CN117767289A - Determination method and device for micro-grid energy scheduling, electronic equipment and storage medium - Google Patents
Determination method and device for micro-grid energy scheduling, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a method and a device for determining micro-grid energy scheduling, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring micro-grid energy data corresponding to a target micro-grid; inputting the energy data of the micro-grid into a pre-constructed micro-grid energy management model to obtain an energy analysis result corresponding to the target micro-grid; the micro-grid energy management model comprises at least one of an energy supply and demand balance sub-model, an energy value attribute sub-model and an energy storage sub-model; and determining an energy scheduling scheme corresponding to the target micro-grid based on the energy analysis result. By analyzing the energy data of the micro-grid, the influence factors in the operation process of the target micro-grid are comprehensively considered from multiple dimensions, so that the operation condition of the target micro-grid is accurately predicted, the energy supply and demand balance of the target micro-grid is met, and the diversity and time variability of the user demands are met.
Description
Technical Field
The present invention relates to the field of power grid control technologies, and in particular, to a method and apparatus for determining energy scheduling of a micro-grid, an electronic device, and a storage medium.
Background
With the rapid development of renewable energy sources, a new energy micro-grid is taken as a flexible, efficient and reliable energy supply mode, and has become one of important ways for solving energy problems and realizing sustainable development.
Currently, many challenges still remain in new energy microgrids, where the problems of energy management and optimization are most prominent. Due to the instability and unpredictability of renewable energy sources in new energy microgrids, as well as the diversity and time-varying nature of user demands, the problems of balancing energy supply and demand, optimizing energy storage and release become very complex and difficult, which cannot be effectively solved in the prior art.
In order to solve the above problems, an improvement in a determination method of the micro grid energy schedule is required.
Disclosure of Invention
The invention provides a method, a device, electronic equipment and a storage medium for determining micro-grid energy scheduling, which are used for solving the problems that in the prior art, energy supply and demand are unbalanced in the energy scheduling process of a micro-grid system, or the diversity and time variability of the energy demand of a user on a grid cannot be met.
In a first aspect, an embodiment of the present invention provides a method for determining energy scheduling of a micro-grid, including:
Acquiring micro-grid energy data corresponding to a target micro-grid;
inputting the energy data of the micro-grid into a pre-constructed micro-grid energy management model to obtain an energy analysis result corresponding to the target micro-grid; the micro-grid energy management model comprises at least one of an energy supply and demand balance sub-model, an energy value attribute sub-model and an energy storage sub-model;
and determining an energy scheduling scheme corresponding to the target micro-grid based on the energy analysis result.
In a second aspect, an embodiment of the present invention further provides a device for determining energy scheduling of a micro-grid, including:
the data acquisition module is used for acquiring micro-grid energy data corresponding to the target micro-grid;
the result determining module is used for inputting the energy data of the micro-grid into a pre-constructed micro-grid energy management model to obtain an energy analysis result corresponding to the target micro-grid; the micro-grid energy management model comprises at least one of an energy supply and demand balance sub-model, an energy value attribute sub-model and an energy storage sub-model;
and the scheme determining module is used for determining an energy scheduling scheme corresponding to the target micro-grid based on the energy analysis result.
In a third aspect, an embodiment of the present invention further provides an electronic device, including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of determining a microgrid energy schedule according to any one of the embodiments of the present invention.
In a fourth aspect, an embodiment of the present invention further provides a computer readable storage medium, where computer instructions are stored, where the computer instructions are configured to cause a processor to implement a method for determining energy scheduling of a micro grid according to any embodiment of the present invention when executed.
