CN117578420B - Park electric energy management system and method based on data analysis - Google Patents
Park electric energy management system and method based on data analysis Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
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Abstract
The invention relates to the technical field of smart power grids, in particular to a park electric energy management system and method based on data analysis, comprising the following steps: the power generation system comprises a load prediction module, a power generation prediction module, a stability evaluation module, a sheet area allocation module and a power storage module, wherein the load prediction module is used for predicting the power consumption of each load device in the next period, the power generation prediction module is used for predicting the power generation capacity of each power generation device, the stability evaluation module is used for calculating the maximum error power, the sheet area allocation module is used for reallocating the load for each power generation device, the power storage module is used for calculating the energy storage quantity and storing the corresponding quantity of electric energy.
Description
Technical Field
The invention relates to the technical field of smart grids, in particular to a park electric energy management system and method based on data analysis.
Background
Administrative parks, which refer to areas that are built and managed separately from cities, are intended to provide a centralized, convenient and specialized environment to support the development and operation of businesses, organizations and institutions. A mature administrative park generally adopts a distributed power generation technology, and uses small solar energy and wind power generation equipment installed in various places such as buildings, roofs, parking lots, fields and the like to convert renewable energy into electric energy, so that self-sufficiency on the energy can be realized without depending on an external power grid.
Because of the characteristics of the distributed power generation technology, the output power of each power generation device is not balanced in time, and the electricity consumption of a park in different time is also unbalanced, which can cause the problems of unstable voltage of some areas, underpower and over-power of the device and the like, and the power supply and the power demand are difficult to match.
Although storage batteries can be used for power load balancing, miniaturized power generation devices often do not support large capacity batteries, and therefore require higher costs in storing energy. In addition, batteries that store large amounts of energy for long periods of time also present a significant safety hazard due to the openness of small power plants.
Disclosure of Invention
The invention aims to provide a park electric energy management system and method based on data analysis, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a campus power management system based on data analysis, comprising: the system comprises a load prediction module, a power generation prediction module, a stability evaluation module, a chip area allocation module and an electric power storage module;
The load prediction module is used for obtaining the historical electricity consumption of each load device in the park, fitting the electricity consumption rule of the devices by using a linear regression algorithm, and predicting the electricity consumption of each device in the next period;
The power generation prediction module is used for converting the power generation efficiency coefficient of the wind motor and the photovoltaic panel according to the power generation data of the power generation equipment in the park, further acquiring the change of the future wind speed and the illumination intensity, and predicting the power generation capacity of each equipment according to the power generation efficiency of the equipment;
the stability evaluation module is used for calculating an error coefficient between a predicted result and an actual result, evaluating the stability of the equipment power load and the stability of power generation of the power generation equipment, and calculating the maximum error electric quantity;
the on-chip area allocation module is used for reallocating the load for each power generation device through a selective switch in the intelligent power grid according to the power generation amount of the power generation device and the power consumption amount of the load device predicted in the next period;
The power storage module is used for storing surplus electric energy of the power generation equipment, and calculating the energy storage quantity, so that the battery stores as little electric energy as possible under the condition of ensuring stable operation of the load equipment in one period.
Further, the load prediction module includes: the data classifying unit and the regression fitting unit;
The data classifying unit is used for acquiring the historical electricity consumption of each load device under different scales and classifying the historical electricity consumption;
The regression fitting unit is used for predicting the electricity consumption of the load equipment in the next period by using a linear regression algorithm.
Further, the power generation prediction module includes: the system comprises an environment monitoring unit, an efficiency acquisition unit and an energy prediction unit;
The environment monitoring unit is used for monitoring the wind speed during the power generation of the wind power equipment and the illumination intensity of the photovoltaic power generation equipment during the power generation;
The efficiency acquisition unit is used for acquiring historical power generation data of each power generation device and historical environment data of the environment monitoring unit and calculating average efficiency of the power generation devices;
The energy prediction unit is used for predicting future environmental data by combining with meteorological data issued by a meteorological department, and further calculating the generated energy of each power generation device.
Further, the stability evaluation module includes: the system comprises a residual calculation unit, a stability analysis unit and a peak-valley time division unit;
the residual calculation unit is used for calculating residual errors of each data point and the regression function in the process of predicting the electricity consumption of the load equipment so as to obtain fluctuation conditions of the data points;
the stability analysis unit is used for analyzing the stability of the power consumption load according to the fluctuation condition of the data points and calculating the load range of each load device;
The peak-valley time division unit is used for calculating the electric energy consumption range of the power generation equipment according to the distribution condition of the load equipment and the load range of the load equipment;
Further, the area allocation module includes: a load distribution unit and a connection replacement unit;
The load distribution unit is used for distributing access equipment for each power generation equipment according to the predicted load of the load equipment and the predicted power generation capacity of the power generation equipment, so that the power supply task of the access equipment is borne;
The connection replacement unit is used for controlling a selective switch in the smart grid, and the load is redistributed once for each power generation device in each period.
