Control method, system, device and storage medium for energy supply system

Abstract

The present application provides a control method, system, device and storage medium for an energy supply system, wherein the control method of the energy supply system includes: obtaining an expected influence matrix according to the initial energy supply demand of each energy-consuming object in each time interval in each time period, and the energy supply parameters in each time interval in each time period; constructing an optimized influence matrix under the condition of minimizing the rank of the influence matrix according to the expected influence matrix and the matrix position set; determining the actual energy supply parameters of the energy supply system according to the optimized influence matrix, so as to control the energy supply system to supply energy to each energy-consuming object according to the actual energy supply parameters. The present application can determine the actual energy supply parameters according to the initial energy supply demand and the energy supply parameters, so that when energy is supplied by the actual energy supply parameters, the phenomenon of users changing their energy consumption behavior and the phenomenon of energy waste can be reduced, thereby ensuring the smooth operation of the power system.

Description

Technical Field

[0001] The present application relates to the technical field of control systems and methods, and in particular to a control method, system, device and storage medium for an energy supply system. Background Art

[0002] At present, due to the emergence of energy storage devices, users can achieve the purpose of reducing their energy loss while meeting their energy needs by performing storage control (i.e., changing the load curve) after owning the energy storage device, or users can achieve the purpose of reducing their energy loss by directly changing their load curve by changing their energy behavior. In this case, when multiple users in an energy system (such as a power system, a thermal energy system, etc.) implement the above behavior, it will cause energy waste and is not conducive to the development and smooth operation of the entire energy system.

[0003] Therefore, considering the situation that users may change their load curves, how to stabilize the energy consumption of each user in the energy system, rationalize it, avoid energy waste and ensure the stable operation of the energy system has become a technical problem that the industry needs to solve urgently. Summary of the invention

[0004] In view of the shortcomings of the related technologies mentioned above, the purpose of the present application is to provide a control method, system, device and storage medium for an energy supply system, which are used to provide reliable energy supply parameters to stabilize the user's energy consumption and solve the problems of the prior art.

[0005] To achieve the above-mentioned purpose and other related purposes, the first aspect of the present application provides a control method for an energy supply system, wherein the energy supply system supplies energy to a plurality of energy-consuming objects; the control method of the energy supply system comprises: obtaining an expected influence quantity matrix according to the initial energy supply demand of each energy-consuming object in each time interval in each time period, and the energy supply parameters in each time interval in each time period; wherein the expected influence quantity matrix comprises the expected energy supply parameter influence of each energy-consuming object on the energy supply system in each said time period; a noise matrix related to the initial energy supply demand is superimposed on the initial influence quantity matrix to obtain the expected influence quantity matrix; According to the expected influence matrix and the matrix position set, an optimized influence matrix is ​​constructed under the condition of minimizing the rank of the influence matrix; wherein the optimized influence matrix includes the influence of each energy-consuming object on the optimized energy supply parameters of the energy supply system in each time period; the influence matrix and the expected influence matrix are of the same type, and the matrix position set includes the corresponding element positions of part of the expected influence matrix and the influence matrix; according to the optimized influence matrix, the actual energy supply parameters of the energy supply system are determined to control the energy supply system to supply energy to each energy-consuming object according to the actual energy supply parameters.

[0006] In certain embodiments of the first aspect of the present application, the expected impact matrix is ​​obtained based on the initial energy supply demand of each energy-consuming object in each time interval in each time period and the energy supply parameters in each time interval in each time period, including: obtaining the expected energy supply demand of each energy-consuming object in each time interval in each time period based on the initial energy supply demand of each energy-consuming object in each time interval in each time period and the energy supply parameters in each time interval in each time period; determining the expected energy supply parameter influence of each energy-consuming object on the energy supply system in each time period based on the expected energy supply demand of each energy-consuming object in each time interval in each time period, so as to obtain the expected impact matrix.

[0007] In certain embodiments of the first aspect of the present application, the influence of each energy-consuming object on the energy supply parameters of the energy supply system in each time period is determined based on the energy supply demand of each energy-consuming object in each time interval in each time period and the energy supply parameters in each time interval in each time period.

[0008] In certain embodiments of the first aspect of the present application, the total amount of initial energy supply demand of a single energy-consuming object in each time interval in a single time period is equal to the total amount of expected energy supply demand; the sum of the differences between the initial energy supply demand of a single energy-consuming object in each time interval in a single time period and the expected energy supply demand in each time interval satisfies a preset ratio; the expected energy supply demand of each energy-consuming object in each time interval in each time period is obtained according to the initial energy supply demand of each energy-consuming object in each time interval in each time period and the energy supply parameters in each time interval in each time period, including: determining the expected energy supply demand of the single energy-consuming object in each time interval in a single time period corresponding to the minimum energy loss of the single energy-consuming object in a single time period according to the initial energy supply demand of the single energy-consuming object in each time interval in a single time period and the energy supply parameters in each time interval in a single time period.

[0009] In certain embodiments of the first aspect of the present application, an optimized influence matrix is ​​constructed based on the expected influence matrix and the matrix position set under the condition of minimizing the rank of the influence matrix, including: using a matrix completion algorithm to construct the optimized influence matrix under the condition of minimizing the rank of the influence matrix and under a preset error constraint; wherein the preset error constraint is that the norm of the matrix obtained by projecting the difference between the influence matrix and the expected influence matrix according to the matrix position set satisfies the preset error.

[0010] In certain embodiments of the first aspect of the present application, the matrix completion algorithm includes: fixed point continuation algorithm, accelerated proximal gradient algorithm, semidefinite programming method, singular value threshold algorithm, augmented Lagrange multiplier method, etc.

[0011] In certain embodiments of the first aspect of the present application, an initial matrix of the influence variable matrix in the fixed point continuation algorithm is determined based on a singular value matrix corresponding to the expected influence variable matrix.

[0012] In certain embodiments of the first aspect of the present application, the actual energy supply loss parameter of each energy-consuming object is the average value of the optimized energy supply parameter influence of each energy-consuming object in each time period in the optimized influence matrix.

[0013] To achieve the above-mentioned purpose and other related purposes, the second aspect of the present application provides a control system for an energy supply system, wherein the energy supply system supplies energy to multiple energy-consuming objects; the control system of the energy supply system includes: an expected influence quantity matrix determination module, which is used to obtain an expected influence quantity matrix according to the initial energy supply demand of each energy-consuming object in each time interval in each time period, and the energy supply parameters in each time interval in each time period; wherein the expected influence quantity matrix includes the expected energy supply parameter influence of each energy-consuming object on the energy supply system in each said time period; a noise matrix related to the initial energy supply demand is superimposed on the initial influence quantity matrix to obtain the expected influence quantity matrix; and an optimization influence quantity matrix is ​​used to determine the expected influence quantity matrix. An array determination module is used to construct an optimized influence matrix under the condition of minimizing the rank of the influence matrix according to the expected influence matrix and the matrix position set; wherein the optimized influence matrix includes the influence of each energy-consuming object on the optimized energy supply parameters of the energy supply system in each time period; the influence matrix and the expected influence matrix are of the same type, and the matrix position set includes the corresponding element positions of the expected influence matrix and part of the influence matrix; an energy supply parameter determination module is used to determine the actual energy supply parameters of the energy supply system according to the optimized influence matrix, so as to control the energy supply system to supply energy to each energy-consuming object according to the actual energy supply parameters.

[0014] In certain embodiments of the second aspect of the present application, the expected impact matrix determination module obtains the expected impact matrix based on the initial energy supply demand of each energy-consuming object in each time interval in each time period and the energy supply parameters in each time interval in each time period, including: obtaining the expected energy supply demand of each energy-consuming object in each time interval in each time period based on the initial energy supply demand of each energy-consuming object in each time interval in each time period and the energy supply parameters in each time interval in each time period; determining the influence of each energy-consuming object on the expected energy supply parameters of the energy supply system in each time period based on the expected energy supply demand of each energy-consuming object in each time interval in each time period, so as to obtain the expected impact matrix.

[0015] In certain embodiments of the second aspect of the present application, the influence of each energy-consuming object on the energy supply parameters of the energy supply system in each time period is determined based on the energy supply demand of each energy-consuming object in each time interval in each time period and the energy supply parameters in each time interval in each time period.

[0016] In certain embodiments of the second aspect of the present application, the total amount of initial energy supply demand of a single energy-consuming object in each time interval in a single time period is equal to the total amount of expected energy supply demand; the sum of the differences between the initial energy supply demand of a single energy-consuming object in each time interval in a single time period and the expected energy supply demand in each time interval satisfies a preset ratio; the expected energy supply demand of each energy-consuming object in each time interval in each time period is obtained according to the initial energy supply demand of each energy-consuming object in each time interval in each time period and the energy supply parameters in each time interval in each time period, including: determining the expected energy supply demand of the single energy-consuming object in each time interval in a single time period corresponding to the minimum energy loss of the single energy-consuming object in a single time period according to the initial energy supply demand of the single energy-consuming object in each time interval in a single time period and the energy supply parameters in each time interval in a single time period.

[0017] In certain embodiments of the second aspect of the present application, the optimized influence matrix determination module constructs an optimized influence matrix based on the expected influence matrix and the matrix position set under the condition of minimizing the rank of the influence matrix, including: using a matrix completion algorithm to construct the optimized influence matrix under the condition of minimizing the rank of the influence matrix and under a preset error constraint; wherein the preset error constraint is that the norm of the matrix obtained by projecting the difference between the influence matrix and the expected influence matrix based on the matrix position set satisfies the preset error.

