CN113408924B - Planning method of park comprehensive energy system based on statistical machine learning - Google Patents
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Abstract
The invention discloses a planning method of a park comprehensive energy system based on statistical machine learning, which comprises the following steps: and acquiring historical meteorological data, carrying out tide calculation on the power generation power of the distributed photovoltaic power supply, the power generation power of the thermodynamic system and the gas supply power of the natural gas system to obtain an operation state variable of the park comprehensive energy system, establishing a target function according to the operation state variable, setting constraint conditions, and carrying out iterative solution on a planning problem formed by the target function and the constraint conditions to obtain a distributed photovoltaic power supply site-selection volume-fixing planning scheme. According to the planning method, the influence of the multi-energy flow associated coupling characteristics of distributed photovoltaic power supply and power load, building heating thermal load and natural gas load fluctuation on the safe operation of the park comprehensive energy system is considered, and the probability energy flow of the park comprehensive energy system is calculated by constructing a fine scene, so that the comprehensive utilization efficiency of energy is greatly improved.
Description
Technical Field
The invention relates to the field of comprehensive energy system operation and analysis, in particular to a planning method of a park comprehensive energy system based on statistical machine learning and a computer readable storage medium.
Background
With the wide access of renewable energy sources, the park comprehensive energy system has more and more uncertainty and complexity, and difficulties and challenges are brought to the safe operation of the comprehensive energy system. In order to solve the problem of uncertainty of a park comprehensive energy system, the uncertainty of new energy output needs to be accurately modeled. In addition, the randomness of renewable energy sources can also lead to the randomness of the injection load or power of the nodes of the park comprehensive energy source system, so that the probability distribution characteristics of all state variables of the system can be calculated through probability tide, and the method plays an important role in analyzing the safe and economic operation of the multi-energy source system. Modeling renewable energy uncertainty using probabilistic models in the related art has the following problems: the model has small capacity, the digital features can only capture local data features, can not fully characterize the complex high-dimensional large data features of renewable energy output, and can not meet the calculation requirements of the probability energy flow of the park comprehensive energy system containing various uncertainty related variables. Moreover, modeling renewable energy uncertainty by using a probability model in the related art will result in lower final energy system planning decision accuracy, safety condition accuracy and energy utilization efficiency.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems in the related art to some extent.
Therefore, the first object of the present invention is to provide a planning method for a park comprehensive energy system based on statistical machine learning, which combines a natural gas and cogeneration technology with photovoltaic power generation, considers the influence of a distributed photovoltaic power supply and an electric load, a building heating heat load and a natural gas load fluctuation multi-energy flow associated coupling characteristic on the safe operation of the park comprehensive energy system, calculates the probability energy flow of the park comprehensive energy system by constructing a fine scene, and can greatly improve the comprehensive utilization efficiency of energy. And the weather scene is simulated through seasonal time sequence characteristics of the temperature, a photovoltaic output scene is generated, and the accuracy of the simulation of the complex operation scene is improved. Compared with the traditional probability model, the method avoids explicitly specifying the fitting probability distribution of the random model, greatly improves the efficiency of random production simulation, and provides decision suggestions for the safe and economic operation of the park comprehensive energy system.
A second object of the present invention is to propose a computer readable storage medium.
To achieve the above objective, an embodiment of a first aspect of the present invention provides a method for planning a park comprehensive energy system based on statistical machine learning, including: acquiring historical meteorological data affecting a distributed photovoltaic power supply and a building heating load, and processing the historical meteorological data to obtain a simulation sample set; calculating the power generation power of the distributed photovoltaic power supply and the building heating load according to the simulation sample set; calculating the thermodynamic system power generation power of the park comprehensive energy system and the natural gas system gas supply power according to the building heating load; carrying out tide calculation according to the generated power of the distributed photovoltaic power supply, the generated power of the thermodynamic system and the supplied power of the natural gas system to obtain the running state variable of the park comprehensive energy system; and establishing an objective function according to the operation state variable of the park comprehensive energy system, forming a planning problem by the objective function and constraint conditions, and carrying out iterative solution on the planning problem to obtain a distributed photovoltaic power source location and volume-fixing planning scheme.