Further, the power storage module includes: a battery unit and an electricity storage control unit;
the battery unit is used for storing redundant power of the power generation equipment;
the electric power storage control unit is used for calculating the energy storage proportion according to the data of power generation and power consumption in one period, so that the electric power storage in the storage battery is reduced under the condition that the stable operation of the load equipment is ensured.
A park electric energy management method based on data analysis comprises the following steps:
s100, acquiring the historical electricity consumption of each load electric equipment in a park, observing the periodicity of historical data, fitting the electricity consumption rule of the equipment, and predicting the electricity consumption of each equipment in the next period;
S200, converting the power generation efficiency coefficient of the wind motor and the photovoltaic panel according to the historical output power and the historical environment data of the power generation equipment in the park, acquiring the change of future wind speed and illumination intensity through a meteorological department, and predicting the power generation capacity of each equipment according to the power generation efficiency of the equipment;
s300, redistributing loads for all the power generation devices through selective switches in the intelligent power grid according to the power generation amount of the power generation devices and the power consumption amount of the load devices, which are predicted in the next period;
s400, analyzing fluctuation conditions of the power consumption data points, calculating a mean square error between a prediction result and an actual result, evaluating the stability of the power consumption load of the equipment, and calculating the maximum error power in the next period;
S500, calculating the underelectricity compensation quantity according to the electricity consumption and the generated energy, so that the electric energy storage in the battery is reduced under the condition that the stable operation of the load equipment is ensured.
Further, step S100 includes:
S101, acquiring electricity consumption of electric equipment in n time periods, wherein n is a system preset value, and recording all historical data into a data set A according to time sequence, wherein A= { a1, a2, …, an }, a1 represents the earliest record, and an represents the latest record;
s102, filtering noise in the data set A by using a wavelet transformation matrix, and calculating the average value of the data set A Said/>Where i is the data number, and the variance of data set A:
where ai represents the i-th element in dataset A;
When S 2 =0, the data in the representative dataset a is a fixed value, and the power consumption r=a1 of the electric equipment;
when S 2 +.0, the periodicity of dataset a is hypothesis tested:
Wherein a i-k represents the i-th element in the data set A, ACF is the autocorrelation coefficient of the data set, k is the assumed period, k epsilon {1,2, …, n/2}, and 0 is less than or equal to ACF and less than or equal to 1, S 2 is more than 0;
Sequentially substituting all the values which can be obtained by k into the above hypothesis test formula to calculate to obtain a value set { ACF1, ACF2, …, ACFn } of the ACF, wherein ACFn represents the nth element in the set, the maximum value of the element in the set is marked as t, and a hypothesis period k0 corresponding to the maximum value t is the electricity utilization period of the electric equipment;
S103, carrying out periodic segmentation on the data set A in sequence, segmenting every k0 data to obtain n/k0 sub-data sequences, respectively marking Y1, Y2, … and Y n/k0, and marking the number of elements in the data sequence Y n/k0 as c;
if c is not equal to k0, extracting the (c+1) th element from other sub-data sequences, and marking the extracted element as yj, wherein j is the subscript of the sub-data sequence to which the element belongs;
If c=k0, extracting the first element in other sub-data sequences, and marking the first element as yj, wherein j is the subscript of the sub-data sequence to which the element belongs;
the expected power consumption in the next period is calculated according to the following formula:
Wherein t is an autocorrelation coefficient of a period k0, and w is expected electricity consumption of the electric equipment in a next period;
and S104, repeating the steps, and calculating the expected electricity consumption of all the electric equipment in the park in the next period.
Further, step S200 includes:
Step S201, acquiring historical output power and historical environment data of power generation equipment, wherein the environment data are wind speed during power generation if the power generation equipment is a wind driven generator, and the environment data are illumination intensity during power generation if the power generation equipment is a photovoltaic generator;
multiplying the historical environment data by a conversion coefficient U preset by a system to obtain an environment variable, calculating the ratio of the output power of the power generation equipment to the environment variable at the same moment, and arranging the ratio in time sequence to form a discrete sequence T of historical efficiency;
constructing a time sequence ARIMA model, performing model training by taking a discrete sequence T as a training set, and fitting the sequence T to obtain a linear regression function of the sequence T;
obtaining the power generation efficiency L of the power generation equipment in the next period by using the linear regression function;
s202, acquiring environmental data in a next period through a meteorological department, and performing energy conversion by using a Scikit-learn tool to obtain energy U provided by the environment, wherein the expected power generation capacity Q=L.U of the power generation equipment;
And calculating the expected power generation amount of all the power generation equipment according to the steps.