[0018] In certain embodiments of the second aspect of the present application, the matrix completion algorithm includes: fixed point continuation algorithm, accelerated proximal gradient algorithm, semidefinite programming method, singular value threshold algorithm, augmented Lagrange multiplier method, etc.

[0019] In certain implementations of the second aspect of the present application, an initial matrix of the influence variable matrix in the fixed point continuation algorithm is determined based on a singular value matrix corresponding to the expected influence variable matrix.

[0020] In certain embodiments of the second aspect of the present application, the actual energy supply loss parameter of each energy-consuming object is the average value of the optimized energy supply parameter influence of each energy-consuming object in each time period in the optimized influence matrix.

[0021] To achieve the above-mentioned purpose and other related purposes, the third aspect of the present application provides a computer device, including: a storage device for storing at least one program; a processing device for calling the at least one program to execute a control method for an energy supply system as described in any one of the first aspects of the present application.

[0022] In certain embodiments of the third aspect of the present application, the computer device further includes a communication device for communicating with an energy supply object to output the actual energy supply parameter.

[0023] To achieve the above-mentioned purpose and other related purposes, the fourth aspect of the present application provides a computer-readable storage medium storing at least one program, which executes and implements the control method for the energy supply system as described in any one of the first aspects of the present application when called.

[0024] In summary, the control method, system, device and storage medium for the energy supply system disclosed in the present application obtain an expected influence matrix according to the initial energy supply demand of each energy-consuming object in each time interval in each time period and the energy supply parameters in each time interval in each time period; according to the expected influence matrix and the matrix position set, an optimized influence matrix is ​​constructed under the condition of minimizing the rank of the influence matrix; according to the optimized influence matrix, the actual energy supply parameters of the energy supply system are determined to control the energy supply system to supply energy to each energy-consuming object according to the actual energy supply parameters. The present application can customize long-term energy supply parameters that can meet the actual energy demand of each user of the power system, considering that the user may change the load curve. When the energy supply system supplies energy through long-term energy supply parameters, it can not only meet the actual energy demand of the user, but also prevent the user from actively changing the load curve, thereby improving the phenomenon of energy waste and ensuring the smooth operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The specific features of the invention involved in this application are shown in the attached claims. The features and advantages of the invention involved in this application can be better understood by referring to the exemplary embodiments and drawings described in detail below. A brief description of the drawings is as follows:

[0026] Figure 1 Shown is a structural diagram of an application scenario of the present application in one embodiment.

[0027] Figure 2 Shown is a flow chart of a control method for an energy supply system in one embodiment of the present application.

[0028] Figure 3 The figure is a flow chart of a method for determining expected energy demand in a single time period according to a greedy algorithm in one embodiment.

[0029] Figure 4 Shown is a schematic diagram of the structure of a control system used for an energy supply system in the present application in one embodiment.

[0030] Figure 5 Shown is a schematic diagram of the structure of a computer device in one embodiment of the present application.

[0031] Figure 6 Shown is a schematic diagram of the structure of a computer device of the present application in another embodiment. DETAILED DESCRIPTION

[0032] The following is an explanation of the implementation of the present application by means of specific embodiments. People familiar with the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification.

[0033] In the following description, reference is made to the accompanying drawings, which describe several embodiments of the present application. It should be understood that other embodiments may also be used, and that changes in module or unit composition, electrical and operational aspects may be made without departing from the spirit and scope of the present disclosure. The following detailed description should not be considered restrictive, and the scope of the embodiments of the present application is limited only by the claims of the published patents. The terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the present application.

[0034] As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless there is an indication to the contrary in the context. It should be further understood that the terms "comprise", "include" indicate the presence of the described features, steps, operations, elements, components, projects, kinds, and / or groups, but do not exclude the presence, occurrence or addition of one or more other features, steps, operations, elements, components, projects, kinds, and / or groups. The terms "or" and "and / or" used herein are interpreted as inclusive, or mean any one or any combination. Therefore, "A, B or C" or "A, B and / or C" means "any of the following: A; B; C; A and B; A and C; B and C; A, B and C". Exceptions to this definition will only occur when the combination of elements, functions, steps or operations is inherently mutually exclusive in some way.

[0035] The power system is an electric energy production and consumption system consisting of power plants, power transmission and transformation lines, power distribution stations and power consumption. Its function is to convert primary energy from nature into electric energy through power generation devices, and then supply the electric energy to various users through transmission, transformation and distribution. To achieve this function, the power system also needs to include a control system to measure, regulate, control, protect, communicate, and dispatch the process of electric energy production and distribution.

[0036] The main structure of the power system includes power sources, substations, transmission / distribution lines, and load centers. The power sources include, but are not limited to, hydropower stations, thermal power plants, and nuclear power plants. Depending on the type of energy, the types of power systems include, but are not limited to: power grids based on traditional fuel power generation, microgrids based on new energy power generation, etc. The new energy includes, but is not limited to: one or more of coal, natural gas, wind energy, hydropower, photovoltaics, biomass energy, ocean energy, and other forms.

[0037] The production and supply of electric energy should be balanced. Existing technologies usually obtain load forecasts for a period of time in the future based on the user's load curve, and then formulate different energy supply strategies for different users according to the forecast results to meet the user's load. However, the user's energy consumption pattern, that is, the load curve, tends to change over time. For example, after having an energy storage device, the user will perform storage control, so that the purpose of reducing the amount of energy loss can be achieved while meeting its energy demand, or the user can directly achieve the purpose of reducing the amount of energy loss by changing its energy use behavior. In this case, the predicted energy supply strategy will not match the user's actual energy consumption, resulting in energy waste and being detrimental to the development and smooth operation of the entire power system.

[0038] Therefore, in order to more efficiently control the operation of the energy supply system, stable energy supply parameters (such as energy supply, energy supply instructions corresponding to energy supply, or energy supply loss parameters) are determined for different users in a targeted manner taking into account that users may change the load curve. This can prevent arbitrageurs from making profits, improve the control efficiency of the energy supply system operation on the one hand, and increase the reliability and stability of the energy supply system on the other.

[0039] Based on this, the present application provides a control system for an energy supply system to obtain optimized actual energy supply parameters.

[0040] See also Figure 1 , Figure 1 Shown is a structural diagram of an application scenario of the present application in one embodiment.

[0041] like Figure 1 As shown, the energy supply system 100 supplies energy to a plurality of energy-consuming objects 200. The energy supply system can supply energy such as electrical energy, thermal energy, etc.; the energy supply system can be applied to any energy-consuming places such as homes, enterprises, shopping malls, schools, etc.

[0042] The energy-consuming object 200 refers to one or more devices that require energy, such as industrial equipment, such as manufacturing / processing equipment, assembly line equipment, etc.; and household appliances, such as televisions, refrigerators, air conditioners, etc., and lamps of lighting systems, etc. In some examples, the energy-consuming object may be a general term for at least one energy-consuming device connected to the same metering device.

[0043] The type of the energy supply system and the transmission medium for supplying energy to the energy-consuming object can be determined according to the type of energy. The type of energy includes but is not limited to electrical energy, thermal energy, etc. For example, when the energy is electrical energy, the energy supply system includes but is not limited to a power grid, and the transmission medium includes but is not limited to electric wires.

[0044] The application scenario of this application can be a traditional thermal power generation grid or a microgrid based on distributed energy, etc., which is not limited here.

[0045] In some embodiments, the energy-consuming object may also include an energy storage device for storing a certain amount of energy. The energy storage device includes but is not limited to a battery, a supercapacitor, etc. The battery may be a lead-acid battery, a nickel-cadmium battery, a nickel-hydrogen battery, a sodium-sulfur battery, a lithium battery, or a fuel cell.

[0046] It can be understood that since the energy type is not limited to electrical energy, its description is also not limited to electrical energy. It is expressed in this article by "energy" and "energy"; accordingly, the power grid system, power system, etc. are also expressed alternatively by "energy supply system".

[0047] It should be noted that the energy supply system is only a general expression and does not limit it to an independent physical device. The coupling relationship between the energy supply system and the energy-consuming object includes a direct or indirect connection relationship, as long as the energy-consuming object can obtain energy from the energy supply system, and its specific implementation form is not limited.

[0048] See also Figure 2 , Figure 2 Shown is a flow chart of the control method for the energy supply system of the present application in one embodiment. The control method is used to determine long-term stable energy supply parameters for different energy-consuming objects in a targeted manner, taking into account that the actual load curve of the energy-consuming object may change relative to the historical load curve. The energy supply parameters include, but are not limited to: the energy supply of each energy-consuming object, the energy supply instruction corresponding to the energy supply, or the energy supply loss parameter, which is used to stabilize the energy consumption behavior of each user in the energy system, rationalize it, and avoid energy waste. In some embodiments, the energy supply loss parameter may be a parameter representing compensation for the loss caused by supplying energy to the energy supply system.

[0049] The control method for the energy supply system may be executed by the computer device described in the present application, or by other computer devices that can execute the control method of the present application.

[0050] like Figure 2 As shown, the control method includes step S110, step S120, and step S130.

[0051] Step S110: The computer device obtains an expected impact matrix based on the initial energy supply demand of each energy-consuming object in each time interval in each time period and the energy supply parameters in each time interval in each time period; wherein the expected impact matrix includes the expected energy supply parameter impact of each energy-consuming object on the energy supply system in each said time period; and the noise matrix related to the initial energy supply demand is superimposed on the initial impact matrix to obtain the expected impact matrix.

[0052] In the following embodiments, examples are given based on the energy loss parameters.

[0053] In some embodiments, the energy supply parameter impact data is measured, evaluated, fitted, or calculated within a period of time. The period of time refers to the energy consumption duration of the energy-consuming object; the energy consumption duration includes but is not limited to: one or more days, one or more weeks, one or more months, or one or more years.

