CN117394457A - New energy collaborative management and control system for power generation prediction - Google Patents
New energy collaborative management and control system for power generation prediction Download PDFInfo
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Abstract
The invention provides a new energy collaborative management and control system for power generation prediction, belonging to the collaborative management and control field; the problems of power generation prediction and management and control are solved. The management and control system comprises the following specific steps: the new energy data acquisition module is used for acquiring real-time data of the new energy power generation equipment to obtain new energy power generation prediction basic data; the new energy data processing module is used for processing the new energy power generation prediction basic data, predicting the yield and efficiency of new energy power generation according to the historical data and the real-time data, and obtaining a new energy power generation prediction result; the power generation optimizing module is used for carrying out cooperative control according to the new energy power generation prediction result, optimizing the operation and the scheduling of the new energy power generation equipment and obtaining monitoring reference information; the new energy power generation visual monitoring module is used for visually displaying monitoring reference information and real-time data; according to the invention, the new energy power generation equipment is subjected to power generation prediction and scheduling, so that the accuracy of power generation prediction and the power generation efficiency of the power generation equipment are improved.
Description
Technical Field
The invention discloses a new energy collaborative management and control system for power generation prediction, and relates to the field of collaborative management and control.
Background
The traditional new energy collaborative management and control system has the following partial limitations:
1. the technical requirements are high: the traditional new energy collaborative management and control system needs to have advanced technology and algorithm support, and has higher requirements on technicians.
2. Lack of regulation functions of the power generation equipment: the traditional new energy collaborative management and control system only has power generation prediction and abnormal alarm, and has no relation to how to control power generation equipment to normally work, so that the abnormal power generation cannot be timely processed, and energy waste is caused.
Inaccuracy in data processing: when the traditional new energy collaborative management and control system is used for power generation prediction, a large amount of real-time data and historical data need to be processed, meanwhile, the prediction result is completely based on the historical data, and if power generation equipment is upgraded or the address is changed, the accuracy of the prediction result of the power generation prediction is greatly reduced.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention aims to provide a new energy collaborative management and control system for power generation prediction, aiming at improving the power generation efficiency of new energy; in order to achieve the above object, the present invention is realized by the following technical scheme:
a new energy collaborative management and control system for power generation prediction comprising:
The new energy data acquisition module is used for acquiring real-time data of the new energy power generation equipment and taking the real-time data as new energy power generation prediction basic data;
the new energy data processing module is used for processing the new energy power generation prediction basic data, predicting the yield and efficiency of new energy power generation according to the historical data and the real-time data, and obtaining a new energy power generation prediction result;
the power generation optimizing module is used for carrying out cooperative control according to the new energy power generation prediction result, optimizing the operation and the scheduling of the new energy power generation equipment and obtaining monitoring reference information;
the new energy power generation visual monitoring module is used for visually displaying monitoring reference information and real-time data;
the system also comprises a database for recording the historical data of the new energy power generation equipment.
Further, the workflow of the new energy data acquisition module is as follows:
acquiring the illumination intensity of photovoltaic power generation equipment on the same day, and marking the illumination intensity as lx as solar power generation prediction data;
acquiring the current wind speed of the wind power generation equipment, and recording the current wind speed as vw as wind power generation prediction data;
acquiring the water level height in the hydroelectric power generation equipment, and recording the water level height as l to be used as hydroelectric power generation prediction data;
acquiring the temperature of a geothermal heat conductor in geothermal power generation equipment, and recording the temperature as t as geothermal power generation prediction data;
Acquiring the density of microorganisms in a fermentation chamber and the density of methane in a methane storage chamber in methane power generation equipment, and respectively recording the density as pd and md as methane power generation prediction data;
the new energy data acquisition module is used for sending solar power generation prediction data, wind power generation prediction data, hydroelectric power generation prediction data, geothermal power generation prediction data and biogas power generation prediction data to the new energy data processing module as new energy power generation prediction basic data.
Further, the working process of the new energy data processing module is as follows:
the new energy data processing module receives new energy power generation prediction basic data;
the new energy data processing module detects whether the database has historical power generation data of new energy power generation equipment in the last year; if historical power generation data exists in the database in the last year, a first power generation prediction calculation method is used; if the database has no historical power generation data in the past year, a second power generation prediction calculation method is used.
Further, the workflow of the first power generation prediction calculation method of the new energy data processing module is as follows:
the new energy data processing module obtains the total power generation amount and the average illumination intensity of the photovoltaic power generation equipment in the past year from a database, and the total power generation amount and the average illumination intensity are respectively recorded as Is0 and lx0; acquiring the total power generation amount and the average wind speed of the wind power generation equipment in the last year, and respectively recording the total power generation amount and the average wind speed as Iw0 and vw0; acquiring the total power generation amount and the average horizontal plane height of the hydroelectric power generation equipment in the last year, and respectively recording the total power generation amount and the average horizontal plane height as Ih0 and l0; acquiring the total power generation amount and the average temperature of the geothermal power generation equipment in the last year, and respectively recording the total power generation amount and the average temperature as Ie0 and t0; acquiring the total power generation amount and the average microorganism density of the methane power generation equipment in the last year, and respectively marking the total power generation amount and the average microorganism density as Im0 and pd0;
The new energy data processing module calculates a current day power generation predicted value of the new energy equipment;
the current solar power generation predicted value of the photovoltaic power generation Is recorded as Is1, and Is1=lx/lx0 Is0/365;
the current day power generation predicted value of wind power generation is recorded as Iw1, iw1=vw/vw0, iw0/365;
the current day power generation predicted value of hydroelectric power generation is recorded as Ih1, ih1=l/l 0, ih0/365;
the current day power generation predicted value of geothermal power generation is recorded as Ie1, ie1 = t/t0 ae 0/365;
the current-day power generation predicted value of methane power generation is recorded as Im1, and im1=pd/pd 0 is equal to or less than Im0/365;
the new energy data processing module detects whether the database contains historical power generation data of new energy power generation equipment in the last year; if historical data exists, calculating to obtain Is1, iw1, ih1, ie1 and Im1 according to the first power generation prediction calculation method, wherein the Is1, iw1, ih1 and Im1 are used as new energy power generation prediction results.
Further, the workflow of the second power generation prediction calculation method of the new energy data processing module is as follows:
the new energy data processing module reads the data base, and the energy conversion efficiency of solar power generation is between 10% and 20%; the energy conversion efficiency of a wind power plant is typically between 30% and 50%; the energy conversion efficiency of a hydropower plant is typically between 80% and 90%; the energy conversion efficiency of geothermal power plants is typically between 30% and 50%; the energy conversion efficiency of biogas power generation is usually between 25% and 40%; calculating the specific energy conversion efficiency and taking respective average values;
The new energy data processing module reads the illumination area of the photovoltaic power generation plate in the database, and marks the illumination area as A1, calculates the current solar power generation predicted value of the photovoltaic power generation according to a light energy formula, and marks the current solar power generation predicted value as IS2; is2=15% > lx×12;
the new energy data processing module reads the wind wheel area of wind power generation equipment in the database, and marks the wind wheel area as A2, calculates the current day power generation predicted value of wind power generation according to a wind energy formula, and marks the current day power generation predicted value as Iw2; iw2=40% > 1.29 a2 (vw) 3;
the new energy data processing module reads the maximum flow of the hydroelectric power generation equipment in the database and the efficiency of the water turbine, and respectively marks Q2 and eta 2, calculates the current day power generation predicted value of the hydroelectric power generation according to a water energy formula, and marks Ih2; ih2=85% > 9.8×q2×η2;
the new energy data processing module reads the weight and specific heat capacity of a geothermal heat conductor in geothermal power generation equipment in a database and respectively records the weight and specific heat capacity as m2 and c2; calculating a current day power generation predicted value of geothermal power generation according to a geothermal energy formula, and recording the current day power generation predicted value as Ie2; lie 2 = 40%. Geothermal energy; lie 2 = 40% ×m2×c2×t;
the new energy data processing module reads the volumes of biogas storage chambers in biogas power generation equipment in a database, v2 respectively; calculating a current day power generation predicted value of methane power generation according to a methane energy formula, and recording the current day power generation predicted value as Im2; im2=32.5% > md v2 5.2 x 103 x 65%;
The new energy data processing module detects whether the database contains historical power generation data of new energy power generation equipment in the last year; if no history data exists, calculating to obtain IS2, iw2, ih2, ie2 and Im2 according to the second power generation prediction calculation method, and taking the IS2, iw2, ih2, ie2 and Im2 as new energy power generation prediction results; and the new energy data processing module sends the new energy power generation prediction result to the power generation optimizing module.