Further, step S300 includes:
S301, forming a power generation distribution sequence of all power generation equipment according to the sequence from the large power generation amount Q to the small power generation amount Q;
all electric equipment forms an electricity distribution sequence according to the sequence of the expected electricity consumption W from large to small;
step S302, distributing electric equipment in the electricity distribution sequence to first generating equipment in the electricity distribution sequence in sequence, keeping the sum of expected electricity consumption not to exceed expected electricity generation capacity, distributing second generating equipment in the electricity distribution sequence after the first generating equipment is distributed, and the like until all the electric equipment in the electricity distribution sequence is distributed;
When all power generation equipment in the power generation distribution sequence is completely finished and unassigned electric equipment still exists in the power utilization distribution sequence, the unassigned electric equipment is distributed to a storage battery, and the expected electric quantity of the electric equipment is added to obtain the battery usage D;
and S303, controlling a selective switch in the intelligent power grid, and reconnecting each electric equipment to the independent power grid of the corresponding power generation equipment according to the distribution result.
Further, step S400 includes:
S401, evaluating accuracy of a prediction result:
obtaining prediction results of power generation equipment history m times, wherein m is a preset value, recording all the prediction results as a data set W according to time sequence, wherein W= { W1, W2, …, wm }, W1 represents the earliest prediction result, wm represents the latest prediction result, and the corresponding actual measurement result is recorded as a data set G, and G= { G1, G2, …, gm };
calculating root mean square error of the prediction result:
wherein σ 2 represents the root mean square error of the prediction result, gh represents the h element in the dataset G, wh represents the h element in the dataset W;
S402, constructing a probability distribution function of an actual measurement result by using a normal distribution function:
Wherein, W is a predicted value of electricity consumption in the next period, and e is a base number of natural logarithms;
S403, carrying out interval estimation on a probability distribution function of the actual measurement result, and taking a confidence interval as [ (1+t) & w, (1-t) & w ], wherein t is an autocorrelation coefficient of a period k0 obtained in the step S103;
the deviation value z of the electricity consumption of the electric equipment is:
z＝F(w)-F(t·w+w)
The estimation interval of the electricity consumption of the electric equipment is [ w-z, w+z ];
and S404, calculating the power consumption estimation interval of all the electric equipment according to the steps.
Further, step S500 includes:
Step S501, according to the distribution condition, adding the estimation intervals of the electricity consumption of all the electric equipment distributed by the power generation equipment to obtain an estimation interval [ w0-z0, w0+z0] of the total electricity consumption, wherein w0 is the sum of expected electricity consumption of all the electric equipment under the name of the power generation equipment, and z0 represents the sum of deviation values of all the electric equipment under the name of the power generation equipment;
s502, calculating the maximum underpower S of the power generation equipment, wherein if S is more than 0, the maximum underpower S is the underpower compensation quantity of the battery, and if S is less than or equal to 0, the underpower compensation is not needed;
S503, calculating surplus electric quantity S0 of the power generation equipment in the current period, storing the electric quantity S into the battery in the current period if S0 is more than or equal to S, storing all surplus electric quantity into the battery if S0 is less than S, and supplementing the electric quantity through an external power grid until the electric quantity in the battery reaches S so as to ensure stable operation of electric equipment.
Compared with the prior art, the invention has the following beneficial effects:
1. According to the intelligent power supply system and the intelligent power supply method, the power consumption of users in a park can be analyzed to predict the power consumption of the users in the next period, the intelligent power grid equipment is used for dynamically distributing the connection between each power generation equipment and the load, effective management and optimal scheduling of energy can be achieved, the operation efficiency of a power system is improved through intelligent monitoring and control, the waste of energy is reduced, the regulation and management of power requirements are further carried out according to the relation between the power generation equipment and the power supply equipment, the balance of power supply and demand is achieved, and the load pressure of a power grid is reduced.
2. According to the method, the power generation efficiency coefficient of the wind motor and the photovoltaic panel can be converted according to the power generation data of the power generation equipment in the park, the power generation capacity of each equipment is calculated according to the change of the future wind speed and the illumination intensity, the stability of the future wind speed and the illumination is evaluated, the data error caused by the power generation efficiency difference of different power generation equipment is avoided, and the accuracy of a prediction algorithm is ensured.