According to the technical scheme, the micro-grid energy data corresponding to the target micro-grid are obtained; inputting the energy data of the micro-grid into a pre-constructed micro-grid energy management model to obtain an energy analysis result corresponding to the target micro-grid; the micro-grid energy management model comprises at least one of an energy supply and demand balance sub-model, an energy value attribute sub-model and an energy storage sub-model; and determining an energy scheduling scheme corresponding to the target micro-grid based on the energy analysis result. In the technical scheme, after the micro-grid energy data are acquired, the micro-grid energy data are input into a pre-built micro-grid energy management model, so that data analysis can be performed on the micro-grid energy data, and the information such as energy supply data and energy demand data corresponding to a target micro-grid in a period of time in the future, energy value attributes, charging and discharging strategies corresponding to an energy storage system in the target micro-grid, energy storage capacity and the like is predicted, so that a more reasonable energy scheduling scheme is designated for the target micro-grid, and the target micro-grid achieves the effect of optimal economic benefit under the energy scheduling scheme. Meanwhile, through analysis of the energy data of the micro-grid, influence factors in the operation process of the target micro-grid are comprehensively considered from multiple dimensions, so that the operation condition of the target micro-grid is accurately predicted, the energy supply and demand balance of the target micro-grid is met, and the diversity and time variability of the user demands are met.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for determining a micro grid energy schedule according to a first embodiment of the present invention;
fig. 2 is a schematic structural diagram of a target micro grid according to a first embodiment of the present invention;
fig. 3 is a flowchart of a method for determining a micro grid energy schedule according to a second embodiment of the present invention;
fig. 4 is a schematic structural diagram of a determining device for energy scheduling of a micro grid according to a third embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device implementing a method for determining a micro-grid energy schedule according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein.
Example 1
Fig. 1 is a flowchart of a method for determining a micro-grid energy schedule according to an embodiment of the present invention, where the method may be implemented by a micro-grid energy schedule determining device, which may be implemented in hardware and/or software, and the micro-grid energy schedule determining device may be configured in a computing device that may perform the micro-grid energy schedule determining method, by collecting micro-grid energy data in a micro-grid system and performing data analysis on the micro-grid energy data based on a pre-built micro-grid energy management model, to obtain an energy analysis result, such as a micro-grid energy supply-demand balance relationship, an energy value attribute, an energy storage capacity and a charge-discharge policy of an energy storage system in the micro-grid, and the like, and determining a situation of an energy schedule scheme corresponding to the micro-grid based on the energy analysis result.
As shown in fig. 1, the method includes:
s110, acquiring micro-grid energy data corresponding to the target micro-grid.
In the technical scheme, the target micro-grid can be understood as a new energy micro-grid needing micro-grid energy scheduling. The micro grid energy data may be understood as micro grid related data that needs to be used when analyzing an optimal energy scheduling scheme corresponding to a target micro grid. The micro-grid energy data can be energy data acquired in real time in the running process of the target micro-grid, and can also be historical energy data in a historical period.
Illustratively, as shown in fig. 2, in the present technical solution, a boad distribution network scheduling system, a distribution network substation, a distribution network scheduling layer, a distribution network centralized control layer, a management center, a control center, an in-situ control layer, a photovoltaic power plant, a load, a micro gas turbine, a wind power plant, an energy storage battery (i.e., an energy storage system), and the like in the target micro grid. The micro-grid energy data may include micro-grid energy generation amount, micro-grid energy demand amount, micro-grid energy storage amount, micro-grid energy outlet amount, basic energy value attribute, energy consumption amount, energy value attribute change information corresponding to a target micro-grid, electric quantity information corresponding to an energy storage system of the target micro-grid, charging power corresponding to the energy storage system, discharging power corresponding to the energy storage system, charging efficiency corresponding to the energy storage system, discharging efficiency corresponding to the energy storage system, and the like.
Specifically, in order to ensure energy supply and demand balance, energy storage and release in the target micro-grid and meet diversity and time variability of user demands in the operation process of the target micro-grid, the micro-grid energy data corresponding to the target micro-grid needs to be acquired in the technical scheme, and an energy scheduling scheme corresponding to the target micro-grid needs to be determined based on the micro-grid energy data.
S120, inputting the energy data of the micro-grid into a pre-constructed micro-grid energy management model to obtain an energy analysis result corresponding to the target micro-grid.
The micro-grid energy management model comprises at least one of an energy supply and demand balance sub-model, an energy value attribute sub-model and an energy storage sub-model. The energy analysis result can be understood as a prediction result of the energy data of the target micro-grid in a future period of time based on the energy data of the micro-grid. For example, the energy import amount, the energy output amount, the micro-grid energy generation amount, the micro-grid energy demand amount, the micro-grid energy storage amount, the energy value attribute, and the like of the target micro-grid in the next month are predicted based on the micro-grid energy data.