[0054] Each of the time periods includes one or more periodic time intervals with different time characteristic information. For example, assuming that the time period is one day, the time interval is each hour of the day. Alternatively, in a day, the time intervals can be divided into multiple time intervals according to the different energy loss parameters of the energy supply system, namely: 0AM-7AM time interval, 7AM-11PM time interval, 11PM-5PM time interval, 5PM-7PM time interval, 7PM-12PM time interval.

[0055] It should be noted that the timing distribution of each time interval within the time period is only an example and is not limited to this.

[0056] In the examples of the present application, T represents the total number of time intervals in the period, and t represents the time interval, wherein the feasible domain of t is [1, T]. For example, if the time interval is one hour, the period can be one day, that is, T can be 24.

[0057] In actual scenarios, the energy consumption between energy-consuming objects i Different, among which, The energy consumption of energy-consuming object i in each time interval t in a period of time. The energy supply parameter influence amount can evaluate the energy consumption of energy-consuming object The marginal impact on the energy loss of the energy supply system.

[0058] In some embodiments, the impact of each energy-consuming object on the energy supply parameters of the energy supply system in each time period is determined based on the energy supply demand of each energy-consuming object in each time interval in each time period and the energy supply parameters in each time interval in each time period.

[0059] Formula (1) gives the energy supply parameter influence MCI of each energy-consuming object i: i Definition:

[0060]

[0061] In the above formula, is the total energy consumption of the energy-consuming object i during the period; It is based on the energy consumption of energy-consuming object i in each time interval t during the period Calculated.

[0062] It should be noted that, in other embodiments, the energy used and total energy The calculation method may also be changed. For example, the total energy consumption in each time period may be the weighted sum of the energy consumption in each time interval, and the energy consumption in each time interval may be the weighted sum of the energy consumption at each moment, etc., but the present invention is not limited thereto.

[0063] In one example, the mathematical relationship between energy supply loss and energy consumption is represented by the loss function shown in formula (2):

[0064]

[0065] in, N represents the number of energy-consuming objects supplied by the energy supply system. The loss function is used to represent the energy consumption L of N energy-consuming objects within a time interval t. t and the energy loss of the energy supply system C(L t ) is the energy consumption of energy-consuming object i within the time interval t, that is, the energy supply of the energy supply system to energy-consuming object i within the time interval t; a is the quadratic term coefficient of the loss function, b is the linear term coefficient of the loss function, and c is the constant term of the loss function.

[0066] Of course, although the energy consumption L of the N energy-consuming objects is represented by a quadratic function in this embodiment t and the energy loss of the energy supply system C(L t ), but is not limited to this, and in other embodiments it can be replaced by other polynomial forms.

[0067] In some embodiments, the energy supply loss parameter is, for example, an energy supply price p(t) of the energy supply system in each time interval.

[0068] In one example, The energy loss parameter p(t) in each time interval can be expressed by formula (3):

[0069]

[0070] In one example, combining formula (1) to formula (3), the energy supply parameter influence amount MCI is i It can be expressed by the following formula:

[0071]

[0072] In formula (4), p(t) is the energy loss parameter in each time interval, is the energy consumption of energy-consuming object i in each time interval t, for 1-norm of .

[0073] In some embodiments, the energy demand of energy-consuming object i in each time interval in a period of time can be directly calculated by its energy consumption. To indicate that, is the energy consumption of the energy-consuming object i in each time interval in the period. In other embodiments, the energy supply demand of the energy-consuming object i in each time interval in a period can also be expressed as the normalized energy consumption To indicate that, For energy The normalized expression of .

[0074] Based on formula (4), the energy demand of energy-consuming object i in each time interval in a period and the energy loss parameter p in each time interval can be calculated. t Determine the energy supply parameter influence MCI of the energy-consuming object i i .

[0075] It should be noted that, in other embodiments, the energy supply parameter influence amount MCI i The calculation method of can also be changed, for example, by adding other weight coefficients on the basis of formula (4), but it is not limited to this.

[0076] Based on the above understanding, step S110 includes step S111 and step S112.

[0077] Step S111: the computer device obtains the expected energy supply demand of each energy-consuming object in each time interval in each time period according to the initial energy supply demand of each energy-consuming object in each time interval in each time period and the energy supply parameters in each time interval in each time period.

[0078] The initial energy supply demand is the energy supply demand corresponding to the historical data of actual energy consumption. The energy supply parameters in each time interval are exemplified by energy supply loss parameters in each time interval. The expected energy supply demand includes the estimated possible energy supply demand. For example, in the actual energy consumption process, in order to keep the total energy consumption of the energy object in a time period the same and to reduce the energy loss in the time period under the action of the energy supply loss parameter, the energy consumption in each time interval of the time period will be adjusted. The energy loss amount is exemplified by energy cost. The energy loss amount is used to compensate for the energy supply loss amount of the energy supply system.

[0079] Specifically, based on the initial energy demand and the energy loss parameters in each time interval, the energy demand in each time interval can be adjusted in anticipation, so that the energy loss in this time period is reduced under the effect of the energy loss parameters. After normalization, the energy consumption of the energy-consuming object i in each time interval t can be used as the initial energy demand, which can improve the calculation efficiency of the control method for the energy supply system. The expected energy demand is obtained as the initial energy demand.

[0080] In some embodiments, the initial energy demand Expected energy demand Satisfies the following formula:

[0081]

[0082]

[0083] Formula (5) represents the total amount of initial energy demand of a single energy-consuming object at each time interval in a single period of time: Total expected energy demand Equal. Formula (6) indicates that the sum of the differences between the initial energy demand of a single energy-consuming object in each time interval in a single period and the expected energy demand in each time interval satisfies the preset ratio θ. for

[0084] The initial energy demand Expected energy demand Under the condition that the constraints described in formula (5) and formula (6) are satisfied, the expected energy demand of the single energy-consuming object in each time interval in a single time period corresponding to minimizing the energy loss of the single energy-consuming object in a single time period is determined according to the initial energy demand of the single energy-consuming object in each time interval in a single time period and the energy supply parameters in each time interval in a single time period.

[0085] The energy supply parameter is taken as the energy supply loss parameter p t As an example, under the constraints of formula (5) and formula (6), the expected energy demand d in each time interval corresponding to the minimum energy loss shown in formula (7) is determined. i ' t .

[0086]

[0087] It should be noted that a time limit can also be set for formula (5) and formula (6). For example, according to actual needs, the energy usage change behavior is implemented in part of the time interval in a period. That is, when solving problem (7), a segmented solution can be adopted, or the solution can be performed only in several consecutive time intervals in a period.

[0088] In other embodiments, the energy consumption of an energy-consuming object i in each time interval t may also be used. As the initial energy demand, the expected energy demand is solved Formula (5), Formula (6), and Formula (7) can be adaptively changed to be transformed into optimization problems as shown in Formula (8), Formula (9), and Formula (10):

[0089]

[0090]

[0091]

[0092] in, Under the constraints of formula (8) and formula (9), determine the energy consumption in each time interval corresponding to the minimum energy loss shown in formula (10): The energy consumption in each time interval can be directly used as the expected energy demand To determine the expected energy supply parameter influence.

[0093] In some examples, a greedy algorithm can be applied to solve the optimization problem corresponding to Formula (7) and Formula (10) based on the initial energy supply demand of a single energy-consuming object in each time interval in a single time period and the energy loss parameter in each time interval in the time period to determine the expected energy supply demand.

[0094] Specifically, the normalized energy consumption of energy-consuming object i in a single period is As an example of the initial energy demand. Figure 3 , Figure 3 The figure is a flow chart of a method for determining expected energy demand in a single time period according to a greedy algorithm in one embodiment. Figure 3 The method shown includes: step S121, step S122, step S123, step S124, step S125.

[0095] Step S121: sort the energy loss parameters in each time interval in the single period to obtain an ascending order of loss parameter ranking p(1)<p(2)<.....p(T), set the initial value of the current energy supply change Δd to 0, set the initial value of the current energy supply loss parameter ranking j to 1, and determine the energy supply change threshold. In this embodiment, the energy supply change threshold is θ·sum 1 ,in In other embodiments, if the initial energy demand directly uses the energy consumption for a period of time To express, the sum of the energy supply involved in the variable threshold is 1 You can directly use the above mentioned to replace.

[0096] Step S122: Determine whether the current energy supply change Δd is less than or equal to the energy supply change threshold θ·sum 1 If yes, then execute step S123, if no, then end. The current energy supply change Δd will be updated according to step S125.

[0097] Step S123: Determine the initial energy demand d of the high energy loss parameter sequence corresponding to the current energy loss parameter sequence j i (T+1-j) Whether the difference between the energy supply change threshold and the current energy supply change is θ·sum 1 -Δd and above:

[0098] If yes, execute step S124, if no, execute step S125. The current energy loss parameter sequence j will be updated according to step S125.

[0099] Step S124: The initial energy demand d under the current energy loss parameter sequence j is i (j) Add the difference between the energy supply change threshold and the current energy supply change θ·sum 1 -Δd is the expected energy demand d under the current energy loss parameter sequence i ' (j) , the initial energy demand d of the high energy loss parameter sequence corresponding to the current energy loss parameter sequence i (T+1-j) Subtract the difference θ·sum between the energy supply change threshold and the current energy supply change 1 -Δd as the expected energy demand d under the current high energy loss parameter sequence i ' (T+1-j) . Then the operation of obtaining the expected energy supply demand of the energy-consuming object in the period is terminated.