Further, the workflow of the power generation optimization module is as follows:
the power generation optimization module detects whether the database contains historical power generation data of new energy power generation equipment in the last year;
if no history data exists, the new energy power generation equipment is updated or the address is migrated, and the power generation optimizing module receives the received new energy power generation prediction result and directly sends the new energy power generation prediction result to the new energy power generation visual monitoring module as monitoring reference information;
if the historical data exists, the power generation optimizing module can optimize the operation and the scheduling of the new energy power generation equipment according to the new energy power generation prediction result.
Further, the workflow of the power generation optimization module is as follows:
the power generation optimization module reads the total power generation amount and average illumination intensity of the photovoltaic power generation equipment in the last year in the database, and respectively marks as Is0; acquiring the total power generation amount and the average horizontal plane height of the hydroelectric power generation equipment in the last year, and respectively recording the total power generation amount and the average horizontal plane height as Ih0; acquiring the total power generation amount and the average temperature of the geothermal power generation equipment in the last year, and respectively marking the total power generation amount and the average temperature as Ie0; acquiring the total power generation amount and the average microorganism density of the biogas power generation equipment in the last year, and respectively marking the total power generation amount and the average microorganism density as Im0;
The power generation optimizing module reads a new energy power generation prediction result to obtain Is1, ih1, ie1 and Im1;
the power generation optimizing module optimizes photovoltaic power generation;
the power generation optimization module judges whether the result of Is1/Is0 x 365 Is more than or equal to 1; if the result is more than or equal to 1, the result shows that when the power generation requirement of the solar photovoltaic power generation equipment reaches the standard, adjustment is not needed; if the result is smaller than 1, the illumination intensity in the current day is poor, the power generation requirement of the photovoltaic power generation equipment does not reach the standard, and the photovoltaic power generation equipment needs to be adjusted;
the power generation optimization module acquires the gradient angle of the installation of the photovoltaic power generation equipment in the database, and marks the gradient angle as alpha; adjusting the reflector of the photovoltaic power generation equipment to enable the angle of the reflector to be beta (beta=90 degrees-alpha) with the ground angle, so that sunlight around the photovoltaic power generation equipment irradiates on the solar panel; adjusting the angle and slope of the lower solar panel of the photovoltaic power generation equipment to be beta, adjusting the angle and slope of the upper solar panel of the photovoltaic power generation equipment to be beta, and increasing the contact area of the solar panel and sunlight;
the power generation optimization module optimizes hydroelectric power generation;
the power generation optimization module judges whether the result of Ih1/Ih0 x 365 is more than or equal to 1; if the result is more than or equal to 1, the power generation requirement of the hydroelectric power generation equipment on the same day reaches the standard, and adjustment is not needed; if the result is smaller than 1, the level in the dam on the same day is lower than the standard value, the power generation requirement of the hydroelectric power generation equipment does not reach the standard, and the photovoltaic power generation equipment needs to be adjusted;
The power generation optimizing module sends information to an upstream dam to open a gate to drain water, so that the water level exceeds (L+d), and the water level is stabilized between (L+d) and (0.9×LA);
the power generation optimizing module optimizes geothermal power generation;
the power generation optimization module judges whether the result of Ie1/Ie0 x 365 is greater than or equal to 1; if the result is more than or equal to 1, the power generation requirement of the geothermal power generation equipment meets the standard, and adjustment is not needed; if the result is smaller than 1, the input power of the generator set is insufficient in the current day, the power generation requirement of the geothermal power generation equipment does not reach the standard, and the geothermal power generation equipment needs to be adjusted;
the power generation optimizing module sends information to a gas output valve in geothermal power generation equipment, and increases the steam output of the gas output valve;
the power generation optimizing module optimizes biogas power generation;
the power generation optimization module judges whether the result of Im1/Im0 x 365 is greater than or equal to 1; if the result is more than or equal to 1, the power generation requirement of the biogas power generation equipment meets the standard, and adjustment is not needed; if the result is less than 1, the fact that the combustion temperature of the biogas is insufficient in the same day is indicated, the power generation requirement of the biogas power generation equipment does not reach the standard, and the biogas power generation equipment needs to be adjusted;
the power generation optimizing module sends information to a methane storage room in the methane power generation equipment, the pressure is increased, and the specific increased pressure is recorded as delta P, delta P=nr (tm 0-tm)/VV; substituting Δp into the specific data yields Δp=md/22.4×8.314[ (tm 0-tm) 9/5+32];
After the power generation optimizing module completes optimizing the new energy power generation equipment, the power generation optimizing module generates an adjustment record log, and the adjustment record log records the operation of the power generation optimizing module on the new energy power generation equipment; and the power generation optimizing module sends the adjustment record log and the new energy power generation prediction result to the new energy power generation visual monitoring module as monitoring reference information.
Further, the working process of the new energy power generation visual monitoring module is as follows:
the new energy power generation visual monitoring module receives monitoring reference information;
the new energy power generation visual monitoring module obtains real power generation data of solar photovoltaic power generation, wind power generation, hydroelectric power generation, geothermal power generation and biogas power generation, and the real power generation data are respectively recorded as Is, iw, ih, ie, im;
the new energy power generation visual monitoring module displays three parts of data, namely a power generation prediction result, real power generation data and a mark, in sequence from left to right;
the new energy power generation visual monitoring module detects whether the monitoring reference information contains an adjustment record log to display different power generation prediction results and marks; if no log exists, the new energy power generation equipment Is updated or the address Is changed, and the monitoring reference information Is 'Is 2, iw2, ih2, ie2 and Im 2'; the new energy power generation visual monitoring module adopts a first monitoring method; if the log exists, the new energy power generation equipment Is regulated, and the monitoring reference information Is 'Is 1, iw1, ih1, ie1 and Im 1'; the new energy power generation visual monitoring module adopts a second monitoring method.
Further, the working flow of the new energy power generation visual monitoring module adopting the first monitoring method is as follows:
the new energy power generation visual monitoring module sequentially calculates the ratio of the predicted value to the real power generation data;
if 0 < Is2/Is < 0.5, the mark Is 'photoelectric short circuit'; if 0.5 Is less than or equal to 1, marking as 'photoelectric normal'; if 1 < Is2/Is, the mark Is 'photoelectric break';
if Iw2/Iw is more than 0 and less than 0.5, marking the wind power weakening; if Iw2/Iw is more than 0.5 and less than or equal to 1, marking the wind power is normal; if 1 is less than Iw2/Iw, the wind power is enhanced;
if 0 is less than Ih2/Ih is less than 0.5, the sign is 'hydropower weakening'; if the ratio of Ih2/Ih is more than 0.5 and less than or equal to 1, marking the optical-electrical normal; if 1 is less than Ih2/Ih, the water and electricity enhancement is marked;
if 0 < Ie2/Ie < 0.5, the flag is "geothermal mitigation"; if the value of Ie2/Ie is more than 0.5 and less than or equal to 1, marking the geothermal energy is normal; if 1 < Ie2/Ie, the flag is "geothermal up";
if the ratio of Im2/Im is more than 0 and less than 0.5, the mark is 'biogas power generation weakening'; if the ratio of Im2/Im is more than 0.5 and less than or equal to 1, the biogas is marked as normal; if 1 < Im2/Im, the sign is "biogas power generation increase".
