CN113516268A - Hybrid energy network strategy application system based on multi-energy complementary energy hub - Google Patents

Hybrid energy network strategy application system based on multi-energy complementary energy hub Download PDF

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CN113516268A
CN113516268A CN202010278063.9A CN202010278063A CN113516268A CN 113516268 A CN113516268 A CN 113516268A CN 202010278063 A CN202010278063 A CN 202010278063A CN 113516268 A CN113516268 A CN 113516268A
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李俊辉
周海明
韩笑
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
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China Electric Power Research Institute Co Ltd CEPRI
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Abstract

The invention relates to a hybrid energy network strategy application system based on a multi-energy complementary energy hub, which comprises the following steps: the system comprises a network construction unit, a data acquisition unit, an interface transmission unit, a data storage unit, a hybrid energy management unit, a comprehensive effect evaluation unit, a strategy application unit and an actual effect detection unit. The multi-layer architecture is adopted to realize the parameter standard distribution of the comprehensive energy system, so that the parameters are better utilized, the number of memories is reduced, the input data of the energy comprehensive system and the nonlinear processing of the parameters are increased, and the comprehensive energy system is convenient to apply a better strategy in the real-time operation process.

Description

Hybrid energy network strategy application system based on multi-energy complementary energy hub
Technical Field
The invention relates to the technical field of energy Internet, in particular to a hybrid energy network strategy application system based on a multi-energy complementary energy hub.
Background
The global economy is rapidly developed, the energy crisis and the environmental crisis brought by the large-scale development and utilization of fossil energy are prominent, the industrial civilization built on the basis of the traditional utilization mode of the fossil energy gradually falls into the predicament, and the sustainable development of human beings is threatened. Therefore, the development of low-carbon cities has led to an increasingly strong call for the construction of distributed energy systems and the development of energy planning in urban areas.
At present, complementarity among various energy sources is utilized in a plurality of places to realize multi-energy source complementation, and the aim is to realize fusion, coordination and linkage of a plurality of energy sources so as to promote the comprehensive utilization rate of the energy sources and the capability of renewable energy consumption. The energy hub in the energy internet has various forms and complex scenes, has different forms of energy hub systems such as a cold, heat and electricity combined supply system, a solar energy and methane power supply hybrid system and the like, has different application scenes such as a garden, a smart ecological city, a living community and the like, and is one of key difficult problems of the energy internet multi-energy complementary system in terms of effective carbon emission and energy efficiency comprehensive monitoring and evaluation.
Aiming at the characteristics of various forms, complex scenes, multi-target constraint and the like of an energy internet multi-energy complementary hub system, the existing methods for solving the difficult problems mainly comprise two categories: one class is based on fundamental physical modeling methods and the other is based on data-driven methods. The accuracy of the physical model method mainly depends on the domain knowledge and modeling accuracy of an actual energy hub system, but the comprehensive utilization efficiency and renewable energy consumption of the energy hub are also influenced by factors such as the climate environment of an application area, the use scale, the user demand response and the like. The data-driven method is mainly based on the analysis of the real-time acquired data by the system in the actual operation process, and the parameters of the multi-energy complementary energy hub system are dynamically planned and dynamically adjusted, so that the method has high flexibility and universality, and has high applicability in project practice. With the development of the evolution technology, modern heuristic algorithms such as genetic algorithm, ant colony algorithm, particle swarm optimization algorithm, artificial neural network algorithm and the like arouse the research interest of people and show great application potential.
However, in the process of constructing the multi-energy comprehensive system by the energy internet, a scientific assessment and monitoring method for the systematicness of the multi-energy comprehensive system is lacked, and the method can be used for effectively managing the real-time operation process of the comprehensive energy system, facilitating the optimization of system parameter configuration and promoting the real-time online multi-target optimization of the comprehensive energy system aiming at the characteristics of various forms, complex scenes, multi-target constraints and the like of the energy internet multi-energy complementary system.
Disclosure of Invention
The invention provides a hybrid energy network strategy application system based on a multi-energy complementary energy hub from the perspective of a deep neural network by utilizing the characteristics of strong universality and wide extension scale of the neural network, the deep bidirectional long-short memory neural network based on the long-short memory neural network designed by the invention is an important branch of the development of a recurrent neural network, the phenomena of gradient explosion and gradient diffusion in an RNN neural network are relieved by skillfully designing a memory unit, an input gate, a forgetting gate and an output gate, and the hybrid energy network strategy application system has stronger generalization capability and long-time memory capability and can accurately realize the dynamic comprehensive management evaluation and the online dynamic system parameter optimization configuration of the multi-energy complementary hub of an energy internet.