According to the planning method for the park comprehensive energy system based on statistical machine learning, historical meteorological data affecting distributed photovoltaic power sources and building heating loads are obtained, the historical meteorological data are processed to obtain a simulation sample set, then power generation power of the distributed photovoltaic power sources and the building heating loads are calculated according to the simulation sample set, the building heating loads are further calculated to obtain power generation power of a thermodynamic system and gas supply power of the park comprehensive energy system, then power flow calculation is conducted on the power generation power of the distributed photovoltaic power sources, the power generation power of the thermodynamic system and the gas supply power of the gas system to obtain operation state variables of the park comprehensive energy system, finally, an objective function is established according to the operation state variables of the park comprehensive energy system, and planning problems consisting of the objective function and constraint conditions are solved in an iterative mode to obtain a planning scheme for site selection and volume selection of the distributed photovoltaic power sources.
Therefore, the planning method of the park comprehensive energy system based on statistical machine learning combines the natural gas and heat and power combined supply technology with photovoltaic power generation, considers the influence of the multi-energy flow associated coupling characteristics of distributed photovoltaic power supply and power load, building heating heat load and natural gas load fluctuation on the safe operation of the park comprehensive energy system, calculates the probability energy flow of the park comprehensive energy system by constructing a fine scene, and can greatly improve the comprehensive utilization efficiency of energy. And the weather scene is simulated through seasonal time sequence characteristics of the temperature, a photovoltaic output scene is generated, the accuracy of the simulation of the complex operation scene is improved, the efficiency of random production simulation is greatly improved, and decision suggestions are provided for the safe and economic operation of the park comprehensive energy system.
In addition, the planning method of the park comprehensive energy system based on statistical machine learning according to the embodiment of the invention can also have the following additional technical characteristics:
alternatively, according to one embodiment of the present invention, a solar illumination intensity simulation sample set is obtained according to a shape parameter of Beta distribution and a Markov chain model, including: acquiring a static edge cumulative probability distribution function of solar illumination intensity based on the shape parameters of Beta distribution and Beta distribution; performing time sequence reconstruction on the static edge cumulative probability distribution function of the solar illumination intensity by using a Markov chain model to obtain a dynamic edge cumulative probability distribution function; and randomly generating a preset number of edge cumulative probability distribution values according to the dynamic edge cumulative probability distribution function, and substituting the edge cumulative probability distribution values into the corresponding edge cumulative probability distribution function to obtain the solar illumination intensity simulation sample set.
Optionally, according to an embodiment of the present invention, the processing the raw data set of the outdoor environment temperature by using a time series model to obtain an outdoor environment temperature simulation sample set includes: setting amplitude, frequency, phase constant and sequence items of a sinusoidal model, and processing an original data set of the outdoor environment temperature according to the sinusoidal model to obtain a fitting curve determination component of the outdoor environment temperature; obtaining random components of an outdoor environment temperature fitting curve based on white noise by using a seasonal autoregressive model and a least square method; estimating time distribution parameters by using a maximum likelihood estimation method to obtain a probability distribution function of outdoor environment temperature residual variation; and creating a time-shifting sequence matrix, determining components according to the time-shifting sequence matrix and a fitting curve of outdoor environment temperature, and obtaining a time-sequence simulation sample set of the outdoor environment temperature by using a probability distribution function of random components of the fitting curve of the outdoor environment temperature and residual variables of the outdoor environment temperature.
Optionally, according to an embodiment of the present invention, calculating the generated power of the distributed photovoltaic power source and the building heating load according to the simulation sample set includes: obtaining the power generation power of the distributed photovoltaic power supply based on the simulation sample set and the rated power of the distributed photovoltaic power supply by using a mathematical model of the photovoltaic power generation system; and calculating the building heating load based on the simulation sample set by using a building heating load mathematical model.
Optionally, according to an embodiment of the present invention, calculating the thermodynamic system generated power and the natural gas system supplied power of the park comprehensive energy system according to the building heating load includes: acquiring heat source node output heat power of the park comprehensive energy system, and calculating the power generation of the thermodynamic system by utilizing a mathematical model of a cogeneration system according to the building heating heat load and the heat source node output heat power; and acquiring a power system power flow calculation result of the park comprehensive energy system, and calculating the natural gas system gas supply power by using a mathematical model of the cogeneration system according to the heat source node output heat power and the power system power flow calculation result.
Optionally, according to an embodiment of the present invention, obtaining the heat source node output heat power of the campus integrated energy system includes: and under the heat and power cogeneration system in a heat and power working mode, calculating the flow of the thermodynamic system to obtain the heat power output by the heat source node.
Optionally, according to an embodiment of the present invention, obtaining a power system power flow calculation result of the park comprehensive energy system includes: and carrying out power flow calculation of the power system based on the generated power of the distributed photovoltaic power supply, the generated power of the thermodynamic system and the obtained power load to obtain the output electric power of the power supply node.