Further, the working flow of the new energy power generation visual monitoring module adopting the second monitoring method is as follows:
If Is1/Is less than or equal to 0, the mark Is 'photoelectric short circuit'; if 0 < Is1/Is < 0.5, the mark Is 'photoelectric attenuation'; if 0.5 Is less than or equal to 1/Is less than or equal to 1, marking as 'photoelectric normal'; if 1 Is less than or equal to 1.2, marking the photoelectric increase; if 1.2 < Is1/Is, the mark Is 'photoelectric break';
if Iw1/Iw is more than 0 and less than 0.5, marking the wind power weakening; if Iw1/Iw is more than 0.5 and less than or equal to 1, marking the wind power is normal; if Iw1/Iw is more than 1 and less than or equal to 1.2, marking the wind power enhancement; if Iw1/Iw is less than 1.2, the wind power is marked as too strong;
if 0 is less than Ih1/Ih is less than 0.5, the sign is 'hydropower weakening'; if the ratio of Ih1/Ih is more than 0.5 and less than or equal to 1, marking the optical-electrical normal; if 1 is less than Ih1/Ih, the water and electricity enhancement is marked;
if 0 < Ie1/Ie < 0.5, the flag is "geothermal mitigation"; if the value of Ie1/Ie is more than 0.5 and less than or equal to 1, marking the geothermal energy is normal; if 1 < Ie1/Ie, the flag is "geothermal up";
if the ratio of Im1/Im is more than 0 and less than 0.5, the mark is 'biogas power generation weakening'; if Im1/Im is more than 0.5 and less than or equal to 1, the biogas is marked as normal; if the ratio of Im1/Im is more than 1 and less than or equal to 1.2, the mark is 'biogas power generation increase'; if 1.2 is less than Im1/Im, the mark is "biogas generation too much".
Compared with the prior art, the invention has the beneficial effects that:
1. The power generation efficiency is improved: the new energy collaborative management and control system can intelligently schedule the power generation equipment according to factors such as weather, environment and the like, and can utilize renewable energy to the greatest extent, so that the power generation efficiency is improved.
2. Improving the accuracy of data prediction: according to the invention, different calculation methods are selected according to whether historical data exists in a database; the time span is only one year, errors caused by upgrading of power generation equipment or address transition are avoided, and excessive dependence on historical data in conventional power generation prediction is avoided.
3. Reducing carbon emission: the new energy collaborative management and control system reduces the dependence on the traditional energy, reduces the carbon emission and reduces the influence on the environment through more accurate power generation prediction and power generation equipment scheduling.
Drawings
FIG. 1 is a functional schematic of the system of the present invention;
FIG. 2 is a schematic view of the hydro-power generation optimization of the present invention;
FIG. 3 is a flow chart of the data processing of the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Referring to fig. 1, the present application provides a new energy collaborative management and control system for power generation prediction, the management and control system includes:
The new energy data acquisition module is used for acquiring real-time data of the new energy power generation equipment and taking the real-time data as new energy power generation prediction basic data;
the new energy data processing module is used for processing the new energy power generation prediction basic data, predicting the yield and efficiency of new energy power generation according to the historical data and the real-time data, and obtaining a new energy power generation prediction result;
the power generation optimizing module is used for carrying out cooperative control according to the new energy power generation prediction result, optimizing the operation and the scheduling of the new energy power generation equipment, improving the power generation amount and the power generation efficiency in a future period of time and reducing the energy waste;
the new energy power generation visual monitoring module is used for visually displaying the prediction result and the real-time data, helping operators to know the power generation condition of the new energy in real time and correspondingly adjusting and managing the power generation condition;
the new energy power generation fault diagnosis module is used for diagnosing faults of the new energy power generation equipment and performing maintenance functions; by monitoring and analyzing the operation data of the new energy equipment, the system can provide fault diagnosis and maintenance advice so as to reduce equipment faults and downtime and improve the reliability and availability of new energy power generation;
the system also comprises a database for recording historical data, equipment parameters and equipment installation geographic parameters of the new energy power generation equipment.
Wherein the device parameters include: the method comprises the following steps of (1) illuminating area of a photovoltaic power generation plate, wind wheel area of wind power generation equipment, height of a water turbine, limit height of a dam, maximum flow rate and water turbine efficiency of hydraulic power generation equipment, weight and specific heat capacity of a geothermal heat conductor in geothermal power generation equipment and volume in a biogas storage chamber in biogas power generation equipment;
the device installation geographic parameters include: slope angle of installation of photovoltaic power generation equipment and distance from dam of hydroelectric power generation equipment to water turbine.
The new energy data acquisition module is used for acquiring real-time data of the new energy power generation equipment and taking the real-time data as new energy power generation prediction basic data;
the working flow of the new energy data acquisition module is as follows:
the new energy data acquisition module acquires real-time data of the four types of new energy power generation equipment through sensors carried on the new energy power generation equipment;
acquiring the illumination intensity of photovoltaic power generation equipment on the same day, and marking the illumination intensity as lx as solar power generation prediction data;
acquiring the current wind speed of the wind power generation equipment, and recording the current wind speed as vw as wind power generation prediction data;
acquiring the water level height in the hydroelectric power generation equipment, and recording the water level height as l to be used as hydroelectric power generation prediction data;
acquiring the temperature of a geothermal heat conductor in geothermal power generation equipment, and recording the temperature as t as geothermal power generation prediction data;
Acquiring the density of microorganisms in a fermentation chamber and the density of methane in a methane storage chamber in methane power generation equipment, and respectively recording the density as pd and md as methane power generation prediction data;
the new energy data acquisition module is used for sending solar power generation prediction data, wind power generation prediction data, hydroelectric power generation prediction data, geothermal power generation prediction data and biogas power generation prediction data to the new energy data processing module as new energy power generation prediction basic data.
The process of obtaining the population density of the microorganisms in the biogas power generation equipment is as follows:
the first step, adding fluorescent protein genes into microorganisms for decomposing crop straws, food residues and other organic matters by using a genetic engineering method;
a second step, a fluorescence detector is arranged above a fermentation chamber of the biogas generating equipment and is used for detecting the microorganism quantity on the surface layer of organic matters in the fermentation chamber; an infrared area measuring instrument is installed and used for detecting the area of the organic matter surface layer of the fermentation chamber;
thirdly, calculating the microorganism density, wherein the microorganism density in the biogas power generation equipment is=the microorganism number of the organic matter surface layer of the fermentation chamber/the area of the organic matter surface layer of the fermentation chamber.
The new energy data processing module is used for processing the new energy power generation prediction basic data, predicting the yield and efficiency of new energy power generation according to the historical data and the real-time data, and obtaining a new energy power generation prediction result;
The working flow of the new energy data processing module is as follows:
the new energy data processing module receives new energy power generation prediction basic data;
the new energy data processing module detects whether the database has historical power generation data of new energy power generation equipment in the last year; if historical power generation data exists in the database in the last year, a first power generation prediction calculation method is used; if the database has no historical power generation data in the past year, a second power generation prediction calculation method is used.
