Power resource allocation method and device, electronic equipment and storage medium

Abstract

Embodiments of the present application provide a power resource allocation method and device, electronic equipment and storage medium, the method comprises: acquiring the computing power information corresponding to each computing node, the computing power information at least includes logical operation capability, parallel computing capability and neural network computing capability; according to the logical operation capability, parallel computing capability and neural network computing capability corresponding to each computing node, a hybrid control system is constructed, and the hybrid control system is configured with a computing power model; through the hybrid control system, the power demand information corresponding to the power consumption equipment and the historical resource configuration information are acquired, and the computing power model is used to operate the power demand information and the historical resource configuration information, and output the output power reference value corresponding to the power supply system; through the power supply system, the corresponding power resource is output to the power consumption equipment based on the output power reference value.

Description

Technical Field

[0001] This invention relates to the field of energy distribution technology, and in particular to a method for distributing electrical resources, a device for distributing electrical resources, an electronic device, and a computer-readable storage medium. Background Technology

[0002] With societal development, human demand for energy is increasing daily. The extensive use of fossil fuels has not only led to the depletion of traditional energy sources but also caused severe environmental pollution. Against this backdrop, renewable distributed power sources have gained significant attention due to their advantages such as sustainability, environmental friendliness, and flexible installation. Peak daily electricity loads are concentrated at midday and evening, with air conditioning equipment being the primary load factor. Load fluctuations are minimal in summer and winter. Due to the absence of seasonal loads and the assumption that air conditioning output is maximized, the annual load remains relatively stable except for May and September. The annual load is mainly influenced by air conditioning equipment; the total system load is lower in May and September when air conditioning is not used. However, problems exist in the allocation of relevant power resources, including unreasonable allocation and low efficiency. Summary of the Invention

[0003] The present invention provides a method, apparatus, electronic device, and computer-readable storage medium for allocating power resources, in order to solve or partially solve the problems of unreasonable power resource allocation and low allocation efficiency.

[0004] This invention discloses a method for allocating power resources, comprising:

[0005] Obtain computing power information corresponding to each computing node, wherein the computing power information includes at least logical operation capability, parallel computing capability, and neural network computing capability;

[0006] A hybrid control system for power resource allocation is constructed based on the logical operation capability, parallel computing capability, and neural network computing capability of each computing node. The hybrid control system is equipped with a computational power model for demand forecasting.

[0007] The hybrid control system acquires the power demand information and historical resource configuration information corresponding to the electrical equipment, and calculates the power demand information and historical resource configuration information using the computational power model to output a reference value of output power corresponding to the power supply system.

[0008] The power supply system outputs corresponding power resources to the electrical equipment based on the output power reference value.

[0009] Optionally, the hybrid control system for power resource allocation, constructed based on the logical operation capabilities, parallel computing capabilities, and neural network computing capabilities of each computing node, includes:

[0010] Obtain the available computing power corresponding to each computing node;

[0011] Target computing nodes are those whose idle computing power is greater than or equal to a preset computing power threshold.

[0012] The computational power model is obtained by using the logical operation capabilities, parallel computing capabilities, and neural network computing capabilities of each target computing node to construct the model.

[0013] The target computing nodes are combined into a hybrid control system for power resource allocation.

[0014] Optionally, the computing node includes at least n logic operation chips corresponding to the logic operation capability, m parallel computing chips corresponding to the parallel computing capability, and p neural network acceleration chips corresponding to the neural network computing capability. The computing power scaling model is as follows:

[0015]

[0016] Among them, the Used to characterize the total computing power requirement. For logical operation capabilities, the For parallel computing capabilities, the For neural network acceleration capabilities; the The above and the For the same mapping function, a, b, and c are mapping scaling coefficients. To provide redundant computing power for logical operations, the To enable redundant computing power in parallel computing, the To accelerate redundant computing power for neural networks.

[0017] Optionally, the power supply system includes an energy storage subsystem, a photovoltaic subsystem, and a battery pack subsystem. The historical resource configuration information includes the output power of the energy storage subsystem, the photovoltaic subsystem, and the battery pack subsystem at the same time. The step of calculating the power demand information and the historical resource configuration information using the computational power quantitative model to output a reference value of the output power corresponding to the power supply system includes:

[0018] Obtain the cost function for the electricity demand information, and the cost function is as follows:

[0019] L(x,P_wref,P_sref,P_bref)=α(P D -P_wref- P_sref- P_bref) 2 +βP_sref 2 +γP_bref 2 ;

[0020] The power demand information and the historical resource allocation information are simulated using the computational power model according to the cost function, and the solution is obtained by applying the following constraints:

[0021] P_wref(t)≤P_wmax, P_sref(t)≤P_smax, P_bref(t)≤P_bmax, ;

[0022] P_wref((j+1)Δ)-P_wref(jΔ)≤d P_wmax ;

[0023] P_sref((j+1)Δ)-P_sref(jΔ)≤d P_smax ;

[0024] P_bref((j+1)Δ)-P_bref(jΔ)≤d P_bmax ;

[0025] Output the first output power value corresponding to the energy storage subsystem, the second output power value corresponding to the photovoltaic subsystem, and the third output power value corresponding to the battery pack subsystem;

[0026] Wherein, P_wmax is the upper limit of the output power of the energy storage subsystem, P_smax is the upper limit of the output power of the photovoltaic subsystem, and P_bmax is the upper limit of the output power of the battery pack subsystem; d p_wmax The d represents the maximum change in output power of the energy storage subsystem within a unit time interval. p_smax The d represents the maximum change in output power of the photovoltaic subsystem within a unit time interval. p_bmax α represents the maximum change in output power of the battery pack subsystem within a unit time interval; α, β, and γ are different weighting factors.