3. According to the invention, on the premise of ensuring stable electricity consumption, the electricity consumption stability analysis mode is adopted to reduce the storage capacity of the storage battery as much as possible, reduce the storage cost, avoid potential safety hazards caused by a large amount of storage, realize automatic storage regulation of the power system and improve the safety and reliability of the power supply system.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of a system for campus power management based on data analysis according to the present invention;
fig. 2 is a schematic diagram of steps of a method for managing power of a campus based on data analysis according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention provides the following technical solutions:
A campus power management system based on data analysis, comprising: the system comprises a load prediction module, a power generation prediction module, a stability evaluation module, a chip area allocation module and an electric power storage module;
The load prediction module is used for obtaining the historical electricity consumption of each load device in the park, fitting the electricity consumption rule of the devices by using a linear regression algorithm, and predicting the electricity consumption of each device in the next period;
The load prediction module includes: the data classifying unit and the regression fitting unit;
The data classifying unit is used for acquiring the historical electricity consumption of each load device under different scales and classifying the historical electricity consumption;
The regression fitting unit is used for predicting the electricity consumption of the load equipment in the next period by using a linear regression algorithm.
The power generation prediction module is used for converting the power generation efficiency coefficient of the wind motor and the photovoltaic panel according to the power generation data of the power generation equipment in the park, further acquiring the change of the future wind speed and the illumination intensity, and predicting the power generation capacity of each equipment according to the power generation efficiency of the equipment;
The power generation prediction module includes: the system comprises an environment monitoring unit, an efficiency acquisition unit and an energy prediction unit;
The environment monitoring unit is used for monitoring the wind speed during the power generation of the wind power equipment and the illumination intensity of the photovoltaic power generation equipment during the power generation;
The efficiency acquisition unit is used for acquiring historical power generation data of each power generation device and historical environment data of the environment monitoring unit and calculating average efficiency of the power generation devices;
The energy prediction unit is used for predicting future environmental data by combining with meteorological data issued by a meteorological department, and further calculating the generated energy of each power generation device.
The stability evaluation module is used for calculating an error coefficient between a predicted result and an actual result, evaluating the stability of the equipment power load and the stability of power generation of the power generation equipment, and calculating the maximum error electric quantity;
The stability assessment module includes: the system comprises a residual calculation unit, a stability analysis unit and a peak-valley time division unit;
the residual calculation unit is used for calculating residual errors of each data point and the regression function in the process of predicting the electricity consumption of the load equipment so as to obtain fluctuation conditions of the data points;
the stability analysis unit is used for analyzing the stability of the power consumption load according to the fluctuation condition of the data points and calculating the load range of each load device;
The peak-valley time division unit is used for calculating the electric energy consumption range of the power generation equipment according to the distribution condition of the load equipment and the load range of the load equipment.
The on-chip area allocation module is used for reallocating the load for each power generation device through a selective switch in the intelligent power grid according to the power generation amount of the power generation device and the power consumption amount of the load device predicted in the next period;
The chip area allocation module includes: a load distribution unit and a connection replacement unit;
The load distribution unit is used for distributing access equipment for each power generation equipment according to the predicted load of the load equipment and the predicted power generation capacity of the power generation equipment, so that the power supply task of the access equipment is borne;
The connection replacement unit is used for controlling a selective switch in the smart grid, and the load is redistributed once for each power generation device in each period.
The power storage module is used for storing surplus electric energy of the power generation equipment, and calculating the energy storage quantity, so that the battery stores as little electric energy as possible under the condition of ensuring stable operation of the load equipment in one period.
The power storage module includes: a battery unit and an electricity storage control unit;
the battery unit is used for storing redundant power of the power generation equipment;
the electric power storage control unit is used for calculating the energy storage proportion according to the data of power generation and power consumption in one period, so that the electric power storage in the storage battery is reduced under the condition that the stable operation of the load equipment is ensured.
As shown in fig. 2, a method for managing electric energy of a campus based on data analysis includes the following steps:
s100, acquiring the historical electricity consumption of each load electric equipment in a park, observing the periodicity of historical data, fitting the electricity consumption rule of the equipment, and predicting the electricity consumption of each equipment in the next period;
The step S100 includes:
S101, acquiring electricity consumption of electric equipment in n time periods, wherein n is a system preset value, and recording all historical data into a data set A according to time sequence, wherein A= { a1, a2, …, an }, a1 represents the earliest record, and an represents the latest record;
s102, filtering noise in the data set A by using a wavelet transformation matrix, and calculating the average value of the data set A Said/>Where i is the data number, and the variance of data set A:
When S 2 =0, the data in the representative dataset a is a fixed value, and the power consumption r=a1 of the electric equipment;
when S 2 +.0, the periodicity of dataset a is hypothesis tested:
Wherein ACF is the autocorrelation coefficient of the dataset, k is the assumed period, k ε {1,2, …, n/2}, and 0.ltoreq.ACF.ltoreq.1, S 2 > 0;
substituting the possible values of k into the above formula to calculate to obtain a value set { ACF1, ACF2, …, ACFn } of ACF, wherein the maximum value of elements in the set is marked as t, and the assumed period k0 corresponding to the maximum value t is the electricity utilization period of the electric equipment;
S103, carrying out periodic segmentation on the data set A in sequence, segmenting every k0 data to obtain n/k0 sub-data sequences, respectively marking Y1, Y2, … and Y n/k0, and marking the number of elements in the data sequence Y n/k0 as c;
if c is not equal to k0, extracting the (c+1) th element from other sub-data sequences, and marking the extracted element as yj, wherein j is the subscript of the sub-data sequence to which the element belongs;
If c=k0, extracting the first element in other sub-data sequences, and marking the first element as yj, wherein j is the subscript of the sub-data sequence to which the element belongs;
the expected power consumption in the next period is calculated according to the following formula:
Wherein t is an autocorrelation coefficient of a period k0, and w is expected electricity consumption of the electric equipment in a next period;
and S104, repeating the steps, and calculating the expected electricity consumption of all the electric equipment in the park in the next period.