In the technical scheme, the micro-grid energy management model is mainly used for carrying out data analysis on micro-grid energy data so that a target micro-grid can ensure energy supply and demand balance of energy inlet quantity and output quantity of the micro-grid, the energy value attribute is the lowest (mainly refers to the input value attribute of the target micro-grid), and the energy storage and release meet the energy supply and demand balance of an energy storage system.
In practical applications, a microgrid energy management model corresponding to a target microgrid is pre-built before analysis based on the microgrid energy data.
In the technical scheme, the energy supply and demand balance sub-model can be expressed by the following formula:
wherein E is supply Micro-grid energy production representing target micro-grid, E demand Micro-grid energy demand representing target micro-grid, E storage Micro-grid energy storage representing target micro-grid, E import Representing energy inlet quantity of target micro-grid and external energy system, E export Representing energy outlet amount of target micro-grid and external energy system。
It should be noted that the above formula may also be referred to as an energy balance equation.
In the technical scheme, the energy value attribute submodel can be expressed by the following formula:
P＝P base +f(E)
wherein P represents the energy value attribute corresponding to the target micro-grid, and P base And (3) representing the basic energy value attribute corresponding to the target micro-grid, wherein E represents the energy consumption in the target micro-grid, and f (E) represents the change function of the energy value attribute.
Where f (E) may take the form of f (E) =ae+b, where a and b may be constants set in advance.
In this technical solution, the energy storage submodel may be represented by the following formula:
SOC(t+1)＝SOC(t)+η ch P ch (t)-η dis P dis (t)
Wherein, SOC represents battery power information of an energy storage system of a target micro-grid, t represents current time and P represents ch (t) represents the charging power of the energy storage system at the current moment, P dis (t) represents the discharge power, eta of the energy storage system at the current moment ch Representing the charging efficiency, eta of an energy storage system dis Representing the discharge efficiency of the energy storage system.
On the basis, the micro-grid energy management model comprises an energy supply and demand balance sub-model, the energy analysis result is a first analysis result, the energy prediction result is input into the pre-constructed micro-grid energy management model to obtain an energy analysis result corresponding to a target micro-grid, and the method comprises the following steps: acquiring first power grid related data corresponding to a target micro power grid; and carrying out energy balance analysis on the first power grid related data based on the energy supply and demand balance sub-model to obtain a first analysis result.
The first power grid related data comprises at least one of micro-grid energy generation amount, micro-grid energy demand amount, micro-grid energy storage amount, micro-grid energy outlet amount and micro-grid energy outlet amount. The first power grid related data can be obtained by calling historical load data and historical energy power supply data of the target micro power grid.
Specifically, the first power grid related data is input into an energy supply and demand balance sub-model, and is used for analyzing the energy supply and energy demand change condition of the target micro-grid so as to better predict the energy demand and energy supply change trend. For example, a statistical analysis or machine learning method may be preset in the energy supply and demand balance sub-model, and when the first grid-related data is received, the change trend of the energy demand and the energy supply corresponding to the target micro-grid may be determined based on the statistical analysis or the machine learning method, so as to obtain a first analysis result, so as to adjust the energy supply and the energy demand in a period of time in the future of the target micro-grid based on the first analysis result.
The method has the advantage that when the first power grid related data is analyzed and processed based on the energy supply and demand balance sub-model, the balance states of the energy supply and the energy demand in the target micro-grid can be accurately analyzed by taking the changes of the energy supply and the energy demand into consideration and combining the energy balance equation in the energy supply and demand balance sub-model.
On the basis, the micro-grid energy management model comprises an energy value attribute sub-model, the energy analysis result is a second analysis result, the energy prediction result is input into the pre-constructed micro-grid energy management model to obtain an energy analysis result corresponding to the target micro-grid, and the method comprises the following steps: acquiring second power grid related data corresponding to the target micro power grid; and carrying out energy value attribute analysis on the second power grid associated data based on the energy value attribute sub-module to obtain a second analysis result.
The second power grid related data comprise basic energy value attributes, energy consumption and energy value attribute change information corresponding to the target micro power grid.