Optionally, according to an embodiment of the present invention, the power flow calculation is performed according to the generated power of the distributed photovoltaic power source, the generated power of the thermodynamic system, and the supplied power of the natural gas system, to obtain an operation state variable of the park comprehensive energy system, including: and calculating the flow of the natural gas system based on the generated power of the distributed photovoltaic power supply, the output electric power of the power supply node, the generated power of the thermodynamic system and the gas supply power of the natural gas system to obtain the operation state variables of the park comprehensive energy system, wherein the operation state variables comprise the node voltage of the electric power system, line tide, network loss, the heat supply temperature of the thermodynamic system, the heat return temperature, the network loss and the gas supply quantity of a pipeline of the natural gas system.
To achieve the above object, a second aspect of the present invention provides a computer-readable storage medium having stored thereon a planning program for a statistical machine learning-based park comprehensive energy system, which when executed by a processor, implements the planning method for a statistical machine learning-based park comprehensive energy system of the above embodiment.
According to the computer readable storage medium provided by the embodiment of the invention, when the stored planning program of the park comprehensive energy system based on statistical machine learning is executed, the natural gas and heat and power cogeneration technology is combined with the photovoltaic power generation, the influence of the distributed photovoltaic power supply and power load, the building heating heat load and the natural gas load fluctuation multi-energy flow associated coupling characteristic on the safe operation of the park comprehensive energy system is considered, and the probability energy flow of the park comprehensive energy system is calculated by constructing a fine scene, so that the comprehensive utilization efficiency of energy can be greatly improved. And the weather scene is simulated through seasonal time sequence characteristics of the temperature, a photovoltaic output scene is generated, the accuracy of the simulation of the complex operation scene is improved, the efficiency of random production simulation is greatly improved, and decision suggestions are provided for the safe and economic operation of the park comprehensive energy system.
Drawings
FIG. 1 is a flow chart of a method for planning a campus integrated energy system based on statistical machine learning according to one embodiment of the present invention;
FIG. 2 is a flow chart of a method for planning a campus integrated energy system based on statistical machine learning, according to one embodiment of the invention;
FIG. 3 is a flow chart of processing historical meteorological data to obtain a set of simulation samples, according to an embodiment of the present invention;
FIG. 4 is a flowchart of obtaining an outdoor ambient temperature simulation sample set from a time series model according to an embodiment of the present invention;
FIG. 5 is a flow chart of calculating the thermal system power generation of the campus integrated energy system based on building heating heat load, according to one embodiment of the invention;
fig. 6 is a flow chart for calculating the gas supply power of a natural gas system using a mathematical model of a cogeneration system, according to an embodiment of the invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments.
Figure 1 is a flow chart of a method for planning a campus integrated energy system based on statistical machine learning according to one embodiment of the present invention. As shown in fig. 1, the planning method of the park comprehensive energy system based on statistical machine learning comprises the following steps:
and S01, acquiring historical meteorological data affecting the distributed photovoltaic power supply and the building heating load, and processing the historical meteorological data to obtain a simulation sample set.
Wherein, the historical meteorological data that influences distributed photovoltaic power and building heating load includes outdoor ambient temperature, sun illumination intensity. Specifically, raw data sets of 96 points daily outdoor ambient temperature and solar illumination intensity during heating may be obtained from the earth's meteorological office. The simulation sample set comprises a solar illumination intensity simulation sample set and an outdoor environment temperature simulation sample set.
Optionally, in an embodiment of the present invention, maximum likelihood estimation is performed on an original data set of solar illumination intensity by using Beta distribution, so as to obtain shape parameters of the Beta distribution, and a solar illumination intensity simulation sample set is obtained according to the shape parameters of the Beta distribution and a markov chain model; and processing the original data set of the outdoor environment temperature by adopting a time sequence model to obtain an outdoor environment temperature simulation sample set. According to the embodiment of the invention, the weather scene is simulated through the time sequence model, the accuracy of the simulation of the complex operation scene is improved, the efficiency of random production simulation is greatly improved, and decision suggestions are provided for the safe and economic operation of the park comprehensive energy system.
