CN114938008A - Energy storage capacity and heat storage capacity configuration method and device and terminal equipment - Google Patents
Energy storage capacity and heat storage capacity configuration method and device and terminal equipment Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
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Abstract
The application is applicable to the technical field of power systems, and provides a method and a device for configuring energy storage capacity and heat storage capacity and terminal equipment. The method comprises the following steps: performing time sequence production simulation processing on the acquired historical data, and determining a wind power output time sequence, a photovoltaic output time sequence and a load time sequence; determining a reference energy storage capacity and a reference heat storage capacity according to the obtained wind power, photovoltaic and photo-thermal installed capacities; based on the data, a time sequence production simulation model is constructed by taking the optimal economy as a target function and taking power balance constraint, new energy power generation output constraint, photo-thermal power station constraint and electrochemical energy storage battery constraint as constraint conditions; solving the time sequence production simulation model by using a CPLEX solver, and determining the optimal energy storage capacity and the optimal heat storage capacity according to a solving result, the reference energy storage capacity and the reference heat storage capacity. This application can accurately dispose energy storage capacity and heat-retaining capacity effectively, and then improves new forms of energy utilization ratio.
Description
Technical Field
The application relates to the technical field of power systems, in particular to a method and a device for configuring energy storage capacity and heat storage capacity and terminal equipment.
Background
In recent years, with the introduction of the "dual carbon" goal, new energy power generation has received much attention. Wind power and photovoltaic power generation are increasingly applied to power systems due to the advantages of zero emission and no pollution. However, the new energy output has fluctuation and randomness, and influences the operation of the power system. In order to ensure the stable operation of the power system, the adjustable power supplies such as the energy storage and the heat storage stabilize the influence generated by the fluctuation and the randomness of new energy by adjusting output.
In the prior art, a typical daily method is usually adopted to select a representative daily scene of wind power generation and photovoltaic power generation, and the output of the daily scene represents the output of the whole wind power generation and photovoltaic power generation in the whole time period to optimize the energy storage capacity and the heat storage capacity.
However, the optimization of energy storage and heat storage by using a typical daily method has limitations, and cannot reflect the actual conditions of wind power generation and photovoltaic power generation in a long time scale, so that the energy storage capacity and the heat storage capacity cannot be accurately and effectively configured, and further the utilization rate of new energy is low.
Disclosure of Invention
In view of this, embodiments of the present application provide a method and an apparatus for configuring energy storage capacity and heat storage capacity, and a terminal device, so as to solve the technical problem in the prior art that the energy storage capacity and the heat storage capacity cannot be accurately and effectively configured, and therefore the utilization rate of new energy is low.
In a first aspect, an embodiment of the present application provides a method for configuring energy storage capacity and heat storage capacity, including:
acquiring historical data, wind power installed capacity, photovoltaic installed capacity and photo-thermal installed capacity; performing time sequence production simulation processing on the historical data, and determining a wind power output time sequence, a photovoltaic output time sequence and a load time sequence; determining a reference energy storage capacity according to the installed wind power capacity and the installed photovoltaic capacity, and determining a reference heat storage capacity according to the installed photothermal capacity;
constructing a time sequence production simulation model based on the wind power output time sequence, the photovoltaic output time sequence and the load time sequence, taking the optimal economy as a target function, and taking power balance constraint, new energy power generation output constraint, photo-thermal power station constraint and electrochemical energy storage battery constraint as constraint conditions;
and solving the time sequence production simulation model by using a CPLEX solver, and determining the optimal energy storage capacity and the optimal heat storage capacity according to a solving result, the reference energy storage capacity and the reference heat storage capacity.
In one possible embodiment of the first aspect, the historical data is historical data of a unit time length, and the historical data includes wind speed historical data, light intensity historical data and load historical data;
the time sequence production simulation processing is carried out on the historical data, and the wind power output time sequence, the photovoltaic output time sequence and the load time sequence are determined, and the method comprises the following steps:
sampling and clustering the wind speed historical data, the illumination intensity historical data and the load historical data by adopting a Latin cube sampling method and a central point clustering algorithm, and determining a wind speed time sequence, an illumination intensity time sequence and a load time sequence of a unit time length;
determining a wind power output time sequence of unit time length according to the wind speed time sequence of unit time length, and determining a photovoltaic output time sequence of unit time length according to the illumination intensity time sequence of unit time length;
and acquiring a plurality of unit time length historical data, repeating the steps, and respectively determining a wind power output time sequence, a photovoltaic output time sequence and a load time sequence according to the wind power output time sequence, the photovoltaic output time sequence and the load time sequence of the unit time lengths.
