Energy maximum utilization method, device and equipment based on power grid physical information system and medium

Abstract

The invention discloses an energy maximum utilization method, device and equipment based on a power grid physical information system, and a storage medium, and relates to the technical field of smart power grids. According to the maximum energy utilization method based on the power grid physical information system, the electricity utilization demand of the target family is predicted based on the obtained internal electricity utilization information and external weather information of the target family, and accurate prediction of the electricity utilization demand of the family is achieved. And then defining an objective function to minimize the power consumption cost of the target family or maximize the utilization rate of renewable energy, calculating an expected value of the objective function based on a power consumption demand prediction result and a renewable energy power generation prediction result to obtain a scheduling strategy, and then performing power consumption scheduling on the target family by adopting the scheduling strategy. Therefore, the power generation plan is adjusted based on the power consumption demand prediction result on the basis of realizing accurate prediction of the target household power consumption demand, intelligent scheduling of renewable energy sources is realized, the utilization rate of the renewable energy sources is maximized, the household power consumption cost is reduced, and the energy utilization efficiency and the environmental protection property of a power grid are improved.

Description

Technical Field

[0001] The present invention relates to the field of smart grid technology, and in particular to a method, device, equipment and storage medium for maximizing energy utilization based on a physical information system of a power grid. Background Art

[0002] With the continued growth of global energy demand and increasing environmental awareness, the use of renewable energy has become a key approach to addressing the energy crisis and reducing environmental pollution. However, the intermittent and uncertain nature of renewable energy sources, such as solar and wind power, poses significant challenges to the stable operation of power grids and the balance of supply and demand. Traditional power grid generation scheduling methods often struggle to effectively address the fluctuations of renewable energy, leading to energy waste and grid instability issues.

[0003] Smart grid technology has developed rapidly in recent years. By integrating advanced sensing, communication, and control technologies, smart grids enable real-time monitoring and precise control of grid operation. Demand-based forecasting technologies are crucial for optimizing power generation scheduling and increasing renewable energy utilization. Various methods, such as time series analysis and machine learning, have been developed for household electricity consumption forecasting. However, these methods often rely on simple predictions based on historical data, resulting in limitations in accuracy and real-time performance. These methods are unable to meet the smart grid's demand for maximizing energy utilization based on grid-physical cyber systems. Summary of the Invention

[0004] Embodiments of the present invention provide a method, apparatus, device, and storage medium for maximizing energy utilization based on a power grid physical information system, which can accurately predict household electricity demand and adjust power generation plans based on household electricity demand to maximize the utilization of renewable energy.

[0005] In a first aspect, an embodiment of the present invention provides a method for maximizing energy utilization based on a power grid physical information system, the method comprising:

[0006] Obtaining internal electricity usage information of a target household and external weather information of the target household's environment, wherein the internal electricity usage information includes electrical equipment information, historical electricity usage data, and renewable energy generation data;

[0007] performing electricity demand forecasting for the target household based on the internal electricity consumption information and the external weather information to obtain an electricity demand forecasting result;

[0008] defining an objective function as minimizing the electricity cost of the target household or maximizing the utilization rate of renewable energy, and calculating an expected value of the objective function based on the electricity demand forecast result and the renewable energy generation forecast result to obtain a scheduling strategy;

[0009] The scheduling strategy is used to schedule electricity consumption for the target household.

[0010] Furthermore, the performing electricity demand forecasting for the target household based on the internal electricity consumption information and the external meteorological information to obtain the electricity demand forecast result includes:

[0011] The internal electricity consumption information and the external meteorological information are input into a preset household electricity demand prediction model, and a power demand prediction result of the target household is output.

[0012] Furthermore, the energy maximization method based on the grid physical cyber system also includes:

[0013] Set the feasible region of the objective function.

[0014] Furthermore, the calculating the expected value of the objective function based on the electricity demand forecast result and the renewable energy generation forecast result to obtain the scheduling strategy includes:

[0015] Based on the electricity demand forecast result and the renewable energy power generation forecast result, the expected value of the objective function is calculated using stochastic approximation to obtain the scheduling strategy.

[0016] Furthermore, the calculating the expected value of the objective function based on the electricity demand forecast result and the renewable energy generation forecast result to obtain the scheduling strategy includes:

[0017] Based on the electricity demand forecast result and the renewable energy power generation forecast result, the expected value of the objective function is calculated using sample average approximation to obtain the scheduling strategy.

[0018] Furthermore, the prediction of the renewable energy power generation prediction result includes:

[0019] Based on the external meteorological information and the performance of the renewable energy power generation equipment of the target household, power generation prediction is performed on the renewable energy power generation equipment to obtain the renewable energy power generation prediction result.