Optionally, in one embodiment of the present invention, a static edge cumulative probability distribution function of solar illumination intensity is obtained according to the shape parameter of the Beta distribution and the Beta distribution; performing time sequence reconstruction on the static edge cumulative probability distribution function of the solar illumination intensity by using a Markov chain model to obtain a dynamic edge cumulative probability distribution function; and randomly generating a preset number of edge cumulative probability distribution values according to the dynamic edge cumulative probability distribution function, and substituting the edge cumulative probability distribution values into the corresponding edge cumulative probability distribution function to obtain a solar illumination intensity simulation sample set.
Alternatively, in one embodiment of the present invention, the fitted curve determination component of the outdoor environment temperature is obtained by setting the amplitude, frequency, phase constant and sequence terms of a sinusoidal model, and processing the original data set of the outdoor environment temperature according to the sinusoidal model; obtaining random components of an outdoor environment temperature fitting curve based on white noise by using a seasonal autoregressive model and a least square method; estimating time distribution parameters by using a maximum likelihood estimation method to obtain a probability distribution function of outdoor environment temperature residual variation; and creating a time-shifting sequence matrix, determining components according to the time-shifting sequence matrix and the fitting curve of the outdoor environment temperature, and obtaining a time-sequence simulation sample set of the outdoor environment temperature by using a probability distribution function of a random component of the fitting curve of the outdoor environment temperature and a residual variable of the outdoor environment temperature. Based on the weather scene simulation based on the time sequence model in the embodiment of the invention, the accuracy of the complex operation scene simulation is improved, the random production simulation efficiency is greatly improved, and decision suggestions are provided for the safe and economic operation of the park comprehensive energy system.
And S02, calculating the power generation power of the distributed photovoltaic power supply and the building heating load according to the simulation sample set.
Specifically, in one embodiment of the present invention, the generated power of the distributed photovoltaic power source is obtained based on the simulated sample set and the rated power of the distributed photovoltaic power source using a mathematical model of the photovoltaic power generation system, wherein the rated power of the distributed photovoltaic power source may be obtained from the photovoltaic power station. And calculating the heating load of the building based on the simulation sample set by using the mathematical model of the heating load of the building.
S03, calculating the thermodynamic system power generation power of the park comprehensive energy system and the natural gas system air supply power according to the building heating load.
Optionally, in one embodiment of the present invention, the mathematical model of the cogeneration system is used to calculate the power generated by the thermodynamic system by obtaining the heat source node output heat power of the campus integrated energy system and based on the building heating heat load and the heat source node output heat power; and calculating the gas supply power of the natural gas system by using a mathematical model of the cogeneration system according to the power flow calculation result of the power system of the park comprehensive energy system and the output thermal power of the heat source node.
Optionally, in an embodiment of the present invention, in the cogeneration system in the heat and power working mode, a thermodynamic system flow calculation is performed to obtain the heat source node output heat power.
Optionally, in one embodiment of the present invention, the power system trend calculation is performed based on the generated power of the distributed photovoltaic power source, the generated power of the thermodynamic system, and the obtained power load, so as to obtain the power source node output electric power. Wherein the electrical load may be counted by smart meters in the local industrial park for electrical load data during heating.
Optionally, in one embodiment of the present invention, the natural gas system flow calculation is performed based on the generated power of the distributed photovoltaic power source, the power source node output electric power, the thermodynamic system generated power and the natural gas system supply power to obtain the operation state variables of the park comprehensive energy system, where the operation state variables include the power system node voltage, the line trend, the network loss, the thermodynamic system heating temperature, the regenerative temperature, the network loss and the natural gas system pipeline supply. According to the planning method for the park comprehensive energy system based on statistical machine learning, the natural gas and heat and power cogeneration technology is combined with the photovoltaic power generation, the influence of the distributed photovoltaic power supply and power load, building heating heat load and natural gas load fluctuation multi-energy flow associated coupling characteristics on the safe operation of the park comprehensive energy system is considered, and the probability energy flow of the park comprehensive energy system is calculated by constructing a fine scene, so that the comprehensive utilization efficiency of energy can be greatly improved.
And S04, carrying out tide calculation according to the generated power of the distributed photovoltaic power supply, the generated power of the thermodynamic system and the supplied power of the natural gas system to obtain the operation state variable of the park comprehensive energy system.
S05, establishing an objective function according to the operation state variable of the park comprehensive energy system, forming a planning problem by the objective function and constraint conditions, and carrying out iterative solution on the planning problem to obtain a distributed photovoltaic power source location and volume-fixing planning scheme.