A hybrid energy network strategy application system based on a multi-energy complementary energy hub is characterized by comprising:
the network construction unit is used for constructing a micro energy network of the multi-energy complementary energy hub;
the data acquisition unit is used for acquiring various required data information, wherein the various data comprise energy input data and load output data of the multi-energy complementary energy hub micro-energy network;
the interface transmission unit is used for configuring interface types corresponding to different data acquisition and transmission;
the data storage unit is used for receiving and storing the acquired energy input data and load output data and preprocessing the data;
the hybrid energy management unit is used for constructing a micro energy network neural network model and predicting distribution coefficients of various energy inputs corresponding to load demands;
the comprehensive energy efficiency evaluation unit is used for calculating, judging and analyzing based on the distribution coefficients input by the various energy sources and the acquired data information, predicting the comprehensive energy efficiency grade of the micro energy source network and generating an evaluation analysis report;
the strategy application unit is used for making a hybrid energy input regulation strategy according to the evaluation analysis report and applying the strategy to the multi-energy complementary energy hub micro-energy network;
and the actual effect detection unit is used for detecting the actual effect of the hybrid energy input regulation strategy.
The invention aims to solve the problem that a scientific evaluation and monitoring method for the systematicness of a multi-energy comprehensive system is lacked in the construction of the multi-energy comprehensive system by an energy internet, and aims at the characteristics of various forms, complex scenes, multi-target constraint and the like of the multi-energy complementary system of the energy internet, a data driving method is utilized to expand the memory characteristic of an LSTM neural network (long-short memory neural network), a bidirectional long-short memory neural network is realized, memory cells in the LSTM are utilized, the 'past' and 'future' information is kept, the prediction capability of the neural network in the system monitoring and evaluation process is enhanced, a deep framework of the bidirectional long-short memory neural network is realized on the basis of the bidirectional long-short memory neural network, and the parameter standard distribution of the comprehensive energy system is realized through a multilayer framework, so that the parameters are better utilized, the number of memories is reduced, and the nonlinear processing of the input data and the parameters of the energy comprehensive system is increased, therefore, the comprehensive energy system can be managed more effectively in the real-time operation process, the system parameter configuration can be optimized conveniently, and the real-time online multi-objective optimization of the comprehensive energy system is promoted.
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Embodiments of the invention are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which like reference numerals refer to similar elements and in which:
FIG. 1 is a system framework diagram of the present invention;
FIG. 2 is a deep bidirectional long-short memory neural network model according to the present invention.
Detailed Description
In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced without these specific details. The following detailed description includes references to the accompanying drawings, which form a part of the detailed description. The drawings show illustrations in accordance with exemplary embodiments. These exemplary embodiments, which are also referred to herein as "examples," are described in sufficient detail to enable those skilled in the art to practice the present subject matter. The embodiments may be combined, other embodiments may be utilized, or structural and logical changes may be made without departing from the scope of the claims. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope is defined by the appended claims and their equivalents.
As shown in fig. 1, the present invention provides a hybrid energy network policy application system based on a multi-energy complementary energy hub, including:
the network construction unit is used for constructing a micro energy network of the multi-energy complementary energy hub;
the data acquisition unit is used for acquiring various required data information, wherein the various data comprise energy input data and load output data of the multi-energy complementary energy hub micro-energy network;
the interface transmission unit is used for configuring interface types corresponding to different data acquisition and transmission;
the data storage unit is used for receiving and storing the acquired energy input data and load output data and preprocessing the data;
the hybrid energy management unit is used for constructing a micro energy network neural network model and predicting distribution coefficients of various energy inputs corresponding to load demands;
the comprehensive energy efficiency evaluation unit is used for calculating, judging and analyzing based on the distribution coefficients input by the various energy sources and the acquired data information, predicting the comprehensive energy efficiency grade of the micro energy source network and generating an evaluation analysis report;
the strategy application unit is used for making a hybrid energy input regulation strategy according to the evaluation analysis report and applying the strategy to the multi-energy complementary energy hub micro-energy network;
and the actual effect detection unit is used for detecting the actual effect of the hybrid energy input regulation strategy.
Preferably, the multi-energy complementary energy hub micro-energy network constructed by the network construction unit is a solar-biogas micro-energy network combined by solar heat collection, photovoltaic power generation, a cogeneration unit and a biogas digester.