[0020] Furthermore, the energy maximization method based on the grid physical cyber system also includes:

[0021] The scheduling strategy is optimized based on the electricity demand forecast result, the renewable energy power generation forecast result, and the scheduling result, where the scheduling result is a result of scheduling electricity consumption for the target household using the scheduling strategy.

[0022] In a second aspect, an embodiment of the present invention provides an energy maximization utilization device based on a physical information system of a power grid, the energy maximization utilization device based on a physical information system of a power grid comprising:

[0023] an acquisition unit, configured to acquire internal electricity usage information of a target household and external meteorological information of the target household's environment, wherein the internal electricity usage information includes electrical equipment information, historical electricity usage data, and renewable energy generation data;

[0024] a prediction unit, configured to predict the electricity demand of the target household based on the internal electricity consumption information and the external meteorological information, and obtain an electricity demand prediction result;

[0025] a scheduling strategy generating unit, configured to define an objective function as minimizing the electricity cost of the target household or maximizing the utilization rate of renewable energy, and calculate an expected value of the objective function based on the electricity demand forecast result and the renewable energy generation forecast result to obtain a scheduling strategy;

[0026] An execution unit is used to use the scheduling strategy to schedule electricity consumption for the target household.

[0027] In a third aspect, an embodiment of the present invention further provides an energy maximization utilization device based on a power grid physical information system, comprising a processor and a memory, wherein the memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of any one of the energy maximization utilization methods based on a power grid physical information system provided by the embodiments of the present invention.

[0028] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which includes a computer program. When the computer program runs on an electronic device, the computer program is used to enable the electronic device to execute any step of the energy maximization utilization method based on the power grid physical information system provided by the embodiment of the present invention.

[0029] The beneficial effects of the present invention are:

[0030] The present invention's method for maximizing energy utilization based on a power grid physical information system obtains internal electricity usage information of a target household and external meteorological information about the target household's environment, and then predicts the target household's electricity demand based on this information to obtain a power demand forecast result, thereby achieving accurate prediction of household electricity demand. The objective function is then defined as minimizing the target household's electricity cost or maximizing the utilization rate of renewable energy. The expected value of the objective function is calculated based on the power demand forecast result and the renewable energy generation forecast result to obtain a scheduling strategy, which is then used to schedule electricity for the target household. This allows for accurate prediction of the target household's electricity demand and adjusts the power generation plan based on the power demand forecast result, achieving intelligent scheduling of renewable energy, maximizing renewable energy utilization, reducing household electricity costs, and improving the energy efficiency and environmental friendliness of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0032] Figure 1 1 is a flow chart of a method for maximizing energy utilization based on a power grid physical information system provided in an embodiment of the present invention;

[0033] Figure 2 Schematic diagram of the structure of an energy maximization utilization device based on a power grid physical information system provided in an embodiment of the present invention;

[0034] Figure 3 It is a structural diagram of an energy maximization utilization device based on a power grid physical information system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0036] The following disclosure provides many different embodiments or examples for implementing different features of the subject matter provided. Specific examples of components and arrangements are described below to simplify the disclosure. Of course, these are merely examples and are not intended to be limiting. For example, in the following description, a first feature formed on or formed on a second feature may include an embodiment in which the first feature and the second feature are formed in direct contact, and may also include an embodiment in which an additional feature may be formed between the first feature and the second feature so that the first feature and the second feature may not be in direct contact. In addition, the disclosure may reuse reference numbers and / or letters in various examples. This repetition is for the purpose of simplicity and clarity and does not inherently dictate the relationship between the various embodiments and / or configurations discussed.

[0037] In addition, the descriptions of "first", "second", etc. in the present invention are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0038] The term "and / or" herein simply describes an association relationship between associated objects, indicating that three relationships can exist. For example, "A and / or B" can represent the existence of three situations: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" herein refers to any combination of at least two of any one or more of a plurality of items. For example, "at least one of A, B, and C" can represent any one or more elements selected from the set consisting of A, B, and C.

[0039] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0040] In addition, numerous specific details are provided in the following detailed description to better illustrate the present invention. Those skilled in the art will appreciate that the present invention can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of the present invention.

[0041] As described above in the background technology, the inventors have found that:

[0042] With the increasing prevalence of smart homes and the rapid development of renewable energy technologies, effectively integrating household electricity demand with renewable energy generation capacity has become a hot topic of research. Traditional power scheduling methods often rely on simple predictions based on historical data, making them incapable of addressing complex and changing electricity consumption patterns and climatic conditions. This leads to problems such as low renewable energy utilization and large grid load fluctuations. Therefore, a smart power scheduling method is needed that can predict household electricity demand in real time and intelligently schedule renewable energy generation.