S200, converting the power generation efficiency coefficient of the wind motor and the photovoltaic panel according to the historical output power and the historical environment data of the power generation equipment in the park, acquiring the change of future wind speed and illumination intensity through a meteorological department, and predicting the power generation capacity of each equipment according to the power generation efficiency of the equipment;
Step S200 includes:
Step S201, acquiring historical output power and historical environment data of power generation equipment, wherein the environment data are wind speed during power generation if the power generation equipment is a wind driven generator, and the environment data are illumination intensity during power generation if the power generation equipment is a photovoltaic generator;
multiplying the historical environment data by a conversion coefficient U preset by a system to obtain an environment variable, calculating the ratio of the output power of the power generation equipment to the environment variable at the same moment, and arranging the ratio in time sequence to form a discrete sequence T of historical efficiency;
constructing a time sequence ARIMA model, performing model training by taking a discrete sequence T as a training set, and fitting the sequence T to obtain a linear regression function of the sequence T;
obtaining the power generation efficiency L of the power generation equipment in the next period by using the linear regression function;
s202, acquiring environmental data in a next period through a meteorological department, and performing energy conversion by using a Scikit-learn tool to obtain energy U provided by the environment, wherein the expected power generation capacity Q=L.U of the power generation equipment;
And calculating the expected power generation amount of all the power generation equipment according to the steps.
S300, redistributing loads for all the power generation devices through selective switches in the intelligent power grid according to the power generation amount of the power generation devices and the power consumption amount of the load devices, which are predicted in the next period;
Step S300 includes:
S301, forming a power generation distribution sequence of all power generation equipment according to the sequence from the large power generation amount Q to the small power generation amount Q;
all electric equipment forms an electricity distribution sequence according to the sequence of the expected electricity consumption W from large to small;
step S302, distributing electric equipment in the electricity distribution sequence to first generating equipment in the electricity distribution sequence in sequence, keeping the sum of expected electricity consumption not to exceed expected electricity generation capacity, distributing second generating equipment in the electricity distribution sequence after the first generating equipment is distributed, and the like until all the electric equipment in the electricity distribution sequence is distributed;
When all power generation equipment in the power generation distribution sequence is completely finished and unassigned electric equipment still exists in the power utilization distribution sequence, the unassigned electric equipment is distributed to a storage battery, and the expected electric quantity of the electric equipment is added to obtain the battery usage D;
and S303, controlling a selective switch in the intelligent power grid, and reconnecting each electric equipment to the independent power grid of the corresponding power generation equipment according to the distribution result.
S400, analyzing fluctuation conditions of the power consumption data points, calculating a mean square error between a prediction result and an actual result, evaluating the stability of the power consumption load of the equipment, and calculating the maximum error power in the next period;
step S400 includes:
S401, evaluating accuracy of a prediction result:
obtaining prediction results of power generation equipment history m times, wherein m is a preset value, recording all the prediction results as a data set W according to time sequence, wherein W= { W1, W2, …, wm }, W1 represents the earliest prediction result, wm represents the latest prediction result, and the corresponding actual measurement result is recorded as a data set G, and G= { G1, G2, …, gm };
calculating root mean square error of the prediction result:
wherein σ 2 represents the root mean square error of the prediction result, gh represents the h element in the dataset G, wh represents the h element in the dataset W;
S402, constructing a probability distribution function of an actual measurement result by using a normal distribution function:
Wherein, W is a predicted value of electricity consumption in the next period, and e is a base number of natural logarithms;
S403, carrying out interval estimation on a probability distribution function of the actual measurement result, and taking a confidence interval as [ (1+t) & w, (1-t) & w ], wherein t is an autocorrelation coefficient of a period k0 obtained in the step S103;
the deviation value z of the electricity consumption of the electric equipment is:
z＝F(w)-F(t·w+w)
The estimation interval of the electricity consumption of the electric equipment is [ w-z, w+z ];
and S404, calculating the power consumption estimation interval of all the electric equipment according to the steps.