Preferably, the data storage unit preprocesses the data, and specifically includes: and prejudging the rationality of the data according to a threshold value, if the data is abnormal, rejecting the data, and otherwise, keeping the data.
Preferably, the energy input data collected by the data collection unit includes load demand variables, environmental parameters, and device node data of the micro energy network;
the environmental parameters comprise illumination conditions, environment average temperature, photovoltaic panel power temperature and unit volume methane concentration; the equipment node data is acquired by a sensor and comprises system energy input quantity, system configuration parameter quantity, energy carbon emission quantity and energy transfer efficiency.
Preferably, the load output data collected by the data collection unit includes power load output, cold load output, heat load output, and gas load output of the micro energy source network.
Preferably, the hybrid energy management unit is configured to construct a micro energy network neural network model, and predict distribution coefficients of various types of energy inputs corresponding to load demands, and specifically includes:
the sample preparation module is used for forming a micro-energy network multi-input multi-output neural network model training data sample according to the energy input data and the load output data;
the training construction module is used for determining various energy input distribution coefficients of the micro energy network, and constructing a micro energy network neural network model which accords with the energy input and load output relation of a micro energy network environment by combining the neural network model training data sample;
the prediction output module is used for predicting the output of the neural network model, namely the distribution coefficient of various energy inputs by utilizing the constructed micro energy network neural network model and inputting load requirements; and the output of the neural network model is a distribution coefficient of the load demand corresponding to various energy inputs.
Preferably, the training construction module is configured to determine various energy input distribution coefficients of the micro energy network, and construct, in combination with the neural network model training data sample, a micro energy network neural network model that conforms to an energy input and load output relationship of a micro energy network environment, and specifically includes:
the foundation construction module is used for constructing a foundation depth bidirectional long and short memory neural network model:
stacking a plurality of layers of bidirectional long and short memory neural network models to form a deep bidirectional long and short memory neural network model, and obtaining a basic formula of the deep bidirectional long and short memory neural network model:
Figure BDA0002445494090000041
Figure BDA0002445494090000042
Figure BDA0002445494090000043
Figure BDA0002445494090000051
Figure BDA0002445494090000052
Figure BDA0002445494090000053
wherein the content of the first and second substances,
Figure BDA0002445494090000054
g，
Figure BDA0002445494090000055
respectively representing the output values of an input gate, a forgetting gate, a current input unit state transition and an output gate in the 1 st layer of the neural network at the time of t;
Figure BDA0002445494090000056
respectively representing the weight matrixes of an input gate, a forgetting gate, a current input unit state transition gate and an output gate in the 1 st layer of the neural network;
Figure BDA0002445494090000057
respectively representing bias items of an input gate, a forgetting gate, a current input unit state and an output gate in the layer 1 of the neural network;
Figure BDA0002445494090000058
respectively representing the states of a hidden layer of a current layer and a hidden layer of a previous layer in the layer 1 of the neural network;
Figure BDA0002445494090000059
respectively representing the states of a cell pre-layer and a current layer in the 1 st layer of the neural network;
in the deep bidirectional long and short memory neural network, the first layer takes characteristic data as input, and the input of each other layer is the output of the previous layer;
the characteristic data comprises power grid output power, solar energy output power and methane output power;
where, σ is the activation function,
Figure BDA00024454940900000510
characteristic data input at the time t for the first layer of the neural network;
the forward-broadcast construction module is used for constructing a forward-propagation multilayer long and short memory neural network model:
Figure BDA00024454940900000511
Figure BDA00024454940900000512
Figure BDA00024454940900000513
Figure BDA00024454940900000514
Figure BDA00024454940900000515
Figure BDA00024454940900000516
the back-broadcast construction module is used for constructing a back-propagation multilayer long and short memory neural network model:
Figure BDA0002445494090000061
Figure BDA0002445494090000062
Figure BDA0002445494090000063
Figure BDA0002445494090000064
Figure BDA0002445494090000065
Figure BDA0002445494090000066
wherein, the arrow → represents the forward propagation of the multi-layer long and short memory neural network to obtain the output value
Figure BDA0002445494090000067
Arrow ← representing backward propagation of multilayer long-and-short memory neural networks to obtain output value
Figure BDA0002445494090000068
A combined construction module for transmitting the output values of the forward-propagation multi-layer long-short memory neural network model and the backward-propagation multi-layer long-short memory neural network model
Figure 2
And
Figure 1
combining and constructing a final deep bidirectional long and short memory neural network model, namely micro-energyAnd (3) obtaining an output result by the source network neural network model:
Figure BDA00024454940900000611
wherein the deep bidirectional long-short memory neural network model outputs stIs the distribution coefficient of various energy inputs corresponding to the load demand. The deep bidirectional long and short memory neural network model output stThe load demand can be obtained by training the neural network through characteristic data input and load demand historical data
Figure BDA00024454940900000612
The weight value is the distribution coefficient of various energy power inputs corresponding to the W weight value.
