the current moment. The maximum power demand of the photovoltaic charging equipment is also the maximum charging demand of the electric vehicle; Indicates the power generated by the charging terminal at the current moment; It represents the minimum power requirement at the current moment, which is also the minimum charging requirement for electric vehicles.

[0113] S303: Taking the minimum power demand as the lower limit of the initial power demand prediction.

[0114] It should be noted that the predicted lower limit of the initial power demand of the photovoltaic charging equipment can be determined based on the minimum charging demand of the electric vehicles and the number of electric vehicles.

[0115] For example, assume that all electric vehicles are charged at the minimum charging rate and there are no new electric vehicles in the future; calculate the SOC value of all electric vehicles in each time period. If an electric vehicle reaches the target SOC, subtract the electric vehicle from the number of electric vehicles at that moment; finally, calculate the minimum charging demand of electric vehicles in each time period and use it as the minimum power demand of the photovoltaic charging equipment at the current moment, which is also the lower limit of the initial power demand forecast.

[0116] S304: Constructing an initial power demand prediction model of the charging terminal from the current moment to the end moment according to the initial power demand prediction upper limit, the initial power demand prediction lower limit, the power demand upper limit and the power demand lower limit.

[0117] As an example, from the current moment To the end time The initial power demand model of the photovoltaic charging equipment can be:

[0118]

[0119] in, express Charging power of photovoltaic charging equipment in the time period; express The lower limit of power demand at the moment; express The upper limit of power demand at the moment; Represents the minimum power demand, which is also the lower limit of the initial power demand forecast; Represents the upper limit of the initial power demand forecast.

[0120] S104: Optimizing the initial power demand prediction model according to the power grid fluctuation at the power grid end to obtain a power demand prediction model.

[0121] Specifically, considering the impact of grid fluctuations on the distribution network, the future power demand model of photovoltaic charging equipment is optimized, and its optimization coefficient can be: ;in, represents the power grid fluctuation function, .

[0122] Therefore, the power demand prediction model of photovoltaic charging equipment can be constructed as follows:

[0123] ;

[0124] in, Indicates that the charging terminal is The lower limit of power demand at the moment Indicates that the charging terminal is The power generation at the moment; Indicates that the charging terminal is The upper limit of power demand at the moment Indicates the minimum power requirement; represents the upper limit of initial power demand forecast; Indicates the lower limit of power demand forecast; Indicates the upper limit of power demand forecast.

[0125] S105: Construct a demand-side response model based on the power consumption data dynamic generation model and the power demand prediction model.

[0126] As a specific embodiment of the present invention, the demand-side response model constructed may be:

[0127] ;

[0128] ;

[0129]

[0130] in, Represents the dynamic generation model of electricity consumption data, which is from the current moment To the end time Electricity consumption data; Indicates that from the current moment To the end time Electricity plan, , Indicates that the power consumption end is The power consumption at the time, ; Indicates that from the current moment To the end time The power consumption plan coefficient vector, whose elements are all 1 and whose dimension is correspond; represents the coefficient vector; Represents the power demand forecasting model, which is from the current moment To the end time Total power demand; , Indicates that the charging terminal is The lower limit of power demand at the moment; , Indicates that the charging terminal is The upper limit of power demand at the moment.

[0131] S106: Controlling the power consumption of the power consumption end to change between the upper limit and the lower limit of the power demand prediction at the charging end according to the demand-side response model, so as to realize power consumption control of the power consumption equipment integrated system.

[0132] Specifically, by controlling the charging power at the power consumption end to vary between the upper and lower limits of the power demand forecast at the charging end as the power consumption data changes in different time periods, that is, during peak power consumption periods, the power consumption of all power consumption devices at the power consumption end is allocated according to the minimum power; during low power consumption periods, the power consumption of all power consumption devices at the power consumption end is allocated according to the maximum power; thereby realizing power consumption control of the integrated system of power consumption equipment.

[0133] As a specific embodiment of the present invention, the installed capacity of the photovoltaic charging equipment is set to 150KW, the number of electric vehicles in the photovoltaic charging equipment is 60, the charging target SOC is 0.88, the ampere-hour capacity is 60Ah, the kilowatt-hour capacity is 22.3kWh, the maximum charging power is 6KW, and the charging start time, charging end time and initial SOC of the electric vehicle are as follows: Figure 4 As shown, the simulation period is 7:00~18:00, the simulation time interval is 1 minute, and there are 660 time intervals in total.

