CN117543723A - Large-scale generator operation strategy determination method and system based on power supply and demand data - Google Patents

Large-scale generator operation strategy determination method and system based on power supply and demand data Download PDF

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CN117543723A
CN117543723A CN202410028252.9A CN202410028252A CN117543723A CN 117543723 A CN117543723 A CN 117543723A CN 202410028252 A CN202410028252 A CN 202410028252A CN 117543723 A CN117543723 A CN 117543723A
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power consumption
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CN117543723B (en
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王波
潘常青
安东辉
梁刚
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Shenzhen Testeck Technology Co ltd
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    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46Controlling of the sharing of output between the generators, converters, or transformers
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
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    • G06Q50/06Energy or water supply
    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

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Abstract

The invention discloses a method and a system for determining a large-scale generator operation strategy based on power supply and demand data, which relate to the technical field of generator operation and comprise the following steps: determining the maximum power generation efficiency of the large-scale power generator; acquiring historical power consumption data of a power supply area corresponding to a large-sized generator; predicting a power consumption prediction amount of each statistical period in the current statistical period; fitting a correlation change function of power consumption predicted quantity and time of each statistical period in the current statistical period; judging whether the large-sized generator can meet the power supply requirement of a corresponding power supply area or not; and (3) planning a real-time operation power generation efficiency scheme of the large-scale generator for the large-scale generator which can meet the power supply requirement. The invention has the advantages that: the intelligent operation adjustment of the large-sized generator can be effectively realized, the operation load of the large-sized generator is reduced, and the service life of the large-sized generator is further prolonged, so that the high-efficiency utilization of energy and the reduction of cost are realized.

Description

Large-scale generator operation strategy determination method and system based on power supply and demand data
Technical Field
The invention relates to the technical field of generator operation, in particular to a method and a system for determining a large-scale generator operation strategy based on power supply and demand data.
Background
The generator is a mechanical device for converting mechanical energy into electric energy, and is driven by a water turbine, a steam turbine, a diesel engine or other power machines, so that energy generated by combustion of water flow, air flow and fuel is converted into mechanical energy to be transmitted to the generator, and then the mechanical energy is converted into electric energy by the generator. The generator has wide application. The form of the generator is many, but the working principle of the generator is based on the law of electromagnetic induction and the law of electromagnetic force. The general principle of its construction is therefore: the magnetic circuit and the circuit which mutually perform electromagnetic induction are formed by proper magnetic conduction and electric conduction materials so as to generate electromagnetic power and achieve the purpose of energy conversion.
The large-scale generator is mainly applied to a power station, and because the power consumption requirement generally shows seasonal periodic fluctuation, in the operation strategy of the power station, the redundant quantity of the large-scale generator power generation is generally stored through energy storage equipment in the electricity consumption valley period, and the electric energy stored by the energy storage equipment is released in the electricity consumption peak period so as to assist in power supply, so that the power supply strategy of peak clipping and valley filling is realized.
Disclosure of Invention
In order to solve the technical problems, the technical scheme provides a method and a system for determining the operation strategy of a large-sized generator based on power supply and demand data, and solves the problems that the prior art lacks comprehensive analysis on power supply and demand data, usually needs to keep the large-sized generator always at the maximized operation efficiency, is difficult to perform intelligent operation adjustment of the large-sized generator, causes larger operation load of the large-sized generator and affects the service life of the large-sized generator.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a large-scale generator operation strategy determining method based on power supply and demand data comprises the following steps:
acquiring operation data of a large-sized generator, and determining the maximum power generation efficiency of the large-sized generator based on the operation data of the large-sized generator;
acquiring historical power consumption data of a power supply area corresponding to a large-sized generator;
taking one year as a statistics period, and dividing the year into different statistics periods according to a set time length;
based on the historical power consumption data of the power supply area corresponding to the large-scale generator, calculating the historical power consumption efficiency of different statistical periods in each statistical period;
predicting a predicted amount of power consumption for each of the statistical periods within the current statistical period based on the historical power consumption efficiencies for different statistical periods within each statistical period;
drawing a correlation curve of the power consumption prediction amount and time of each statistical period in the current statistical period in a coordinate system by taking time as an abscissa and the power consumption prediction amount of each statistical period in the current statistical period as an ordinate;
fitting a correlation change function of the power consumption pre-measurement and time of each statistical period in the current statistical period based on the correlation curve of the power consumption pre-measurement and time of each statistical period in the current statistical period;
judging whether the large-scale generator can meet the power supply requirement of a corresponding power supply area or not based on a related change function of power consumption pre-measurement and time in each statistical period in the current statistical period and the maximum power generation efficiency of the large-scale generator, if so, judging that the large-scale generator can meet the power supply requirement, if not, judging that the large-scale generator is difficult to meet the power supply requirement, and outputting a generating capacity shortage signal;
for a large-sized generator capable of meeting the power supply requirement, planning a real-time operation power generation efficiency scheme of the large-sized generator based on the maximum power generation efficiency of the large-sized generator and the power consumption prediction amount of each statistical period in the current statistical period;
and controlling the large-scale generator to generate electricity according to the scheme of the real-time operation power generation efficiency of the large-scale generator.