[0027] Optionally, the hybrid control system includes an energy storage controller communicatively connected to the energy storage subsystem, a photovoltaic controller communicatively connected to the photovoltaic subsystem, and a battery pack controller communicatively connected to the battery pack. The step of outputting corresponding power resources to the electrical equipment through the power supply system based on the output power reference value includes:

[0028] The energy storage controller transmits the first output power value to the energy storage subsystem, and controls the energy storage subsystem to output corresponding power resources to the electrical equipment based on the first output power value.

[0029] The photovoltaic controller transmits the second output power value to the photovoltaic subsystem, and controls the photovoltaic subsystem to output corresponding power resources to the electrical equipment based on the second output power value.

[0030] The battery pack controller transmits the third output power value to the battery pack subsystem, and controls the battery pack subsystem to output corresponding power resources to the electrical equipment based on the third output power value.

[0031] This invention also discloses a power resource allocation device, comprising:

[0032] The computing power acquisition module is used to acquire computing power information corresponding to each computing node. The computing power information includes at least logical operation capability, parallel computing capability, and neural network computing capability.

[0033] The system construction module is used to construct a hybrid control system for power resource allocation based on the logical operation capability, parallel computing capability and neural network computing capability of each computing node. The hybrid control system is configured with a computational quantitative model for demand forecasting.

[0034] The power calculation module is used to obtain the power demand information and historical resource configuration information of the electrical equipment through the hybrid control system, and to calculate the power demand information and the historical resource configuration information through the power calculation model, and output the output power reference value corresponding to the power supply system.

[0035] The power resource processing module is used to output corresponding power resources to the electrical equipment through the power supply system based on the output power reference value.

[0036] Optionally, the system construction module is specifically used for:

[0037] Obtain the available computing power corresponding to each computing node;

[0038] Target computing nodes are those whose idle computing power is greater than or equal to a preset computing power threshold.

[0039] The computational power model is obtained by using the logical operation capabilities, parallel computing capabilities, and neural network computing capabilities of each target computing node to construct the model.

[0040] The target computing nodes are combined into a hybrid control system for power resource allocation.

[0041] Optionally, the computing node includes at least n logic operation chips corresponding to the logic operation capability, m parallel computing chips corresponding to the parallel computing capability, and p neural network acceleration chips corresponding to the neural network computing capability. The computing power scaling model is as follows:

[0042]

[0043] Among them, the Used to characterize the total computing power requirement. For logical operation capabilities, the For parallel computing capabilities, the For neural network acceleration capabilities; the The above and the For the same mapping function, a, b, and c are mapping scaling coefficients. To provide redundant computing power for logical operations, the To enable redundant computing power in parallel computing, the To accelerate redundant computing power for neural networks.

[0044] Optionally, the power supply system includes an energy storage subsystem, a photovoltaic subsystem, and a battery pack subsystem. The historical resource configuration information includes the output power of the energy storage subsystem, the photovoltaic subsystem, and the battery pack subsystem at the same time. The power calculation module is specifically used for:

[0045] Obtain the cost function for the electricity demand information, and the cost function is as follows:

[0046] L(x,P_wref,P_sref,P_bref)=α(PD-P_wref-P_sref-P_bref) 2 +βP_sref 2 +γP_bref 2 ;

[0047] The power demand information and the historical resource allocation information are simulated using the computational power model according to the cost function, and the solution is obtained by applying the following constraints:

[0048] P_wref(t)≤P_wmax, P_sref(t)≤P_smax, P_bref(t)≤P_bmax, ;

[0049] P_wref((j+1)Δ)-P_wref(jΔ)≤dP_wmax;

[0050] P_sref((j+1)Δ)-P_sref(jΔ)≤dP_smax;

[0051] P_bref((j+1)Δ)-P_bref(jΔ)≤dP_bmax;

[0052] Output the first output power value corresponding to the energy storage subsystem, the second output power value corresponding to the photovoltaic subsystem, and the third output power value corresponding to the battery pack subsystem;

[0053] Wherein, P_wmax is the upper limit of the output power of the energy storage subsystem, P_smax is the upper limit of the output power of the photovoltaic subsystem, and P_bmax is the upper limit of the output power of the battery pack subsystem; dp_wmax is the maximum change in output power of the energy storage subsystem within a unit time interval, dp_smax is the maximum change in output power of the photovoltaic subsystem within a unit time interval, and dp_bmax is the maximum change in output power of the battery pack subsystem within a unit time interval; α, β, and γ are different weighting factors.

[0054] Optionally, the hybrid control system includes an energy storage controller communicatively connected to the energy storage subsystem, a photovoltaic controller communicatively connected to the photovoltaic subsystem, and a battery pack controller communicatively connected to the battery pack. The power resource processing module is specifically used for:

[0055] The energy storage controller transmits the first output power value to the energy storage subsystem, and controls the energy storage subsystem to output corresponding power resources to the electrical equipment based on the first output power value.

[0056] The photovoltaic controller transmits the second output power value to the photovoltaic subsystem, and controls the photovoltaic subsystem to output corresponding power resources to the electrical equipment based on the second output power value.

[0057] The battery pack controller transmits the third output power value to the battery pack subsystem, and controls the battery pack subsystem to output corresponding power resources to the electrical equipment based on the third output power value.

[0058] This invention also discloses an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0059] The memory is used to store computer programs;

[0060] When the processor executes a program stored in the memory, it implements the method described in the embodiments of the present invention.

[0061] This invention also discloses a computer-readable storage medium storing instructions that, when executed by one or more processors, cause the processors to perform the methods described in this invention.