In a possible implementation manner of the first aspect, the determining a reference energy storage capacity according to the installed wind power capacity and the installed photovoltaic capacity and determining a reference heat storage capacity according to the installed photothermal capacity includes:
determining a reference energy storage capacity according to the product of the sum of the wind power installed capacity and the photovoltaic installed capacity and a preset coefficient;
and determining the reference heat storage capacity according to the photo-thermal installed capacity and the heat storage time.
In a possible implementation manner of the first aspect, the objective function is:
in the formula (I), the compound is shown in the specification,
in order to reduce the power generation cost of the photo-thermal power station,
for the overall discharge cost, lambda, of an electrochemical energy storage cell RC A penalty coefficient for the new energy power abandonment,
as new energyWaste electricity amount, lambda LC In order to make the load-shedding penalty factor,
in order to cut the load, C is the construction cost of the unit electrochemical energy storage battery, S OC_max For reference energy storage capacity, xi bat The energy storage capacity coefficient is T, and the production simulation duration is T.
In a possible implementation manner of the first aspect, the power balance constraint is:
in the formula (I), the compound is shown in the specification,
real-time output is scheduled for the ith photo-thermal unit of the photo-thermal power station in the t time period,
real-time output is dispatched for the photovoltaic in the period of t,
real-time output is scheduled for the wind power in the time period t,
for the time period t to participate in scheduling the balanced load,
for the discharge power of the mth electrochemical energy storage battery in the period t,
charging power of mth electrochemical energy storage battery for t time period, N csp Number of photothermal units in photothermal power station, N bat The number of the electrochemical energy storage batteries is;
the new energy power generation output constraint is as follows:
in the formula (I), the compound is shown in the specification,
the maximum output of the wind power is obtained in the time period of t,
the maximum photovoltaic output is obtained in the period of t,
is the installed capacity of the wind power generation,
is the photovoltaic installed capacity.
In one possible embodiment of the first aspect, the photothermal power station constraints include a thermal storage capacity constraint, a thermal collection field heat dynamic balance constraint, a thermal tank heat dynamic balance constraint, a photothermal power station power generation output constraint, and a photothermal power station ramp constraint;
the constraint of the electrochemical energy storage battery comprises energy storage capacity constraint, energy storage charging and discharging power constraint and energy storage capacity balance constraint.
In a possible implementation manner of the first aspect, the solving the time series production simulation model by using a CPLEX solver, and determining an optimal energy storage capacity and an optimal heat storage capacity according to a solution result, the reference energy storage capacity, and the reference heat storage capacity includes:
solving the time sequence production simulation model by using a CPLEX solver to determine an optimal energy storage capacity coefficient and an optimal heat storage capacity coefficient;
and determining the optimal energy storage capacity according to the reference energy storage capacity and the optimal energy storage capacity coefficient, and determining the optimal heat storage capacity according to the reference heat storage capacity and the optimal heat storage capacity coefficient.
In a second aspect, an embodiment of the present application provides an energy storage capacity and heat storage capacity configuration device, including:
the acquisition module is used for acquiring historical data, wind power installed capacity, photovoltaic installed capacity and photo-thermal installed capacity;
the first determining module is used for performing time sequence production simulation processing on the historical data and determining a wind power output time sequence, a photovoltaic output time sequence and a load time sequence;
the second determining module is used for determining a reference energy storage capacity according to the wind power installed capacity and the photovoltaic installed capacity and determining a reference heat storage capacity according to the photo-thermal installed capacity;
the construction module is used for constructing a time sequence production simulation model based on the wind power output time sequence, the photovoltaic output time sequence and the load time sequence, with optimal economy as a target function and with power balance constraint, new energy power generation output constraint, photo-thermal power station constraint and electrochemical energy storage battery constraint as constraint conditions;
and the configuration module is used for solving the time sequence production simulation model by using a CPLEX solver, and determining the optimal energy storage capacity and the optimal heat storage capacity according to the solving result, the reference energy storage capacity and the reference heat storage capacity.
In a third aspect, an embodiment of the present application provides a terminal device, including a memory and a processor, where the memory stores a computer program executable on the processor, and the processor implements the method for configuring the energy storage capacity and the heat storage capacity according to any one of the first aspect when executing the computer program.
In a fourth aspect, embodiments provide a computer-readable storage medium storing a computer program, which when executed by a processor, implements the energy storage capacity and heat storage capacity configuration method according to any one of the first aspect.