[0043] Based on this, an embodiment of the present invention proposes a method, device, equipment and storage medium for maximizing energy utilization based on a physical information system of a power grid. The method for maximizing energy utilization based on a physical information system of a power grid obtains the internal electricity consumption information of a target household and the external meteorological information of the target household's environment, and predicts the electricity demand of the target household based on the internal electricity consumption information and the external meteorological information to obtain the electricity demand prediction result, thereby achieving an accurate prediction of the household's electricity demand. Then, the objective function is defined as minimizing the target household's electricity cost or maximizing the utilization rate of renewable energy, and the expected value of the objective function is calculated based on the electricity demand prediction result and the renewable energy generation prediction result to obtain a scheduling strategy, and then the scheduling strategy is used to schedule the electricity consumption of the target household, thereby adjusting the power generation plan based on the electricity demand prediction result on the basis of achieving an accurate prediction of the target household's electricity demand, realizing intelligent scheduling of renewable energy, maximizing the utilization rate of renewable energy, reducing household electricity costs, and improving the energy utilization efficiency and environmental protection of the power grid.

[0044] Specifically, this embodiment will be described from the perspective of an energy maximization utilization device based on a power grid physical information system. The energy maximization utilization device based on a power grid physical information system can be specifically integrated into an energy maximization utilization device based on a power grid physical information system, that is, the energy maximization utilization method based on a power grid physical information system in an embodiment of the present invention can be executed by an energy maximization utilization device based on a power grid physical information system.

[0045] The following detailed description is provided in conjunction with the accompanying drawings. This embodiment uses an energy maximization device based on a power grid cyber-physical system as an example. It should be noted that the order in which the following embodiments are described does not limit the preferred order of implementation. Although the flowcharts illustrate a logical order, in some cases, the steps shown or described may be performed in a different order than that shown in the accompanying drawings.

[0046] Please refer to Figure 1 The specific process of the energy maximization utilization method based on the grid physical information system can be as follows: Step S101 to Step S104, wherein:

[0047] Step S101 : obtaining the internal electricity consumption information of the target household and the external weather information of the environment where the target household is located.

[0048] The target household is the household targeted for energy maximization based on the grid-physical information system in embodiments of the present invention. The target household can be one or more. The target household's internal electricity usage information includes electrical device information, historical electricity usage data, and renewable energy generation data. It should be understood that the electrical device information refers to information about various electrical devices within the target household, including power parameters and usage time, such as the power and usage time of televisions, refrigerators, and air conditioners. Historical electricity usage data refers to the target household's electricity consumption per unit time in the past, such as daily, weekly, and monthly electricity consumption records. Renewable energy generation data refers to the electricity generated by the target household's renewable energy generation equipment per unit time in the past, such as the electricity generated by solar photovoltaic panels and wind turbines. The target household's external meteorological information refers to external meteorological information at the target household's location, such as light intensity, wind speed, temperature, and other meteorological information related to renewable energy generation. External meteorological information includes past, present, and future external meteorological information. Embodiments of the present invention can predict the electricity generation of the target household's renewable energy generation equipment based on this external meteorological information.

[0049] In some embodiments, the internal electricity usage information and external weather information of the target household can be collected in real time through sensor devices such as smart meters and weather stations.

[0050] Step S102 : forecasting the electricity demand of the target household based on the internal electricity consumption information and the external weather information to obtain an electricity demand forecast result.

[0051] Specifically, the embodiment of the present invention first aligns the internal electricity consumption information (electrical equipment information, historical electricity consumption data, and renewable energy generation data) and external meteorological information of the target household obtained in step S101 according to the timestamp and with a specific time unit as the granularity, and then preprocesses the above data to fill in missing values and handle outliers. In some embodiments, linear interpolation or the mean based on adjacent time points can be used to fill missing values, and isolation forest or 3σ principle can be used to detect abnormal electricity consumption (such as sudden increase / drop). For the preprocessed data, a pre-trained model is used to predict electricity demand to obtain an electricity demand forecast result. Among them, the pre-trained model can be a traditional time series model (such as SARIMA, Prophet), a machine learning model (such as support vector machine, random forest), a deep learning model (such as LSTM / GRU, Transformer), or a hybrid model of the aforementioned models. For example, in the hybrid model, SARIMA can be used to capture linear trends and LightGBM can be used to fit the residual term. It can be understood that the execution subject of this embodiment can be a processor of the power grid physical information system, and the internal power consumption information and external meteorological information can be stored in the memory of the power grid physical information system.

[0052] In some embodiments, step S102 may include:

[0053] Internal electricity consumption information and external meteorological information are input into a preset household electricity demand forecasting model, and the electricity demand forecast results of the target household are output.