[0134] If the conventional power consumption control method of the power consumption equipment integration system is used, no demand response strategy is set, that is, all electric vehicles are charged as they arrive until the electric vehicles are charged to the target SOC. The simulation results are as follows Figure 5As shown in the figure, since the conventional power control method does not control the charging behavior, as the number of electric vehicles increases, the charging power also gradually increases, resulting in a peak charging load between 8:00 and 10:00 in the morning. At this time, the charging power is much greater than the photovoltaic power, so more electricity needs to be obtained from the power grid, resulting in a waste of resources. When the peak charging load occurs, the charging power is much greater than the photovoltaic power. If more electricity cannot be obtained from the power grid in time, it will cause the power equipment integration system to collapse, and even the charging end to collapse, and it will not be able to meet the power demand of the user end.

[0135] According to the present invention, the power consumption data of the day is output through the power consumption data dynamic generation model, and the power demand prediction model is optimized by using the power grid fluctuation of the distribution network, so as to realize the power consumption control of the power consumption equipment integration system based on the demand side response model. The simulation results are as follows: Figure 6 As shown in the figure, it can be seen that the number of electric vehicles charged before 11 o'clock reached the maximum value. At this time, the photovoltaic power was not enough to meet the charging demand. In order to ensure the power demand, the charging power of all electric vehicles was allocated according to the minimum power; between 11:00 and 12:00, since the photovoltaic output was sufficient to meet the charging demand, the charging power followed the change of the photovoltaic output, and the allocation power of the charging power of all electric vehicles increased accordingly. Since most of the photovoltaic output between 12:00 and 16:30 was less than the minimum power demand or the maximum power demand, the charging power during this period changed between the upper and lower limits of the photovoltaic charging equipment power demand as the power consumption data changed. After 16:30, since the power consumption data was relatively high and most electric vehicles had reached the charging target SOC at this time, the charging power after 16:30 followed the change of photovoltaics, thereby reducing the power obtained from the power grid, thus avoiding resource waste, reducing the adverse impact on the power grid, meeting the power demand of the power consumption end, and to a certain extent improving the stability of the power consumption equipment integration system, providing a strong guarantee for the reliable operation of the power consumption equipment integration system.

[0136] It should be noted that, for the above-mentioned various embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0137] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0138] In the several embodiments provided in this application, it should be understood that the disclosed method or system can be implemented in other ways. For example, the above-described embodiments are only schematic, and the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0139] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0140] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0141] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application.

[0142] Those skilled in the art will appreciate that all or part of the various circuits in the above embodiments may be completed by entering a program to instruct related hardware, and the program may be stored in a computer-readable memory, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0143] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

[0144] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0145] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for controlling power consumption of an integrated power consumption system based on a demand-side response model, wherein the integrated power consumption system comprises a charging terminal and a power consumption terminal; characterized in that: The method comprises: Acquire historical electricity consumption data, coordinate the historical electricity consumption data, and obtain multiple historical coordination centers; Acquire the real-time electricity consumption data published on the day, and construct a dynamic generation model of electricity consumption data based on the real-time electricity consumption data and multiple historical coordination centers, including: acquire the real-time electricity consumption data of the day, calculate the change rate of the electricity consumption data before the current moment and the coordination center change rate of each coordination center before the current moment; obtain the distance vector between the electricity consumption data change rate and the change rate of each coordination center based on the electricity consumption data change rate and the coordination center change rate; acquire the predicted coordination center of the electricity consumption data of the day based on the distance vector and multiple historical coordination centers to obtain predicted electricity consumption data; obtain the dynamic generation model of electricity consumption data of the electricity user from the current moment to the end moment based on the electricity consumption data at the current moment and the predicted electricity consumption data at the end moment; Obtain the historical power demand and power generation of the charging terminal, as well as the power generation power, maximum power demand and minimum power demand of the charging terminal at the current moment, and build an initial power demand prediction model; According to power grid fluctuations, the initial power demand prediction model is optimized to obtain a power demand prediction model; A demand-side response model is constructed based on the power consumption data dynamic generation model and the power demand prediction model; the demand-side response model includes: ； ； in, Indicates the dynamic generation model of the power consumption data, which is from the current moment To the end time Electricity consumption data; From the current moment To the end time Electricity plan, , Indicates that the power consumption end is The power consumption at the time, ; From the current moment To the end time The power consumption plan coefficient vector, whose elements are all 1 and whose dimension is correspond; represents the coefficient vector; Represents the power demand prediction model, which is from the current moment To the end time Total power demand; , Indicates that the charging terminal is The lower limit of power demand at the moment; , Indicates that the charging terminal is The upper limit of power demand at the moment; The power consumption of the power-consuming end is controlled to vary between the predicted upper limit and the predicted lower limit of the power demand at the charging end according to the demand-side response model, thereby realizing power consumption control of the power-consuming equipment integrated system.