In summary, according to the planning method for the park comprehensive energy system based on statistical machine learning, the natural gas and heat and power cogeneration technology is combined with the photovoltaic power generation, the influence of the distributed photovoltaic power supply and power load, the building heating heat load and natural gas load fluctuation multi-energy flow associated coupling characteristic on the safe operation of the park comprehensive energy system is considered, and the probability energy flow of the park comprehensive energy system is calculated by constructing a fine scene, so that the comprehensive utilization efficiency of energy can be greatly improved. And the weather scene is simulated through seasonal time sequence characteristics of the temperature, a photovoltaic output scene is generated, the accuracy of the simulation of the complex operation scene is improved, the efficiency of random production simulation is greatly improved, and decision suggestions are provided for the safe and economic operation of the park comprehensive energy system.
As shown in fig. 2, an embodiment of a planning method for a campus integrated energy system based on statistical machine learning according to the present invention includes the following steps:
s10, acquiring an original data set of outdoor ambient temperature and solar illumination intensity of 96 points per day during heating from a local weather bureau, and acquiring rated power of a distributed photovoltaic power supply from a photovoltaic power station;
for example, 96 points of outdoor ambient temperature data per day throughout the heating period may be obtained from a weather data collection system of a local weather station, wherein the weather data collection system includes a plurality of sensors, a solar radiometer, an intelligent weather data collection device, and a GPRS DTU communication module. Specifically, solar radiation measuring instrument can be utilized to collect solar illumination intensity data of 96 points per day in a heating period, and the rated power data of the distributed photovoltaic power supply can be obtained through the photovoltaic power station.
S20, historical meteorological data is processed, and a simulation sample set is obtained.
As shown in fig. 3, the specific implementation steps include:
s201, performing maximum likelihood estimation on the solar illumination intensity data acquired in the S10 by using Beta distribution to obtain the shape parameters of the Beta distribution.
S202, acquiring a static edge cumulative probability distribution function of the solar illumination intensity G according to the shape parameters of the Beta distribution and the Beta distribution.
The specific formula is as follows:
wherein: alpha and Beta are shape parameters of Beta distribution, Γ is Gamma function, G max At the mostHigh light intensity.
S203, performing time sequence reconstruction on the static edge cumulative probability distribution function of the solar illumination intensity G obtained in the S202 by using a Markov transition probability matrix to obtain a dynamic edge cumulative probability distribution function of the solar illumination intensity G.
The specific formula of the Markov transition probability matrix is as follows:
P ij ＝P(X n+1 ＝s i |X n ＝s j ),s i ,s j ∈s
wherein: p (P) ij Representing the state transition probability, X, of the ith row and jth column in a Markov transition probability matrix P n Representing the current state of a variable, X n+1 Representing the next state of the variable, s is the variable state sequence, s i Representing the state of the ith variable, s j Representing the state of the j-th variable, wherein the variable includes solar illumination intensity and outdoor ambient temperature.
Calculation using the Chapman-Kolmogorov equation
Representing the transition probability of the ith row and jth column of the n+h step state transition matrix,/>Representing the transition probability of the ith row and the kth column of the n-step state transition matrix, +.>Representing the transition probability of the kth row and the jth column of the h-step state transition matrix.
S204, randomly generating a preset number of edge cumulative probability distribution values according to the dynamic edge cumulative probability distribution function of the solar illumination intensity G obtained in the S203, and substituting the edge cumulative probability distribution values into the corresponding edge cumulative probability distribution function to obtain a solar illumination intensity simulation sample set.
S30, obtaining an outdoor environment temperature simulation sample set through a time sequence model based on outdoor environment temperature data of set time.
As shown in fig. 4, the specific implementation steps include:
s301, setting amplitude, frequency, phase constant and sequence items of a sinusoidal model, and obtaining a fitting curve determining component of the outdoor environment temperature according to the outdoor environment temperature data acquired in the S10.
The specific formula is as follows:
model＝fit(x,T fit )
wherein T is fit A temperature fit curve representing a given time for a given year, x representing a vector of dates per hour, which the system needs to convert to a sequence date number, a i Represents the i-th amplitude, omega i Represents the i-th frequency, c i The phase constant representing the i-th sine wave term, n representing the sequence term, indicates that the equation parameter is calculated using a nonlinear least squares method when n=2. It should be noted that the fitted curve of the outdoor ambient temperature includes a temperature fitted curve for a given time of n given years.
S302, obtaining random components of an outdoor environment temperature fitting curve based on white noise by using a seasonal autoregressive model and a least square method.