[0100] For example, the initial energy demand in a period is The energy loss parameters corresponding to the six time intervals in this period are (1, 3, 6, 5, 4, 2) respectively. The energy loss parameters in this period are sorted in ascending order of loss parameters as follows: 1<2<3<4<5<6. Assuming the value of θ is 0.2, the initial energy demand corresponding to the time interval with the lowest energy loss parameter is 0.05. When the first judgment is made, the initial energy demand corresponding to the high energy loss parameter sequence j=6 corresponding to the current energy loss parameter sequence j=1 is 0.4. Since 0.4>(0.2-0), the energy demand corresponding to the energy loss parameter sequence j=6 is changed from 0.4 to 0.2, and 0.2 is taken as the expected energy demand in the time interval corresponding to the energy loss parameter sequence j=6. Accordingly, the energy demand corresponding to the energy loss parameter sequence j=1 is changed from 0.05 to 0.25, and 0.25 is taken as the expected energy demand in the time interval corresponding to the energy loss parameter sequence j=1. Take (0.25, 0.25, 0.2, 0.15, 0.1, 0.05) as the expected energy demand for this period

[0101] Step S125: The initial energy demand d under the high energy loss parameter corresponding to the current energy loss parameter is converted into i (T+1-j) Set to 0 as the expected energy demand under the current high energy loss parameters d i ' (T+1-j) , will be compared with the initial energy demand d under the current energy loss parameters i (j) Add the initial energy demand d under the high energy loss parameter corresponding to the current energy loss parameter i(T+1-j) Then it is used as the expected energy demand under the current energy loss parameters d i ' (j) , and add 1 to the current energy supply loss parameter order j, and sum the current energy supply change and the initial energy supply demand under the high energy supply loss parameter corresponding to the current energy supply loss parameter d i (T+1-j) +Δd as the next current energy supply change. The updated current energy supply change Δd and the current energy supply loss parameter sequence j are respectively used as the current energy supply change Δd when the step S122 is executed next time and the current energy supply loss parameter sequence j when the step S123 is executed next time.

[0102] For example, the initial energy demand in a period is The energy loss parameters corresponding to the ten time intervals in this period are (1, 8, 7, 11, 6, 9, 5, 4, 3, 2) respectively. The energy loss parameters in this period are sorted in ascending order of the loss parameters as follows: 1<2<3<4<5<6<7<8<9<11. Assuming the value of θ is 0.3, the initial energy demand corresponding to the time interval with the lowest energy loss parameter is 0. When the first judgment is made, the energy loss parameter corresponding to the high energy loss parameter sequence j=10 corresponding to the current energy loss parameter sequence j=1 is 11, and the initial energy demand corresponding to the high energy loss parameter sequence j=10 corresponding to the current energy loss parameter sequence j=1 is 0.25. Since 0.25<(0.3-0), the energy demand corresponding to the energy loss parameter sequence j=10 is changed from 0.25 to 0, and 0 is the expected energy demand corresponding to the energy loss parameter sequence j=10. Accordingly, the energy demand corresponding to the energy loss parameter sequence j=1 is changed from 0 to (0.25+0), and 0.25 is taken as the expected energy demand corresponding to the energy loss parameter sequence j=1. Then, the energy loss parameter sequence j is increased by 1, and the current energy loss parameter sequence j is now 2. The energy change Δd is added to the energy demand corresponding to the energy loss parameter sequence j=10, and now the current energy change Δd is 0.25. Since the initial energy demand of the high energy loss parameter sequence j=9 corresponding to the current energy loss parameter sequence j=2 is 0.25>(0.3-0.25), the energy demand corresponding to the energy loss parameter sequence j=2 is changed from 0 to ((0.3-0.25)+0), and 0.05 is taken as the expected energy demand corresponding to the energy loss parameter sequence j=2. The energy demand corresponding to the energy loss parameter sequence j = 9 is changed from 0.25 to (0.25-(0.3-0.25)), and 0.2 is the expected energy demand corresponding to the energy loss parameter sequence j = 9. (0.25, 0.05, 0.1, 0, 0.1, 0.2, 0.15, 0.05, 0.05, 0.05) is taken as the expected energy demand for this period

[0103] The current energy supply change is initially the initial value 0, and the current energy supply loss parameter sequence j is initially 1. The energy supply loss parameter p(1) corresponding to the current energy supply loss parameter at the initial time is the lowest. The current energy supply change Δd and the current energy supply loss parameter sequence j are updated according to step S125 to act on steps S122 and S123.

[0104] After determining the expected energy supply demand of each energy-consuming object i in each time period, step S112 may be executed.

[0105] Step S112: Determine the expected energy supply parameter impact of each energy-consuming object on the energy supply system in each time period according to the expected energy supply demand of each energy-consuming object in each time interval in each time period to obtain the expected impact matrix.

[0106] Specifically, the expected energy supply demands of multiple energy-consuming objects in multiple time periods are substituted into formula (4) to obtain the expected energy supply parameter influences of multiple energy-consuming objects in multiple time periods, so as to obtain the expected influence matrix Y. Each element in the expected influence matrix Y represents the expected energy supply parameter influence of each energy-consuming object on the energy supply system in each time period.

[0107] Each element in the expected impact matrix represents the expected energy supply parameter impact y of each energy-consuming object on the energy supply system in each time period. i,j The expected energy supply parameter influence amount y i,j Satisfies the following formula:

[0108]

[0109] in, is the energy supply parameter impact corresponding to the initial energy supply demand of each energy-consuming object in each period, ε i,j is the energy supply parameter impact corresponding to the noise of each energy-consuming object in each time period. M is the number of energy-consuming objects, and N is the number of time periods in the observation period. Among them, the noise is the difference between the expected energy supply demand and the initial energy supply demand.

[0110] Therefore, the noise matrix related to the initial energy supply demand is superimposed on the initial influence matrix to obtain the expected influence matrix, and the expected influence matrix Y can be expressed by the following formula:

[0111] Y=Θ * +Z (12)

[0112] Among them, Θ *is the initial influence matrix, the initial influence matrix θ * Each element in is the energy supply parameter influence corresponding to the initial energy supply demand of each energy-consuming object in each period Z is the noise matrix related to the initial energy supply demand. Each element in the noise matrix Z is the energy supply parameter influence ε corresponding to the noise of each energy-consuming object in each period. i,j .

[0113] It should be noted that in reality, users will not change their energy demand in every period during the entire observation period. Therefore, in practice, only some of the energy supply parameter influences in the expected influence matrix are noisy, that is, The energy supply parameter influence ε corresponding to the noise in other energy supply parameter influences in the expected influence matrix i,j is 0. In this case, the initial influence matrix Θ * is low-rank, and the expected influence matrix Y is also low-rank. Therefore, we can use some elements of the expected influence matrix Y obtained in the previous solution to construct the optimized influence matrix, so that the actual energy supply parameters corresponding to the optimized influence matrix are closer to the user's actual energy supply demand. Therefore, when the energy supply system uses the long-term stable actual energy supply parameters obtained based on the optimized influence matrix for energy supply, users will not change their own energy demand in order to reduce energy loss.

[0114] Step S120 may construct an optimized influence matrix according to some elements of the expected influence matrix Y obtained by solving the above.

[0115] Step S120: The computer device constructs an optimized influence matrix under the condition of minimizing the rank of the influence matrix based on the expected influence matrix and the matrix position set; wherein the optimized influence matrix includes the influence of each energy-consuming object on the optimized energy supply parameters of the energy supply system in each time period; the influence matrix and the expected influence matrix are of the same type, and the matrix position set includes the corresponding element positions of part of the expected influence matrix and the influence matrix.

[0116] In some embodiments, the optimized influence matrix is ​​constructed by using a matrix completion algorithm under the condition of minimizing the rank of the influence matrix and under a preset error constraint. The error constraint can be expressed by the following formula:

[0117] ||P Ω (Θ-Y)|| F <δ (13)

[0118] Among them, δ is the preset error, P ΩFormula (13) indicates that the norm of the matrix obtained by performing a projection operation on the difference between the influence matrix Θ and the expected influence matrix Y according to the matrix position set satisfies the preset error. The projection operation P Ω It can be explained by formula (14):

[0119]

[0120] Wherein, Ω is a matrix position set, the influence matrix Θ and the expected influence matrix Y are isotype matrices, and the matrix position set Ω includes the expected influence matrix Y and the corresponding element positions of some of the influence matrix Θ. For example, the matrix position set Ω includes the diagonal positions of the M×N expected influence matrix Y. Since the influence matrix Θ and the expected influence matrix Y are isotype matrices, the matrix position set Ω also includes the diagonal positions of the influence matrix Θ.

[0121] The matrix position set Ω is determined by a sampling algorithm and the number of matrix positions n expected to be sampled. For example, the sampling algorithm is simple random sampling, the number of matrix positions expected to be sampled is n, and based on the sampling probability Extract n positions in the matrix. The product of M and N is the total number of matrix positions in the expected influence matrix. It should be noted that if the number of samples is too large, the constructed matrix will be closer to Y; if the number of samples is too small, it will be impossible to accurately construct the optimized influence matrix.

[0122] Under the preset error constraint and the condition of minimizing the rank of the influence matrix, the problem of constructing an optimized influence matrix can be transformed into an optimization problem of minimizing the rank corresponding to formula (15).

[0123]

[0124] The above optimization problem can be solved by using a matrix completion algorithm to construct the optimization influence matrix, wherein the optimization influence matrix includes the influence of each energy-consuming object on the optimized energy supply parameters of the energy supply system in each time period.

[0125] The matrix completion algorithm includes but is not limited to a fixed point continuation algorithm, an accelerated proximal gradient algorithm, a semidefinite programming method, a singular value threshold algorithm, an augmented Lagrange multiplier method, and the like.