Preferably, the power consumption prediction amount for predicting each statistical period in the current statistical period based on the historical power consumption efficiency of different statistical periods in each statistical period specifically includes:
numbering a plurality of statistical periods in sequence from far to near according to the time interval with the current time;
and calculating the power consumption prediction amount of each statistical period in the current statistical period according to the power consumption prediction formula.
Preferably, the electricity consumption prediction formula is:
in the method, in the process of the invention,predicted amount of power consumption for a statistical period within a current statistical period, +.>For counting the total number of period numbers>For the historical power consumption efficiency according to the statistical period ranging from far to near in the j-th statistical period with the current time interval, +.>Is a power consumption demand trend parameter.
Preferably, the determining whether the large-scale generator can meet the power supply requirement of the corresponding power supply area based on the related change function of the power consumption prediction amount and the time of each statistical period in the current statistical period and the maximum power generation efficiency of the large-scale generator specifically includes:
acquiring the energy storage conversion rate of energy storage equipment corresponding to the current large-scale generator;
screening a plurality of statistical periods of which the maximum power generation efficiency of the large-sized generator is greater than the power consumption predicted amount, and recording the statistical periods as redundant periods;
screening a plurality of statistical periods that the maximum power generation efficiency of the large-sized generator is smaller than the power consumption predicted amount, and recording the statistical periods as insufficient periods;
calculating the power generation redundancy amount of the large-scale generator in the redundancy period and the power generation shortage amount of the large-scale generator in the shortage period respectively;
constructing a power supply judgment inequality based on the power generation redundancy amount of the large-sized generator, the power generation shortage of the large-sized generator and the energy storage conversion rate of energy storage equipment corresponding to the large-sized generator;
and judging whether the power supply judging inequality is met, if so, the large-scale generator cannot meet the power supply requirement of the corresponding power supply area, and if not, the large-scale generator can meet the power supply requirement of the corresponding power supply area.
Preferably, the power supply determination inequality is:
in the power supply determination inequality,energy storage conversion rate of energy storage equipment corresponding to large-scale generator, < ->Is the power generation redundancy of a large-scale generator, < >>Is insufficient for the power generation of a large-scale generator.
Preferably, the planning of the real-time running power generation efficiency scheme of the large-scale power generator based on the maximum power generation efficiency of the large-scale power generator and the power consumption prediction amount of each statistical period in the current statistical period specifically includes:
acquiring the nearest one or more redundant periods before the insufficient period, and recording the nearest one or more redundant periods as power-up redundant periods;
determining a power-up period in a power-up redundancy period through a power-up demand formula based on a related change function of power consumption pre-measurement and time of each statistical period in the statistical period;
controlling the large-scale generator to generate power according to the maximum power generation efficiency in the power supplementing period, and storing redundant power generation amount in the power supplementing period into energy storage equipment;
and controlling the large-scale generator to generate power according to the power consumption predicted amount corresponding to the current moment in the non-power-supplementing period in the power-supplementing redundancy period.
Preferably, the electricity supplementing requirement formula specifically includes:
in the method, in the process of the invention,for the termination of the power-on redundancy period, +.>For the start time of the power-up period, +.>Is the maximum power generation efficiency of the large-scale generator, < >>For the beginning of the deficient period, +.>For the termination of the deficient period, +.>A power consumption prediction amount and time related change function for each statistical period in the statistical period;
by calculating the starting time of the power-up period meeting the power-up requirement formulaAs power supplement in power supplement redundancy periodA time period.