[0062] The embodiments of the present invention have the following advantages:

[0063] In this embodiment of the invention, by acquiring the computing power information corresponding to each computing node, which includes at least logical operation capability, parallel computing capability, and neural network computing capability, a hybrid control system for power resource allocation is constructed based on the logical operation capability, parallel computing capability, and neural network computing capability corresponding to each computing node. The hybrid control system is configured with a computing power quantitative model for demand prediction. Then, the power demand information and historical resource configuration information corresponding to the electrical equipment can be acquired through the hybrid control system, and the power demand information and historical resource configuration information are calculated through the computing power quantitative model to output an output power reference value corresponding to the power supply system. Then, the power supply system outputs the corresponding power resources to the electrical equipment based on the output power reference value. Thus, in the process of power resource allocation, on the one hand, by constructing a hybrid control system and configuring the corresponding computing power quantitative model, the power allocation system has sufficient quantitative computing power for power dispatching, ensuring the efficiency of power resource allocation and dispatching. On the other hand, by using the data corresponding to the actual power demand and historical resource configuration information to predict the power resources required by the electrical equipment and allocate them, the normal power consumption of the electrical equipment is guaranteed. Attached Figure Description

[0064] Figure 1 This is a flowchart of the steps of a power resource allocation method provided in an embodiment of the present invention;

[0065] Figure 2 This is a schematic diagram of the control system provided in an embodiment of the present invention;

[0066] Figure 3 This is a structural block diagram of a power resource distribution device provided in an embodiment of the present invention;

[0067] Figure 4 This is a block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0068] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0069] As an example, with societal development, human demand for energy is increasing daily. The extensive use of fossil fuels has not only led to the depletion of traditional energy sources but also caused severe environmental pollution. Against this backdrop, renewable distributed power sources have attracted significant attention due to their advantages such as sustainability, environmental friendliness, and flexible installation. In recent years, the protection and development of islands has become a hot topic in marine affairs. Islands and their surrounding waters possess abundant fishery, oil, tourism, port, and mineral resources, making island development of great economic and strategic importance. Increasingly sophisticated renewable energy power generation methods, such as wind, solar, and tidal power, can effectively reduce dependence on conventional energy sources and diesel generators. With the emergence of microgrids, island microgrid systems based primarily on new energy sources have arisen. However, simply connecting diverse distributed power sources in parallel cannot constitute a stable power supply network; the system's power quality, protection, and operation are poor. Therefore, finding an island energy supply solution based on renewable energy utilization is crucial for solving the sustainable development of islands and building ecological islands. It is evident that for power supply networks with hybrid energy storage methods, the allocation of relevant power resources is prone to problems of unreasonable allocation and low efficiency.

[0070] One of the core inventive points of this invention lies in acquiring the computing power information corresponding to each computing node. This computing power information includes at least logical operation capability, parallel computing capability, and neural network computing capability. Based on the logical operation capability, parallel computing capability, and neural network computing capability corresponding to each computing node, a hybrid control system for power resource allocation is constructed. The hybrid control system is configured with a computing power quantitative model for demand prediction. Then, the hybrid control system can acquire the power demand information and historical resource configuration information corresponding to the electrical equipment, and perform calculations on the power demand information and historical resource configuration information through the computing power quantitative model to output an output power reference value corresponding to the power supply system. Then, the power supply system outputs the corresponding power resources to the electrical equipment based on the output power reference value. Thus, in the process of power resource allocation, on the one hand, by constructing a hybrid control system and configuring the corresponding computing power quantitative model, the power allocation system has sufficient quantitative computing power for power dispatching calculations, ensuring the efficiency of power resource allocation and dispatching. On the other hand, by using the data corresponding to the actual power demand and historical resource configuration information to predict the power resources required by the electrical equipment and allocate them, the normal power consumption of the electrical equipment is guaranteed.

[0071] Reference Figure 1The diagram illustrates a flowchart of a method for allocating power resources according to an embodiment of the present invention, which may specifically include the following steps:

[0072] Step 101: Obtain the computing power information corresponding to each computing node. The computing power information includes at least logical operation capability, parallel computing capability, and neural network computing capability.

[0073] Optionally, based on power demand and the output of photovoltaic and wind turbines, the capacity of each device can be configured as follows (due to insufficient and incomplete on-site data, the output of distributed power sources is temporarily reserved as a margin in theoretical analysis; the following analysis is the analysis after deducting the margin). Renewable resources can be fully utilized through control system modeling and simulation to reduce battery consumption, thereby compensating for the shortcomings of existing technology modeling and simulation calculations being slow and affecting the inability of renewable resources to be used at full capacity.

[0074] In this embodiment of the invention, the power hybrid control system management includes cold and thermal energy storage subsystems (energy storage), battery energy storage subsystems (energy storage), grid-connected photovoltaic power station subsystems, and smart electricity consumption and demand response subsystems. Upon receiving an output power reference value, the system outputs a specified amount of energy through a local controller. The actual control objective is to output sufficient energy to meet load demand while also satisfying other system requirements. System modeling provides output power references for the energy storage and photovoltaic subsystems to ensure sufficient energy output to meet load demand. Simultaneously, simulation aims to ensure that the output power meets the overall energy demand.

[0075] In practical implementation, a hybrid control system can be constructed and a quantitative computing model can be built to obtain the quantified computing power for system control, power dispatching, and other operations, providing sufficient computing power resources for the normal operation and function of the hybrid control system. Then, simulation is performed, using historical actual data on the distribution of real load demand. Through this data and the data obtained from real-time measurements, the load demand at the next moment can be predicted, which is also called the simulation result.

[0076] In this context, computing nodes can be devices used to control the power supply system. Different devices, based on differences in hardware and software, can correspond to different computing powers (i.e., the ability to perform data operations). Computing power information can include different computing capabilities such as logical operation capabilities, parallel computing capabilities, and neural network computing capabilities. The higher the computing power, the more efficient and complex data operations the device can perform. Therefore, by using computing power information, suitable computing nodes can be selected to form a corresponding hybrid control system, achieving efficient, accurate, and rational allocation of power resources during the power resource allocation process.

[0077] Step 102: Construct a hybrid control system for power resource allocation based on the logical operation capability, parallel computing capability and neural network computing capability of each computing node. The hybrid control system is equipped with a computational power model for demand forecasting.