The first power generation prediction calculation method is that the database has historical power generation data of new energy power generation equipment in the last year;
the new energy data processing module obtains the total power generation amount and the average illumination intensity of the photovoltaic power generation equipment in the past year from a database, and the total power generation amount and the average illumination intensity are respectively recorded as Is0 and lx0; acquiring the total power generation amount and the average wind speed of the wind power generation equipment in the last year, and respectively recording the total power generation amount and the average wind speed as Iw0 and vw0; acquiring the total power generation amount and the average horizontal plane height of the hydroelectric power generation equipment in the last year, and respectively recording the total power generation amount and the average horizontal plane height as Ih0 and l0; acquiring the total power generation amount and the average temperature of the geothermal power generation equipment in the last year, and respectively recording the total power generation amount and the average temperature as Ie0 and t0; acquiring the total power generation amount and the average microorganism density of the methane power generation equipment in the last year, and respectively marking the total power generation amount and the average microorganism density as Im0 and pd0;
the new energy data processing module calculates a current day power generation predicted value of the new energy equipment;
The current solar power generation predicted value of the photovoltaic power generation Is recorded as Is1, and Is1=lx/lx0 Is0/365;
the current day power generation predicted value of wind power generation is recorded as Iw1, iw1=vw/vw0, iw0/365;
the current day power generation predicted value of hydroelectric power generation is recorded as Ih1, ih1=l/l 0, ih0/365;
the current day power generation predicted value of geothermal power generation is recorded as Ie1, ie1 = t/t0 ae 0/365;
the current-day power generation predicted value of methane power generation is recorded as Im1, and im1=pd/pd 0 is equal to or less than Im0/365;
the second power generation prediction calculation method is that no historical power generation data of new energy power generation equipment in the past year exists in a database;
the new energy data processing module reads the statistical results of the actual energy consumption and the generated energy of various power plants according to big data in the database: the energy conversion efficiency of solar power generation is between 10% and 20%; the energy conversion efficiency of a wind power plant is typically between 30% and 50%; the energy conversion efficiency of a hydropower plant is typically between 80% and 90%; the energy conversion efficiency of geothermal power plants is typically between 30% and 50%; the energy conversion efficiency of biogas power generation is usually between 25% and 40%; when calculating the specific energy conversion efficiency, the energy conversion ratio takes the average value of each energy conversion ratio;
the new energy data processing module reads the illumination area of the photovoltaic power generation plate in the database, and marks the illumination area as A1, calculates the current solar power generation predicted value of the photovoltaic power generation according to a light energy formula, and marks the current solar power generation predicted value as IS2; is2=15% > light energy, light energy=light intensity×light time×light area (A1), wherein light intensity Is lx, light time takes 12 hours;
The new energy data processing module reads the wind wheel area and the air density of wind power generation equipment in a database, wherein the wind wheel area is marked as A2, and the air density is 1.29kg/m3; calculating a current day power generation predicted value of wind power generation according to a wind energy formula, and recording the current day power generation predicted value as Iw2; iw2=40% > wind energy, iw2=40% > 0.5 (vw) 3 a2 1.29; wherein vw is wind speed;
the new energy data processing module reads the maximum flow rate and the water turbine efficiency of the hydraulic power generation equipment in the database, and the maximum flow rate and the water turbine efficiency are respectively recorded as Q2 and eta 2, and the gravity acceleration is 9.8m/s2; calculating a current day power generation predicted value of the hydroelectric power generation according to a water energy formula, and recording the current day power generation predicted value as Ih2; ih2=85% of water energy, ih2=85% of 9.8 q2 η2; wherein l is the water surface height;
the new energy data processing module reads the weight and specific heat capacity of a geothermal heat conductor in geothermal power generation equipment in a database and respectively records the weight and specific heat capacity as m2 and c2; calculating a current day power generation predicted value of geothermal power generation according to a geothermal energy formula, and recording the current day power generation predicted value as Ie2; lie 2 = 40%. Geothermal energy;
geothermal equation 1: geothermal energy = geothermal flow density x geothermal resource utilization time x effective area of geothermal well or pipe;
geothermal formula 2: ground heat flux = heat flux/ground surface area;
geothermal formula 3: heat flow = heat conductivity = temperature gradient;
Combining with the design of a real geothermal heat conductor, the ground area of the geothermal heat conductor completely wraps a heat source in order to fully utilize the collected geothermal heat; thus, as can be seen from geothermal formula 1 and geothermal formula 2, the result of "geothermal flow density x effective area of geothermal well or pipe" is equal to the heat flow; then according to thermodynamic principle (Q= mcT; Q represents heat, m represents mass of the object, c represents specific heat capacity of the object, T represents temperature), and geothermal formula 3, so that the predicted current-day power generation value Ie 2=40% > m 2T of geothermal power generation is combined; wherein t is the temperature of the geothermal heat conductor;
the new energy data processing module reads the volumes of biogas storage chambers in biogas power generation equipment in a database, v2 respectively; calculating a current day power generation predicted value of methane power generation according to a methane energy formula, and recording the current day power generation predicted value as Im2; im2=32.5% biogas energy; biogas energy = biogas yield biogas heating value; wherein the biogas yield is the biogas amount in the biogas storage chamber, and the biogas heat value is 5.2kJ/m 3 The method comprises the steps of carrying out a first treatment on the surface of the And because the content of methane in the biogas is 60% -70%; so the current daily power generation predicted value im2=32.5% > md×v2×5.2×103×65%; wherein md is methane density;
the new energy data processing module detects whether the database contains historical power generation data of new energy power generation equipment in the last year; if historical data exists, calculating to obtain Is1, iw1, ih1, ie1 and Im1 according to the first power generation prediction calculation method, and taking the Is1, iw1, ih1 and Im1 as a new energy power generation prediction result; if no history data exists, calculating to obtain IS2, iw2, ih2, ie2 and Im2 according to the second power generation prediction calculation method, and taking the IS2, iw2, ih2, ie2 and Im2 as new energy power generation prediction results; and the new energy data processing module sends the new energy power generation prediction result to the power generation optimizing module.
It should be noted that, the new energy data processing module adopts two reasons for power generation prediction calculation: the usability and the accuracy of the new energy collaborative management and control system for power generation prediction are improved; the first generation prediction calculation is calculated on the basis of the generation of new energy power generation equipment in the last year, the time span is only one year, and calculation errors caused by the upgrading or failure of the power generation equipment are reduced; when the system uses the second method to predict the power generation, the power generation equipment is updated or the address is changed, and the parameters adopted by the second method are based on the statistical results of big data, and the statistical results are stored in a database, so that the data maintenance is convenient.
The power generation optimizing module is used for carrying out cooperative control according to the new energy power generation prediction result, optimizing the operation and the scheduling of the new energy power generation equipment, improving the power generation amount and the power generation efficiency in a future period of time and reducing the energy waste;
the power generation optimization module detects whether the database contains historical power generation data of new energy power generation equipment in the last year;
if no history data exists, the new energy power generation equipment is updated or the address is migrated, and the power generation optimizing module receives the received new energy power generation prediction result and directly sends the new energy power generation prediction result to the new energy power generation visual monitoring module as monitoring reference information;
If the historical data exists, the power generation optimizing module can optimize the operation and the scheduling of the new energy power generation equipment according to the new energy power generation prediction result;
the working flow of the power generation optimizing module is as follows:
the power generation optimization module reads the total power generation amount and average illumination intensity of the photovoltaic power generation equipment in the last year in the database, and respectively marks as Is0; acquiring the total power generation amount and the average horizontal plane height of the hydroelectric power generation equipment in the last year, and respectively recording the total power generation amount and the average horizontal plane height as Ih0; acquiring the total power generation amount and the average temperature of the geothermal power generation equipment in the last year, and respectively marking the total power generation amount and the average temperature as Ie0; acquiring the total power generation amount and the average microorganism density of the biogas power generation equipment in the last year, and respectively marking the total power generation amount and the average microorganism density as Im0;