In practical applications, in the operation management of the target micro-grid, it is generally required to consider the value attribute corresponding to the target micro-grid, such as the operational expenditure value attribute of the micro-grid and the operational income value attribute of the micro-grid. Based on this, in order to maximize the economic benefit of the target micro grid, it is necessary to improve the operation income value attribute while reducing the operation expense value attribute.
Specifically, the energy value attribute sub-model is mainly used for evaluating or predicting the energy value attribute of the target micro-grid according to the second grid related data, and in the evaluation process, as many factors which can influence the energy value attribute of the target micro-grid need to be comprehensively considered from multiple dimensions as possible. For example, the factors include energy market competition factors, energy expenditure attributes (e.g., production expenditure attributes, transportation expenditure attributes, etc.) of the micro-grid, and energy management rules factors within the region of the target micro-grid, etc. Based on the energy value attribute, after the second power grid related data is input into the energy value attribute sub-model, a second analysis result can be obtained, so that the energy value attribute of the target micro-grid in a period of time in the future is priced according to the second analysis result, and the priced result is ensured to be the energy value attribute corresponding to the maximum economic benefit corresponding to the target micro-grid.
On the basis, the micro-grid energy management model comprises an energy storage sub-model, the energy analysis result is a third analysis result, the energy prediction result is input into the pre-constructed micro-grid energy management model to obtain an energy analysis result corresponding to the target micro-grid, and the method comprises the following steps: acquiring third grid related data corresponding to the target micro grid; and carrying out power analysis on the third power grid associated data based on the energy storage sub-model to obtain a third analysis result.
The third power grid related data comprise electric quantity information corresponding to an energy storage system of the target micro-grid, charging power corresponding to the energy storage system, discharging power corresponding to the energy storage system, charging efficiency corresponding to the energy storage system and discharging efficiency corresponding to the energy storage system.
Specifically, when the target micro-grid is subjected to grid analysis, relevant information of energy storage devices in the target micro-grid, such as device types, energy storage capacity, efficiency, cycle life and the like corresponding to the energy storage system, needs to be considered. Based on the above, after the third power grid related data is input into the energy storage sub-model, power analysis can be performed on the energy supply and the energy demand in the target micro-grid in combination with the power grid performance of the target micro-grid, so as to obtain a third analysis result. Further, based on the third analysis result, a charge-discharge strategy, energy storage capacity setting and the like of the energy storage system of the target micro-grid in a period of time in the future can be determined.
And S130, determining an energy scheduling scheme corresponding to the target micro-grid based on the energy analysis result.
The energy scheduling scheme comprises setting information of energy supply and energy demand corresponding to the target micro-grid, energy value attribute information, and information such as charging and discharging strategies, energy storage capacity and the like of an energy storage system of the micro-grid.
Based on the above explanation, the energy analysis results in the present technical solution include a first analysis result, a second analysis result and a third analysis result, and further, according to the energy analysis results, energy supply data and energy demand data of the micro grid corresponding to the target micro grid, energy market data, battery power data of the energy storage system and the like can be determined, so that an energy scheduling scheme corresponding to the target micro grid is determined based on the energy analysis results.
According to the technical scheme, the micro-grid energy data corresponding to the target micro-grid are obtained; inputting the energy data of the micro-grid into a pre-constructed micro-grid energy management model to obtain an energy analysis result corresponding to the target micro-grid; the micro-grid energy management model comprises at least one of an energy supply and demand balance sub-model, an energy value attribute sub-model and an energy storage sub-model; and determining an energy scheduling scheme corresponding to the target micro-grid based on the energy analysis result. In the technical scheme, after the micro-grid energy data are acquired, the micro-grid energy data are input into a pre-built micro-grid energy management model, so that data analysis can be performed on the micro-grid energy data, and the information such as energy supply data and energy demand data corresponding to a target micro-grid in a period of time in the future, energy value attributes, charging and discharging strategies corresponding to an energy storage system in the target micro-grid, energy storage capacity and the like is predicted, so that a more reasonable energy scheduling scheme is designated for the target micro-grid, and the target micro-grid achieves the effect of optimal economic benefit under the energy scheduling scheme. Meanwhile, through analysis of the energy data of the micro-grid, influence factors in the operation process of the target micro-grid are comprehensively considered from multiple dimensions, so that the operation condition of the target micro-grid is accurately predicted, the energy supply and demand balance of the target micro-grid is met, and the diversity and time variability of the user demands are met.