[0078] In this embodiment of the invention, a corresponding hybrid control system can be constructed based on the computing power information corresponding to the computing nodes, so as to achieve efficient, accurate and reasonable power resource allocation in the process of power resource allocation.

[0079] In practical implementation, the idle computing power of each computing node can be obtained. Then, computing nodes with idle computing power greater than or equal to a preset computing power threshold are selected as target computing nodes. Next, the logical operation capabilities, parallel computing capabilities, and neural network computing capabilities of each target computing node are used to construct a model, obtaining a computational power model. Simultaneously, these target computing nodes are combined into a hybrid control system for power resource allocation. The preset computing power threshold can be a pre-configured judgment threshold for computing nodes, which can be used to filter out computing nodes with higher computing power to form the corresponding hybrid control system.

[0080] The computing node includes at least n logic operation chips corresponding to logic operation capabilities, m parallel computing chips corresponding to parallel computing capabilities, and p neural network acceleration chips corresponding to neural network computing capabilities. The computing power quantification model is as follows:

[0081]

[0082] Among them, the Used to characterize the total computing power requirement. For logical operation capabilities, the For parallel computing capabilities, the For neural network acceleration capabilities; the The above and the For the same mapping function, a, b, and c are mapping scaling coefficients. To provide redundant computing power for logical operations, the To enable redundant computing power in parallel computing, the To accelerate redundant computing power in neural networks. Furthermore... , , These represent the number of logic operation chips, parallel computing chips, and neural network acceleration chips, respectively.

[0083] In one example, after matching different computing power requirements to business scenarios, computing power is uniformly quantified. This uniform quantification of computing power is the foundation for computing power scheduling and utilization. After calculating the idle computing power of each computing node through a model, computing nodes with more idle computing power are allocated to business requests.

[0084] Computing power refers to the key core capability of a device or platform in processing business information to complete a certain task. It involves the computing power of the device or platform, including logical operation capabilities, parallel computing capabilities, and neural network acceleration. Based on the algorithms executed and the types of data computation involved, computing power can be categorized into logical operation capabilities, parallel computing capabilities, and neural network computing capabilities.

[0085] Computing power requirements can be categorized into three types: logic operation capability, parallel computing capability, and neural network acceleration capability. Furthermore, different manufacturers' chips have different designs for different computing types, which necessitates a unified measurement of heterogeneous computing power. The computing power provided by different chips can be mapped to a unified dimension through a metric function. For heterogeneous computing power devices and platforms, assuming there are n logic operation chips, m parallel computing chips, and p neural network acceleration chips, the computing power requirements of the business can be uniformly described by the above-mentioned computing power quantification model. Taking parallel computing capability as an example, assuming there are three different types of parallel computing chip resources, b1, b2, and b3, then... Let q2 be a mapping function representing the parallel computing capability that the j-th parallel computing chip b can provide, and let q2 represent the redundant computing power of parallel computing.

[0086] Step 103: Obtain the power demand information and historical resource configuration information corresponding to the electrical equipment through the hybrid control system, and perform calculations on the power demand information and the historical resource configuration information through the computing power quantitative model to output the output power reference value corresponding to the power supply system.

[0087] The electricity demand information can be the power value corresponding to the power resources required by the electrical equipment, and the historical resource configuration information can be the resource configuration method adopted by the power supply system to provide power resources to the electrical equipment in the past, such as the power supply power value of the power supply system at different times, the allocation ratio of different power supply systems and the power supply power value, etc. This invention does not limit this.

[0088] In practical implementation, the power supply system includes an energy storage subsystem, a photovoltaic subsystem, and a battery pack subsystem. Historical resource configuration information includes the output power of the energy storage subsystem, photovoltaic subsystem, and battery pack subsystem at the same time. The reference value for the output power can be calculated as follows:

[0089] The cost function for obtaining electricity demand information is as follows:

[0090] L(x,P_wref,P_sref,P_bref)=α(P D -P_wref- P_sref- P_bref) 2 +βP_sref 2 +γP_bref 2 ;

[0091] The power demand information and historical resource allocation information are simulated using a computational power-based model according to a cost function, and the solution is obtained under the following constraints:

[0092] P_wref(t)≤P_wmax, P_sref(t)≤P_smax, P_bref(t)≤P_bmax, ;

[0093] P_wref((j+1)Δ)-P_wref(jΔ)≤d P_wmax ;

[0094] P_sref((j+1)Δ)-P_sref(jΔ)≤d P_smax ;

[0095] P_bref((j+1)Δ)-P_bref(jΔ)≤d P_bmax ;

[0096] The first output power value corresponding to the energy storage subsystem, the second output power value corresponding to the photovoltaic subsystem, and the third output power value corresponding to the battery pack subsystem are output.

[0097] Where P_wmax is the upper limit of the output power of the energy storage subsystem, P_smax is the upper limit of the output power of the photovoltaic subsystem, and P_bmax is the upper limit of the output power of the battery pack subsystem; d p_wmax d represents the maximum change in output power of the energy storage subsystem within a unit time interval. p_smax Let d be the maximum change in output power of the photovoltaic subsystem within a unit time interval. p_bmax α represents the maximum change in output power of the battery pack subsystem within a unit time interval; α, β, and γ are different weighting factors.