[0126] The non-convex optimization problem corresponding to formula (15) can be transformed into a minimum value problem as shown in the following formula:

[0127]

[0128] Among them, ||Θ|| *is the sum of the singular values ​​of the influence matrix Θ, ||.|| F Represents the F-norm of the matrix. The F-norm of the matrix is ​​the sum of the squares of the element values ​​at each matrix position. Power, Wherein, M is the number of energy-consuming objects, N is the number of time periods in the observation period, p is the sampling probability of determining the matrix position set Ω, and σ is the standard deviation of the noise matrix Z. The noise matrix can be obtained by solving the expected influence matrix Y and the initial influence matrix Θ according to the above solution. * The difference between them is obtained.

[0129] When using the fixed point continuation algorithm to solve the minimum problem of formula (16), it is necessary to obtain the solution of the following differential equation:

[0130]

[0131] in, It represents the sub-gradient of the influence matrix Θ. The fixed point continuation algorithm constructs matrices A, B, and multiplies the transpose of the B matrix with the product of the A matrix AB. T As the optimized influence matrix.

[0132] Among them, the matrix A and matrix B are based on the initial matrix A 0 and the initial matrix B 0 , obtained by iterating the fixed point algorithm. It should be noted that the initial matrix A 0 and the initial matrix B 0 The setting of is related to the convergence speed of the fixed point algorithm.

[0133] In some embodiments, the initial matrix A 0 and the initial matrix B 0 It is related to the expected influence matrix Y obtained by solving the above.

[0134] Specifically, performing singular value decomposition on the expected influence matrix Y can obtain the singular value matrix Σ of the expected influence matrix Y: M×N , the singular value matrix Σ M×N is an M×N diagonal matrix, where The initial matrix A can be constructed by simplifying the singular value matrix 0 and the initial matrix B 0 .

[0135] For example, the diagonal elements in the singular value matrix are (σ1, σ2, ..., σn). M×NThe diagonal elements in are sorted by size to obtain a sorted singular value matrix. The diagonal elements in the sorted singular value matrix are (σ1', σ2', ..., σn'), where (σ1'>σ2'>...>σn'). When the diagonal element in the singular value matrix is ​​less than the singular value threshold, the corresponding diagonal element is set to 0 to obtain a simplified singular value matrix. The singular value threshold may be one tenth, one hundredth, or one thousandth of the maximum value of the diagonal element, but is not limited thereto. It can be expressed by the following formula:

[0136]

[0137] Among them, K is the simplified singular value matrix The number of non-zero diagonal elements in . is a diagonal matrix, each diagonal element is a singular value matrix Σ M×N The diagonal elements in that are above the singular value threshold. m×n Represents an m×n dimensional zero matrix.

[0138] According to the simplified singular value matrix The initial matrix A obtained 0 and the initial matrix B 0 It can be expressed by the following formula:

[0139]

[0140] in, is the diagonal matrix The square root of each element in . is the initial matrix of the optimized influence matrix.

[0141] In other embodiments, the initial matrix of the optimized influence matrix may be an M×N dimensional 0 matrix, or may be the expected influence matrix Y, but is not limited thereto.

[0142] It should be noted that in step S120, the number of energy-consuming objects M is used as rows and the number of time periods in the observation period N is used as columns. In other embodiments, the number of time periods in the observation period N is used as rows and the number of energy-consuming objects M is used as columns.

[0143] Step S130 may be performed according to the optimized influence matrix constructed in step S120.

[0144] Step S130: determining actual energy supply parameters of the energy supply system according to the optimized influence matrix, so as to control the energy supply system to supply energy to each energy-consuming object according to the actual energy supply parameters.

[0145] The actual energy supply parameters include but are not limited to: the actual energy supply of each energy-consuming object, the actual energy supply instruction corresponding to the actual energy supply, or the actual energy supply loss parameter. The actual energy supply parameters are used to stabilize the energy consumption behavior of each user in the energy system, rationalize it, and avoid energy waste.

[0146] In some embodiments, the actual energy loss parameter is the average of the optimized energy parameter influence of each energy-consuming object in each time period in the optimized influence matrix. For example, the optimized energy parameter influence of an energy-consuming object in N time periods in the optimized influence matrix is ​​(a 1 ,a 2 ,....,a N ), the ratio of the sum of the influencing amounts of each optimized energy supply parameter to N is taken as the stable actual energy supply loss parameter.

[0147] In some embodiments, the actual energy supply amount of each energy-consuming object, or the actual energy supply instruction corresponding to the actual energy supply amount, is determined based on the obtained actual energy supply loss parameter. For example, the energy supply amount corresponding to the actual energy supply loss parameter is used as the long-term actual energy supply amount to control the energy supply system that does not include an energy storage device to produce energy according to the actual energy supply amount to supply each energy-consuming object. For another example, the actual energy supply instruction is determined after the energy supply amount corresponding to the actual energy supply loss parameter is used as the stable actual energy supply amount, so that the energy supply system including the energy storage device can produce energy, store energy, and release energy according to the actual energy supply instruction. Among them, the actual energy supply instruction includes: the actual energy production amount for each time interval, the actual energy storage amount for each time interval, and the actual energy release amount for each time interval.

[0148] It should be noted that when the energy supply system supplies energy through the actual energy supply parameters, it can not only meet the actual energy demand of users but also prevent users from easily changing their load curves, thereby improving the phenomenon of energy waste and ensuring the smooth operation of the power system.

[0149] The present application also provides a control system for an energy supply system, which is used to determine long-term stable energy supply parameters for different energy-consuming objects in a targeted manner, taking into account that the actual load curve of the energy-consuming object may change relative to the historical load curve. The energy supply parameters include, but are not limited to: the energy supply of each energy-consuming object, the energy supply instruction corresponding to the energy supply, or the energy supply loss parameter, which is used to stabilize the energy consumption behavior of each user in the energy system, rationalize it, and avoid energy waste. In some embodiments, the energy supply loss parameter may be a parameter that represents compensation for the loss caused by the energy supply system.

[0150] The control system can be applied in Figure 5 or Figure 6 In the computer system shown in FIG. For example, in the form of software installed in Figure 5 or Figure 6 The corresponding functions are implemented in the computer system shown.

[0151] See also Figure 4 , Figure 4 As shown in FIG4 , the control system 40 for the energy supply system includes: an expected influence matrix determination module 41 , an optimized influence matrix determination module 42 , and an energy supply parameter determination module 43 .

[0152] The expected impact matrix determination module 41 is used to obtain the expected impact matrix according to the initial energy supply demand of each energy-consuming object in each time interval in each time period, and the energy supply parameters in each time interval in each time period; wherein the expected impact matrix includes the expected energy supply parameter impact of each energy-consuming object on the energy supply system in each said time period; the noise matrix related to the initial energy supply demand superimposed on the initial impact matrix is ​​the expected impact matrix.

[0153] In the following embodiments, examples are given based on the energy loss parameters.

[0154] In actual scenarios, the energy consumption between energy-consuming objects i Different, among which, The energy consumption of energy-consuming object i in each time interval t in a period of time. The energy supply parameter influence amount can evaluate the energy consumption of energy-consuming object The marginal impact on the energy loss of the energy supply system.

[0155] In some embodiments, the impact of each energy-consuming object on the energy supply parameters of the energy supply system in each time period is determined based on the energy supply demand of each energy-consuming object in each time interval in each time period and the energy supply parameters in each time interval in each time period.

[0156] Formula (1) gives the energy supply parameter influence MCI of each energy-consuming object i: i Definition:

[0157]

[0158] In the above formula, is the total energy consumption of the energy-consuming object i during the period; It is based on the energy consumption of energy-consuming object i in each time interval t during the period Calculated.

[0159] It should be noted that, in other embodiments, the energy used and total energy The calculation method may also be changed. For example, the total energy consumption in each time period may be the weighted sum of the energy consumption in each time interval, and the energy consumption in each time interval may be the weighted sum of the energy consumption at each moment, etc., but the present invention is not limited thereto.

[0160] In one example, the mathematical relationship between energy supply loss and energy consumption is represented by the loss function shown in formula (2):

[0161]

[0162] in, N represents the number of energy-consuming objects supplied by the energy supply system. The loss function is used to represent the energy consumption L of N energy-consuming objects within a time interval t. t and the energy loss of the energy supply system C(L t ) is the energy consumption of energy-consuming object i within the time interval t, that is, the energy supply of the energy supply system to energy-consuming object i within the time interval t; a is the quadratic term coefficient of the loss function, b is the linear term coefficient of the loss function, and c is the constant term of the loss function.

[0163] Of course, although the energy consumption L of the N energy-consuming objects is represented by a quadratic function in this embodiment t and the energy loss of the energy supply system C(L t ), but is not limited to this, and in other embodiments it can be replaced by other polynomial forms.

[0164] In some embodiments, the energy loss parameter is exemplified by the energy price p(t) of the energy supply system in each time interval. The energy loss parameter p(t) in each time interval can be expressed by formula (3):

[0165]

[0166] In one example, combining formula (1) to formula (3), the energy supply parameter influence amount MCI is i It can be expressed by the following formula:

[0167]

[0168] In formula (4), p(t) is the energy loss parameter in each time interval, is the energy consumption of energy-consuming object i in each time interval t, for 1-norm of .

[0169] In some embodiments, the energy demand of energy-consuming object i in each time interval in a period of time can be directly calculated by its energy consumption. To indicate that, is the energy consumption of the energy-consuming object i in each time interval in the period. In other embodiments, the energy supply demand of the energy-consuming object i in each time interval in a period can also be expressed as the normalized energy consumption To indicate that, For energy The normalized expression of .