The specific formula is as follows:
T res,k ＝a 0 +a 1 T res,k-1 +…+a p T res,k-p +ε k
wherein T is res,k Representing random component of fitting curve of outdoor environment temperature, T res,k-1 Represents the random component, T of the k-1 outdoor environment temperature fitting curve res,k-p Represents the kth-p outdoor ringRandom component epsilon of ambient temperature fitting curve k Represents the kth white noise sequence, a 0 ，a 1 ，…，a p Coefficients representing multiple linear regression are solved using the least squares method.
S303, estimating the distribution parameters of t distribution by using a maximum likelihood estimation method to obtain a probability distribution function of the outdoor environment temperature residual variable.
S304, creating a time shift sequence matrix, wherein positive hysteresis corresponds to delay, negative hysteresis corresponds to advance, determining components according to the acquired fitting curve of the outdoor environment temperature, random components of the fitting curve of the outdoor environment temperature and residual variables of the environment temperature, and calculating a time sequence simulation sample set of the outdoor environment temperature.
S40, calculating the power generated by the distributed photovoltaic power supply.
Specifically, the mathematical model of the photovoltaic power generation system is utilized, and the power generated by the distributed photovoltaic power supply is obtained according to the solar illumination intensity simulation sample set obtained in the step S204, the outdoor environment temperature simulation sample set obtained in the step S30 and the rated power of the distributed photovoltaic power supply obtained in the step S1O.
Calculated by the following formula:
wherein Y is PV To obtain rated capacity [ kW ] of photovoltaic power supply connected to power distribution network]；f PV Is the power derating factor of the photovoltaic power system; g T Is the current solar illumination intensity [ kW/-square meter ]]；G T，STC Solar light intensity [ kW/-square meter ] under standard test conditions]；T C Battery temperature [ DEGC ] for photovoltaic power supply]；T C，STC Cell temperature [ DEGC ] of photovoltaic power supply under standard test conditions]。
S50, the solar illumination intensity simulation sample set obtained in the S204 and the outdoor environment temperature simulation sample set obtained in the S30 are used as input variables to be input into a mathematical model of the building heating heat load, and the building heating heat load is obtained.
S60, calculating the power generation power of the thermodynamic system of the park comprehensive energy system according to the building heating heat load.
As shown in fig. 5, the specific implementation steps include:
s601, under the condition that the cogeneration system is in a heat and power working mode, calculating the flow of the thermodynamic system to obtain the output heat power of the heat source node.
S602, calculating the power generation power of the thermodynamic system according to the building heating heat load, the output heat power of the heat source node and the mathematical model of the cogeneration system.
And S70, calculating the gas supply power of the natural gas system by using a mathematical model of the cogeneration system according to the heat source node output heat power and the power flow calculation result of the power system.
As shown in fig. 6, the specific implementation steps include:
and S701, carrying out power flow calculation of the power system according to the power generated by the distributed photovoltaic power supply, the power generated by the thermodynamic system and obtained in S602, and the power load during the statistical heating period of each intelligent ammeter in the local industrial park, so as to obtain the output power of the power supply node.
In this embodiment, the industrial park is only used as the range to be calculated, and the specific calculation range is not limited.
S702, calculating the gas supply power of the natural gas system by using a mathematical model of the cogeneration system according to the heat source node output heat power and the power source node output electric power.
And S80, calculating the flow of the natural gas system according to the power generated by the distributed photovoltaic power supply, the power generated by the thermodynamic system and the power supplied by the natural gas system, and obtaining the running state variable results of the park comprehensive energy system, wherein the running state variable results comprise the node voltage of the power system, the line tide, the network loss, the heating temperature of the thermodynamic system, the regenerative temperature, the network loss and the air supply quantity of a pipeline of the natural gas system.
And S90, establishing an objective function according to the operation state variable of the park comprehensive energy system, and carrying out iterative solution to obtain the minimum value of the objective function as a result, thereby obtaining the distributed photovoltaic power supply location and volume-fixing planning scheme.
Specifically, the power generated by the distributed photovoltaic power supply is used as the injection power of a PV node in a power system, an objective function is established according to the network loss of the power system and the network loss of a thermodynamic system, constraint conditions are established according to the node voltage of the power system, the heat supply temperature of the thermodynamic system, the heat return temperature and the air supply quantity of a natural gas system pipeline, finally, the objective function and the constraint conditions are combined into a random programming problem, the random programming problem is subjected to iterative solution, the minimum value of the objective function is obtained as a result, and a site-selection and volume-fixation programming scheme of the park distributed photovoltaic power supply is obtained.