2. The power consumption control method of the power consumption equipment integration system based on the demand side response model according to claim 1 is characterized in that: Building an initial power demand forecast model includes: Obtain the historical power demand and power generation of the charging terminal to obtain the upper limit of the initial power demand forecast; Obtain the power generation power, maximum power demand and minimum power demand of the charging terminal at the current moment, and obtain the upper limit and lower limit of the power demand of the charging terminal at the current moment; Taking the minimum power demand as the lower limit of initial power demand prediction; According to the initial power demand prediction upper limit, the initial power demand prediction lower limit, the power demand upper limit and the power demand lower limit, an initial power demand prediction model of the charging terminal from the current moment to the end moment is constructed.

3. The power consumption control method of the power consumption equipment integration system based on the demand side response model according to claim 1 is characterized in that: The power consumption data dynamic generation model include: in, Indicates the day Electricity consumption data for a certain period of time; Indicates each forecast coordination center; Represents a distance vector.

4. The power consumption control method of the power consumption equipment integration system based on the demand side response model according to claim 1, characterized in that: The power demand prediction model includes: ； in, represents the optimization coefficient based on power grid fluctuations, , represents the power grid fluctuation function, ; Indicates that the charging terminal is The lower limit of power demand at the moment; Indicates that the charging terminal is The power generation at the time; Indicates that the charging terminal is The upper limit of power demand at the moment; Indicates the minimum power requirement; represents the upper limit of the initial power demand forecast; represents the predicted lower limit of the power demand; represents the predicted upper limit of the power demand.

5. The power consumption control method of the power consumption equipment integration system based on the demand side response model according to claim 3 is characterized in that: The calculation method of the power consumption data change rate and the coordination center change rate includes: Get real-time electricity consumption data for the day for: ;in, Indicates the day Electricity consumption data for a certain period of time; The power consumption data change rate The calculation methods include: in, Indicates The rate of change of electricity consumption data in a time period; Indicates the day Electricity consumption data for a certain period of time; The rate of change of the co-ordination center The calculation methods include: in, Indicates The rate of change of each coordinating center during the time period; Indicates various coordinating centres; Indicates the number of coordination centers.

6. The power consumption control method of the power consumption equipment integration system based on the demand side response model according to claim 5, characterized in that: The distance vector The calculation methods include: , ； in, Indicates The rate of change of electricity consumption data in a time period; Indicates The rate of change of each coordinating center during the time period.

7. The power consumption control method of the power consumption equipment integration system based on the demand side response model according to claim 1, characterized in that: At the present moment The power requirement upper limit and the power demand lower limit The calculation methods include: ； ； in, represents the upper limit of the initial power demand forecast, , , , is an undetermined coefficient, which is determined based on the historical power demand and power generation power of the charging terminal; Indicates the maximum power demand at the current moment; Indicates the power generated by the charging terminal at the current moment; Indicates the minimum power requirement at the current moment.

8. The power consumption control method of the power consumption equipment integration system based on the demand side response model according to claim 1, characterized in that: The coordination includes: Get the historical electricity consumption data for multiple days before today and get the historical electricity consumption data set ; , Indicates Daily electricity consumption data; Set the number of coordination centers and the initial value of the coordination center ; , Indicates The coordinating centre for the category; Calculate the Euclidean distance from each historical electricity consumption data to the initial value of each coordination center ; The historical electricity consumption data is coordinated according to the Euclidean distance, and the minimum Euclidean distance from the historical electricity consumption data to the initial value of each coordination center is used as the objective function ; Indicates The number of data in a pool; The type with the smallest center distance from the historical electricity consumption data to the initial values ​​of each coordination center is taken as the type of the historical electricity consumption data; according to The initial values ​​of each coordination center are continuously updated, and the Euclidean distance from the historical power consumption data to the initial values ​​of each coordination center is repeatedly calculated; until the initial values ​​of all coordination centers converge, and multiple converged historical coordination centers are obtained.

Images