In a fifth aspect, the present application provides a computer program product, which when run on a terminal device, causes the terminal device to perform the method for configuring energy storage capacity and heat storage capacity as described in any one of the first aspects above.
It is to be understood that, for the beneficial effects of the second aspect to the fifth aspect, reference may be made to the relevant description in the first aspect, and details are not described herein again.
According to the method, the device and the terminal equipment for configuring the energy storage capacity and the heat storage capacity, the wind power output time sequence, the photovoltaic output time sequence and the load time sequence are determined by performing time sequence production simulation processing on the acquired historical data, the reference energy storage capacity and the reference heat storage capacity are determined according to the wind power installed capacity, the photovoltaic installed capacity and the photo-thermal installed capacity, a time sequence production simulation model is constructed by taking the economic optimality as a target function and taking power balance constraint, new energy generation output constraint, photo-thermal power station constraint and electrochemical energy storage battery constraint as constraint conditions based on the data, a CPLEX solver is used for solving the time sequence production simulation model, the optimal energy storage capacity and the optimal heat storage capacity are determined according to the solving result, the reference energy storage capacity and the reference heat storage capacity, and the energy storage capacity and the heat storage capacity can be accurately and effectively configured, thereby improving the utilization rate of new energy.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the specification.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required to be used in the embodiments or the prior art description will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings may be obtained according to these drawings without inventive labor.
FIG. 1 is a schematic flow chart diagram illustrating a method for configuring energy storage capacity and heat storage capacity according to an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart diagram of a method for configuring energy storage capacity and heat storage capacity according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of an arrangement of energy storage capacity and heat storage capacity provided by an embodiment of the present application;
fig. 4 is a schematic structural diagram of a terminal device according to an embodiment of the present application.
Detailed Description
The present application will be described more clearly with reference to specific examples. The following examples will assist those skilled in the art in further understanding the role of the present application, but are not intended to limit the application in any way. It should be noted that various changes and modifications can be made by one skilled in the art without departing from the spirit of the application. All falling within the scope of protection of the present application.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather mean "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
To make the objects, technical solutions and advantages of the present application more apparent, the following description is given by way of example with reference to the accompanying drawings.
In recent years, with the introduction of the "dual carbon" goal, new energy power generation has received much attention. The new energy power generation forms are various, wherein the wind power generation and the photovoltaic power generation are increasingly applied to a power system due to the advantage of zero emission and no pollution. However, as the new energy power generation is connected to the power grid in a large scale, the fluctuation and randomness of the new energy output have increasingly serious influence on the operation of the power system. In order to ensure the stable operation of the power system, the adjustable power supplies such as the energy storage and the heat storage stabilize the influence generated by the fluctuation and the randomness of new energy by adjusting output. The solar-thermal power generation is another utilization form of solar power generation, has the advantages of stable and adjustable output, storable heat and the like, and the electrochemical energy storage can convert the electric energy of the new energy abandoned into the electric energy for storage, so that the two can be used as an energy storage and heat storage adjustable power supply.
In the existing method for determining the energy storage capacity and the heat storage capacity, a typical day method is usually adopted to select a representative day scene of wind power generation and photovoltaic power generation, and the output of the day scene represents the output of the whole wind power generation and photovoltaic power generation whole time period to optimize the energy storage capacity and the heat storage capacity. However, the output of wind power and photovoltaic power generation has randomness and volatility in a short time scale and seasonal characteristics in a long time scale, and the method of a typical day cannot reflect the actual operation conditions of wind power and photovoltaic power generation in the long time scale, that is, the method of the typical day is limited in optimizing the energy storage capacity and the heat storage capacity, so that the energy storage capacity and the heat storage capacity cannot be accurately and effectively configured, and the utilization rate of new energy is low.
Based on the problems, the inventor finds that the optimized time sequence simulation method can be adopted to simulate the output of wind power and photovoltaic power generation and the load of a power generation system under a long-time scale, namely, the time sequence of the wind power and photovoltaic output and the time sequence of the load are simulated, then a time sequence production simulation model which takes the optimal economy as an objective function and takes the power balance constraint, the new energy power generation output constraint, the photothermal power station constraint and the electrochemical energy storage battery constraint as constraint conditions is constructed according to the time sequence, and the time sequence production simulation model is solved to configure the optimal energy storage capacity and the optimal heat storage capacity.