In summary, according to the planning method of the park comprehensive energy system based on statistical machine learning provided by the embodiment of the invention, the natural gas and heat and power cogeneration technology and the photovoltaic power generation are combined together, the influence of the multi-energy flow associated coupling characteristics of distributed photovoltaic power supply and power load, building heating heat load and natural gas load fluctuation on the safe operation of the park comprehensive energy system is considered, and the probability energy flow of the park comprehensive energy system is calculated by constructing a fine scene, so that the comprehensive utilization efficiency of energy can be greatly improved. And the weather scene is simulated through seasonal time sequence characteristics of the temperature, a photovoltaic output scene is generated, the accuracy of the simulation of the complex operation scene is improved, the efficiency of random production simulation is greatly improved, and decision suggestions are provided for the safe and economic operation of the park comprehensive energy system.
Further, the embodiment of the invention also provides a computer readable storage medium, on which a planning program of the park comprehensive energy system based on statistical machine learning is stored, and when the planning program of the park comprehensive energy system based on statistical machine learning is executed by a processor, the planning method of the park comprehensive energy system based on statistical machine learning of the embodiment is realized.
According to the computer readable storage medium provided by the embodiment of the invention, when the stored planning program of the park comprehensive energy system based on statistical machine learning is executed, the natural gas and heat and power cogeneration technology is combined with the photovoltaic power generation, the influence of the distributed photovoltaic power supply and power load, the building heating heat load and the natural gas load fluctuation multi-energy flow associated coupling characteristic on the safe operation of the park comprehensive energy system is considered, and the probability energy flow of the park comprehensive energy system is calculated by constructing a fine scene, so that the comprehensive utilization efficiency of energy can be greatly improved. And the weather scene is simulated through seasonal time sequence characteristics of the temperature, a photovoltaic output scene is generated, the accuracy of the simulation of the complex operation scene is improved, the efficiency of random production simulation is greatly improved, and decision suggestions are provided for the safe and economic operation of the park comprehensive energy system.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention 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 invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element being referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention.
Furthermore, the terms "first," "second," and the like, as used in embodiments of the present invention, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or as implying any particular number of features in the present embodiment. Thus, a feature of an embodiment of the invention that is defined by terms such as "first," "second," etc., may explicitly or implicitly indicate that at least one such feature is included in the embodiment. In the description of the present invention, the word "plurality" means at least two or more, for example, two, three, four, etc., unless explicitly defined otherwise in the embodiments.
In the present invention, unless explicitly stated or limited otherwise in the examples, the terms "mounted," "connected," and "fixed" as used in the examples should be interpreted broadly, e.g., the connection may be a fixed connection, may be a removable connection, or may be integral, and it may be understood that the connection may also be a mechanical connection, an electrical connection, etc.; of course, it may be directly connected, or indirectly connected through an intermediate medium, or may be in communication with each other, or in interaction with each other. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to specific embodiments.
In the present invention, unless expressly stated or limited otherwise, a first feature "up" or "down" a second feature may be the first and second features in direct contact, or the first and second features in indirect contact via an intervening medium. Moreover, a first feature being "above," "over" and "on" a second feature may be a first feature being directly above or obliquely above the second feature, or simply indicating that the first feature is level higher than the second feature. The first feature being "under", "below" and "beneath" the second feature may be the first feature being directly under or obliquely below the second feature, or simply indicating that the first feature is less level than the second feature.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (9)
1. A method for planning a campus integrated energy system based on statistical machine learning, comprising:
acquiring historical meteorological data affecting a distributed photovoltaic power supply and a building heating load, and processing the historical meteorological data to obtain a simulation sample set;
calculating the power generation power of the distributed photovoltaic power supply and the building heating load according to the simulation sample set;
calculating the thermodynamic system power generation power of the park comprehensive energy system and the natural gas system gas supply power according to the building heating load;
carrying out tide calculation according to the generated power of the distributed photovoltaic power supply, the generated power of the thermodynamic system and the supplied power of the natural gas system to obtain the running state variable of the park comprehensive energy system;
establishing an objective function according to the operation state variable of the park comprehensive energy system, forming a planning problem by the objective function and constraint conditions, and carrying out iterative solution on the planning problem to obtain a distributed photovoltaic power supply location and volume-fixing planning scheme;
processing the original data set of the outdoor environment temperature by adopting a time sequence model to obtain an outdoor environment temperature simulation sample set, wherein the method comprises the following steps:
setting amplitude, frequency, phase constant and sequence items of a sinusoidal model, and processing an original data set of the outdoor environment temperature according to the sinusoidal model to obtain a fitting curve determination component of the outdoor environment temperature;
obtaining random components of an outdoor environment temperature fitting curve based on white noise by using a seasonal autoregressive model and a least square method;
estimating time distribution parameters by using a maximum likelihood estimation method to obtain a probability distribution function of outdoor environment temperature residual variation;
and creating a time-shifting sequence matrix, determining components according to the time-shifting sequence matrix and a fitting curve of outdoor environment temperature, and obtaining a time-sequence simulation sample set of the outdoor environment temperature by using a probability distribution function of random components of the fitting curve of the outdoor environment temperature and residual variables of the outdoor environment temperature.