Example two
Fig. 3 is a flowchart of a method for determining energy scheduling of a micro-grid according to a second embodiment of the present invention.
As shown in fig. 3, the method includes:
s210, acquiring micro-grid energy data corresponding to the target micro-grid.
S220, inputting the energy data of the micro-grid into a pre-constructed micro-grid energy management model to obtain an energy analysis result corresponding to the target micro-grid.
And S230, determining an energy scheduling scheme corresponding to the target micro-grid based on the energy analysis result.
In practical application, determining an energy scheduling scheme corresponding to the target micro grid based on the energy analysis result includes: taking the energy analysis result as input data corresponding to an input layer of the convolutional neural network, and determining a loss value corresponding to the convolutional neural network; when the loss value is minimum, outputting an energy prediction result corresponding to the target micro-grid based on an output layer of the convolutional neural network; and carrying out model optimization on the micro-grid energy management model according to the energy prediction result to obtain a target processing model, and determining an energy scheduling scheme corresponding to the target micro-grid based on the target processing model.
In one specific example, convolutional neural networks include an input layer, a convolutional layer, a pooling layer, a fully-connected layer, and an output layer.
The convolution layer may be a one-dimensional convolution layer, and the number, the size, the step length, the filling mode and the like of convolution kernels in the convolution layer are set in a self-defined manner, and specifically are as follows:
y[t]＝sum(x[i]*w[t-i])
where y t represents the output data, x i represents the input data, and w t-i represents the convolution kernel.
Further, a pooling layer is added after the convolution layer to perform space dimension reduction and feature extraction. The pooling layer can use a maximum pooling pool or an average pooling layer to downsample the output data of the convolution layer, so that the feature dimension is reduced.
Wherein the maximum pooling can be expressed by the following formula:
y[t]＝max(x[i：i+k])
wherein y [ t ] is output data of the pooling layer, x [ i ]: i+k is a subsequence of input data of the pooling layer, k represents the pooling window size.
The average pooling may be expressed by the following formula:
y[t]＝mean(x[i：i+k])
wherein y [ t ] is output data of the pooling layer, x [ i ]: i+k is a subsequence of input data of the pooling layer, k represents the pooling window size.
On the basis, the output data of the pooling layer are flattened into a one-dimensional vector, and one or more fully connected layers are added, and the following formula can be seen for the concrete:
y＝activation(Wx+b)
where y represents the output of the fully connected layer, x represents the input of the fully connected layer, W represents the weight matrix, and b represents the bias vector.
Further, the number of neurons of the output layer is preset, and energy supply data, energy demand data, energy value attribute, battery charge and discharge strategy and energy storage capacity information of an energy storage system of the target micro-grid and the like of the c-standard micro-grid in a period of time in the future are predicted.
Furthermore, in order to ensure the accuracy of the output result of the convolutional neural network, the convolutional neural network further comprises a preset loss function, so that whether the convolutional neural network reaches the optimum is determined according to the magnitude of a loss value corresponding to the loss function, and when the loss value is minimum, the convolutional neural network is used as a target processing function, so that an energy scheduling scheme corresponding to the target micro-grid is determined based on the target processing function.
Specifically, the loss function may be expressed by the following formula:
wherein RMSE represents the loss value, y pred Representing the predicted value of the loss function output, y true Representing the true value of the loss function output, n representing the number of samples.
Based on the formula, the prediction accuracy of the convolutional neural network can be evaluated, and when the loss value is minimum, the accuracy is highest, namely, the convolutional neural network corresponding to the minimum loss value is used as a target processing model.
Further, determining an energy scheduling scheme corresponding to the target micro-grid based on the target processing model includes: acquiring energy supply demand data corresponding to a target micro-grid and power grid dispatching data corresponding to the target micro-grid; determining initialization model parameters corresponding to the target process model based on the energy supply demand data and the grid scheduling data; and carrying out iterative optimization on the initial model parameters based on a genetic algorithm in the target processing model to obtain an energy scheduling scheme corresponding to the target micro-grid.
Based on the above example, the change relation between the energy supply and the demand can be determined according to the energy supply data and the energy demand data corresponding to the target micro-grid, so as to determine the time range and the time interval of the scheduling management.