the power generation optimizing module reads a new energy power generation prediction result to obtain Is1, ih1, ie1 and Im1;
the power generation optimizing module optimizes photovoltaic power generation;
the power generation optimization module judges whether the result of Is1/Is0 x 365 Is more than or equal to 1; if the result is more than or equal to 1, the result shows that when the power generation requirement of the solar photovoltaic power generation equipment reaches the standard, adjustment is not needed; if the result is smaller than 1, the illumination intensity in the current day is poor, the power generation requirement of the photovoltaic power generation equipment does not reach the standard, and the photovoltaic power generation equipment needs to be adjusted;
the photovoltaic power generation equipment is adjusted as follows:
the power generation optimization module acquires the gradient angle of the installation of the photovoltaic power generation equipment in the database, and marks the gradient angle as alpha; adjusting the reflector of the photovoltaic power generation equipment to enable the angle of the reflector to be beta (beta=90 degrees-alpha) with the ground angle, so that sunlight around the photovoltaic power generation equipment irradiates on the solar panel; adjusting the angle and slope of the lower solar panel of the photovoltaic power generation equipment to be beta, adjusting the angle and slope of the upper solar panel of the photovoltaic power generation equipment to be beta, and increasing the contact area of the solar panel and sunlight;
The power generation optimization module optimizes hydroelectric power generation;
the power generation optimization module judges whether the result of Ih1/Ih0 x 365 is more than or equal to 1; if the result is more than or equal to 1, the power generation requirement of the hydroelectric power generation equipment on the same day reaches the standard, and adjustment is not needed; if the result is smaller than 1, the level in the dam on the same day is lower than the standard value, the power generation requirement of the hydroelectric power generation equipment does not reach the standard, and the photovoltaic power generation equipment needs to be adjusted;
referring to fig. 2, the hydroelectric power generation device is adjusted as follows:
the power generation optimization module obtains the distance from the dam of the hydraulic power generation equipment in the database to the water turbine, and the distance is recorded as d; acquiring the height of the water turbine, and marking the height as L; obtaining the limit height LA of the dam;
calculation principle of a standard value of the level height in a dam of hydroelectric power equipment: the essence of hydroelectric generation is to convert gravitational potential Energy (EP) accumulated by a dam into kinetic Energy (EK) of an impulse turbine according to the law of conservation of energy and the law of energy conversion; however, because the height difference between the dam and the water turbine is effective only in the horizontal direction, the gravitational potential energy of the water in the dam cannot be fully converted into kinetic energy; setting the included angle theta between water sprayed from a water outlet of a dam and a water turbine, wherein the height of the water surface is LL; so the gravitational potential Energy (EP) of the water converts kinetic Energy (EK), (EP) converts kinetic Energy (EK), the energy of the effective impulse turbine is (EK) sin θ, and the ineffectiveness energy is (EK) cos θ; the ratio of effective energy/non-effective energy is tan theta; according to trigonometric function properties, the ratio of effective energy/non-effective energy is just "1" at θ of 45O, tan θ= (LL-L)/d, ll=l+d, where the height L of the turbine and the dam-to-turbine distance d are constant, so the water level in the dam is within a safe range, which means that the level is at most 0.9 x la; the water level in the dam is adjusted to exceed (L+d) on the basis of the original water level, and the total power generation amount is increased; adjusting to be lower than (L+d), and reducing the total power generation amount;
The power generation optimizing module sends information to an upstream dam to open a gate to drain water, so that the water level exceeds (L+d), and the water level is stabilized between (L+d) and (0.9×LA);
the power generation optimizing module optimizes geothermal power generation;
the power generation optimization module judges whether the result of Ie1/Ie0 x 365 is greater than or equal to 1; if the result is more than or equal to 1, the power generation requirement of the geothermal power generation equipment meets the standard, and adjustment is not needed; if the result is smaller than 1, the input power of the generator set is insufficient in the current day, the power generation requirement of the geothermal power generation equipment does not reach the standard, and the geothermal power generation equipment needs to be adjusted;
brief description of the principles of adjustment of geothermal power plants: the geothermal power generation is to collect heat through a geothermal heat conductor, then transmit the heat to a steam turbine, generate high-pressure steam by the steam turbine, and drive a generator set to work by the high-pressure steam; because the geothermal energy is uncontrollable, the adjustment of the total power generation amount of geothermal power generation equipment is realized by adjusting a gas output valve of a steam turbine connected with an engine unit;
the power generation optimizing module sends information to a gas output valve in geothermal power generation equipment, and increases the steam output of the gas output valve;
the power generation optimizing module optimizes biogas power generation;
the power generation optimization module judges whether the result of Im1/Im0 x 365 is greater than or equal to 1; if the result is more than or equal to 1, the power generation requirement of the biogas power generation equipment meets the standard, and adjustment is not needed; if the result is less than 1, the fact that the combustion temperature of the biogas is insufficient in the same day is indicated, the power generation requirement of the biogas power generation equipment does not reach the standard, and the biogas power generation equipment needs to be adjusted;
The adjustment principle of the biogas power generation equipment: the biogas power generation equipment utilizes microorganisms to decompose organisms to generate biogas, then burns the biogas to generate heat energy, and finally converts the heat energy into electric energy by adopting the same method as the geothermal power generation equipment; pv=nrt according to the ideal gas equation; wherein P represents the pressure of the gas, V represents the volume of the gas, n represents the amount of the substance of the gas, R is the gas constant, and T represents the temperature of the gas (in Kelvin); the equation shows that the pressure of the gas is proportional to the temperature of the gas on the premise of a certain volume and the amount of the substance; amount of gas substance = volume of gas/molar volume of gas (n = V/Vm), the molar volume of gas being 22.4L at standard atmospheric pressure; the gas constant value was 8.314J/(mol.K); celsius 9/5+32=degrees fahrenheit; according to the law of molecular thermal motion, when the temperature of methane molecules rises, the more violent the methane molecules collide with oxygen molecules, the more heat is released by methane combustion;
the power generation optimization module obtains the volume, the temperature and the methane density in a methane storage room in methane power generation equipment and respectively records as VV, tm and md; the power generation optimizing module acquires the temperature (in degrees centigrade) in a biogas storage chamber in the biogas power generation equipment in the past year, and records the temperature as tm0;
The power generation optimizing module sends information to a methane storage room in the methane power generation equipment, the pressure is increased, and the specific increased pressure is recorded as delta P, delta P=nr (tm 0-tm)/VV; substituting Δp into the specific data yields Δp=md/22.4×8.314[ (tm 0-tm) 9/5+32];
after the power generation optimizing module completes the optimization of the new energy power generation equipment, the power generation optimizing module generates an adjustment record log, and the adjustment record log records the operation of the power generation optimizing module on the new energy power generation equipment; and the power generation optimizing module sends the adjustment record log and the new energy power generation prediction result to the new energy power generation visual monitoring module as monitoring reference information.
It should be noted that, because the power source wind power of wind power generation is not controlled by manpower at all, the power generation optimizing module has no optimizing scheme for the operation and the dispatching of wind power generation equipment.
The new energy power generation visual monitoring module is used for visually displaying the prediction result and the real-time data, helping operators to know the power generation condition of the new energy in real time and correspondingly adjusting and managing the power generation condition;
the new energy power generation visual monitoring module receives monitoring reference information;
the new energy power generation visual monitoring module obtains real power generation data of solar photovoltaic power generation, wind power generation, hydroelectric power generation, geothermal power generation and biogas power generation, and the real power generation data are respectively recorded as Is, iw, ih, ie, im;
The new energy power generation visual monitoring module displays three parts of data, namely a power generation prediction result, real power generation data and a mark, in sequence from left to right;
the new energy power generation visual monitoring module detects whether the monitoring reference information contains an adjustment record log to display different power generation prediction results and marks; if no log exists, the new energy power generation equipment Is updated or the address Is changed, and the monitoring reference information Is 'Is 2, iw2, ih2, ie2 and Im 2'; the new energy power generation visual monitoring module adopts a first monitoring method; if the log exists, the new energy power generation equipment Is regulated, and the monitoring reference information Is 'Is 1, iw1, ih1, ie1 and Im 1'; the new energy power generation visual monitoring module adopts a second monitoring method.