2. The method of claim 1, wherein the historical weather data comprises raw data sets of outdoor ambient temperature and solar light intensity of a campus during heating, and wherein processing the historical weather data to obtain a simulated sample set comprises:
performing maximum likelihood estimation on the original data set of the solar illumination intensity by using Beta distribution to obtain shape parameters of the Beta distribution, and obtaining a solar illumination intensity simulation sample set according to the shape parameters of the Beta distribution and a Markov chain model;
and processing the original data set of the outdoor environment temperature by adopting a time sequence model to obtain an outdoor environment temperature simulation sample set.
3. The method of claim 2, wherein obtaining the solar illumination intensity simulation sample set from the shape parameters of the Beta distribution and the markov chain model comprises:
acquiring a static edge cumulative probability distribution function of solar illumination intensity based on the shape parameters of Beta distribution and Beta distribution;
performing time sequence reconstruction on the static edge cumulative probability distribution function of the solar illumination intensity by using a Markov chain model to obtain a dynamic edge cumulative probability distribution function;
and randomly generating a preset number of edge cumulative probability distribution values according to the dynamic edge cumulative probability distribution function, and substituting the edge cumulative probability distribution values into the corresponding edge cumulative probability distribution function to obtain the solar illumination intensity simulation sample set.
4. A method according to any one of claims 1-3, wherein calculating the generated power of the distributed photovoltaic power source and the building heating load from the simulated sample set comprises:
obtaining the power generation power of the distributed photovoltaic power supply based on the simulation sample set and the rated power of the distributed photovoltaic power supply by using a mathematical model of the photovoltaic power generation system;
and calculating the building heating load based on the simulation sample set by using a building heating load mathematical model.
5. A method according to any one of claims 1-3, wherein calculating thermodynamic system generated power and natural gas system supplied power of the campus integrated energy system from the building heating load comprises:
acquiring heat source node output heat power of the park comprehensive energy system, and calculating the power generation of the thermodynamic system by utilizing a mathematical model of a cogeneration system according to the building heating heat load and the heat source node output heat power;
and acquiring a power system power flow calculation result of the park comprehensive energy system, and calculating the natural gas system gas supply power by using a mathematical model of the cogeneration system according to the heat source node output heat power and the power system power flow calculation result.
6. The method of claim 5, wherein obtaining the thermal power output by the heat source node of the campus integrated energy system comprises:
and under the heat and power cogeneration system in a heat and power working mode, calculating the flow of the thermodynamic system to obtain the heat power output by the heat source node.
7. The method of claim 6, wherein obtaining power system flow calculations for the campus integrated energy system comprises:
and carrying out power flow calculation of the power system based on the generated power of the distributed photovoltaic power supply, the generated power of the thermodynamic system and the obtained power load to obtain the output electric power of the power supply node.
8. The method of claim 7, wherein performing a tidal current calculation based on the generated power of the distributed photovoltaic power source, the generated power of the thermodynamic system, and the supplied power of the natural gas system to obtain an operational state variable of the campus integrated energy system comprises:
and calculating the flow of the natural gas system based on the generated power of the distributed photovoltaic power supply, the electric power output by the power supply node, the generated power of the thermodynamic system and the gas supply power of the natural gas system to obtain the operation state variables of the park comprehensive energy system, wherein the operation state variables comprise the node voltage of the electric power system, the line tide, the heat supply temperature of the thermodynamic system, the heat regeneration temperature, the network loss and the gas supply quantity of a pipeline of the natural gas system.
9. A computer-readable storage medium, on which a planning program for a statistical machine-learning-based campus integrated energy system is stored, which, when executed by a processor, implements the statistical machine-learning-based campus integrated energy system planning method according to any one of claims 1 to 8.
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