Specifically, the genetic algorithm parameters related to the distributed energy sources in the target micro-grid system are initialized. (i.e., initial model parameters) and randomly generating an initial set of energy scheduling schemes from any subset of the parameters in the initialization set. Further, a genetic algorithm is deployed in advance in the target processing model, any subset parameter related function is optimized and solved based on the genetic algorithm, the quality degree of each individual is evaluated, and on the basis, a part of individuals are selected from the current population corresponding to the initial energy scheduling scheme to serve as parents for subsequent crossing and mutation operations. The interleaving operation is to use multi-point interleaving operation to perform interleaving treatment on the parent individuals to generate new offspring individuals. The mutation operation refers to mutation of sub-individuals, and a certain degree of randomness is introduced, so that a new population can be generated through selection operation, crossover operation and mutation operation.
On the basis, the initial energy scheduling scheme is subjected to continuous iterative optimization until the preset iterative times are reached, and according to the evaluation of the fitness function in the genetic algorithm, the individual corresponding to the highest fitness is taken as the optimal individual, and then the energy scheduling scheme corresponding to the optimal individual is taken as the energy scheduling scheme corresponding to the target micro-grid.
According to the technical scheme, the micro-grid energy data corresponding to the target micro-grid are obtained; inputting the energy data of the micro-grid into a pre-constructed micro-grid energy management model to obtain an energy analysis result corresponding to the target micro-grid; the micro-grid energy management model comprises at least one of an energy supply and demand balance sub-model, an energy value attribute sub-model and an energy storage sub-model; and determining an energy scheduling scheme corresponding to the target micro-grid based on the energy analysis result. In the technical scheme, after the micro-grid energy data are acquired, the micro-grid energy data are input into a pre-built micro-grid energy management model, so that data analysis can be performed on the micro-grid energy data, and the information such as energy supply data and energy demand data corresponding to a target micro-grid in a period of time in the future, energy value attributes, charging and discharging strategies corresponding to an energy storage system in the target micro-grid, energy storage capacity and the like is predicted, so that a more reasonable energy scheduling scheme is designated for the target micro-grid, and the target micro-grid achieves the effect of optimal economic benefit under the energy scheduling scheme. Meanwhile, through analysis of the energy data of the micro-grid, influence factors in the operation process of the target micro-grid are comprehensively considered from multiple dimensions, so that the operation condition of the target micro-grid is accurately predicted, the energy supply and demand balance of the target micro-grid is met, and the diversity and time variability of the user demands are met.
Example III
Fig. 4 is a schematic structural diagram of a determining device for energy scheduling of a micro-grid according to a third embodiment of the present invention. As shown in fig. 4, the apparatus includes: a data acquisition module 310, a result determination module 320, and a schema determination module 330.
The data acquisition module 310 is configured to acquire micro-grid energy data corresponding to a target micro-grid;
the result determining module 320 is configured to input the energy data of the micro-grid into a pre-constructed micro-grid energy management model, so as to obtain an energy analysis result corresponding to the target micro-grid; the micro-grid energy management model comprises at least one of an energy supply and demand balance sub-model, an energy value attribute sub-model and an energy storage sub-model;
the scheme determining module 330 is configured to determine an energy scheduling scheme corresponding to the target micro grid based on the energy analysis result.
According to the technical scheme, the micro-grid energy data corresponding to the target micro-grid are obtained; inputting the energy data of the micro-grid into a pre-constructed micro-grid energy management model to obtain an energy analysis result corresponding to the target micro-grid; the micro-grid energy management model comprises at least one of an energy supply and demand balance sub-model, an energy value attribute sub-model and an energy storage sub-model; and determining an energy scheduling scheme corresponding to the target micro-grid based on the energy analysis result. In the technical scheme, after the micro-grid energy data are acquired, the micro-grid energy data are input into a pre-built micro-grid energy management model, so that data analysis can be performed on the micro-grid energy data, and the information such as energy supply data and energy demand data corresponding to a target micro-grid in a period of time in the future, energy value attributes, charging and discharging strategies corresponding to an energy storage system in the target micro-grid, energy storage capacity and the like is predicted, so that a more reasonable energy scheduling scheme is designated for the target micro-grid, and the target micro-grid achieves the effect of optimal economic benefit under the energy scheduling scheme. Meanwhile, through analysis of the energy data of the micro-grid, influence factors in the operation process of the target micro-grid are comprehensively considered from multiple dimensions, so that the operation condition of the target micro-grid is accurately predicted, the energy supply and demand balance of the target micro-grid is met, and the diversity and time variability of the user demands are met.