A deep two-way long and short memory neural network model is built by using Google Tensflow, a dropout (random inactivation) value is set to be 0.5, the batch _ size of a training set is set to be 32, the batch-size of a testing set is set to be 64, the dimensionality of a hidden state in the front-back direction in Bi-LSTM is 100, the training learning rate is 0.001, an Adam (adaptive moment estimation) optimizer is adopted, and all training data are disordered before training of each round.
Through the simulation comparison test of CNN, LSTM and Deep Bi-LSTM neural networks, model evaluation is carried out by adopting recall rate, accuracy and F1 value, and the experimental results are compared as follows:
accuracy (%) Recall (%) F value (%)
CNN 86.93 85.38 86.79
LSTM 87.28 86.12 86.63
Deep Bi-LSTM 92.43 89.67 90.89
The experimental comparison result shows that the Deep bidirectional long and short memory neural network model based on Deep Bi-LSTM is better than the CNN and LSTM methods as a whole, because the Deep bidirectional long and short memory neural network model can acquire the characteristics from the sequence to the front and the back, more comprehensive sequence knowledge information can be acquired, and meanwhile, the Deep bidirectional long and short memory neural network model is constructed by combining with the Deep neural network, so that the model can acquire the sequence characteristics better.
The invention solves the problem that a scientific evaluation and monitoring method for the systematicness of a multi-energy comprehensive system is lacked in the process of constructing the multi-energy comprehensive system by an energy internet, and aims at the characteristics of various forms, complex scenes, multi-target constraint and the like of the multi-energy complementary system of the energy internet, a data driving method is utilized to expand the memory characteristic of an LSTM neural network (long-short memory neural network), a bidirectional long-short memory neural network is realized, memory cells in the LSTM are utilized, the information of 'past' and 'future' is kept, the prediction capability of the neural network in the process of system monitoring and evaluation is enhanced, a deep framework of the bidirectional long-short memory neural network is realized on the basis of the bidirectional long-short memory neural network, and the standardized distribution of the parameters of the comprehensive energy system is realized through a multilayer framework, so that the parameters are better utilized, the number of memories is reduced, and the nonlinear processing of the input data and the parameters of the energy comprehensive system is increased, therefore, the comprehensive energy system can be monitored more effectively in the real-time operation process, the system parameter configuration can be optimized conveniently, and the real-time online multi-target optimization of the comprehensive energy system is promoted.
While embodiments have been described with reference to specific exemplary embodiments thereof, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader spirit and scope of the inventive subject matter. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (7)

1. A hybrid energy network strategy application system based on a multi-energy complementary energy hub is characterized by comprising:
the network construction unit is used for constructing a micro energy network of the multi-energy complementary energy hub;
the data acquisition unit is used for acquiring various required data information, wherein the various data comprise energy input data and load output data of the multi-energy complementary energy hub micro-energy network;
the interface transmission unit is used for configuring interface types corresponding to different data acquisition and transmission;
the data storage unit is used for receiving and storing the acquired energy input data and load output data and preprocessing the data;
the hybrid energy management unit is used for constructing a micro energy network neural network model and predicting distribution coefficients of various energy inputs corresponding to load demands;
the comprehensive energy efficiency evaluation unit is used for calculating, judging and analyzing based on the distribution coefficients input by the various energy sources and the acquired data information, predicting the comprehensive energy efficiency grade of the micro energy source network and generating an evaluation analysis report;
the strategy application unit is used for making a hybrid energy input regulation strategy according to the evaluation analysis report and applying the strategy to the multi-energy complementary energy hub micro-energy network;
and the actual effect detection unit is used for detecting the actual effect of the hybrid energy input regulation strategy.
2. The system of claim 1, wherein the multi-energy complementary energy hub micro-energy network constructed by the network construction unit is a solar-biogas micro-energy network combined by solar heat collection, photovoltaic power generation, cogeneration units and biogas digesters.
3. The system of claim 1, wherein the data storage unit preprocesses the data, specifically comprising: and prejudging the rationality of the data according to a threshold value, if the data is abnormal, rejecting the data, and otherwise, keeping the data.