A first monitoring method; the new energy power generation visual monitoring module sequentially calculates the ratio of the predicted value to the real power generation data;
if 0 Is less than 0.5 and Is2/Is less than 0.5, the short circuit phenomenon of the photovoltaic power generation equipment Is shown, maintenance Is needed, and the photovoltaic power generation equipment Is marked as 'photoelectric short circuit'; if the Is2/Is more than 0.5 and less than or equal to 1, the photovoltaic power generation equipment Is indicated to work normally, and the sign Is 'photoelectric normal'; if 1 Is less than IS2/IS, the photovoltaic power generation equipment Is indicated to have a disconnection phenomenon, maintenance Is needed, and the sign Is 'photoelectric disconnection';
If Iw2/Iw is more than 0 and less than 0.5, the wind speed is reduced, and the wind speed is marked as 'wind power reduction'; if Iw2/Iw is more than 0.5 and less than or equal to 1, indicating that the wind speed is normal, and marking the wind power as normal; if 1 is less than Iw2/Iw, the wind speed is increased, and the wind speed is marked as wind power enhancement;
if 0 is less than Ih2/Ih is less than 0.5, the hydroelectric power generation is weakened, dam water storage is additionally added, and the mark is 'hydroelectric power weakening'; if the Ih2/Ih is more than 0.5 and less than or equal to 1, indicating that the hydroelectric power generation equipment works normally, and marking the hydroelectric power generation equipment as 'photoelectric normal'; if the water power is 1 to Ih2/Ih, the water discharging speed of the dam needs to be reduced, and the water power is marked as 'water power enhancement';
if 0 < Ie2/Ie < 0.5, indicating that geothermal power generation is reduced, the steam output of the gas output valve needs to be increased again, and the flag is 'geothermal reduction'; if the Ie2/Ie is more than 0.5 and less than or equal to 1, the geothermal power generation equipment works normally, and the geothermal power generation equipment is marked as 'geothermal normal'; if 1 < Ie2/Ie, the geothermal power generation increases, the steam output of the gas output valve needs to be reduced, and the flag is 'geothermal increase';
if the ratio of Im2/Im is more than 0 and less than 0.5, the biogas generation is weakened, the pressure of the biogas storage chamber needs to be increased again, and the mark is 'biogas generation weakening'; if Im2/Im is more than 0.5 and less than or equal to 1, the biogas power generation equipment works normally, and the mark is 'biogas power generation normal'; if 1 is less than Im2/Im, the biogas power generation is increased, the pressure of the biogas storage chamber needs to be reduced, and the mark is 'biogas power generation increase'.
A second monitoring method; the new energy power generation visual monitoring module sequentially calculates the ratio of the predicted value to the real power generation data;
if Is1/Is less than or equal to 0, the short circuit phenomenon of the photovoltaic power generation equipment Is indicated, maintenance Is needed, and the photovoltaic power generation equipment Is marked as 'photoelectric short circuit'; if 0 Is less than 0.5, the photovoltaic power generation Is weakened, and the sign Is 'photoelectric weakening'; if the Is1/Is less than or equal to 1 and more than 0.5, the photovoltaic power generation equipment works normally, and the sign Is 'photoelectric normal'; if the ratio Is more than 1 and Is1/Is less than or equal to 1.2, the photovoltaic power generation equipment works normally and Is marked as 'photoelectric increase'; if 1.2 Is less than IS1/IS, the photovoltaic power generation equipment Is indicated to have a disconnection phenomenon, maintenance Is needed, and the sign Is 'photoelectric disconnection';
if Iw1/Iw is more than 0 and less than 0.5, the wind speed is reduced, and the wind speed is marked as 'wind power reduction'; if Iw1/Iw is more than 0.5 and less than or equal to 1, indicating that the wind speed is normal, and marking the wind power as normal; if Iw1/Iw is more than 1 and less than or equal to 1.2, the wind speed is increased, and the wind power is increased; if Iw1/Iw is smaller than 1.2, the wind speed is too large, the service time of the wind power equipment needs to be reduced to prevent faults, and the wind power equipment is marked as 'wind power too strong';
if 0 is less than Ih1/Ih is less than 0.5, the hydroelectric power generation is weakened, dam water storage is additionally added, and the mark is 'hydroelectric power weakening'; if the Ih1/Ih is more than 0.5 and less than or equal to 1, indicating that the hydroelectric power generation equipment works normally, and marking the hydroelectric power generation equipment as 'photoelectric normal'; if the water power is smaller than Ih1/Ih, the water power is increased, the water discharging speed of the dam is required to be reduced, and the water power is increased;
If 0 < Ie1/Ie < 0.5, indicating that geothermal power generation is reduced, the steam output of the gas output valve needs to be increased again, and the flag is 'geothermal reduction'; if the Ie1/Ie is more than 0.5 and less than or equal to 1, the geothermal power generation equipment works normally, and the geothermal power generation equipment is marked as 'geothermal normal'; if 1 < Ie1/Ie, the geothermal power generation increases, the steam output of the gas output valve needs to be reduced, and the flag is 'geothermal increase';
if the ratio of Im1/Im is more than 0 and less than 0.5, the biogas generation is weakened, the pressure of the biogas storage chamber needs to be increased again, and the mark is 'biogas generation weakening'; if Im1/Im is more than 0.5 and less than or equal to 1, the biogas power generation equipment works normally, and the mark is 'biogas power generation normal'; if Im1/Im is more than 1 and less than or equal to 1.2, the biogas power generation is increased, and the sign is 'biogas power generation increase'; if the ratio of the methane power generation is smaller than 1.2 and is smaller than 1/Im, the methane power generation is further increased, the pressure of the methane storage chamber needs to be reduced, and the condition that the methane power generation is excessive is marked.
The above formulas are all formulas for removing dimensions and taking numerical calculation, the formulas are formulas for obtaining the latest real situation by collecting a large amount of data and performing software simulation, preset parameters in the formulas are set by a person skilled in the art according to the actual situation, if weight coefficients and proportion coefficients exist, the set sizes are specific numerical values obtained by quantizing the parameters, the subsequent comparison is convenient, and the proportional relation between the weight coefficients and the proportion coefficients is not influenced as long as the proportional relation between the parameters and the quantized numerical values is not influenced.
It should be noted that, the monitoring method of the new energy power generation visual monitoring module only appears one kind, and the parts with the same content of the mark cannot appear at the same time.
And the new energy power generation fault diagnosis module is used for diagnosing faults of the new energy power generation equipment and performing maintenance functions. By monitoring and analyzing the operation data of the new energy equipment, fault diagnosis and maintenance advice are provided, so that equipment faults and downtime are reduced, and the reliability and availability of new energy power generation are improved.
The new energy collaborative management and control system for power generation prediction further comprises: other new energy definition modules; the module is used for adding new energy sources for generating power and analysis parameters, calculation methods and prediction standard values corresponding to the new energy sources in the invention; after new energy is developed, power practitioners can use the invention to predict the generated energy of the new energy.
Finally, it should be noted that: the above examples are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, but it should be understood by those skilled in the art that the present invention is not limited thereto, and that the present invention is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A new forms of energy cooperation management and control system for electricity generation prediction, its characterized in that, management and control system includes:
the new energy data acquisition module is used for acquiring real-time data of the new energy power generation equipment to obtain new energy power generation prediction basic data;
the new energy data processing module is used for processing the new energy power generation prediction basic data, predicting the yield and efficiency of new energy power generation according to the historical data and the real-time data, and obtaining a new energy power generation prediction result;
the power generation optimizing module is used for carrying out cooperative control according to the new energy power generation prediction result, optimizing the operation and the scheduling of the new energy power generation equipment and obtaining monitoring reference information;
the new energy power generation visual monitoring module is used for visually displaying monitoring reference information and real-time data;
the system also comprises a database for recording the historical data of the new energy power generation equipment.
2. The new energy collaborative management and control system for power generation prediction according to claim 1, wherein the workflow of the new energy data acquisition module is as follows:
acquiring the illumination intensity of photovoltaic power generation equipment on the same day, and marking the illumination intensity as lx as solar power generation prediction data;
acquiring the current wind speed of the wind power generation equipment, and recording the current wind speed as vw as wind power generation prediction data;
Acquiring the water level height in the hydroelectric power generation equipment, and recording the water level height as l to be used as hydroelectric power generation prediction data;
acquiring the temperature of a geothermal heat conductor in geothermal power generation equipment, and recording the temperature as t as geothermal power generation prediction data;
acquiring the density of microorganisms in a fermentation chamber and the density of methane in a methane storage chamber in methane power generation equipment, and respectively recording the density as pd and md as methane power generation prediction data;
the new energy data acquisition module is used for sending solar power generation prediction data, wind power generation prediction data, hydroelectric power generation prediction data, geothermal power generation prediction data and biogas power generation prediction data to the new energy data processing module as new energy power generation prediction basic data.