Optionally, the result determining module includes: the first acquisition unit is used for acquiring first power grid related data corresponding to the target micro power grid when the micro power grid energy management model comprises an energy supply and demand balance sub-model and the energy analysis result is a first analysis result; the first power grid related data comprises at least one of micro-grid energy generation amount, micro-grid energy demand amount, micro-grid energy storage amount, micro-grid energy inlet amount and micro-grid energy outlet amount;
and the first analysis result determining unit is used for carrying out energy balance analysis on the first power grid related data based on the energy supply and demand balance sub-model to obtain a first analysis result.
Optionally, the result determining module includes: the second acquisition unit is used for acquiring second power grid related data corresponding to the target micro power grid when the micro power grid energy management model comprises an energy value attribute sub-model and the energy analysis result is a second analysis result; the second power grid related data comprise basic energy value attributes, energy consumption and energy value attribute change information corresponding to the target micro power grid;
and the second analysis result determining unit is used for carrying out energy value attribute analysis on the second power grid associated data based on the energy value attribute sub-module to obtain a second analysis result.
Optionally, the result determining module includes: the third acquisition unit is used for acquiring third power grid related data corresponding to the target micro power grid when the micro power grid energy management model comprises an energy storage sub-model and the energy analysis result is a third analysis result; the third power grid related data comprise electric quantity information corresponding to an energy storage system of the target micro-grid, charging power corresponding to the energy storage system, discharging power corresponding to the energy storage system, charging efficiency corresponding to the energy storage system and discharging efficiency corresponding to the energy storage system;
and the third analysis result determining unit is used for carrying out power analysis on the third power grid associated data based on the energy storage sub-model to obtain a third analysis result.
Optionally, the scheme determining module includes: the loss value determining unit is used for taking the energy analysis result as input data corresponding to the input layer of the convolutional neural network and determining a loss value corresponding to the convolutional neural network;
the prediction result determining unit is used for outputting an energy prediction result corresponding to the target micro-grid based on an output layer of the convolutional neural network when the loss value is minimum;
and the scheme determining unit is used for carrying out model optimization on the micro-grid energy management model according to the energy prediction result to obtain a target processing model, and determining an energy scheduling scheme corresponding to the target micro-grid based on the target processing model.
Optionally, the scheme determining unit includes: the data acquisition subunit is used for acquiring the energy supply demand data corresponding to the target micro-grid and the power grid dispatching data corresponding to the target micro-grid;
a parameter determination subunit configured to determine an initialization model parameter corresponding to the target processing model based on the energy supply demand data and the grid scheduling data;
and the scheme determining subunit is used for carrying out iterative optimization on the initial model parameters based on a genetic algorithm in the target processing model to obtain an energy scheduling scheme corresponding to the target micro-grid.
The determining device for the micro-grid energy scheduling provided by the embodiment of the invention can execute the determining method for the micro-grid energy scheduling provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the executing method.
Example IV
Fig. 5 shows a schematic structural diagram of the electronic device 10 of the embodiment of the present invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the determination of the microgrid energy schedule.
In some embodiments, the method of determining the microgrid energy schedule may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method of determining a microgrid energy schedule described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the method of determining the microgrid energy schedule in any other suitable way (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
The computer program for implementing the method of determining the microgrid energy schedule of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method for determining a microgrid energy schedule, comprising:
acquiring micro-grid energy data corresponding to a target micro-grid;
inputting the energy data of the micro-grid into a pre-constructed micro-grid energy management model to obtain an energy analysis result corresponding to the target micro-grid; the micro-grid energy management model comprises at least one of an energy supply and demand balance sub-model, an energy value attribute sub-model and an energy storage sub-model;
And determining an energy scheduling scheme corresponding to the target micro-grid based on the energy analysis result.