[0098] In one example, the simulation predicts the load demand at the next moment using historical data on the actual load demand distribution, along with data obtained from real-time measurements. The main objective of the simulation is to ensure that the output power meets the overall energy demand P. Simultaneously, the rates of change of the reference values ​​for cold and hot energy storage, photovoltaics, and battery storage outputs, P_wref, P_sref, and P_bref, are limited. In the aforementioned cost function, the first term ensures that the system provides energy to meet the load demand as much as possible. Since battery lifespan is affected by charging and discharging, a third term is added to minimize the number of times and duration the battery is used as a backup power source. Because there are infinitely many solutions (x, P_wref, P_sref, P_bref) that satisfy the first and third terms, a second term is added to ensure a unique solution to the optimization problem. In practice, the value of β can be very small. This makes the energy storage system the main power supply component of the hybrid system, with the photovoltaic subsystem and battery pack only providing power when the wind power system cannot provide more energy. Furthermore, to meet load demands, real-time tracking is unavoidable. To address this, historical load demand distribution data was referenced, and load demand at the next moment was predicted using this data and real-time measurements. Since this estimation is not directly relevant to the project's research, the predicted load distribution, which the system needs to track in real-time, is directly presented in the simulation. The corresponding output power value is obtained by solving the aforementioned constraints. These constraints reflect the actual subsystem capacity and limit the subsystem's maximum output power and rate of change, thus protecting the system.

[0099] Furthermore, dynamic programming can be used to achieve real-time output of reference values ​​for each subsystem of the hybrid system. For example, assuming the horizontal axis represents time, reflecting the load demand and subsystem output reference values ​​over 12 hours, and the vertical axis represents power, assuming that the system load demand changes significantly at time t=2, and due to the limitation of the output power change rate, the system cannot accurately track the load change (although neither system has reached its maximum output value at this time), the battery pack supplements the insufficient power supply at this time. Thus, the simulation results are obtained using actual data on the real load demand.

[0100] Step 104: The power supply system outputs the corresponding power resources to the electrical equipment based on the output power reference value.

[0101] In the specific implementation, refer to Figure 2The diagram illustrates the structure of a control system provided in an embodiment of the present invention. The hybrid control system includes an energy storage controller communicatively connected to the energy storage subsystem, a photovoltaic controller communicatively connected to the photovoltaic subsystem, and a battery pack controller communicatively connected to the battery pack. The energy storage controller can transmit the first output power value to the energy storage subsystem, controlling the energy storage subsystem to output corresponding power resources to the electrical device based on the first output power value. The photovoltaic controller can also transmit the second output power value to the photovoltaic subsystem, controlling the photovoltaic subsystem to output corresponding power resources to the electrical device based on the second output power value. Furthermore, the battery pack controller can transmit the third output power value to the battery pack subsystem, controlling the battery pack subsystem to output corresponding power resources to the electrical device based on the third output power value. Specifically, the main controller sends out reference values ​​for the output power of the energy storage subsystem, photovoltaic subsystem, and battery pack (P_wref, P_sref, P_bref, etc.). The sub-controllers give control commands (u_w, u_s, and u_b, etc.) based on the reference values. The two subsystems and the battery pack subsystem output given power (x_w, x_s, and x_b, etc.) based on the control signals.

[0102] Through the above process, in the process of allocating power resources, on the one hand, by constructing a hybrid control system and configuring a corresponding computational quantitative model, the power allocation system has sufficient quantitative computing power for power dispatching, ensuring the efficiency of power resource allocation and dispatching. On the other hand, by using real power demand and data corresponding to historical resource allocation information to predict the power resources required by electrical equipment and allocate them, the normal power consumption of electrical equipment is guaranteed.

[0103] In this embodiment of the invention, by acquiring the computing power information corresponding to each computing node, which includes at least logical operation capability, parallel computing capability, and neural network computing capability, a hybrid control system for power resource allocation is constructed based on the logical operation capability, parallel computing capability, and neural network computing capability corresponding to each computing node. The hybrid control system is configured with a computing power quantitative model for demand prediction. Then, the power demand information and historical resource configuration information corresponding to the electrical equipment can be acquired through the hybrid control system, and the power demand information and historical resource configuration information are calculated through the computing power quantitative model to output an output power reference value corresponding to the power supply system. Then, the power supply system outputs the corresponding power resources to the electrical equipment based on the output power reference value. Thus, in the process of power resource allocation, on the one hand, by constructing a hybrid control system and configuring the corresponding computing power quantitative model, the power allocation system has sufficient quantitative computing power for power dispatching, ensuring the efficiency of power resource allocation and dispatching. On the other hand, by using the data corresponding to the actual power demand and historical resource configuration information to predict the power resources required by the electrical equipment and allocate them, the normal power consumption of the electrical equipment is guaranteed.

[0104] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0105] Reference Figure 3 The diagram illustrates a structural block diagram of a power resource allocation device provided in an embodiment of the present invention, which may specifically include the following modules:

[0106] The computing power acquisition module 301 is used to acquire computing power information corresponding to each computing node. The computing power information includes at least logical operation capability, parallel computing capability, and neural network computing capability.

[0107] The system construction module 302 is used to construct a hybrid control system for power resource allocation based on the logical operation capability, parallel computing capability and neural network computing capability of each computing node. The hybrid control system is configured with a computational quantitative model for demand forecasting.

[0108] The power calculation module 303 is used to obtain the power demand information and historical resource configuration information corresponding to the electrical equipment through the hybrid control system, and to calculate the power demand information and the historical resource configuration information through the power calculation model, and output the output power reference value corresponding to the power supply system.

[0109] The power resource processing module 304 is used to output corresponding power resources to the electrical equipment through the power supply system based on the output power reference value.

[0110] In one alternative embodiment, the system construction module 302 is specifically used for:

[0111] Obtain the available computing power corresponding to each computing node;

[0112] Target computing nodes are those whose idle computing power is greater than or equal to a preset computing power threshold.

[0113] The computational power model is obtained by using the logical operation capabilities, parallel computing capabilities, and neural network computing capabilities of each target computing node to construct the model.

[0114] The target computing nodes are combined into a hybrid control system for power resource allocation.