[0054] The household electricity demand forecasting model is pre-trained and, when fed with internal electricity usage information and external weather information, can output a target household's electricity demand forecast. It is understood that the execution entity of this embodiment can be a processor in the grid-physical cyber system, and the household electricity demand forecasting model can be stored in a memory in the grid-physical cyber system.

[0055] The household electricity demand prediction model can be the aforementioned machine learning model or deep learning model, and is trained using a training dataset consisting of the internal electricity consumption information and external meteorological information obtained in step S101. Therefore, the household electricity demand prediction model comprehensively considers multiple factors, including the target household's historical electricity consumption patterns (such as the difference between weekdays and weekends, and the impact of seasonal changes on electricity consumption), external meteorological conditions (such as light intensity directly affecting solar power generation and wind speed affecting wind power generation), and household members' living habits (such as waking times, bedtimes, and cooking times). The model is trained using the training dataset to accurately predict the target household's electricity demand over a future period of time (such as 24 hours, 48 hours, or a week).

[0056] Step S103 , defining the objective function as minimizing the target household's electricity cost or maximizing the utilization rate of renewable energy, and calculating the expected value of the objective function based on the electricity demand forecast result and the renewable energy generation forecast result to obtain a scheduling strategy.

[0057] The renewable energy generation forecast result is a forecast of the power generation of the target household's renewable energy generation equipment. The target household's renewable energy generation equipment may include solar photovoltaic panels, wind turbines, and other power generation equipment capable of generating renewable energy. The specific renewable energy generation equipment that the target household can deploy needs to be known. The expected value of the objective function is the expected value of the function calculated based on the electricity demand forecast result and the renewable energy generation forecast result when minimizing the target household's electricity cost or maximizing the utilization rate of renewable energy. This expected value represents the specific decision variables in the scheduling strategy obtained in step S103, which may be decision variables such as the target household's renewable energy generation in each time period and the amount of electricity purchased from the power grid. These decision variables determine the specific plan for the target household's electricity scheduling. It is understandable that the execution entity of this embodiment may be a processor of the power grid physical information system, and the scheduling strategy may be stored in the memory of the power grid physical information system.

[0058] Specifically, the objective function aims to minimize the electricity cost of the target household or maximize the utilization rate of renewable energy, which can be:

[0059]

[0060] Among them, x is the decision variable in the scheduling strategy. X is the feasible domain of the decision variable. d is the electricity demand random variable, which represents the household electricity demand of the target household in a certain period of time in the future, that is, the electricity demand forecast result. As mentioned above, d is affected by many factors. P is the probability distribution of electricity demand, which can be calculated from the electricity demand forecast results. C(x,d) represents the cost function under a given scheduling strategy and electricity demand, and also represents the total cost under a given scheduling strategy and electricity demand, including the electricity purchase cost, renewable energy power generation cost and possible penalty costs (such as fines for exceeding the grid capacity limit). It can be understood that by minimizing C(x,d), the function expectation value x can be calculated based on the electricity demand forecast results and the renewable energy power generation forecast results, that is, the decision variable of the scheduling strategy is calculated, so that the scheduling strategy can be determined.

[0061] In some embodiments, x may include the amount of power generated by renewable energy and the amount of power purchased from the grid in each period, that is, the decision variables in the scheduling strategy include the amount of power generated by renewable energy and the amount of power purchased from the grid in each period. It can be understood that in this embodiment, the objective function is:

[0062]

[0063] Where g represents the amount of renewable energy generated during each period, such as solar and wind power. p represents the amount of electricity purchased from the grid. C(g,p,d) represents the total cost for a given amount of power generated, purchased, and electricity demand (derived from electricity demand forecasts). This includes the cost of electricity purchases, renewable energy generation costs, maintenance costs, and possible penalties (such as fines for exceeding grid capacity limits).

[0064] To improve the reliability of the dispatching strategy, in some embodiments, a method for maximizing energy utilization based on a grid-physical cyber system may include:

[0065] Set the feasible region X of the objective function.

[0066] In some embodiments, the feasible domain X takes into account constraints such as the capacity limit of the power grid to which the target household is connected and the maximum output power of the renewable energy power generation equipment, so that the decision variables of the determined scheduling strategy satisfy a reasonable feasible domain X, thereby improving the reliability of the scheduling strategy. It can be understood that the execution subject of this embodiment can be a processor of the power grid physical information system, and the scheduling strategy can be stored in the memory of the power grid physical information system.

[0067] It is understandable that in some cases, the objective function may not be directly solvable (e.g., it may not converge), that is, it is not possible to obtain the decision variables of the scheduling policy by directly calculating the objective function. To this end, in some embodiments, the calculation of the objective function may include:

[0068] Based on the electricity demand forecast results and renewable energy generation forecast results, the expected value of the objective function is calculated using stochastic approximation (SA) to obtain the scheduling strategy.