Further, a system for determining a large-scale generator operation strategy based on the power supply and demand data is provided, which is used for implementing the method for determining the large-scale generator operation strategy based on the power supply and demand data, and includes:
the generator data acquisition module is used for acquiring the operation data of the large generator and determining the maximum power generation efficiency of the large generator based on the operation data of the large generator;
the power supply demand determining module is used for obtaining historical power consumption data of a power supply area corresponding to the large-scale generator, taking one year as a statistical period, dividing one year into different statistical periods according to set time length, calculating historical power consumption efficiency of different statistical periods in each statistical period based on the historical power consumption data of the power supply area corresponding to the large-scale generator, predicting power consumption pre-measurement of each statistical period in the current statistical period based on the historical power consumption efficiency of different statistical periods in each statistical period, taking time as an abscissa, taking the power consumption pre-measurement of each statistical period in the current statistical period as an ordinate, drawing a correlation curve of the power consumption pre-measurement of each statistical period in the current statistical period and the time in a coordinate system, and fitting a correlation change function of the power consumption pre-measurement of each statistical period and the time in the current statistical period based on the correlation curve of the power consumption pre-measurement of each statistical period and the time in the current statistical period;
the power generator operation planning module is used for judging whether the large-sized power generator can meet the power supply requirement of a corresponding power supply area or not and for the large-sized power generator which can meet the power supply requirement based on the correlation change function of the power consumption pre-measurement value and time of each statistical period in the current statistical period and the maximum power generation efficiency of the large-sized power generator, and planning a real-time operation power generation efficiency scheme of the large-sized power generator based on the maximum power generation efficiency of the large-sized power generator and the power consumption pre-measurement value of each statistical period in the current statistical period.
Optionally, the power supply requirement determining module includes:
the historical data acquisition unit is used for acquiring historical power consumption data of a power supply area corresponding to the large-scale generator, taking one year as a statistical period, dividing one year into different statistical periods according to set time length and calculating the historical power consumption efficiency of different statistical periods in each statistical period based on the historical power consumption data of the power supply area corresponding to the large-scale generator;
a power consumption prediction unit for predicting a power consumption prediction amount for each statistical period in a current statistical period based on a historical power consumption efficiency for different statistical periods in each statistical period;
the function fitting unit is used for drawing a correlation curve of the power consumption pre-measurement and the time of each statistical period in the current statistical period and a correlation curve of the power consumption pre-measurement and the time of each statistical period based on the current statistical period in the coordinate system by taking the time as an abscissa and taking the power consumption pre-measurement of each statistical period in the current statistical period as an ordinate, and fitting out a correlation change function of the power consumption pre-measurement and the time of each statistical period in the current statistical period.
Optionally, the generator operation planning module includes:
the judging unit is used for judging whether the large-sized generator can meet the power supply requirement of a corresponding power supply area or not based on a related change function of the power consumption predicted quantity and time of each statistical period in the current statistical period and the maximum power generation efficiency of the large-sized generator;
and the power supply planning unit is used for planning a real-time running power generation efficiency scheme of the large-scale generator based on the maximum power generation efficiency of the large-scale generator and the power consumption prediction amount of each statistical period in the current statistical period.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a large-scale generator operation strategy determination scheme based on power supply and demand data, which is based on comprehensive analysis of the power consumption demand of a power supply area corresponding to a large-scale generator and the power supply and generation efficiency of the generator, and is used for planning a large-scale generator operation strategy which enables the operation load of the large-scale generator to be minimum on the basis that the large-scale generator meets the power supply demand.
Drawings
FIG. 1 is a flow chart of a method for determining the operation strategy of a large-scale generator based on power supply and demand data;
FIG. 2 is a flow chart of a method for predicting a predicted amount of power consumption for each statistical period within a current statistical period in accordance with the present invention;
FIG. 3 is a flow chart of a method for determining whether a large generator can meet the power supply requirements of a corresponding power supply area according to the present invention;
fig. 4 is a flow chart of a method of planning a real-time operating power generation efficiency scheme for a large generator in accordance with the present invention.
Detailed Description
The following description is presented to enable one of ordinary skill in the art to make and use the invention. The preferred embodiments in the following description are by way of example only and other obvious variations will occur to those skilled in the art.