That is to say, the embodiment of the application determines the wind power output time sequence, the photovoltaic output time sequence and the load time sequence by performing time sequence production simulation processing on the acquired historical data, and the reference energy storage capacity and the reference heat storage capacity are determined according to the installed capacity of wind power, the installed capacity of photovoltaic power and the installed capacity of photo-thermal power, based on the data, constructing a time sequence production simulation model by taking the optimal economy as a target function and taking power balance constraint, new energy power generation output constraint, photo-thermal power station constraint and electrochemical energy storage battery constraint as constraint conditions, solving the time sequence production simulation model by using a CPLEX solver, and then determining the optimal energy storage capacity and the optimal heat storage capacity according to the solving result, the reference energy storage capacity and the reference heat storage capacity, and accurately and effectively configuring the energy storage capacity and the heat storage capacity so as to improve the utilization rate of new energy.
Fig. 1 is a schematic flow chart of a method for configuring energy storage capacity and heat storage capacity according to an embodiment of the present disclosure. As shown in fig. 1, the method in the embodiment of the present application may include:
Alternatively, the historical data may include historical wind speed data, historical light intensity data, historical load data and the like of the power plant or the region, and the time resolution may be 1 hour. The historical data may be historical data of a unit time length, the unit time may be a week, a month, a season, a year, or the like, and correspondingly, the historical data may be week historical data, month historical data, season historical data, calendar historical data, or the like. The installed wind power capacity is the sum of rated power of all wind generating sets configured in a power plant or a region, the installed photovoltaic capacity is the sum of rated power of all photovoltaic generating sets configured in the power plant or the region, the installed photothermal capacity is the sum of rated power of all photothermal generating sets configured in the power plant or the region, and the data of the installed wind power capacity, the installed photovoltaic capacity and the installed photothermal capacity can be directly obtained from relevant units of the power plant or the region.
And 102, performing time sequence production simulation processing on the historical data, and determining a wind power output time sequence, a photovoltaic output time sequence and a load time sequence.
In a possible implementation manner, referring to fig. 2, in step 102, performing time-series production simulation processing on the historical data, and determining a wind power output time-series sequence, a photovoltaic output time-series sequence, and a load time-series sequence, may specifically include:
and 1021, sampling and clustering the wind speed historical data, the illumination intensity historical data and the load historical data by adopting a Latin cube sampling method and a central point clustering algorithm, and determining a wind speed time sequence, an illumination intensity time sequence and a load time sequence in unit time length.
Alternatively, for convenience of description, the historical data with the unit time length as the month length is used for explanation in step 1021. Acquiring historical data of the month length, namely month historical data, wherein the historical data of the month length comprises historical wind speed data, historical light intensity data and historical load data, and the time resolution is 1 hour.
Wherein, step 1021 may specifically include:
s1, determining probability distribution parameters corresponding to the wind speed historical data, the illumination intensity historical data and the load historical data of the month length respectively;
s2, sampling the monthly wind speed historical data, the illumination intensity historical data and the load historical data by adopting a Latin cube sampling method according to the probability distribution parameters to obtain wind speed simulation data, illumination intensity simulation data and load simulation data of a plurality of daily lengths;
s3, clustering wind speed simulation data, illumination intensity simulation data and load simulation data of a plurality of day lengths respectively by adopting a central point (k-medoids) clustering algorithm, and determining a plurality of corresponding wind speed typical scenes, illumination intensity typical scenes and load typical scenes;
and S4, clustering the historical wind speed data of the month length according to the plurality of wind speed typical scenes, calculating a state transition probability matrix of each wind speed typical scene under the historical wind speed data of the month length according to a clustering result, and determining a wind speed time sequence of the month length according to the state transition probability matrix. And clustering the historical illumination intensity data of the month length according to the plurality of typical illumination intensity scenes, calculating a state transition probability matrix of each typical illumination intensity scene under the historical illumination intensity data of the month length according to a clustering result, and determining an illumination intensity time sequence of the month length according to the state transition probability matrix. And clustering the load historical data of the month length according to the plurality of load typical scenes, calculating a state transition probability matrix of each load typical scene under the load historical data of the month length according to a clustering result, and determining a load time sequence of the month length according to the state transition probability matrix.
Illustratively, the wind speed historical data, the illumination intensity historical data and the load historical data of the month length are respectively calculated, and the probability distribution obeyed by the wind speed historical data, the illumination intensity historical data and the load historical data of the month length and the corresponding probability distribution parameters are determined. For example, it is calculated that the historical wind speed data of the month length obeys the Weihr distribution, the historical light intensity data obeys the Beta distribution, the historical load data obeys the normal distribution, and the corresponding probability distribution parameters are determined according to the obeyed probability distribution.