[0069] Stochastic approximation is an iterative method for solving equations or optimization problems with random noise, such as the Robbins-Monro algorithm (for solving equation roots) or the Kiefer-Wolfowitz algorithm (for gradient estimation optimization). The core idea of stochastic approximation is to approximate the true solution using noisy observations by gradually adjusting parameters. For example, when the expectation cannot be directly calculated, the estimate is updated with random samples at each iteration, and the step size is typically gradually reduced to ensure convergence.

[0070] Specifically, the embodiment of the present invention adopts a random approximation method to calculate the objective function. In each iteration, a power demand sample d is randomly selected from the power demand forecast result. k , calculate the gradient estimate with noise And according to the gradient estimate and learning rate η k Update the decision variable x of the scheduling policy k+1 The iteration formula is:

[0071]

[0072] Among them, Proj X It represents projecting the updated decision variables back to the feasible region X.

[0073] Understandably, x k+1 represents the decision variable of the scheduling strategy for the k+1th iteration, It is the gradient estimate calculated based on the current (kth iteration) scheduling policy and randomly sampled power demand, and contains noise to simulate the uncertainty in actual conditions. k Used to control the iteration step size, affecting the convergence speed and stability.

[0074] In some embodiments, x may include the amount of power generated by renewable energy and the amount of power purchased from the grid in each period, that is, the decision variables in the scheduling strategy include the amount of power generated by renewable energy and the amount of power purchased from the grid in each period. It can be understood that in this embodiment, a power demand sample d is randomly selected from the power demand forecast result in each iteration. k , calculate the gradient estimate with noise And according to the gradient estimate and learning rate η k Update the decision variables of the scheduling strategy, i.e., the power generation g k+1 and the amount of electricity purchased p k+1 The iteration formula is:

[0075]

[0076] in, It is a gradient estimate calculated based on the current (kth iteration) power generation, power purchase and randomly sampled power demand, and contains noise to simulate the uncertainty in actual conditions; Proj X It means projecting the updated power generation and purchase amount back to the feasible region X.

[0077] It is understandable that the above-mentioned method for calculating the expected value of the objective function using random approximation may be subject to excessive noise influence, resulting in inaccurate and unreliable calculation results. To address this issue and to cope with scenarios where the expected value of the objective function cannot be directly calculated, in other embodiments, the calculation of the objective function may further include:

[0078] Based on the electricity demand forecast results and renewable energy generation forecast results, the sample average approximation (SAA) is used to calculate the expected value of the objective function and obtain the scheduling strategy.

[0079] Sample average approximation is a batch processing method that transforms stochastic optimization problems into deterministic ones. For cases where the objective function contains an expectation, sample average approximation generates a large number of samples, approximates the expectation with the sample mean, constructs a deterministic optimization problem, and then solves it using traditional optimization algorithms.

[0080] Specifically, the embodiment of the present invention uses the average value of a set of randomly selected power demand samples (for example, randomly selected from the probability distribution P of power demand) to approximate the expected value of the objective function and solve the approximate problem. The approximate optimization problem is:

[0081]

[0082] in, It is the sample average approximation of the objective function, which represents the approximate value of the objective function calculated based on N random samples. N is the number of samples, that is, N random samples are {d1, d2, ..., d N}.d i It is understood that by solving this approximate problem, the decision variable x of the scheduling strategy can be obtained.

[0083] It is understood that in some embodiments, x may include the power generation of renewable energy and the amount of electricity purchased from the grid in each period, that is, the decision variables in the scheduling strategy include the power generation of renewable energy and the amount of electricity purchased from the grid in each period. In this embodiment, the approximate optimization problem is:

[0084]

[0085] It can be understood that by solving this approximate problem, the decision variables of the scheduling strategy can be obtained, namely the power generation g * and the amount of electricity purchased p * , and serves as the decision variable for the final scheduling strategy.

[0086] Optionally, in some embodiments, the prediction of renewable energy power generation prediction results may include:

[0087] Based on external meteorological information and the performance of the target household's renewable energy generation equipment, a power generation forecast is performed for the renewable energy generation equipment to obtain a renewable energy power generation forecast result. It is understood that the execution entity of this embodiment may be a processor of the power grid physical information system, and the renewable energy power generation forecast result may be stored in a memory of the power grid physical information system.

[0088] In some embodiments, the renewable energy generation prediction result is obtained based on external meteorological information and performance estimation of the renewable energy generation equipment of the target household.

[0089] S104: Use a scheduling strategy to schedule electricity consumption for target households.