Referring to fig. 1, a method for determining an operation strategy of a large-scale generator based on power supply and demand data includes:
acquiring operation data of a large-sized generator, and determining the maximum power generation efficiency of the large-sized generator based on the operation data of the large-sized generator;
acquiring historical power consumption data of a power supply area corresponding to a large-sized generator;
taking one year as a statistics period, and dividing the year into different statistics periods according to a set time length;
based on the historical power consumption data of the power supply area corresponding to the large-scale generator, calculating the historical power consumption efficiency of different statistical periods in each statistical period;
predicting a predicted amount of power consumption for each of the statistical periods within the current statistical period based on the historical power consumption efficiencies for different statistical periods within each statistical period;
drawing a correlation curve of the power consumption prediction amount and time of each statistical period in the current statistical period in a coordinate system by taking time as an abscissa and the power consumption prediction amount of each statistical period in the current statistical period as an ordinate;
fitting a correlation change function of the power consumption pre-measurement and time of each statistical period in the current statistical period based on the correlation curve of the power consumption pre-measurement and time of each statistical period in the current statistical period;
judging whether the large-scale generator can meet the power supply requirement of a corresponding power supply area or not based on a related change function of power consumption pre-measurement and time in each statistical period in the current statistical period and the maximum power generation efficiency of the large-scale generator, if so, judging that the large-scale generator can meet the power supply requirement, if not, judging that the large-scale generator is difficult to meet the power supply requirement, and outputting a generating capacity shortage signal;
for a large-sized generator capable of meeting the power supply requirement, planning a real-time operation power generation efficiency scheme of the large-sized generator based on the maximum power generation efficiency of the large-sized generator and the power consumption prediction amount of each statistical period in the current statistical period;
and controlling the large-scale generator to generate electricity according to the scheme of the real-time operation power generation efficiency of the large-scale generator.
The scheme is based on comprehensive analysis of the power consumption demand of the power supply area corresponding to the large-sized generator and the power supply and generation efficiency of the generator, and a large-sized generator operation strategy with the minimum operation load of the large-sized generator is planned on the basis that the large-sized generator meets the power supply demand, so that efficient utilization of energy and reduction of cost are realized.
Referring to fig. 2, the power consumption prediction amount for predicting each statistical period in the current statistical period based on the historical power consumption efficiency of different statistical periods in each statistical period specifically includes:
numbering a plurality of statistical periods in sequence from far to near according to the time interval with the current time;
and calculating the power consumption prediction amount of each statistical period in the current statistical period according to the power consumption prediction formula.
The electricity consumption prediction formula is:
in the method, in the process of the invention,predicted amount of power consumption for a statistical period within a current statistical period, +.>For counting the total number of period numbers>For the historical power consumption efficiency according to the statistical period ranging from far to near in the j-th statistical period with the current time interval, +.>Is a power consumption demand trend parameter.
It can be understood that the electricity demand generally shows seasonal periodic fluctuation, based on which, in the scheme, years are taken as statistical periods, and electricity consumption is predicted in different statistical periods based on historical electricity consumption data of past years, so that the supply and demand relationship of the large-scale generator can be effectively rationalized and planned and managed, and further the intelligent formulation of the operation strategy of the large-scale generator is realized.
Referring to fig. 3, based on a time-dependent variation function of power consumption prediction amount and time of each statistical period in a current statistical period and a maximum power generation efficiency of a large-sized power generator, determining whether the large-sized power generator can satisfy power supply requirements of a corresponding power supply area specifically includes:
acquiring the energy storage conversion rate of energy storage equipment corresponding to the current large-scale generator;
screening a plurality of statistical periods of which the maximum power generation efficiency of the large-sized generator is greater than the power consumption predicted amount, and recording the statistical periods as redundant periods;
screening a plurality of statistical periods that the maximum power generation efficiency of the large-sized generator is smaller than the power consumption predicted amount, and recording the statistical periods as insufficient periods;
calculating the power generation redundancy amount of the large-scale generator in the redundancy period and the power generation shortage amount of the large-scale generator in the shortage period respectively;
constructing a power supply judgment inequality based on the power generation redundancy amount of the large-sized generator, the power generation shortage of the large-sized generator and the energy storage conversion rate of energy storage equipment corresponding to the large-sized generator;
and judging whether the power supply judging inequality is met, if so, the large-scale generator cannot meet the power supply requirement of the corresponding power supply area, and if not, the large-scale generator can meet the power supply requirement of the corresponding power supply area.