4. The system of claim 1, wherein the energy input data collected by the data collection unit includes load demand variables, environmental parameters, and device node data of the micro energy network;
the environmental parameters comprise illumination conditions, environment average temperature, photovoltaic panel power temperature and unit volume methane concentration; the equipment node data is acquired by a sensor and comprises system energy input quantity, system configuration parameter quantity, energy carbon emission quantity and energy transfer efficiency.
5. The system of claim 1, wherein the load output data collected by the data collection unit includes power load output, cold load output, heat load output, gas load output of the micro energy network.
6. The system according to any one of claims 1 to 5, wherein the hybrid energy management unit is configured to construct a micro energy network neural network model, and predict distribution coefficients of various types of energy inputs corresponding to load demands, and specifically includes:
the sample preparation module is used for forming a micro-energy network multi-input multi-output neural network model training data sample according to the energy input data and the load output data;
the training construction module is used for determining various energy input distribution coefficients of the micro energy network, and constructing a micro energy network neural network model which accords with the energy input and load output relation of a micro energy network environment by combining the neural network model training data sample;
the prediction output module is used for predicting the output of the neural network model, namely the distribution coefficient of various energy inputs by utilizing the constructed micro energy network neural network model and inputting load requirements; and the output of the neural network model is a distribution coefficient of the load demand corresponding to various energy inputs.
7. The system of claim 6, wherein the training construction module is configured to determine distribution coefficients of various types of energy inputs to the micro energy network, and construct a micro energy network neural network model that conforms to a relationship between an energy input and a load output of a micro energy network environment by combining the neural network model training data samples, and specifically includes:
the foundation construction module is used for constructing a foundation depth bidirectional long and short memory neural network model:
stacking a plurality of layers of bidirectional long and short memory neural network models to form a deep bidirectional long and short memory neural network model, and obtaining a basic formula of the deep bidirectional long and short memory neural network model:
Figure FDA0002445494080000021
Figure FDA0002445494080000022
Figure FDA0002445494080000023
Figure FDA0002445494080000024
Figure FDA0002445494080000025
Figure FDA0002445494080000026
wherein the content of the first and second substances,
Figure FDA0002445494080000027
g，
Figure FDA0002445494080000028
respectively representing the output values of an input gate, a forgetting gate, a current input unit state transition and an output gate in the first layer of the neural network at the moment t;
Figure FDA0002445494080000029
respectively representing the weight matrixes of an input gate, a forgetting gate, a current input unit state transition gate and an output gate in the first layer of the neural network;
Figure FDA0002445494080000031
respectively representing bias items of an input gate, a forgetting gate, a current input unit state and an output gate in the first layer of the neural network;
Figure FDA0002445494080000032
respectively representing the states of a hidden layer of a current layer and a hidden layer of a previous layer in the first layer of the neural network;
Figure FDA0002445494080000033
respectively representing the states of a cell pre-layer and a current layer in the l layer of the neural network;
in the deep bidirectional long and short memory neural network, the first layer takes characteristic data as input, and the input of each other layer is the output of the previous layer;
the characteristic data comprises power grid output power, solar energy output power and methane output power;
where, σ is the activation function,
Figure FDA0002445494080000034
characteristic data input at the time t for the first layer of the neural network;
the forward-broadcast construction module is used for constructing a forward-propagation multilayer long and short memory neural network model:
Figure FDA0002445494080000035
Figure FDA0002445494080000036
Figure FDA0002445494080000037
Figure FDA0002445494080000038
Figure FDA0002445494080000039
Figure FDA00024454940800000310
the back-broadcast construction module is used for constructing a back-propagation multilayer long and short memory neural network model:
Figure FDA00024454940800000311
Figure FDA00024454940800000312
Figure FDA00024454940800000313
Figure FDA00024454940800000314
Figure FDA00024454940800000315
Figure FDA0002445494080000041
wherein, the arrow → represents the forward propagation of the multi-layer long and short memory neural network to obtain the output value
Figure FDA0002445494080000042
Arrow ← representing backward propagation of multilayer long-and-short memory neural networks to obtain output value
Figure FDA0002445494080000043
A combined construction module for transmitting the output values of the forward-propagation multi-layer long-short memory neural network model and the backward-propagation multi-layer long-short memory neural network model
Figure FDA0002445494080000044
And
Figure FDA0002445494080000045
and (3) combining, constructing a final deep bidirectional long and short memory neural network model, namely a micro energy network neural network model, and obtaining an output result:
Figure FDA0002445494080000046
wherein the deep bidirectional long-short memory neural network model outputs stIs the distribution coefficient of various energy inputs corresponding to the load demand.
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