3. The new energy collaborative management and control system for power generation prediction according to claim 1, wherein the new energy data processing module works as follows:
the working flow of the new energy data processing module is as follows:
the new energy data processing module receives new energy power generation prediction basic data;
the new energy data processing module detects whether the database has historical power generation data of new energy power generation equipment in the last year; if historical power generation data exists in the database in the last year, a first power generation prediction calculation method is used; if the database has no historical power generation data in the past year, a second power generation prediction calculation method is used.
4. A new energy collaborative management and control system for power generation prediction according to claim 3, wherein the workflow of the first power generation prediction calculation method of the new energy data processing module is as follows:
the new energy data processing module obtains the total power generation amount and the average illumination intensity of the photovoltaic power generation equipment in the past year from a database, and the total power generation amount and the average illumination intensity are respectively recorded as Is0 and lx0; acquiring the total power generation amount and the average wind speed of the wind power generation equipment in the last year, and respectively recording the total power generation amount and the average wind speed as Iw0 and vw0; acquiring the total power generation amount and the average horizontal plane height of the hydroelectric power generation equipment in the last year, and respectively recording the total power generation amount and the average horizontal plane height as Ih0 and l 0; acquiring the total power generation amount and the average temperature of the geothermal power generation equipment in the last year, and respectively recording the total power generation amount and the average temperature as Ie0 and t0; acquiring the total power generation amount and the average microorganism density of the methane power generation equipment in the last year, and respectively marking the total power generation amount and the average microorganism density as Im0 and pd0;
the new energy data processing module calculates a current day power generation predicted value of the new energy equipment;
the current solar power generation predicted value of the photovoltaic power generation Is recorded as Is1, and Is1=lx/lx0 Is0/365;
the current day power generation predicted value of wind power generation is recorded as Iw1, iw1=vw/vw0, iw0/365;
the current day power generation predicted value of hydroelectric power generation is recorded as Ih1, ih1=l/l 0×ih0/365;
the current day power generation predicted value of geothermal power generation is recorded as Ie1, ie1 = t/t0 ae 0/365;
The current-day power generation predicted value of methane power generation is recorded as Im1, and im1=pd/pd 0 is equal to or less than Im0/365;
the new energy data processing module detects whether the database contains historical power generation data of new energy power generation equipment in the last year; if historical data exists, calculating to obtain Is1, iw1, ih1, ie1 and Im1 according to the first power generation prediction calculation method, wherein the Is1, iw1, ih1 and Im1 are used as new energy power generation prediction results.
5. A new energy collaborative management and control system for power generation prediction according to claim 3, wherein the workflow of the second power generation prediction calculation method of the new energy data processing module is as follows:
the new energy data processing module reads the data base, and the energy conversion efficiency of solar power generation is between 10% and 20%; the energy conversion efficiency of a wind power plant is typically between 30% and 50%; the energy conversion efficiency of a hydropower plant is typically between 80% and 90%; the energy conversion efficiency of geothermal power plants is typically between 30% and 50%; the energy conversion efficiency of biogas power generation is usually between 25% and 40%; calculating the specific energy conversion efficiency and taking respective average values;
the new energy data processing module reads the illumination area of the photovoltaic power generation plate in the database, and marks the illumination area as A1, calculates the current solar power generation predicted value of the photovoltaic power generation according to a light energy formula, and marks the current solar power generation predicted value as IS2; is2=15% > lx×12;
The new energy data processing module reads the wind wheel area of wind power generation equipment in the database, and marks the wind wheel area as A2, calculates the current day power generation predicted value of wind power generation according to a wind energy formula, and marks the current day power generation predicted value as Iw2; iw2=40% > 1.29 a2 (vw) 3;
the new energy data processing module reads the maximum flow of the hydroelectric power generation equipment in the database and the efficiency of the water turbine, and respectively marks Q2 and eta 2, calculates the current day power generation predicted value of the hydroelectric power generation according to a water energy formula, and marks Ih2; ih2=85% > 9.8×q2×η2;
the new energy data processing module reads the weight and specific heat capacity of a geothermal heat conductor in geothermal power generation equipment in a database and respectively records the weight and specific heat capacity as m2 and c2; calculating a current day power generation predicted value of geothermal power generation according to a geothermal energy formula, and recording the current day power generation predicted value as Ie2; lie 2 = 40%. Geothermal energy; lie 2 = 40% ×m2×c2×t;
the new energy data processing module reads the volumes of biogas storage chambers in biogas power generation equipment in a database, v2 respectively; calculating a current day power generation predicted value of methane power generation according to a methane energy formula, and recording the current day power generation predicted value as Im2; im2=32.5% > md v2 5.2 x 103 x 65%;
the new energy data processing module detects whether the database contains historical power generation data of new energy power generation equipment in the last year; if no history data exists, calculating to obtain IS2, iw2, ih2, ie2 and Im2 according to the second power generation prediction calculation method, and taking the IS2, iw2, ih2, ie2 and Im2 as new energy power generation prediction results; and the new energy data processing module sends the new energy power generation prediction result to the power generation optimizing module.
6. The new energy collaborative management and control system for power generation prediction according to claim 1, wherein the workflow of the power generation optimization module is as follows:
the power generation optimization module detects whether the database contains historical power generation data of new energy power generation equipment in the last year;
if no history data exists, the new energy power generation equipment is updated or the address is migrated, and the power generation optimizing module receives the received new energy power generation prediction result and directly sends the new energy power generation prediction result to the new energy power generation visual monitoring module as monitoring reference information;
and if the historical data exists, the power generation optimizing module optimizes the operation and the scheduling of the new energy power generation equipment according to the new energy power generation prediction result.
7. The new energy collaborative management and control system for power generation prediction according to claim 6, wherein the workflow of the power generation optimization module is as follows:
the power generation optimization module reads the total power generation amount and average illumination intensity of the photovoltaic power generation equipment in the last year in the database, and respectively marks as Is0; acquiring the total power generation amount and the average horizontal plane height of the hydroelectric power generation equipment in the last year, and respectively recording the total power generation amount and the average horizontal plane height as Ih0; acquiring the total power generation amount and the average temperature of the geothermal power generation equipment in the last year, and respectively marking the total power generation amount and the average temperature as Ie0; acquiring the total power generation amount and the average microorganism density of the biogas power generation equipment in the last year, and respectively marking the total power generation amount and the average microorganism density as Im0;
The power generation optimizing module reads a new energy power generation prediction result to obtain Is1, ih1, ie1 and Im1;
the power generation optimizing module optimizes photovoltaic power generation;
the power generation optimization module judges whether the result of Is1/Is0 x 365 Is more than or equal to 1; if the result is more than or equal to 1, the result shows that when the power generation requirement of the solar photovoltaic power generation equipment reaches the standard, adjustment is not needed; if the result is smaller than 1, the illumination intensity in the current day is poor, the power generation requirement of the photovoltaic power generation equipment does not reach the standard, and the photovoltaic power generation equipment needs to be adjusted;
the power generation optimization module acquires the gradient angle of the installation of the photovoltaic power generation equipment in the database, and marks the gradient angle as alpha; adjusting the reflector of the photovoltaic power generation equipment to enable the angle of the reflector to be beta (beta=90 degrees-alpha) with the ground angle, so that sunlight around the photovoltaic power generation equipment irradiates on the solar panel; adjusting the angle and slope of the lower solar panel of the photovoltaic power generation equipment to be beta, adjusting the angle and slope of the upper solar panel of the photovoltaic power generation equipment to be beta, and increasing the contact area of the solar panel and sunlight;
the power generation optimization module optimizes hydroelectric power generation;
the power generation optimization module judges whether the result of Ih1/Ih0 x 365 is more than or equal to 1; if the result is more than or equal to 1, the power generation requirement of the hydroelectric power generation equipment on the same day reaches the standard, and adjustment is not needed; if the result is smaller than 1, the level in the dam on the same day is lower than the standard value, the power generation requirement of the hydroelectric power generation equipment does not reach the standard, and the photovoltaic power generation equipment needs to be adjusted;
The power generation optimizing module sends information to an upstream dam to open a gate to drain water, so that the water level exceeds (L+d), and the water level is stabilized between (L+d) and (0.9×LA);
the power generation optimizing module optimizes geothermal power generation;
the power generation optimization module judges whether the result of Ie1/Ie0 x 365 is greater than or equal to 1; if the result is more than or equal to 1, the power generation requirement of the geothermal power generation equipment meets the standard, and adjustment is not needed; if the result is smaller than 1, the input power of the generator set is insufficient in the current day, the power generation requirement of the geothermal power generation equipment does not reach the standard, and the geothermal power generation equipment needs to be adjusted;
the power generation optimizing module sends information to a gas output valve in geothermal power generation equipment, and increases the steam output of the gas output valve;
the power generation optimizing module optimizes biogas power generation;
the power generation optimization module judges whether the result of Im1/Im0 x 365 is greater than or equal to 1; if the result is more than or equal to 1, the power generation requirement of the biogas power generation equipment meets the standard, and adjustment is not needed; if the result is less than 1, the fact that the combustion temperature of the biogas is insufficient in the same day is indicated, the power generation requirement of the biogas power generation equipment does not reach the standard, and the biogas power generation equipment needs to be adjusted;
the power generation optimizing module sends information to a methane storage room in the methane power generation equipment, the pressure is increased, and the specific increased pressure is recorded as delta P, delta P=nr (tm 0-tm)/VV; substituting Δp into the specific data yields Δp=md/22.4×8.314[ (tm 0-tm) 9/5+32];
After the power generation optimizing module completes optimizing the new energy power generation equipment, the power generation optimizing module generates an adjustment record log, and the adjustment record log records the operation of the power generation optimizing module on the new energy power generation equipment; and the power generation optimizing module sends the adjustment record log and the new energy power generation prediction result to the new energy power generation visual monitoring module as monitoring reference information.