The power supply determination inequality is:
in the power supply determination inequality,energy storage conversion rate of energy storage equipment corresponding to large-scale generator, < ->Is the power generation redundancy of a large-scale generator, < >>Is insufficient for the power generation of a large-scale generator.
When judging whether the generated energy of the generator can meet the power consumption of the power supply area or not, dividing the operation period of the generator into a redundant period and an insufficient period based on the supply-demand relationship, and judging whether the large generator can meet the power supply requirement of the corresponding power supply area or not by calculating whether the energy storage output of the redundant period can meet the energy gap generated in the insufficient period or not.
Referring to fig. 4, the scheme for planning the real-time running power generation efficiency of the large-scale power generator based on the maximum power generation efficiency of the large-scale power generator and the power consumption prediction amount of each statistical period in the current statistical period specifically includes:
acquiring the nearest one or more redundant periods before the insufficient period, and recording the nearest one or more redundant periods as power-up redundant periods;
determining a power-up period in a power-up redundancy period through a power-up demand formula based on a related change function of power consumption pre-measurement and time of each statistical period in the statistical period;
controlling the large-scale generator to generate power according to the maximum power generation efficiency in the power supplementing period, and storing redundant power generation amount in the power supplementing period into energy storage equipment;
and controlling the large-scale generator to generate power according to the power consumption predicted amount corresponding to the current moment in the non-power-supplementing period in the power-supplementing redundancy period.
The electricity supplementing demand formula specifically comprises:
in the method, in the process of the invention,for the termination of the power-on redundancy period, +.>For the start time of the power-up period, +.>Is the maximum power generation efficiency of the large-scale generator, < >>For the beginning of the deficient period, +.>For the termination of the deficient period, +.>A power consumption prediction amount and time related change function for each statistical period in the statistical period;
by calculating the starting time of the power-up period meeting the power-up requirement formulaAs a power up period within the power up redundancy period.
It can be understood that certain energy source natural loss exists in the energy storage equipment, so that the traditional method always maintains the charging state of the energy storage equipment in the redundant period of the generator, on one hand, the generator is always in a high-power running state, and on the other hand, the energy source natural loss is increased, therefore, in the scheme, in the redundant period closest to the insufficient period, the power supplementing period of a power supply gap in the insufficient period can be calculated, the power is generated according to the maximum power generation efficiency in the power supplementing period, and the redundant power generation amount in the power supplementing period is stored in the energy storage equipment, so that the energy storage time of the energy storage equipment can be shortened to the greatest extent, the energy source natural loss is reduced, the running power of the large generator can be regulated reasonably, the running load of the large generator is reduced, and the service life of the large generator is further prolonged.
Further, based on the same inventive concept as the above-mentioned large-scale generator operation strategy determination method based on the power supply and demand data, the present solution also provides a large-scale generator operation strategy determination system based on the power supply and demand data, including:
the generator data acquisition module is used for acquiring the operation data of the large generator and determining the maximum power generation efficiency of the large generator based on the operation data of the large generator;
the power supply demand determining module is used for obtaining historical power consumption data of a power supply area corresponding to the large-scale generator, taking one year as a statistical period, dividing one year into different statistical periods according to set time length, calculating historical power consumption efficiency of different statistical periods in each statistical period based on the historical power consumption data of the power supply area corresponding to the large-scale generator, predicting power consumption pre-measurement of each statistical period in the current statistical period based on the historical power consumption efficiency of different statistical periods in each statistical period, taking time as an abscissa, taking the power consumption pre-measurement of each statistical period in the current statistical period as an ordinate, drawing a correlation curve of the power consumption pre-measurement of each statistical period in the current statistical period and the time in a coordinate system, and fitting a correlation change function of the power consumption pre-measurement of each statistical period and the time in the current statistical period based on the correlation curve of the power consumption pre-measurement of each statistical period and the time in the current statistical period;
and the generator operation planning module is used for judging whether the large generator can meet the power supply requirement of a corresponding power supply area or not and planning a real-time operation power generation efficiency scheme of the large generator for the large generator capable of meeting the power supply requirement based on the correlation change function of the power consumption pre-measurement value and time of each statistical period in the current statistical period and the maximum power generation efficiency of the large generator.