[0090] As can be seen from the above, scheduling the electricity consumption of target households according to the decision variable x in the scheduling strategy can minimize the electricity consumption cost of the target households or maximize the utilization rate of renewable energy.

[0091] Specifically, the embodiment of the present invention controls the electrical equipment and renewable energy power generation equipment in the target home according to the decision variable x in the scheduling strategy, thereby realizing intelligent scheduling of electricity.

[0092] For example, the embodiment of the present invention is based on the decision variables (power generation g * and the amount of electricity purchased p * ) uses a smart home system to control the electrical appliances and renewable energy generation equipment within the target home. For example, during periods of abundant solar power generation, solar power generation is prioritized to meet household electricity needs. During periods of high wind power generation but low household electricity demand, excess wind energy is stored or sold online. When renewable energy generation is insufficient, an appropriate amount of electricity is purchased from the grid to meet electricity demand.

[0093] Optionally, in some embodiments, the method for maximizing energy utilization based on a grid-physical cyber system may further include:

[0094] The dispatch strategy is optimized based on the electricity demand forecast, renewable energy generation forecast, and dispatch results. The dispatch results are the results of the dispatch strategy for the target households.

[0095] It can be understood that, through the above comparative analysis and the scheduling strategy optimization process based on the comparative review results, the embodiment of the present invention can improve the accuracy and efficiency of the electricity scheduling for the target households.

[0096] In summary, the embodiments of the present invention employ an optimization algorithm, combining the characteristics of household electricity consumption and renewable energy generation, to achieve precise and intelligent electricity scheduling. Specifically, the embodiments of the present invention obtain the internal electricity consumption information of the target household and the external meteorological information of the target household's environment, and predict the target household's electricity demand based on the internal electricity consumption information and external meteorological information to obtain the electricity demand prediction results, thereby achieving accurate prediction of household electricity demand. The objective function is then defined as minimizing the target household's electricity cost or maximizing the utilization rate of renewable energy. The expected value of the objective function is calculated based on the electricity demand prediction results and the renewable energy generation prediction results to obtain a scheduling strategy, which is then used to schedule electricity for the target household. This allows the power generation plan to be adjusted based on the electricity demand prediction results on the basis of achieving accurate prediction of the target household's electricity demand, achieving intelligent scheduling of renewable energy, maximizing the utilization rate of renewable energy, reducing household electricity costs, and improving the energy efficiency and environmental friendliness of the power grid. The energy maximization utilization method based on the grid physical information system of the embodiments of the present invention has good adaptability and robustness, and can cope with the impact of factors such as different climate conditions and changes in family members' living habits on electricity demand.

[0097] This embodiment also provides an energy maximization utilization device based on a power grid physical information system, which can be integrated into an energy maximization utilization device based on a power grid physical information system. Figure 2 As shown, the energy maximization utilization device based on the grid physical information system may include:

[0098] An acquisition unit 201 is configured to acquire internal electricity usage information of a target household and external weather information of the target household's environment, wherein the internal electricity usage information includes electrical equipment information, historical electricity usage data, and renewable energy generation data;

[0099] The prediction unit 202 is configured to predict the electricity demand of the target household based on the internal electricity consumption information and the external weather information to obtain an electricity demand prediction result;

[0100] a scheduling strategy generating unit 203, configured to define an objective function as minimizing the electricity cost of the target household or maximizing the utilization rate of renewable energy, and calculate an expected value of the objective function based on the electricity demand forecast result and the renewable energy generation forecast result to obtain a scheduling strategy;

[0101] The execution unit 204 is configured to use the scheduling strategy to schedule electricity consumption for the target household.

[0102] like Figure 3 Show, Figure 3A schematic diagram of the structure of an energy maximization utilization device based on a power grid physical information system provided in an embodiment of the present invention. The energy maximization utilization device 1100 based on a power grid physical information system includes a processor 1101 having one or more processing cores, a memory 1102 having one or more computer-readable storage media, and a computer program stored on the memory 1102 and executable on the processor. The processor 1101 is electrically connected to the memory 1102. It will be understood by those skilled in the art that the structure of the energy maximization utilization device based on a power grid physical information system shown in the figure does not constitute a limitation on the energy maximization utilization device based on a power grid physical information system, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0103] Processor 1101 is the control center of device 1100 for maximizing energy utilization based on a power grid-physical cyber system. It connects various components of device 1100 using various interfaces and lines. By running or loading software programs and / or units stored in memory 1102 and invoking data stored in memory 1102, it executes various functions and processes data of device 1100 for maximizing energy utilization based on a power grid-physical cyber system, thereby providing overall monitoring of device 1100 for maximizing energy utilization based on a power grid-physical cyber system. Processor 1101 can be a CPU, a graphics processor (GPU), a network processor (NP), etc., and can implement or execute the various methods, steps, and logic blocks disclosed in the embodiments of the present invention.