[0115] In one optional embodiment, the computing node includes at least n logic operation chips corresponding to the logic operation capability, m parallel computing chips corresponding to the parallel computing capability, and p neural network acceleration chips corresponding to the neural network computing capability. The computing power scaling model is as follows:

[0116]

[0117] Among them, the Used to characterize the total computing power requirement. For logical operation capabilities, the For parallel computing capabilities, the For neural network acceleration capabilities; the The above and the For the same mapping function, a, b, and c are mapping scaling coefficients. To provide redundant computing power for logical operations, the To enable redundant computing power in parallel computing, the To accelerate redundant computing power for neural networks.

[0118] In one optional embodiment, the power supply system includes an energy storage subsystem, a photovoltaic subsystem, and a battery pack subsystem. The historical resource configuration information includes the output power of the energy storage subsystem, the photovoltaic subsystem, and the battery pack subsystem at the same time. The power calculation module 303 is specifically used for:

[0119] Obtain the cost function for the electricity demand information, and the cost function is as follows:

[0120] L(x,P_wref,P_sref,P_bref)=α(PD-P_wref-P_sref-P_bref) 2 +βP_sref 2 +γP_bref 2 ;

[0121] The power demand information and the historical resource allocation information are simulated using the computational power model according to the cost function, and the solution is obtained by applying the following constraints:

[0122] P_wref(t)≤P_wmax, P_sref(t)≤P_smax, P_bref(t)≤P_bmax, ;

[0123] P_wref((j+1)Δ)-P_wref(jΔ)≤dP_wmax;

[0124] P_sref((j+1)Δ)-P_sref(jΔ)≤dP_smax;

[0125] P_bref((j+1)Δ)-P_bref(jΔ)≤dP_bmax;

[0126] Output the first output power value corresponding to the energy storage subsystem, the second output power value corresponding to the photovoltaic subsystem, and the third output power value corresponding to the battery pack subsystem;

[0127] Wherein, P_wmax is the upper limit of the output power of the energy storage subsystem, P_smax is the upper limit of the output power of the photovoltaic subsystem, and P_bmax is the upper limit of the output power of the battery pack subsystem; dp_wmax is the maximum change in output power of the energy storage subsystem within a unit time interval, dp_smax is the maximum change in output power of the photovoltaic subsystem within a unit time interval, and dp_bmax is the maximum change in output power of the battery pack subsystem within a unit time interval; α, β, and γ are different weighting factors.

[0128] In one optional embodiment, the hybrid control system includes an energy storage controller communicatively connected to the energy storage subsystem, a photovoltaic controller communicatively connected to the photovoltaic subsystem, and a battery pack controller communicatively connected to the battery pack. The power resource processing module 304 is specifically used for:

[0129] The energy storage controller transmits the first output power value to the energy storage subsystem, and controls the energy storage subsystem to output corresponding power resources to the electrical equipment based on the first output power value.

[0130] The photovoltaic controller transmits the second output power value to the photovoltaic subsystem, and controls the photovoltaic subsystem to output corresponding power resources to the electrical equipment based on the second output power value.

[0131] The battery pack controller transmits the third output power value to the battery pack subsystem, and controls the battery pack subsystem to output corresponding power resources to the electrical equipment based on the third output power value.

[0132] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0133] In addition, this invention also provides an electronic device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the various processes of the above-described power resource allocation method embodiments and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0134] This invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the various processes of the above-described power resource allocation method embodiments and achieves the same technical effects. To avoid repetition, it will not be described again here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0135] Figure 4 A schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present invention.

[0136] The electronic device 400 includes, but is not limited to, components such as: a radio frequency unit 401, a network module 402, an audio output unit 403, an input unit 404, a sensor 405, a display unit 406, a user input unit 407, an interface unit 408, a memory 409, a processor 410, and a power supply 411. Those skilled in the art will understand that the electronic device structure involved in the embodiments of the present invention does not constitute a limitation on the electronic device. An electronic device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. In the embodiments of the present invention, the electronic device includes, but is not limited to, mobile phones, tablet computers, laptop computers, PDAs, in-vehicle terminals, wearable devices, and pedometers.

[0137] It should be understood that, in this embodiment of the invention, the radio frequency unit 401 can be used for receiving and transmitting signals during information transmission or calls. Specifically, it receives downlink data from the base station and processes it with the processor 410; additionally, it transmits uplink data to the base station. Typically, the radio frequency unit 401 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low-noise amplifier, a duplexer, etc. Furthermore, the radio frequency unit 401 can also communicate with networks and other devices through a wireless communication system.

[0138] The electronic device provides users with wireless broadband internet access through the network module 402, such as helping users send and receive emails, browse web pages, and access streaming media.

[0139] The audio output unit 403 can convert audio data received by the radio frequency unit 401 or the network module 402 or stored in the memory 409 into audio signals and output them as sound. Furthermore, the audio output unit 403 can also provide audio output related to specific functions performed by the electronic device 400 (e.g., call signal reception sound, message reception sound, etc.). The audio output unit 403 includes a speaker, a buzzer, and a receiver, etc.

[0140] Input unit 404 is used to receive audio or video signals. Input unit 404 may include a graphics processing unit (GPU) 4041 and a microphone 4042. The GPU 4041 processes image data of still images or videos acquired by an image capture device (such as a camera) in video capture mode or image capture mode. The processed image frames can be displayed on display unit 406. The image frames processed by GPU 4041 can be stored in memory 409 (or other storage media) or transmitted via radio frequency unit 401 or network module 402. Microphone 4042 can receive sound and process such sound into audio data. The processed audio data can be converted into a format that can be transmitted to a mobile communication base station via radio frequency unit 401 in telephone call mode.