The power supply demand determination module includes:
the historical data acquisition unit is used for acquiring historical power consumption data of a power supply area corresponding to the large-scale generator, taking one year as a statistics period, dividing one year into different statistics periods according to set time length and calculating the historical power consumption efficiency of different statistics periods in each statistics period based on the historical power consumption data of the power supply area corresponding to the large-scale generator;
a power consumption prediction unit for predicting a power consumption prediction amount for each statistical period in the current statistical period based on the historical power consumption efficiency for different statistical periods in each statistical period;
and the function fitting unit is used for drawing a correlation curve of the power consumption pre-measurement and the time of each statistical period in the current statistical period and a correlation curve of the power consumption pre-measurement and the time of each statistical period based on the current statistical period in a coordinate system by taking the time as an abscissa and the power consumption pre-measurement of each statistical period in the current statistical period as an ordinate, and fitting a correlation change function of the power consumption pre-measurement and the time of each statistical period in the current statistical period.
The generator operation planning module includes:
the judging unit is used for judging whether the large-sized generator can meet the power supply requirement of a corresponding power supply area or not based on a related change function of the power consumption predicted quantity and time of each statistical period in the current statistical period and the maximum power generation efficiency of the large-sized generator;
and the power supply planning unit is used for planning a real-time running power generation efficiency scheme of the large-scale generator based on the maximum power generation efficiency of the large-scale generator and the power consumption prediction amount of each statistical period in the current statistical period.
In summary, the invention has the advantages that: the intelligent operation adjustment of the large-sized generator can be effectively realized, the operation load of the large-sized generator is reduced, and the service life of the large-sized generator is further prolonged, so that the high-efficiency utilization of energy and the reduction of cost are realized.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made therein without departing from the spirit and scope of the invention, which is defined by the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (10)

1. A method for determining an operational strategy of a large generator based on power supply and demand data, comprising:
acquiring operation data of a large-sized generator, and determining the maximum power generation efficiency of the large-sized generator based on the operation data of the large-sized generator;
acquiring historical power consumption data of a power supply area corresponding to a large-sized generator;
taking one year as a statistics period, and dividing the year into different statistics periods according to a set time length;
based on the historical power consumption data of the power supply area corresponding to the large-scale generator, calculating the historical power consumption efficiency of different statistical periods in each statistical period;
predicting a predicted amount of power consumption for each of the statistical periods within the current statistical period based on the historical power consumption efficiencies for different statistical periods within each statistical period;
drawing a correlation curve of the power consumption prediction amount and time of each statistical period in the current statistical period in a coordinate system by taking time as an abscissa and the power consumption prediction amount of each statistical period in the current statistical period as an ordinate;
fitting a correlation change function of the power consumption pre-measurement and time of each statistical period in the current statistical period based on the correlation curve of the power consumption pre-measurement and time of each statistical period in the current statistical period;
judging whether the large-scale generator can meet the power supply requirement of a corresponding power supply area or not based on a related change function of power consumption pre-measurement and time in each statistical period in the current statistical period and the maximum power generation efficiency of the large-scale generator, if so, judging that the large-scale generator can meet the power supply requirement, if not, judging that the large-scale generator is difficult to meet the power supply requirement, and outputting a generating capacity shortage signal;
for a large-sized generator capable of meeting the power supply requirement, planning a real-time operation power generation efficiency scheme of the large-sized generator based on the maximum power generation efficiency of the large-sized generator and the power consumption prediction amount of each statistical period in the current statistical period;
and controlling the large-scale generator to generate electricity according to the scheme of the real-time operation power generation efficiency of the large-scale generator.
2. The method according to claim 1, wherein the power consumption prediction measure for predicting each statistical period in the current statistical period based on the historical power consumption efficiency of different statistical periods in each statistical period comprises:
numbering a plurality of statistical periods in sequence from far to near according to the time interval with the current time;
and calculating the power consumption prediction amount of each statistical period in the current statistical period according to the power consumption prediction formula.
3. The method for determining a large-scale generator operation strategy based on power supply and demand data according to claim 2, wherein the electricity consumption prediction formula is:
wherein->Predicted amount of power consumption for a statistical period within a current statistical period, +.>For counting the total number of period numbers>For the historical power consumption efficiency according to the statistical period ranging from far to near in the j-th statistical period with the current time interval, +.>Is a power consumption demand trend parameter.