2. The method of claim 1, wherein the microgrid energy management model comprises an energy supply and demand balance sub-model, the energy analysis result is a first analysis result, the inputting the energy prediction result into a pre-built microgrid energy management model to obtain an energy analysis result corresponding to the target microgrid comprises:
acquiring first grid-related data corresponding to the target micro grid; the first power grid related data comprises at least one of micro-grid energy generation amount, micro-grid energy demand amount, micro-grid energy storage amount, micro-grid energy inlet amount and micro-grid energy outlet amount;
and carrying out energy balance analysis on the first power grid related data based on the energy supply and demand balance sub-model to obtain the first analysis result.
3. The method of claim 1, wherein the microgrid energy management model comprises an energy value attribute sub-model, the energy analysis result is a second analysis result, the inputting the energy prediction result into a pre-built microgrid energy management model to obtain an energy analysis result corresponding to the target microgrid comprises:
Acquiring second power grid related data corresponding to the target micro power grid; the second power grid related data comprise basic energy value attributes, energy consumption and energy value attribute change information corresponding to the target micro power grid;
and carrying out energy value attribute analysis on the second power grid associated data based on the energy value attribute sub-module to obtain the second analysis result.
4. The method of claim 1, wherein the microgrid energy management model comprises an energy storage sub-model, the energy analysis result is a third analysis result, the inputting the energy prediction result into a pre-built microgrid energy management model to obtain an energy analysis result corresponding to the target microgrid comprises:
acquiring third grid related data corresponding to the target micro grid; the third power grid related data comprise electric quantity information corresponding to an energy storage system of the target micro-grid, charging power corresponding to the energy storage system, discharging power corresponding to the energy storage system, charging efficiency corresponding to the energy storage system and discharging efficiency corresponding to the energy storage system;
and carrying out power analysis on the third power grid related data based on the energy storage sub-model to obtain a third analysis result.
5. The method of claim 1, wherein the determining an energy scheduling scheme corresponding to the target microgrid based on the energy analysis results comprises:
taking the energy analysis result as input data corresponding to an input layer of the convolutional neural network, and determining a loss value corresponding to the convolutional neural network;
when the loss value is minimum, outputting an energy prediction result corresponding to the target micro-grid based on an output layer of the convolutional neural network;
and carrying out model optimization on the micro-grid energy management model according to the energy prediction result to obtain a target processing model, and determining an energy scheduling scheme corresponding to the target micro-grid based on the target processing model.
6. The method of claim 5, wherein the determining an energy scheduling scheme corresponding to the target microgrid based on the target process model comprises:
acquiring energy supply demand data corresponding to the target micro-grid and power grid dispatching data corresponding to the target micro-grid;
determining initialization model parameters corresponding to the target process model based on the energy supply demand data and the grid scheduling data;
And carrying out iterative optimization on the initial model parameters based on a genetic algorithm in the target processing model to obtain an energy scheduling scheme corresponding to the target micro-grid.
7. A determining apparatus for energy scheduling of a micro-grid, comprising:
the data acquisition module is used for acquiring micro-grid energy data corresponding to the target micro-grid;
the result determining module is used for inputting the energy data of the micro-grid into a pre-constructed micro-grid energy management model to obtain an energy analysis result corresponding to the target micro-grid; the micro-grid energy management model comprises at least one of an energy supply and demand balance sub-model, an energy value attribute sub-model and an energy storage sub-model;
and the scheme determining module is used for determining an energy scheduling scheme corresponding to the target micro-grid based on the energy analysis result.
8. The apparatus of claim 7, wherein the scheme determination module comprises:
the loss value determining unit is used for taking the energy analysis result as input data corresponding to an input layer of the convolutional neural network and determining a loss value corresponding to the convolutional neural network;
A prediction result determining unit, configured to output an energy prediction result corresponding to the target micro-grid based on an output layer of the convolutional neural network when the loss value is minimum;
and the scheme determining unit is used for carrying out model optimization on the micro-grid energy management model according to the energy prediction result to obtain a target processing model, and determining an energy scheduling scheme corresponding to the target micro-grid based on the target processing model.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of determining a microgrid energy schedule of any one of claims 1-7.
10. A computer readable storage medium storing computer instructions for causing a processor to perform the method of determining a microgrid energy schedule according to any one of claims 1-7.
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