8. The new energy collaborative management and control system for power generation prediction according to claim 1, wherein the working process of the new energy power generation visual monitoring module is as follows:
the new energy power generation visual monitoring module receives monitoring reference information;
the new energy power generation visual monitoring module obtains real power generation data of solar photovoltaic power generation, wind power generation, hydroelectric power generation, geothermal power generation and biogas power generation, and the real power generation data are respectively recorded as Is, iw, ih, ie, im;
the new energy power generation visual monitoring module displays three parts of data, namely a power generation prediction result, real power generation data and a mark, in sequence from left to right;
the new energy power generation visual monitoring module detects whether the monitoring reference information contains an adjustment record log to display different power generation prediction results and marks; if no log exists, the new energy power generation equipment Is updated or the address Is changed, and the monitoring reference information Is 'Is 2, iw2, ih2, ie2 and Im 2'; the new energy power generation visual monitoring module adopts a first monitoring method; if the log exists, the new energy power generation equipment Is regulated, and the monitoring reference information Is 'Is 1, iw1, ih1, ie1 and Im 1'; the new energy power generation visual monitoring module adopts a second monitoring method.
9. The new energy collaborative management and control system for power generation prediction according to claim 8, wherein the new energy power generation visualization monitoring module adopts the workflow of the first monitoring method as follows:
the new energy power generation visual monitoring module sequentially calculates the ratio of the predicted value to the real power generation data;
if 0 < Is2/Is < 0.5, the mark Is 'photoelectric short circuit'; if 0.5 Is less than or equal to 1, marking as 'photoelectric normal'; if 1 < Is2/Is, the mark Is 'photoelectric break';
if Iw2/Iw is more than 0 and less than 0.5, marking the wind power weakening; if Iw2/Iw is more than 0.5 and less than or equal to 1, marking the wind power is normal; if 1 is less than Iw2/Iw, the wind power is enhanced;
if 0 is less than Ih2/Ih is less than 0.5, the sign is 'hydropower weakening'; if the ratio of Ih2/Ih is more than 0.5 and less than or equal to 1, marking the optical-electrical normal; if 1 is less than Ih2/Ih, the water and electricity enhancement is marked;
if 0 < Ie2/Ie < 0.5, the flag is "geothermal mitigation"; if the value of Ie2/Ie is more than 0.5 and less than or equal to 1, marking the geothermal energy is normal; if 1 < Ie2/Ie, the flag is "geothermal up";
if the ratio of Im2/Im is more than 0 and less than 0.5, the mark is 'biogas power generation weakening'; if the ratio of Im2/Im is more than 0.5 and less than or equal to 1, the biogas is marked as normal; if 1 < Im2/Im, the sign is "biogas power generation increase".
10. The new energy collaborative management and control system for power generation prediction according to claim 8, wherein the new energy power generation visualization monitoring module adopts a second monitoring method as follows:
if Is1/Is less than or equal to 0, the mark Is 'photoelectric short circuit'; if 0 < Is1/Is < 0.5, the mark Is 'photoelectric attenuation'; if 0.5 Is less than or equal to 1/Is less than or equal to 1, marking as 'photoelectric normal'; if 1 Is less than or equal to 1.2, marking the photoelectric increase; if 1.2 < Is1/Is, the mark Is 'photoelectric break';
if Iw1/Iw is more than 0 and less than 0.5, marking the wind power weakening; if Iw1/Iw is more than 0.5 and less than or equal to 1, marking the wind power is normal; if Iw1/Iw is more than 1 and less than or equal to 1.2, marking the wind power enhancement; if Iw1/Iw is less than 1.2, the wind power is marked as too strong;
if 0 is less than Ih1/Ih is less than 0.5, the sign is 'hydropower weakening'; if the ratio of Ih1/Ih is more than 0.5 and less than or equal to 1, marking the optical-electrical normal; if 1 is less than Ih1/Ih, the water and electricity enhancement is marked;
if 0 < Ie1/Ie < 0.5, the flag is "geothermal mitigation"; if the value of Ie1/Ie is more than 0.5 and less than or equal to 1, marking the geothermal energy is normal; if 1 < Ie1/Ie, the flag is "geothermal up";
if the ratio of Im1/Im is more than 0 and less than 0.5, the mark is 'biogas power generation weakening'; if Im1/Im is more than 0.5 and less than or equal to 1, the biogas is marked as normal; if the ratio of Im1/Im is more than 1 and less than or equal to 1.2, the mark is 'biogas power generation increase'; if 1.2 is less than Im1/Im, the mark is "biogas generation too much".
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20160028886A (en) * | 2014-09-04 | 2016-03-14 | 정길조 | Methode For Predicting Energy Producing Quantity of piezoelectric generation system In Apartment and It's System |
| CN112200376A (en) * | 2020-10-16 | 2021-01-08 | 国能日新科技股份有限公司 | System and method for predicting medium-term and long-term generated energy of new energy wind power plant |
| CN112598481A (en) * | 2020-12-30 | 2021-04-02 | 山东电力交易中心有限公司 | Computer implementation method for inter-provincial short-term clean energy electric power transaction recommendation |
| US20230214703A1 (en) * | 2021-12-30 | 2023-07-06 | SparkCognition, Inc. | Predicting energy production for energy generating assets |
| CN116805802A (en) * | 2023-05-15 | 2023-09-26 | 深圳市金地楼宇工程有限公司 | Low-carbon self-adaptive energy regulation method and management platform thereof |
-
2023
- 2023-10-31 CN CN202311434362.7A patent/CN117394457B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20160028886A (en) * | 2014-09-04 | 2016-03-14 | 정길조 | Methode For Predicting Energy Producing Quantity of piezoelectric generation system In Apartment and It's System |
| CN112200376A (en) * | 2020-10-16 | 2021-01-08 | 国能日新科技股份有限公司 | System and method for predicting medium-term and long-term generated energy of new energy wind power plant |
| CN112598481A (en) * | 2020-12-30 | 2021-04-02 | 山东电力交易中心有限公司 | Computer implementation method for inter-provincial short-term clean energy electric power transaction recommendation |
| US20230214703A1 (en) * | 2021-12-30 | 2023-07-06 | SparkCognition, Inc. | Predicting energy production for energy generating assets |
| CN116805802A (en) * | 2023-05-15 | 2023-09-26 | 深圳市金地楼宇工程有限公司 | Low-carbon self-adaptive energy regulation method and management platform thereof |
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