4. The method for determining a running strategy of a large-scale generator based on power supply and demand data according to claim 3, wherein the determining whether the large-scale generator can meet the power supply requirement of the corresponding power supply area based on the time-dependent variation function of the power consumption prediction amount and the maximum power generation efficiency of the large-scale generator in each statistical period in the current statistical period specifically comprises:
acquiring the energy storage conversion rate of energy storage equipment corresponding to the current large-scale generator;
screening a plurality of statistical periods of which the maximum power generation efficiency of the large-sized generator is greater than the power consumption predicted amount, and recording the statistical periods as redundant periods;
screening a plurality of statistical periods that the maximum power generation efficiency of the large-sized generator is smaller than the power consumption predicted amount, and recording the statistical periods as insufficient periods;
calculating the power generation redundancy amount of the large-scale generator in the redundancy period and the power generation shortage amount of the large-scale generator in the shortage period respectively;
constructing a power supply judgment inequality based on the power generation redundancy amount of the large-sized generator, the power generation shortage of the large-sized generator and the energy storage conversion rate of energy storage equipment corresponding to the large-sized generator;
and judging whether the power supply judging inequality is met, if so, the large-scale generator cannot meet the power supply requirement of the corresponding power supply area, and if not, the large-scale generator can meet the power supply requirement of the corresponding power supply area.
5. The method of determining a large generator operation strategy based on power supply and demand data of claim 4, wherein the power supply determination inequality is:
in the power supply determination inequality, +.>Is the energy storage conversion rate of energy storage equipment corresponding to the large-scale generator,is the power generation redundancy of a large-scale generator, < >>Is insufficient for the power generation of a large-scale generator.
6. The method for determining a running strategy of a large-scale generator based on power supply and demand data according to claim 5, wherein the planning of a real-time running power generation efficiency scheme of the large-scale generator based on the maximum power generation efficiency of the large-scale generator and the power consumption prediction amount of each statistical period in the current statistical period specifically comprises:
acquiring the nearest one or more redundant periods before the insufficient period, and recording the nearest one or more redundant periods as power-up redundant periods;
determining a power-up period in a power-up redundancy period through a power-up demand formula based on a related change function of power consumption pre-measurement and time of each statistical period in the statistical period;
controlling the large-scale generator to generate power according to the maximum power generation efficiency in the power supplementing period, and storing redundant power generation amount in the power supplementing period into energy storage equipment;
and controlling the large-scale generator to generate power according to the power consumption predicted amount corresponding to the current moment in the non-power-supplementing period in the power-supplementing redundancy period.
7. The method for determining a large-scale generator operation strategy based on power supply and demand data according to claim 6, wherein the power supply and demand formula specifically comprises:
wherein->For the termination of the power-on redundancy period, +.>For the start time of the power-up period, +.>Is the maximum power generation efficiency of the large-scale generator, < >>For the beginning of the deficient period, +.>For the termination of the deficient period, +.>A power consumption prediction amount and time related change function for each statistical period in the statistical period;
by calculating the electricity supplementing time period meeting the electricity supplementing requirement formulaWill be at the start ofAs a power up period within the power up redundancy period.
8. A large-scale generator operation strategy determination system based on power supply and demand data, characterized by being configured to implement the large-scale generator operation strategy determination method based on power supply and demand data as claimed in any one of claims 1 to 7, comprising:
the generator data acquisition module is used for acquiring the operation data of the large generator and determining the maximum power generation efficiency of the large generator based on the operation data of the large generator;
the power supply demand determining module is used for obtaining historical power consumption data of a power supply area corresponding to the large-scale generator, taking one year as a statistical period, dividing one year into different statistical periods according to set time length, calculating historical power consumption efficiency of different statistical periods in each statistical period based on the historical power consumption data of the power supply area corresponding to the large-scale generator, predicting power consumption pre-measurement of each statistical period in the current statistical period based on the historical power consumption efficiency of different statistical periods in each statistical period, taking time as an abscissa, taking the power consumption pre-measurement of each statistical period in the current statistical period as an ordinate, drawing a correlation curve of the power consumption pre-measurement of each statistical period in the current statistical period and the time in a coordinate system, and fitting a correlation change function of the power consumption pre-measurement of each statistical period and the time in the current statistical period based on the correlation curve of the power consumption pre-measurement of each statistical period and the time in the current statistical period;
the power generator operation planning module is used for judging whether the large-sized power generator can meet the power supply requirement of a corresponding power supply area or not and for the large-sized power generator which can meet the power supply requirement based on the correlation change function of the power consumption pre-measurement value and time of each statistical period in the current statistical period and the maximum power generation efficiency of the large-sized power generator, and planning a real-time operation power generation efficiency scheme of the large-sized power generator based on the maximum power generation efficiency of the large-sized power generator and the power consumption pre-measurement value of each statistical period in the current statistical period.