[0104] In an embodiment of the present invention, the processor 1101 in the energy maximization utilization device 1100 based on the power grid physical information system will load the instructions corresponding to the processes of one or more applications into the memory 1102 according to the following steps, and the processor 1101 will run the applications stored in the memory 1102 to realize various functions. Please refer to the previous embodiments and will not repeat them here.

[0105] Optional, such as Figure 3 As shown, the energy maximization utilization device 1100 based on the grid physical information system also includes: a touch screen 1103, a radio frequency circuit 1104, an audio circuit 1105, an input unit 1106 and a power supply 1107. Among them, the processor 1101 is electrically connected to the touch screen 1103, the radio frequency circuit 1104, the audio circuit 1105, the input unit 1106 and the power supply 1107 respectively. Those skilled in the art will understand that Figure 3The illustrated structure of the energy maximization device based on the grid physical information system does not constitute a limitation on the energy maximization device based on the grid physical information system, and may include more or fewer components than shown in the figure, or combine certain components, or arrange components differently.

[0106] The touch screen display 1103 can be used to display a graphical user interface and receive operation instructions generated by the user acting on the graphical user interface. The touch screen display 1103 may include a display panel and a touch panel. Among them, the display panel can be used to display information input by the user or information provided to the user and various graphical user interfaces of energy maximization utilization devices based on the grid physical information system. These graphical user interfaces can be composed of graphics, text, icons, videos and any combination thereof. Optionally, the display panel can be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), etc. The touch panel can be used to collect user touch operations on or near it (such as operations performed by the user using any suitable object or accessory such as a finger, stylus, etc. on or near the touch panel), and generate corresponding operation instructions, and the operation instructions execute corresponding programs. Optionally, the touch panel may include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch direction, detects the signal caused by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into touch point coordinates, and then sends it to the processor 1101, and can receive commands sent by the processor 1101 and execute them. The touch panel can cover the display panel. When the touch panel detects a touch operation on or near it, it is transmitted to the processor 1101 to determine the type of touch event. Then the processor 1101 provides a corresponding visual output on the display panel according to the type of touch event. In an embodiment of the present invention, the touch panel and the display panel can be integrated into the touch display screen 1103 to realize input and output functions. However, in some embodiments, the touch panel and the touch panel can be used as two independent components to realize input and output functions. That is, the touch display screen 1103 can also be used as part of the input unit 1106 to realize the input function.

[0107] The RF circuit 1104 may be configured to transmit and receive RF signals to establish wireless communication with network devices or other devices that maximize energy utilization based on a grid-physical information system through wireless communication, and to transmit and receive signals between network devices or other devices that maximize energy utilization based on a grid-physical information system.

[0108] Audio circuit 1105 can be used to provide an audio interface between the user and the energy maximization device based on the grid-physical cyber system through a speaker and microphone. Audio circuit 1105 can convert received audio data into electrical signals and transmit them to the speaker, which then converts them into sound signals for output. The microphone, on the other hand, converts the collected sound signals into electrical signals, which are then received by audio circuit 1105 and converted into audio data. The audio data is then output to processor 1101 for processing, and then transmitted via RF circuit 1104 to, for example, another energy maximization device based on the grid-physical cyber system. Alternatively, the audio data can be output to memory 1102 for further processing. Audio circuit 1105 may also include an earphone jack to provide communication between external headphones and the energy maximization device based on the grid-physical cyber system.

[0109] The input unit 1106 may be configured to receive input digital, character information, or user feature information (such as fingerprint, iris, or facial information), and to generate keyboard, mouse, joystick, optical, or trackball signal input related to user settings and function control.

[0110] Power supply 1107 is used to power various components of the grid-physical cyber system-based energy maximization device 1100. Optionally, power supply 1107 can be logically connected to processor 1101 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption. Power supply 1107 can also include one or more DC or AC power supplies, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and other arbitrary components.

[0111] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0112] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be accomplished by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0113] To this end, embodiments of the present invention provide a computer-readable storage medium storing multiple computer programs capable of being loaded by a processor to execute any of the methods for maximizing energy utilization based on a power grid-physical cyber system provided by embodiments of the present invention. This computer program can execute the steps of the aforementioned methods for maximizing energy utilization based on a power grid-physical cyber system, as described in the previous embodiments and will not be further elaborated here.