[0141] The electronic device 400 also includes at least one sensor 405, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor includes an ambient light sensor and a proximity sensor. The ambient light sensor can adjust the brightness of the display panel 4061 according to the ambient light level, and the proximity sensor can turn off the display panel 4061 and / or backlight when the electronic device 400 is moved to the ear. As a type of motion sensor, an accelerometer sensor can detect the magnitude of acceleration in various directions (generally three axes). When stationary, it can detect the magnitude and direction of gravity and can be used to identify the posture of the electronic device (such as landscape / portrait switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc. The sensor 405 may also include a fingerprint sensor, pressure sensor, iris sensor, molecular sensor, gyroscope, barometer, hygrometer, thermometer, infrared sensor, etc., which will not be described in detail here.

[0142] The display unit 406 is used to display information input by the user or information provided to the user. The display unit 406 may include a display panel 4061, which may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like.

[0143] User input unit 407 can be used to receive input numerical or character information, and to generate key signal inputs related to user settings and function control of electronic devices. Specifically, user input unit 407 includes a touch panel 4071 and other input devices 4072. Touch panel 4071, also known as a touch screen, can collect touch operations performed by the user on or near it (such as operations performed by the user using a finger, stylus, or any suitable object or accessory on or near touch panel 4071). Touch panel 4071 may include two parts: a touch detection device and a touch controller. The touch detection device detects the user's touch position and the signal generated by the touch operation, and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device, converts it into touch point coordinates, and sends it to the processor 410, which receives and executes commands from the processor 410. In addition, touch panel 4071 can be implemented using various types such as resistive, capacitive, infrared, and surface acoustic wave. Besides touch panel 4071, user input unit 407 may also include other input devices 4072. Specifically, other input devices 4072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, power buttons, etc.), trackballs, mice, joysticks, etc., which will not be described in detail here.

[0144] Furthermore, the touch panel 4071 can cover the display panel 4061. When the touch panel 4071 detects a touch operation on or near it, it transmits the information to the processor 410 to determine the type of touch event. Subsequently, the processor 410 provides corresponding visual output on the display panel 4061 according to the type of touch event. It is understood that in one embodiment, the touch panel 4071 and the display panel 4061 are implemented as two independent components to realize the input and output functions of the electronic device. However, in some embodiments, the touch panel 4071 and the display panel 4061 can be integrated to realize the input and output functions of the electronic device. The specific implementation is not limited here.

[0145] Interface unit 408 serves as an interface for connecting external devices to electronic device 400. For example, external devices may include a wired or wireless headphone port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device with an identification module, an audio input / output (I / O) port, a video I / O port, a headphone port, and so on. Interface unit 408 can be used to receive input from external devices (e.g., data, power, etc.) and transmit the received input to one or more components within electronic device 400, or it can be used to transmit data between electronic device 400 and external devices.

[0146] The memory 409 can be used to store software programs and various data. The memory 409 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, applications required for at least one function (such as sound playback, image playback, etc.), etc.; the data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). Furthermore, the memory 409 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0147] The processor 410 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 409, and by calling data stored in the memory 409, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. The processor 410 may include one or more processing units; preferably, the processor 410 may integrate an application processor and a modem processor. The application processor mainly handles the operating system, user interface, and applications, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 410.

[0148] The electronic device 400 may also include a power supply 411 (such as a battery) that supplies power to various components. Preferably, the power supply 411 can be logically connected to the processor 410 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system.

[0149] In addition, the electronic device 400 includes some functional modules not shown, which will not be described in detail here.

[0150] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0151] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0152] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of the present invention.

[0153] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0154] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0155] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0156] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0157] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0158] If the aforementioned functions are implemented as 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 this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0159] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for allocating electricity resources, characterized in that, include: Obtain computing power information corresponding to each computing node, wherein the computing power information includes at least logical operation capability, parallel computing capability, and neural network computing capability; A hybrid control system for power resource allocation is constructed based on the logical operation capability, parallel computing capability, and neural network computing capability of each computing node. The hybrid control system is equipped with a computational power model for demand forecasting. The hybrid control system acquires the power demand information and historical resource configuration information corresponding to the electrical equipment, and calculates the power demand information and historical resource configuration information using the computational power model to output a reference value of output power corresponding to the power supply system. The power supply system outputs corresponding power resources to the electrical equipment based on the output power reference value; The hybrid control system for power resource allocation, constructed based on the logical operation capabilities, parallel computing capabilities, and neural network computing capabilities of each computing node, includes: Obtain the available computing power corresponding to each computing node; Target computing nodes are those whose idle computing power is greater than or equal to a preset computing power threshold. The computational power model is obtained by using the logical operation capabilities, parallel computing capabilities, and neural network computing capabilities of each target computing node to construct the model. The target computing nodes are combined into a hybrid control system for power resource allocation; The computing node includes at least n logic operation chips corresponding to the logic operation capability, m parallel computing chips corresponding to the parallel computing capability, and p neural network acceleration chips corresponding to the neural network computing capability. The computing power scaling model is as follows: Among them, the Used to characterize the total computing power requirement. For logical operation capabilities, the For parallel computing capabilities, the For neural network acceleration capabilities; the The above and the For the same mapping function, a, b, and c are mapping scaling coefficients. To provide redundant computing power for logical operations, the To enable redundant computing power in parallel computing, the Accelerate redundant computing power for neural networks; The power supply system includes an energy storage subsystem, a photovoltaic subsystem, and a battery pack subsystem. The historical resource configuration information includes the output power of the energy storage subsystem, photovoltaic subsystem, and battery pack subsystem at the same time. The step of calculating the power demand information and the historical resource configuration information using the computational power quantitative model to output a reference value of the output power corresponding to the power supply system includes: Obtain the cost function for the electricity demand information, and the cost function is as follows: L(x,P_wref,P_sref,P_bref)=α(P D -P_wref- P_sref- P_bref) 2 +βP_sref 2 +γP_bref 2 ; The power demand information and the historical resource allocation information are simulated using the computational power model according to the cost function, and the solution is obtained by applying the following constraints: P_wref(t)≤P_wmax,P_sref(t)≤P_smax,P_bref(t)≤P_bmax, ; P_wref((j+1)Δ)- P_wref(jΔ)≤d P_wmax ; P_sref((j+1)Δ) - P_ref(jΔ) ≤ d P_smax ; P_bref((j+1)Δ) - P_bref(jΔ) ≤ d P_bmax ; Output the first output power value corresponding to the energy storage subsystem, the second output power value corresponding to the photovoltaic subsystem, and the third output power value corresponding to the battery pack subsystem; Wherein, P D The total load demand power of the power supply system is defined as follows: P_wmax is the upper limit of the output power of the energy storage subsystem, P_smax is the upper limit of the output power of the photovoltaic subsystem, and P_bmax is the upper limit of the output power of the battery pack subsystem; P_wref is the reference value of the output power of the energy storage subsystem, P_sref is the reference value of the output power of the photovoltaic subsystem, and P_bref is the reference value of the output power of the battery pack subsystem; d p_wmax The d represents the maximum change in output power of the energy storage subsystem within a unit time interval. p_smax The d represents the maximum change in output power of the photovoltaic subsystem within a unit time interval. p_bmax α represents the maximum change in output power of the battery pack subsystem within a unit time interval; α, β, and γ are different weighting factors.