[0170] Based on formula (4), the energy demand of energy-consuming object i in each time interval in a period and the energy loss parameter p in each time interval can be calculated. t Determine the energy supply parameter influence MCI of the energy-consuming object i i .

[0171] It should be noted that, in other embodiments, the energy supply parameter influence amount MCI i The calculation method of can also be changed, for example, by adding other weight coefficients on the basis of formula (4), but it is not limited to this.

[0172] Based on the above understanding, firstly, the expected impact matrix determination module 41 obtains the expected energy supply demand of each energy-consuming object in each time interval in each time period according to the initial energy supply demand of each energy-consuming object in each time interval in each time period and the energy supply parameters in each time interval in each time period.

[0173] The initial energy supply demand is the energy supply demand corresponding to the historical data of actual energy consumption. The energy supply parameters in each time interval are exemplified by energy supply loss parameters in each time interval. The expected energy supply demand includes the estimated possible energy supply demand. For example, in the actual energy consumption process, in order to keep the total energy consumption of the energy object in a time period the same and reduce the energy loss in the time period under the action of the energy supply loss parameter, the energy consumption in each time interval of the time period will be adjusted. The energy loss amount is exemplified by the total energy cost. The energy loss amount is used to compensate for the energy supply loss amount of the energy supply system.

[0174] Specifically, based on the initial energy demand and the energy loss parameters in each time interval, the energy demand in each time interval can be adjusted in anticipation, so that the energy loss in this time period is reduced under the effect of the energy loss parameters. After normalization, the energy consumption of the energy-consuming object i in each time interval t can be used as the initial energy demand to improve the calculation efficiency. The expected energy demand is obtained as the initial energy demand.

[0175] In some embodiments, the initial energy demand Expected energy demand Satisfies the following formula:

[0176]

[0177]

[0178] Formula (5) represents the total amount of initial energy demand of a single energy-consuming object at each time interval in a single period of time: Total expected energy demand Equal. Formula (6) indicates that the sum of the differences between the initial energy demand of a single energy-consuming object in each time interval in a single period and the expected energy demand in each time interval satisfies the preset ratio θ. for

[0179] The initial energy demand Expected energy demand Under the condition that the constraints described in formula (5) and formula (6) are satisfied, the expected energy demand of the single energy-consuming object in each time interval in a single time period corresponding to minimizing the energy loss of the single energy-consuming object in a single time period is determined according to the initial energy demand of the single energy-consuming object in each time interval in a single time period and the energy supply parameters in each time interval in a single time period.

[0180] The energy supply parameter is taken as the energy supply loss parameter p t As an example, under the constraints of formula (5) and formula (6), the expected energy demand d in each time interval corresponding to the minimum energy loss shown in formula (7) is determined. i ' t .

[0181]

[0182] It should be noted that a time limit can also be set for formula (5) and formula (6). For example, according to actual needs, the energy usage change behavior is implemented in part of the time interval in a period. That is, when solving problem (7), a segmented solution can be adopted, or the solution can be performed only in several consecutive time intervals in a period.

[0183] In other embodiments, the energy consumption of an energy-consuming object i in each time interval t may also be used. As the initial energy demand, the expected energy demand is solved Formula (5), Formula (6), and Formula (7) can be adaptively changed to be transformed into optimization problems as shown in Formula (8), Formula (9), and Formula (10):

[0184]

[0185]

[0186]

[0187] in, Under the constraints of formula (8) and formula (9), determine the energy consumption in each time interval corresponding to the minimum energy loss shown in formula (10): The energy consumption in each time interval can be directly used as the expected energy demand To determine the expected energy supply parameter influence.

[0188] In some examples, a greedy algorithm can be applied to solve the optimization problem corresponding to Formula (7) and Formula (10) based on the initial energy supply demand of a single energy-consuming object in each time interval in a single time period and the energy loss parameter in each time interval in the time period to determine the expected energy supply demand.

[0189] Specifically, the normalized energy consumption of energy-consuming object i in a single period is Taking the initial energy supply demand as an example, the expected impact matrix determination module 41 determines the expected energy supply demand in a single period according to a greedy algorithm.

[0190] It should be noted that the method of determining the expected energy demand in a single time period according to the greedy algorithm is the same or similar to that described above and will not be described in detail here.

[0191] After determining the expected energy supply demand of each energy-consuming object i in each time period, the expected impact matrix determination module 41 determines the expected energy supply parameter influence of each energy-consuming object on the energy supply system in each time period according to the expected energy supply demand of each energy-consuming object in each time interval in each time period, so as to obtain the expected impact matrix.

[0192] Substitute the expected energy supply demands of multiple energy-consuming objects in multiple time periods into formula (4) to obtain the expected energy supply parameter influences of multiple energy-consuming objects in multiple time periods, so as to obtain the expected influence matrix Y. Each element in the expected influence matrix Y represents the expected energy supply parameter influence of each energy-consuming object on the energy supply system in each time period.

[0193] Each element in the expected impact matrix represents the expected energy supply parameter impact y of each energy-consuming object on the energy supply system in each time period. i,j The expected energy supply parameter influence amount y i,j Satisfies the following formula:

[0194]

[0195] in, is the energy supply parameter impact corresponding to the initial energy supply demand of each energy-consuming object in each period, ε i,j is the energy supply parameter impact corresponding to the noise of each energy-consuming object in each time period. M is the number of energy-consuming objects, and N is the number of time periods in the observation period. Among them, the noise is the difference between the expected energy supply demand and the initial energy supply demand.

[0196] Therefore, the noise matrix related to the initial energy supply demand is superimposed on the initial influence matrix to obtain the expected influence matrix. The expected influence matrix Y can be expressed by the following formula:

[0197] Y=Θ * +Z (12)

[0198] Among them, Θ * is the initial influence matrix, the initial influence matrix θ * Each element in is the energy supply parameter influence corresponding to the initial energy supply demand of each energy-consuming object in each period Z is the noise matrix related to the initial energy supply demand. Each element in the noise matrix Z is the energy supply parameter influence ε corresponding to the noise of each energy-consuming object in each period. i,j .

[0199] It should be noted that in reality, users will not change their energy demand in every period during the entire observation period. Therefore, in practice, only some of the energy supply parameter influences in the expected influence matrix are noisy, that is, The energy supply parameter influence ε corresponding to the noise in other energy supply parameter influences in the expected influence matrix i,j is 0. In this case, the initial influence matrix Θ *is low-rank, and the expected influence matrix Y is also low-rank. Therefore, we can use some elements of the expected influence matrix Y obtained in the previous solution to construct the optimized influence matrix, so that the actual energy supply parameters corresponding to the optimized influence matrix are closer to the user's actual energy supply demand. Therefore, when the energy supply system uses the long-term stable actual energy supply parameters obtained based on the optimized influence matrix for energy supply, users will not change their own energy demand in order to reduce energy loss.

[0200] The optimized influence matrix determination module 42 can construct an optimized influence matrix according to some elements of the expected influence matrix Y obtained by solving the above text.

[0201] The optimized influence matrix determination module 42 is used to construct an optimized influence matrix under the condition of minimizing the rank of the influence matrix based on the expected influence matrix and the matrix position set; wherein the optimized influence matrix includes the influence of each energy-consuming object on the optimized energy supply parameters of the energy supply system in each time period; the influence matrix and the expected influence matrix are of the same type, and the matrix position set includes the corresponding element positions of part of the expected influence matrix and the influence matrix.

[0202] In some embodiments, the optimized influence matrix is ​​constructed by using a matrix completion algorithm under the condition of minimizing the rank of the influence matrix and under a preset error constraint. The error constraint can be expressed by the following formula:

[0203] ||P Ω (Θ-Y)|| F <δ (13)

[0204] Among them, δ is the preset error, P Ω Formula (13) indicates that the norm of the matrix obtained by performing a projection operation on the difference between the influence matrix Θ and the expected influence matrix Y according to the matrix position set satisfies the preset error. The projection operation P Ω It can be explained by formula (14):

[0205]

[0206] Wherein, Ω is a matrix position set, the influence matrix Θ and the expected influence matrix Y are isotype matrices, and the matrix position set Ω includes the expected influence matrix Y and the corresponding element positions of some of the influence matrix Θ. For example, the matrix position set Ω includes the diagonal positions of the M×N expected influence matrix Y. Since the influence matrix Θ and the expected influence matrix Y are isotype matrices, the matrix position set Ω also includes the diagonal positions of the influence matrix Θ.

[0207] The matrix position set Ω is determined by a sampling algorithm and the number of matrix positions n expected to be sampled. For example, the sampling algorithm is simple random sampling, the number of matrix positions expected to be sampled is n, and based on the sampling probability Extract n positions in the matrix. The product of M and N is the total number of matrix positions in the expected influence matrix. It should be noted that if the number of samples is too large, the constructed matrix will be closer to Y; if the number of samples is too small, it will be impossible to accurately construct the optimized influence matrix.

[0208] Under the preset error constraint and the condition of minimizing the rank of the influence matrix, the problem of constructing an optimized influence matrix can be transformed into an optimization problem of minimizing the rank corresponding to formula (15).

[0209]

[0210] The above optimization problem can be solved by using a matrix completion algorithm to construct the optimization influence matrix, wherein the optimization influence matrix includes the influence of each energy-consuming object on the optimized energy supply parameters of the energy supply system in each time period.

[0211] The matrix completion algorithm includes but is not limited to a fixed point continuation algorithm, an accelerated proximal gradient algorithm, a semidefinite programming method, a singular value threshold algorithm, an augmented Lagrange multiplier method, and the like.