S500, calculating the underelectricity compensation quantity according to the electricity consumption and the generated energy, so that the electric energy storage in the battery is reduced under the condition that the stable operation of the load equipment is ensured.
Step S500 includes:
Step S501, according to the distribution condition, adding the estimation intervals of the electricity consumption of all the electric equipment distributed by the power generation equipment to obtain an estimation interval [ w0-z0, w0+z0] of the total electricity consumption, wherein w0 is the sum of expected electricity consumption of all the electric equipment under the name of the power generation equipment, and z0 represents the sum of deviation values of all the electric equipment under the name of the power generation equipment;
s502, calculating the maximum underpower S of the power generation equipment, wherein if S is more than 0, the maximum underpower S is the underpower compensation quantity of the battery, and if S is less than or equal to 0, the underpower compensation is not needed;
S503, calculating surplus electric quantity S0 of the power generation equipment in the current period, storing the electric quantity S into the battery in the current period if S0 is more than or equal to S, storing all surplus electric quantity into the battery if S0 is less than S, and supplementing the electric quantity through an external power grid until the electric quantity in the battery reaches S so as to ensure stable operation of electric equipment.
Examples:
8 electric equipment and 3 power generation devices exist in a certain park, the electricity consumption of all the electric equipment in the history of 10 hours is obtained, the electricity consumption of the future 1 hour is estimated, and the expected electricity consumption of the 8 electric equipment is 100KW/h, 150KW/h, 120KW/h, 80KW/h, 140KW/h, 100KW/h0, 130KW/h and 125KW/h, and the autocorrelation coefficients are 0.88, 0.75, 0.92, 0.80, 0.68, 0.85, 0.71 and 0.55 respectively;
Estimating historical power generation data of the power generation equipment to obtain 35%, 45% and 40% of power generation efficiency of the power generation equipment, acquiring weather information, and estimating that the energy provided by the weather is 1000KW/h, wherein the expected power generation capacity of the power generation equipment is 350KW/h, 450KW/h and 400KW/h respectively, and distributing electric equipment 2, 5 and 7 to the power generation equipment 2, distributing electric equipment 1,3 and 8 to the power generation equipment 3 and distributing electric equipment 4 and 6 to the electric equipment 1 in 1 hour in the future;
And calculating the stability of each electric device according to the autocorrelation coefficient of the electric device, wherein the deviation values of 8 devices are respectively 10KW/h, 12KW/h, 5KW/h, 8KW/h, 11KW/h, 9KW/h, 15KW/h and 3KW/h, and then the storage batteries of the power generation devices 1, 2 and 3 are respectively set to be 17KW/h, 38KW/h and 18KW/h, and the corresponding quantity of electric energy is stored in the storage batteries of the power generation devices in the current period.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A method for campus power management based on data analysis, the method comprising the steps of:
s100, acquiring the historical electricity consumption of each load electric equipment in a park, observing the periodicity of historical data, fitting the electricity consumption rule of the equipment, and predicting the electricity consumption of each equipment in the next period;
S200, converting the power generation efficiency coefficient of the wind motor and the photovoltaic panel according to the historical output power and the historical environment data of the power generation equipment in the park, acquiring the change of future wind speed and illumination intensity through a meteorological department, and predicting the power generation capacity of each equipment according to the power generation efficiency of the equipment;
s300, redistributing loads for all the power generation devices through selective switches in the intelligent power grid according to the power generation amount of the power generation devices and the power consumption amount of the load devices, which are predicted in the next period;
s400, analyzing fluctuation conditions of the power consumption data points, calculating a mean square error between a prediction result and an actual result, evaluating the stability of the power consumption load of the equipment, and calculating the maximum error power in the next period;
S500, calculating underelectricity compensation quantity according to the electricity consumption and the generated energy, so that the electric energy storage in the battery is reduced under the condition that the stable operation of the load equipment is ensured;
The step S100 includes:
S101, acquiring electricity consumption of electric equipment in n time periods, wherein n is a system preset value, and recording all historical data into a data set A according to time sequence, wherein A= { a1, a2, …, an }, a1 represents the earliest record, and an represents the latest record;
s102, filtering noise in the data set A by using a wavelet transformation matrix, and calculating the average value of the data set A The saidWhere i is the data number, and the variance of data set A:
where ai represents the i-th element in dataset A;
When S 2 =0, the data in the representative dataset a is a fixed value, and the power consumption r=a1 of the electric equipment;
when S 2 +.0, the periodicity of dataset a is hypothesis tested:
Wherein a i-k represents the i-th element in the data set A, ACF is the autocorrelation coefficient of the data set, k is the assumed period, k epsilon {1,2, …, n/2}, and 0 is less than or equal to ACF and less than or equal to 1, S 2 is more than 0;
Sequentially substituting all the values which can be obtained by k into the above hypothesis test formula to calculate to obtain a value set { ACF1, ACF2, …, ACFn } of the ACF, wherein ACFn represents the nth element in the set, the maximum value of all the elements in the set is marked as t, and a hypothesis period k0 corresponding to the maximum value t is the electricity utilization period of the electric equipment;
S103, carrying out periodic segmentation on the data set A in sequence, segmenting every k0 data to obtain n/k0 sub-data sequences, respectively marking Y1, Y2, … and Y n/k0, and marking the number of elements in the data sequence Y n/k0 as c;
if c is not equal to k0, extracting the (c+1) th element from other sub-data sequences, and marking the extracted element as yj, wherein j is the subscript of the sub-data sequence to which the element belongs;
If c=k0, extracting the first element in other sub-data sequences, and marking the first element as yj, wherein j is the subscript of the sub-data sequence to which the element belongs;
the expected power consumption in the next period is calculated according to the following formula:
Wherein t is an autocorrelation coefficient of a period k0, and w is expected electricity consumption of the electric equipment in a next period;
and S104, repeating the steps, and calculating the expected electricity consumption of all the electric equipment in the park in the next period.