[0114] The computer-readable storage medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0115] Since the computer program stored in the computer-readable storage medium can execute any of the energy maximization utilization methods based on the power grid physical information system provided in the embodiments of the present invention, the beneficial effects that can be achieved by any of the energy maximization utilization methods based on the power grid physical information system provided in the embodiments of the present invention can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0116] In the above-mentioned embodiments of the energy maximization utilization device based on the physical information system of the power grid, the computer-readable storage medium, the energy maximization utilization equipment based on the physical information system of the power grid, and the computer program product, the descriptions of each embodiment have different focuses. For parts not described in detail in a particular embodiment, reference can be made to the relevant descriptions of other embodiments. Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working processes and beneficial effects of the above-mentioned energy maximization utilization device based on the physical information system of the power grid, the computer-readable storage medium, the computer program product, the energy maximization utilization equipment based on the physical information system of the power grid, and their corresponding units can be referred to in the description of the energy maximization utilization method based on the physical information system of the power grid in the above embodiments, and the details will not be repeated here.

[0117] The above is a detailed introduction to the energy maximization utilization method based on the physical information system of the power grid, the energy maximization utilization device based on the physical information system of the power grid, the energy maximization utilization equipment based on the physical information system of the power grid, the computer-readable storage medium and the computer program product provided in the embodiments of the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for maximizing energy utilization based on a power grid physical information system, characterized in that: The method comprises: Obtaining internal electricity usage information of a target household and external weather information of the target household's environment, wherein the internal electricity usage information includes electrical equipment information, historical electricity usage data, and renewable energy generation data; performing electricity demand forecasting for the target household based on the internal electricity consumption information and the external weather information to obtain an electricity demand forecasting result; defining an objective function as minimizing the electricity cost of the target household or maximizing the utilization rate of renewable energy, and calculating an expected value of the objective function based on the electricity demand forecast result and the renewable energy generation forecast result to obtain a scheduling strategy; The scheduling strategy is used to schedule electricity consumption for the target household.

2. The method for maximizing energy utilization based on a power grid physical information system according to claim 1, characterized in that: The performing electricity demand forecasting on the target household based on the internal electricity consumption information and the external weather information to obtain the electricity demand forecast result includes: The internal electricity consumption information and the external meteorological information are input into a preset household electricity demand prediction model, and a power demand prediction result of the target household is output.

3. The method for maximizing energy utilization based on a power grid physical information system according to claim 1, characterized in that: The method further comprises: Set the feasible region of the objective function.

4. The method for maximizing energy utilization based on a power grid physical information system according to claim 1, characterized in that: The calculating the expected value of the objective function based on the electricity demand forecast result and the renewable energy generation forecast result to obtain the scheduling strategy includes: Based on the electricity demand forecast result and the renewable energy power generation forecast result, the expected value of the objective function is calculated using stochastic approximation to obtain the scheduling strategy.

5. The method for maximizing energy utilization based on a power grid physical information system according to claim 1, characterized in that: The calculating the expected value of the objective function based on the electricity demand forecast result and the renewable energy generation forecast result to obtain the scheduling strategy includes: Based on the electricity demand forecast result and the renewable energy power generation forecast result, the expected value of the objective function is calculated using sample average approximation to obtain the scheduling strategy.

6. The method for maximizing energy utilization based on a power grid physical information system according to claim 1, characterized in that: The forecast of the renewable energy power generation forecast result includes: Based on the external meteorological information and the performance of the renewable energy power generation equipment of the target household, power generation prediction is performed on the renewable energy power generation equipment to obtain the renewable energy power generation prediction result.

7. The method for maximizing energy utilization based on a grid physical information system according to any one of claims 1 to 6, characterized in that: The method further comprises: The scheduling strategy is optimized based on the electricity demand forecast result, the renewable energy power generation forecast result, and the scheduling result, where the scheduling result is a result of scheduling electricity consumption for the target household using the scheduling strategy.

8. An energy maximization utilization device based on a power grid physical information system, characterized in that: The energy maximization utilization device based on the power grid physical information system includes: an acquisition unit, configured to acquire internal electricity usage information of a target household and external meteorological information of the target household's environment, wherein the internal electricity usage information includes electrical equipment information, historical electricity usage data, and renewable energy generation data; a prediction unit, configured to predict the electricity demand of the target household based on the internal electricity consumption information and the external meteorological information, and obtain an electricity demand prediction result; a scheduling strategy generating unit, configured to define an objective function as minimizing the electricity cost of the target household or maximizing the utilization rate of renewable energy, and calculate an expected value of the objective function based on the electricity demand forecast result and the renewable energy generation forecast result to obtain a scheduling strategy; An execution unit is used to use the scheduling strategy to schedule electricity consumption for the target household.

9. An energy maximization utilization device based on a power grid physical information system, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of the method for maximizing energy utilization based on a grid physical information system according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a computer program. When the computer program is run on an electronic device, the computer program is used to enable the electronic device to execute the steps of the energy maximization utilization method based on the grid physical information system according to any one of claims 1 to 7.

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