[0212] The non-convex optimization problem corresponding to formula (15) can be transformed into a minimum value problem as shown in the following formula:

[0213]

[0214] Among them, ||Θ|| * is the sum of the singular values ​​of the influence matrix Θ, ||.|| F Represents the F-norm of the matrix. The F-norm of the matrix is ​​the sum of the squares of the element values ​​at each matrix position. Power, Wherein, M is the number of energy-consuming objects, N is the number of time periods in the observation period, p is the sampling probability of determining the matrix position set Ω, and σ is the standard deviation of the noise matrix Z. The noise matrix can be obtained by solving the expected influence matrix Y and the initial influence matrix Θ according to the above solution. * The difference between them is obtained.

[0215] When using the fixed point continuation algorithm to solve the minimum problem of formula (16), it is necessary to obtain the solution of the following differential equation:

[0216]

[0217] in, It represents the sub-gradient of the influence matrix Θ. The fixed point continuation algorithm constructs matrices A, B, and multiplies the transpose of the B matrix with the product of the A matrix AB. T As the optimized influence matrix.

[0218] Among them, the matrix A and matrix B are based on the initial matrix A 0 and the initial matrix B 0 , obtained by iterating the fixed point algorithm. It should be noted that the initial matrix A 0 and the initial matrix B 0 The setting of is related to the convergence speed of the fixed point algorithm.

[0219] In some embodiments, the initial matrix A 0 and the initial matrix B 0 It is related to the expected influence matrix Y obtained by solving the above.

[0220] Specifically, performing singular value decomposition on the expected influence matrix Y can obtain the singular value matrix Σ of the expected influence matrix Y: M×N , the singular value matrix Σ M×N is an M×N diagonal matrix, where The initial matrix A can be constructed by simplifying the singular value matrix 0 and the initial matrix B 0 .

[0221] For example, the diagonal elements in the singular value matrix are (σ1, σ2, ..., σn). M×N The diagonal elements in are sorted by size to obtain a sorted singular value matrix. The diagonal elements in the sorted singular value matrix are (σ1', σ2', ..., σn'), where (σ1'>σ2'>...>σn'). When the diagonal element in the singular value matrix is ​​less than the singular value threshold, the corresponding diagonal element is set to 0 to obtain a simplified singular value matrix. The singular value threshold may be one tenth, one hundredth, or one thousandth of the maximum value of the diagonal element, but is not limited thereto. It can be expressed by the following formula:

[0222]

[0223] Among them, K is the simplified singular value matrix The number of non-zero diagonal elements in . is a diagonal matrix, each diagonal element is a singular value matrix Σ M×N The diagonal elements in that are above the singular value threshold.m×n Represents an m×n dimensional zero matrix.

[0224] According to the simplified singular value matrix The initial matrix A obtained 0 and the initial matrix B 0 It can be expressed by the following formula:

[0225]

[0226] in, is the diagonal matrix The square root of each element in . is the initial matrix of the optimized influence matrix.

[0227] In other embodiments, the initial matrix of the optimized influence matrix may be a zero matrix of M×N dimensions, or may be the expected influence matrix Y, but is not limited thereto. It should be noted that in the present application, the number M of energy-consuming objects is used as a row and the number N of time periods during the observation period is used as a column. In other implementations, the number N of time periods during the observation period may be used as a row and the number M of energy-consuming objects may be used as a column.

[0228] The energy supply parameter determination module 43 is used to determine the actual energy supply parameters of the energy supply system according to the optimized influence matrix, so as to control the energy supply system to supply energy to each energy-consuming object according to the actual energy supply parameters.

[0229] The actual energy supply parameters include but are not limited to: the actual energy supply of each energy-consuming object, the actual energy supply instruction corresponding to the actual energy supply, or the actual energy supply loss parameter. The actual energy supply parameters are used to stabilize the energy consumption behavior of each user in the energy system, rationalize it, and avoid energy waste.

[0230] In some embodiments, the actual energy loss parameter is the average of the optimized energy parameter influence of each energy-consuming object in each time period in the optimized influence matrix. For example, the optimized energy parameter influence of an energy-consuming object in N time periods in the optimized influence matrix is ​​(a 1 ,a 2 ,....,a N ), the ratio of the sum of the influencing amounts of each optimized energy supply parameter to N is taken as the stable actual energy supply loss parameter.

[0231] In some embodiments, the actual energy supply amount of each energy-consuming object, or the actual energy supply instruction corresponding to the actual energy supply amount, is determined based on the obtained actual energy supply loss parameter. For example, the energy supply amount corresponding to the actual energy supply loss parameter is used as the long-term actual energy supply amount to control the energy supply system that does not include an energy storage device to produce energy according to the actual energy supply amount to supply each energy-consuming object. For another example, the actual energy supply instruction is determined after the energy supply amount corresponding to the actual energy supply loss parameter is used as the stable actual energy supply amount, so that the energy supply system including the energy storage device can produce energy, store energy, and release energy according to the actual energy supply instruction. Among them, the actual energy supply instruction includes: the actual energy production amount for each time interval, the actual energy storage amount for each time interval, and the actual energy release amount for each time interval.

[0232] It should be noted that when the energy supply system supplies energy through the actual energy supply parameters, it can not only meet the actual energy demand of users but also prevent users from easily changing their load curves, thereby improving the phenomenon of energy waste and ensuring the smooth operation of the power system.

[0233] See also Figure 5 , Figure 5 The structure diagram of the computer device in one embodiment of the present application is shown as follows: Figure 5 As shown, the computer system 50 includes a storage device 51 and a processing device 52 .

[0234] The storage device 51 is used to store at least one program. In some embodiments, the storage device 51 includes one or more memories, which are used to execute the control method for the energy supply system exemplified based on the technical ideas of the present application when executed. In an embodiment, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more disk storage devices, flash memory devices or other non-volatile solid-state storage devices. In some embodiments, the memory may also include a memory away from one or more processors, such as a network attached memory accessed via an RF circuit or an external port and a communication network, wherein the communication network may be the Internet, one or more intranets, a local area network, a wide area network, a storage area network, etc., or a suitable combination thereof. The memory controller may control access to the memory by other components of the device such as the CPU and peripheral interfaces.

[0235] The processing device 52 is connected to the storage device 51. In some embodiments, the processing device 52 includes one or more processors, and the processing device 52 is used to call the at least one program to execute the control method for the energy supply system described in the present application. In an embodiment, the processor is operably coupled to a memory and / or a non-volatile storage device. More specifically, the processor can execute instructions stored in the memory and / or the non-volatile storage device to perform operations in the computer device, such as generating actual energy supply parameters for controlling the energy supply system to supply energy to each of the energy-consuming objects according to the actual energy supply parameters. In this way, the processor may include one or more general-purpose microprocessors, one or more special-purpose processors, one or more field programmable logic arrays, or any combination thereof.

[0236] See also Figure 6 , Figure 6 Shown is a schematic diagram of the structure of a computer device in another embodiment of the present application, such as Figure 6 As shown, the computer device 60 includes a communication device 61, a storage device 62, and a processing device 63. The storage device 62 and the processing device 63 are Figure 5 The storage device 51 and the processing device 52 shown are the same or similar and will not be described in detail herein.

[0237] The communication device 61 is used to communicate with the energy supply object to output the actual energy supply parameter. Through the communication device 61, the computer device 61 can perform data interaction or communication transmission with multiple energy supply objects. For example, the computer device sends the actual energy supply parameter to each energy supply object through the communication device 61, so that each energy supply object can pay the energy cost according to the actual energy supply parameter. Among them, the communication device 61 may include: wired communication circuit and / or wireless communication circuit, wired communication circuit such as wired network card, wireless communication circuit such as 2G-5G, WiFi, 314 / 433 radio frequency circuit, etc., so as to access the communication network, and the communication network may include the Internet, mobile network, local area network (LAN), wide area network (WAN), storage area network (SAN), or one or more intranets, etc., or one or more of any other appropriate communication networks.

[0238] In one embodiment, Figure 5 or Figure 6The computer device shown is, for example, an electronic device loaded with an APP application or having web page / website access capabilities, such as a desktop computer, a smart TV, a server, a mobile terminal (such as a mobile phone, a tablet computer, a laptop computer, etc.), and these electronic devices may also include input and output units such as a display unit / audio unit. The display unit may include a touch component that facilitates user input by detecting the occurrence and / or position of an object touching its screen (for example, the surface of an electronic display); for example, an operator on the energy supply system side touches the screen of the electronic device to input the singular value threshold, etc.

[0239] In addition, the present application also discloses a computer-readable storage medium storing at least one computer program, which, when executed, implements at least one embodiment of the control method for an energy supply system described above.

[0240] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application.

[0241] In the embodiments provided in the present application, the computer readable and writable storage medium may include a read-only memory, a random access memory, an EEPROM, a CD-ROM or other optical disk storage device, a disk storage device or other magnetic storage device, a flash memory, a USB flash drive, a mobile hard disk, or any other medium that can be used to store a desired program code in the form of an instruction or data structure and can be accessed by a computer. In addition, any connection can be appropriately referred to as a computer-readable medium. For example, if the instruction is sent from a website, a server or other remote source using a coaxial cable, an optical fiber cable, a twisted pair, a digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwaves, the coaxial cable, optical fiber cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwaves are included in the definition of the medium. However, it should be understood that computer readable and writable storage media and data storage media do not include connections, carriers, signals, or other temporary media, but are intended to be non-temporary, tangible storage media. Disk and disc, as used in this application, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers.