2. The method for campus power management based on data analysis of claim 1, wherein:
Step S200 includes:
Step S201, acquiring historical output power and historical environment data of power generation equipment, wherein the environment data are wind speed during power generation if the power generation equipment is a wind driven generator, and the environment data are illumination intensity during power generation if the power generation equipment is a photovoltaic generator;
multiplying the historical environment data by a conversion coefficient U preset by a system to obtain an environment variable, calculating the ratio of the output power of the power generation equipment to the environment variable at the same moment, and arranging the ratio in time sequence to form a discrete sequence T of historical efficiency;
constructing a time sequence ARIMA model, performing model training by taking a discrete sequence T as a training set, and fitting the sequence T to obtain a linear regression function of the sequence T;
obtaining the power generation efficiency L of the power generation equipment in the next period by using the linear regression function;
S202, acquiring environmental data in a next period through a meteorological department, performing energy conversion by using a Scikit-learn tool to obtain energy U provided by the environment, and then obtaining the expected power generation Q=of the power generation equipment
L·U；
Calculating expected power generation capacity of all power generation equipment according to the steps;
Step S300 includes:
S301, forming a power generation distribution sequence of all power generation equipment according to the sequence from the large power generation amount Q to the small power generation amount Q;
all electric equipment forms an electricity distribution sequence according to the sequence of the expected electricity consumption W from large to small;
step S302, distributing electric equipment in the electricity distribution sequence to first generating equipment in the electricity distribution sequence in sequence, keeping the sum of expected electricity consumption not to exceed expected electricity generation capacity, distributing second generating equipment in the electricity distribution sequence after the first generating equipment is distributed, and the like until all the electric equipment in the electricity distribution sequence is distributed;
When all power generation equipment in the power generation distribution sequence is completely finished and unassigned electric equipment still exists in the power utilization distribution sequence, the unassigned electric equipment is distributed to a storage battery, and the expected electric quantity of the electric equipment is added to obtain the battery usage D;
and S303, controlling a selective switch in the intelligent power grid, and reconnecting each electric equipment to the independent power grid of the corresponding power generation equipment according to the distribution result.
3. The method for campus power management based on data analysis of claim 2, wherein:
step S400 includes:
S401, evaluating accuracy of a prediction result:
obtaining prediction results of power generation equipment history m times, wherein m is a preset value, recording all the prediction results as a data set W according to time sequence, wherein W= { W1, W2, …, wm }, W1 represents the earliest prediction result, wm represents the latest prediction result, and the corresponding actual measurement result is recorded as a data set G, and G= { G1, G2, …, gm };
calculating root mean square error of the prediction result:
wherein σ 2 represents the root mean square error of the prediction result, gh represents the h element in the dataset G, wh represents the h element in the dataset W;
S402, constructing a probability distribution function of an actual measurement result by using a normal distribution function:
Wherein, W is a predicted value of electricity consumption in the next period, and e is a base number of natural logarithms;
S403, carrying out interval estimation on a probability distribution function of the actual measurement result, and taking a confidence interval as [ (1+t) & w, (1-t) & w ], wherein t is an autocorrelation coefficient of a period k0 obtained in the step S103;
the deviation value z of the electricity consumption of the electric equipment is:
z＝F(w)-F(t·w+w)
The estimation interval of the electricity consumption of the electric equipment is [ w-z, w+z ];
and S404, calculating the power consumption estimation interval of all the electric equipment according to the steps.