2. The method according to claim 1, characterized in that, The hybrid control system includes an energy storage controller communicatively connected to the energy storage subsystem, a photovoltaic controller communicatively connected to the photovoltaic subsystem, and a battery pack controller communicatively connected to the battery pack. The step of outputting corresponding power resources to the electrical equipment through the power supply system based on the output power reference value includes: The energy storage controller transmits the first output power value to the energy storage subsystem, and controls the energy storage subsystem to output corresponding power resources to the electrical equipment based on the first output power value. The photovoltaic controller transmits the second output power value to the photovoltaic subsystem, and controls the photovoltaic subsystem to output corresponding power resources to the electrical equipment based on the second output power value. The battery pack controller transmits the third output power value to the battery pack subsystem, and controls the battery pack subsystem to output corresponding power resources to the electrical equipment based on the third output power value.

3. A power resource distribution device, characterized in that, include: The computing power acquisition module is used to acquire computing power information corresponding to each computing node. The computing power information includes at least logical operation capability, parallel computing capability, and neural network computing capability. The system construction module is used to construct a hybrid control system for power resource allocation based on the logical operation capability, parallel computing capability and neural network computing capability of each computing node. The hybrid control system is configured with a computational quantitative model for demand forecasting. The power calculation module is used to obtain the power demand information and historical resource configuration information of the electrical equipment through the hybrid control system, and to calculate the power demand information and the historical resource configuration information through the power calculation model, and output the output power reference value corresponding to the power supply system. The power resource processing module is used to output corresponding power resources to the electrical equipment through the power supply system based on the output power reference value; Specifically, the system construction module is used for: Obtain the available computing power corresponding to each computing node; Target computing nodes are those whose idle computing power is greater than or equal to a preset computing power threshold. The computational power model is obtained by using the logical operation capabilities, parallel computing capabilities, and neural network computing capabilities of each target computing node to construct the model. The target computing nodes are combined into a hybrid control system for power resource allocation; The computing node includes at least n logic operation chips corresponding to the logic operation capability, m parallel computing chips corresponding to the parallel computing capability, and p neural network acceleration chips corresponding to the neural network computing capability. The computing power scaling model is as follows: Among them, the Used to characterize the total computing power requirement. For logical operation capabilities, the For parallel computing capabilities, the For neural network acceleration capabilities; the The above and the For the same mapping function, a, b, and c are mapping scaling coefficients. To provide redundant computing power for logical operations, the To enable redundant computing power in parallel computing, the Accelerate redundant computing power for neural networks; The power supply system includes an energy storage subsystem, a photovoltaic subsystem, and a battery pack subsystem. The historical resource configuration information includes the output power of the energy storage subsystem, photovoltaic subsystem, and battery pack subsystem at the same time. The power calculation module is specifically used for: Obtain the cost function for the electricity demand information, and the cost function is as follows: L(x,P_wref,P_sref, P_bref)=α(P D -P_wref- P_sref- P_bref) 2 +βP_sref 2 +γP_bref 2 ; The power demand information and the historical resource allocation information are simulated using the computational power model according to the cost function, and the solution is obtained by applying the following constraints: P_wref(t)≤P_wmax,P_sref(t)≤P_smax,P_bref(t)≤P_bmax, ; P_wref((j+1)Δ)- P_wref(jΔ)≤d P_wmax ; P_sref((j+1)Δ) - P_ref(jΔ) ≤ d P_smax ; P_bref((j+1)Δ) - P_bref(jΔ) ≤ d P_bmax ; Output the first output power value corresponding to the energy storage subsystem, the second output power value corresponding to the photovoltaic subsystem, and the third output power value corresponding to the battery pack subsystem; Wherein, P D The total load demand power of the power supply system is defined as follows: P_wmax is the upper limit of the output power of the energy storage subsystem, P_smax is the upper limit of the output power of the photovoltaic subsystem, and P_bmax is the upper limit of the output power of the battery pack subsystem; P_wref is the reference value of the output power of the energy storage subsystem, P_sref is the reference value of the output power of the photovoltaic subsystem, and P_bref is the reference value of the output power of the battery pack subsystem; dp_wmax is the maximum change in output power of the energy storage subsystem within a unit time interval, dp_smax is the maximum change in output power of the photovoltaic subsystem within a unit time interval, and dp_bmax is the maximum change in output power of the battery pack subsystem within a unit time interval; α, β, and γ are different weighting factors.

4. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; The memory is used to store computer programs; When the processor executes a program stored in the memory, it implements the method as described in claim 1 or 2.

5. A computer-readable storage medium having instructions stored thereon that, when executed by one or more processors, cause the processors to perform the method as claimed in claim 1 or 2.

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