[0242] In one or more exemplary aspects, the functions described by the computer program of the method described in the present application can be implemented in hardware, software, firmware, or any combination thereof. When implemented in software, these functions can be stored or transmitted as one or more instructions or codes on a computer-readable medium. The steps of the method or algorithm disclosed in the present application can be embodied in a processor-executable software module, wherein the processor-executable software module can be located on a tangible, non-temporary computer-readable and writable storage medium. A tangible, non-temporary computer-readable and writable storage medium can be any available medium that a computer can access.

[0243] The block diagram in the accompanying drawings above of the present application illustrates the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. Based on this, each box in the block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be realized by a dedicated hardware-based system that performs a specified function or operation, or can be realized by a combination of dedicated hardware and computer instructions.

[0244] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A control method for an energy supply system, characterized in that: The energy supply system supplies energy to a plurality of energy-consuming objects; and the control method of the energy supply system comprises: According to the initial energy supply demand of each energy-consuming object in each time interval in each time period, and the energy loss parameter representing the parameter for compensating the loss caused by energy supplying the energy supply system in each time interval in each time period, an expected influence quantity matrix is ​​obtained; wherein the expected influence quantity matrix includes the expected energy supply parameter influence of each energy-consuming object on the energy supply system in each said time period; the noise matrix related to the initial energy supply demand is superimposed on the initial influence quantity matrix to obtain the expected influence quantity matrix; the step of obtaining the expected influence quantity matrix includes: according to the initial energy supply demand of each energy-consuming object in each time interval in each time period, and the energy loss parameter in each time interval in each time period, obtaining the expected energy supply demand of each energy-consuming object in each time interval in each time period; according to the expected energy supply demand of each energy-consuming object in each time interval in each time period, determining the expected energy supply parameter influence of each energy-consuming object on the energy supply system in each time period to obtain the expected influence quantity matrix; A preset error constraint formula is created based on the influence matrix, the expected influence matrix, the matrix position set, and the preset error; wherein the preset error constraint can be expressed by the formula ||P Ω (Θ-Y)|| F <δ indicates that the norm of the matrix obtained by performing a projection operation on the difference between the influence matrix Θ and the expected influence matrix Y according to the matrix position set Ω satisfies the preset error δ; the optimal solution of the influence matrix is ​​obtained under the condition of satisfying the preset error constraint formula and minimizing the rank of the influence matrix to construct an optimized influence matrix; wherein the optimized influence matrix includes the influence of each energy-consuming object on the optimized energy supply parameter of the energy supply system in each time period; the influence matrix and the expected influence matrix are of the same type, and the matrix position set includes the corresponding element positions of the expected influence matrix and part of the influence matrix; According to the optimized influence matrix, the actual energy supply parameters of the energy supply system are determined to control the energy supply system to supply energy to each of the energy-consuming objects according to the actual energy supply parameters.

2. The control method for an energy supply system according to claim 1, characterized in that: The influence of each energy-consuming object on the energy supply parameters of the energy supply system in each time period is determined according to the energy supply demand of each energy-consuming object in each time interval in each time period and the energy supply loss parameter in each time interval in each time period.

3. The control method for an energy supply system according to claim 1, characterized in that: The total amount of initial energy demand of a single energy-consuming object in each time interval in a single period is equal to the total amount of expected energy demand; the sum of the difference between the initial energy demand of a single energy-consuming object in each time interval in a single period and the expected energy demand in each time interval satisfies a preset ratio; the expected energy demand of each energy-consuming object in each time interval in each period is obtained according to the initial energy demand of each energy-consuming object in each time interval in each period and the energy loss parameter in each time interval in each period, including: Based on the initial energy supply demand of a single energy-consuming object in each time interval in a single time period and the energy supply loss parameters in each time interval in a single time period, determine the expected energy supply demand of the single energy-consuming object in each time interval in a single time period that corresponds to minimizing the energy loss of the single energy-consuming object in a single time period.

4. The control method for an energy supply system according to claim 1, characterized in that: Creating a preset error constraint formula according to the influence amount matrix, the expected influence amount matrix, the matrix position set, and the preset error, obtaining the optimal solution of the influence amount matrix under the condition of satisfying the preset error constraint formula and minimizing the rank of the influence amount matrix to construct an optimized influence amount matrix, including: The optimized influence matrix is ​​constructed by using a matrix completion algorithm under the condition of minimizing the rank of the influence matrix and under a preset error constraint.

5. The control method for an energy supply system according to claim 4, characterized in that: The matrix completion algorithm includes: fixed point continuation algorithm, accelerated proximal gradient algorithm, semidefinite programming method, singular value threshold algorithm, and augmented Lagrange multiplier method.

6. The control method for an energy supply system according to claim 5, characterized in that: An initial matrix of the influence matrix in the fixed point continuation algorithm is determined according to the singular value matrix corresponding to the expected influence matrix.

7. The control method for an energy supply system according to claim 6, characterized in that: The actual energy supply loss parameter of each energy-consuming object is the average value of the optimized energy supply parameter influence quantity of each energy-consuming object in each time period in the optimized influence quantity matrix.

8. A control system for an energy supply system, characterized in that: The energy supply system supplies energy to a plurality of energy-consuming objects; the control system of the energy supply system comprises: An expected impact matrix determination module is used to obtain an expected impact matrix according to the initial energy supply demand of each energy-consuming object in each time interval in each time period, and the energy loss parameter representing the parameter for compensating the loss caused by energy supply to the energy supply system in each time interval in each time period; wherein the expected impact matrix includes the expected energy supply parameter impact of each energy-consuming object on the energy supply system in each said time period; the noise matrix related to the initial energy supply demand is superimposed on the initial impact matrix to obtain the expected impact matrix; the expected impact matrix includes: according to the initial energy supply demand of each energy-consuming object in each time interval in each time period, and the energy loss parameter in each time interval in each time period, the expected energy supply demand of each energy-consuming object in each time interval in each time period is obtained; according to the expected energy supply demand of each energy-consuming object in each time interval in each time period, the expected energy supply parameter impact of each energy-consuming object on the energy supply system in each time period is determined to obtain the expected impact matrix; The influence matrix determination module is optimized, and is used to create a preset error constraint formula according to the influence matrix, the expected influence matrix, the matrix position set, and the preset error; wherein the preset error constraint can be expressed by the formula ||P Ω (Θ-Y)|| F <δ indicates that the norm of the matrix obtained by performing a projection operation on the difference between the influence matrix Θ and the expected influence matrix Y according to the matrix position set Ω satisfies the preset error δ; the optimal solution of the influence matrix is ​​obtained under the condition of satisfying the preset error constraint formula and minimizing the rank of the influence matrix to construct an optimized influence matrix; wherein the optimized influence matrix includes the influence of each energy-consuming object on the optimized energy supply parameter of the energy supply system in each time period; the influence matrix and the expected influence matrix are of the same type, and the matrix position set includes the corresponding element positions of the expected influence matrix and part of the influence matrix; The energy supply parameter determination module is used to determine the actual energy supply parameters of the energy supply system according to the optimized influence matrix, so as to control the energy supply system to supply energy to each energy-consuming object according to the actual energy supply parameters.

9. The control system for an energy supply system according to claim 8, characterized in that: The influence of each energy-consuming object on the energy supply parameters of the energy supply system in each time period is determined according to the energy supply demand of each energy-consuming object in each time interval in each time period and the energy supply loss parameter in each time interval in each time period.

10. The control system for an energy supply system according to claim 8, characterized in that: The total amount of initial energy demand of a single energy-consuming object in each time interval in a single period is equal to the total amount of expected energy demand; the sum of the difference between the initial energy demand of a single energy-consuming object in each time interval in a single period and the expected energy demand in each time interval satisfies a preset ratio; the expected energy demand of each energy-consuming object in each time interval in each period is obtained according to the initial energy demand of each energy-consuming object in each time interval in each period and the energy loss parameter in each time interval in each period, including: Based on the initial energy supply demand of a single energy-consuming object in each time interval in a single time period and the energy supply loss parameters in each time interval in a single time period, determine the expected energy supply demand of the single energy-consuming object in each time interval in a single time period that corresponds to minimizing the energy loss of the single energy-consuming object in a single time period.

11. The control system for an energy supply system according to claim 8, characterized in that: The optimized influence amount matrix determination module creates a preset error constraint formula according to the influence amount matrix, the expected influence amount matrix, the matrix position set, and the preset error, obtains the optimal solution of the influence amount matrix under the condition of satisfying the preset error constraint formula and minimizing the rank of the influence amount matrix to construct the optimized influence amount matrix, including: The optimized influence matrix is ​​constructed by using a matrix completion algorithm under the condition of minimizing the rank of the influence matrix and under a preset error constraint.

12. The control system for an energy supply system according to claim 11, characterized in that: The matrix completion algorithm includes: fixed point continuation algorithm, accelerated proximal gradient algorithm, semidefinite programming method, singular value threshold algorithm, and augmented Lagrange multiplier method.

13. The control system for an energy supply system according to claim 12, characterized in that: An initial matrix of the influence matrix in the fixed point continuation algorithm is determined according to the singular value matrix corresponding to the expected influence matrix.

14. The control system for an energy supply system according to claim 13, characterized in that: The actual energy supply loss parameter of each energy-consuming object is the average value of the optimized energy supply parameter influence quantity of each energy-consuming object in each time period in the optimized influence quantity matrix.

15. A computer device, characterized in that: include: A storage device, used to store at least one program; A processing device, used for calling the at least one program to execute the control method for the energy supply system according to any one of claims 1 to 7.

16. The computer device according to claim 15, characterized in that The computer device further comprises a communication device for communicating with an energy supply object to output the actual energy supply parameter.

17. A computer-readable storage medium, characterized in that: At least one program is stored, and when the at least one program is called, it is executed and implements the control method for the energy supply system as claimed in any one of claims 1 to 7.

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