4. A method of campus power management based on data analysis as claimed in claim 3, wherein:
step S500 includes:
Step S501, according to the distribution condition, adding the estimation intervals of the electricity consumption of all the electric equipment distributed by the power generation equipment to obtain an estimation interval [ w0-z0, w0+z0] of the total electricity consumption, wherein w0 is the sum of expected electricity consumption of all the electric equipment under the name of the power generation equipment, and z0 represents the sum of deviation values of all the electric equipment under the name of the power generation equipment;
s502, calculating the maximum underpower S of the power generation equipment, wherein if S is more than 0, the maximum underpower S is the underpower compensation quantity of the battery, and if S is less than or equal to 0, the underpower compensation is not needed;
S503, calculating surplus electric quantity S0 of the power generation equipment in the current period, storing the electric quantity S into the battery in the current period if S0 is more than or equal to S, storing all surplus electric quantity into the battery if S0 is less than S, and supplementing the electric quantity through an external power grid until the electric quantity in the battery reaches S so as to ensure stable operation of electric equipment.
5. A data analysis based campus power management system that performs a data analysis based campus power management method as claimed in claim 1, the system comprising the following modules: the system comprises a load prediction module, a power generation prediction module, a stability evaluation module, a chip area allocation module and an electric power storage module;
The load prediction module is used for obtaining the historical electricity consumption of each load device in the park, fitting the electricity consumption rule of the devices by using a linear regression algorithm, and predicting the electricity consumption of each device in the next period;
The power generation prediction module is used for converting the power generation efficiency coefficient of the wind motor and the photovoltaic panel according to the power generation data of the power generation equipment in the park, further acquiring the change of the future wind speed and the illumination intensity, and predicting the power generation capacity of each equipment according to the power generation efficiency of the equipment;
the stability evaluation module is used for calculating an error coefficient between a predicted result and an actual result, evaluating the stability of the equipment power load and the stability of power generation of the power generation equipment, and calculating the maximum error electric quantity;
the on-chip area allocation module is used for reallocating the load for each power generation device through a selective switch in the intelligent power grid according to the power generation amount of the power generation device and the power consumption amount of the load device predicted in the next period;
The power storage module is used for storing surplus electric energy of the power generation equipment, and calculating the energy storage quantity, so that the battery stores as little electric energy as possible under the condition of ensuring stable operation of the load equipment in one period.
6. The data analysis based campus power management system of claim 5 wherein:
The load prediction module includes: the data classifying unit and the regression fitting unit;
The data classifying unit is used for acquiring the historical electricity consumption of each load device under different scales and classifying the historical electricity consumption;
The regression fitting unit is used for predicting the electricity consumption of the load equipment in the next period by using a linear regression algorithm.
7. The data analysis based campus power management system of claim 6 wherein:
The power generation prediction module includes: the system comprises an environment monitoring unit, an efficiency acquisition unit and an energy prediction unit;
The environment monitoring unit is used for monitoring the wind speed during the power generation of the wind power equipment and the illumination intensity of the photovoltaic power generation equipment during the power generation;
The efficiency acquisition unit is used for acquiring historical power generation data of each power generation device and historical environment data of the environment monitoring unit and calculating average efficiency of the power generation devices;
The energy prediction unit is used for predicting future environmental data by combining with meteorological data issued by a meteorological department, and further calculating the generated energy of each power generation device.
8. The data analysis based campus power management system of claim 7 wherein:
The stability assessment module includes: the system comprises a residual calculation unit, a stability analysis unit and a peak-valley time division unit;
the residual calculation unit is used for calculating residual errors of each data point and the regression function in the process of predicting the electricity consumption of the load equipment so as to obtain fluctuation conditions of the data points;
the stability analysis unit is used for analyzing the stability of the power consumption load according to the fluctuation condition of the data points and calculating the load range of each load device;
The peak-valley time division unit is used for calculating the electric energy consumption range of the power generation equipment according to the distribution condition of the load equipment and the load range of the load equipment.
9. The data analysis based campus power management system of claim 8 wherein:
The chip area allocation module includes: a load distribution unit and a connection replacement unit;
The load distribution unit is used for distributing access equipment for each power generation equipment according to the predicted load of the load equipment and the predicted power generation capacity of the power generation equipment, so that the power supply task of the access equipment is borne;
the connection replacement unit is used for controlling a selective switch in the intelligent power grid, and redistributing a load for each power generation device in each time period;
The power storage module includes: a battery unit and an electricity storage control unit;
the battery unit is used for storing redundant power of the power generation equipment;
the electric power storage control unit is used for calculating the energy storage proportion according to the data of power generation and power consumption in one period, so that the electric power storage in the storage battery is reduced under the condition that the stable operation of the load equipment is ensured.
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