9. The large generator operation strategy determination system based on power supply and demand data of claim 8, wherein said power supply demand determination module comprises:
the historical data acquisition unit is used for acquiring historical power consumption data of a power supply area corresponding to the large-scale generator, taking one year as a statistical period, dividing one year into different statistical periods according to set time length and calculating the historical power consumption efficiency of different statistical periods in each statistical period based on the historical power consumption data of the power supply area corresponding to the large-scale generator;
a power consumption prediction unit for predicting a power consumption prediction amount for each statistical period in a current statistical period based on a historical power consumption efficiency for different statistical periods in each statistical period;
the function fitting unit is used for drawing a correlation curve of the power consumption pre-measurement and the time of each statistical period in the current statistical period and a correlation curve of the power consumption pre-measurement and the time of each statistical period based on the current statistical period in the coordinate system by taking the time as an abscissa and taking the power consumption pre-measurement of each statistical period in the current statistical period as an ordinate, and fitting out a correlation change function of the power consumption pre-measurement and the time of each statistical period in the current statistical period.
10. The large generator operation strategy determination system based on power supply and demand data of claim 9, wherein said generator operation planning module comprises:
the judging unit is used for judging whether the large-sized generator can meet the power supply requirement of a corresponding power supply area or not based on a related change function of the power consumption predicted quantity and time of each statistical period in the current statistical period and the maximum power generation efficiency of the large-sized generator;
and the power supply planning unit is used for planning a real-time running power generation efficiency scheme of the large-scale generator based on the maximum power generation efficiency of the large-scale generator and the power consumption prediction amount of each statistical period in the current statistical period.
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2001258157A (en) * 2000-03-09 2001-09-21 Hitachi Ltd Device and method of developing operation plan for generator and recording medium storing operation plan program for generator
JP2013038958A (en) * 2011-08-09 2013-02-21 Chugoku Electric Power Co Inc:The Method of checking balance of power supply and demand, and device for checking balance of power supply and demand
US20180233922A1 (en) * 2017-02-14 2018-08-16 Mitsubishi Electric Research Laboratories, Inc. Energy Production and Frequency Regulation Co-Optimization for Power Generation Systems
US20200234209A1 (en) * 2016-05-10 2020-07-23 Conectric, Llc Method and system for adaptively switching prediction strategies optimizing time-variant energy consumption of built environment
CN112634077A (en) * 2020-12-18 2021-04-09 四川大汇大数据服务有限公司 Medium-and-long-term power supply and demand situation analysis method
CN114865627A (en) * 2022-06-20 2022-08-05 南方电网科学研究院有限责任公司 Power distribution method, device and equipment based on supply and demand relationship

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2001258157A (en) * 2000-03-09 2001-09-21 Hitachi Ltd Device and method of developing operation plan for generator and recording medium storing operation plan program for generator
JP2013038958A (en) * 2011-08-09 2013-02-21 Chugoku Electric Power Co Inc:The Method of checking balance of power supply and demand, and device for checking balance of power supply and demand
US20200234209A1 (en) * 2016-05-10 2020-07-23 Conectric, Llc Method and system for adaptively switching prediction strategies optimizing time-variant energy consumption of built environment
US20180233922A1 (en) * 2017-02-14 2018-08-16 Mitsubishi Electric Research Laboratories, Inc. Energy Production and Frequency Regulation Co-Optimization for Power Generation Systems
CN112634077A (en) * 2020-12-18 2021-04-09 四川大汇大数据服务有限公司 Medium-and-long-term power supply and demand situation analysis method
CN114865627A (en) * 2022-06-20 2022-08-05 南方电网科学研究院有限责任公司 Power distribution method, device and equipment based on supply and demand relationship

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
王健, 文福拴, 杨仁刚: "基于发电容量充裕度估计的发电公司检修策略", 电力系统自动化, no. 06, 15 June 2005 (2005-06-15), pages 45 - 50 *

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