Alternatively, the Latin cube sampling method is used as a hierarchical sampling method, and has higher sample space coverage rate and smaller error compared with other sampling methods. Compared with other clustering algorithms such as k-means clustering algorithm and the like, the k-means clustering algorithm can better keep the volatility of the original data.
For example, clustering wind speed simulation data of a plurality of day lengths can obtain wind speed typical scenes such as large fluctuation, medium fluctuation, small fluctuation and the like, and wind speed simulation data of the day lengths after clustering. The illumination intensity simulation data of the lengths of the days are clustered, and illumination intensity typical scenes similar to sunny days, cloudy days, rainy days, cloudy days and the like and the illumination intensity simulation data of the lengths of the days after clustering can be obtained. And clustering the load simulation data of a plurality of day lengths to obtain a plurality of load typical scenes and the load simulation data of the day lengths after clustering. Wherein, the number of the typical scenes is determined according to the actual requirement and the stability of the clustering algorithm. The larger the number of typical scenes, the higher the accuracy of the time sequence obtained in the subsequent calculation.
For example, after the state transition probability matrix of each wind speed typical scene is determined, wind speed simulation data of any day length is randomly selected from the wind speed simulation data of a plurality of day lengths after clustering processing to serve as wind speed data of a day length of a first day, wind speed data of a day length of a second day is calculated according to the state transition probability matrix of each wind speed typical scene, wind speed data of a day length of a third day is calculated according to the wind speed data of the day length of the second day and the state transition probability matrix of each wind speed typical scene, and by analogy, a wind speed time sequence of a month length is determined.
And step 1022, determining the wind power output time sequence of unit time length according to the wind speed time sequence of unit time length, and determining the photovoltaic output time sequence of unit time length according to the illumination intensity time sequence of unit time length.
Optionally, the wind power output time sequence of the unit time length is obtained according to the wind speed time sequence of the unit time length and a wind speed-output conversion formula, and the photovoltaic output time sequence of the unit time length is obtained according to the illumination intensity time sequence of the unit time length and an illumination intensity-output conversion formula.
And 1023, acquiring historical data of a plurality of unit time lengths, repeating the steps 1021-1022, and respectively determining a wind power output time sequence, a photovoltaic output time sequence and a load time sequence according to the wind power output time sequence, the photovoltaic output time sequence and the load time sequence of the plurality of unit time lengths.
Illustratively, historical data of a plurality of unit time lengths are acquired, for example, historical data of a plurality of months of length are acquired, steps 1021 to 1022 are repeated for each unit time length historical data, wind power output time sequence, photovoltaic output time sequence and load time sequence of the plurality of unit time lengths can be acquired, then the wind power output time sequence, photovoltaic output time sequence and load time sequence of the plurality of unit time lengths are respectively sequenced according to the sequence of the unit time, and wind power output time sequence, photovoltaic output time sequence and load time sequence of which the time resolution is 1 hour and the time length is production simulation time length can be acquired, wherein the production simulation time length can be set according to needs.
For example, monthly history data of each month of 1-12 months are acquired, the monthly history data of 1-12 months are respectively calculated to obtain a wind power output time sequence, a photovoltaic output time sequence and a load time sequence of 1-12 months, the wind power output time sequence, the photovoltaic output time sequence and the load time sequence of the months are sequenced according to the month sequence, and a wind power output time sequence, a photovoltaic output time sequence and a load time sequence of 8760 hours (namely one year) of production simulation duration and 1 hour of time resolution are obtained.
Optionally, the month history data may be acquired according to the production simulation duration, for example, if the production simulation duration is one year, the month history data of 12 months may be acquired, and if the production simulation duration is half a year, the month history data of 6 months may be acquired.
In the embodiment, the difference between the historical data of the unit time length is considered, for example, the difference between the historical data of the monthly calendar is considered, the historical data of each month are respectively calculated and processed to obtain the time sequence of the month length, and then the time sequence of the month length of each month is sequenced to obtain the time sequence of the production simulation time length, so that the wind power output time sequence, the photovoltaic output time sequence and the load time sequence can be more accurately and effectively simulated, namely the actual conditions of wind power generation, photovoltaic power generation and load under a long time scale are more accurately and effectively reflected.
And 103, determining a reference energy storage capacity according to the installed wind power capacity and the installed photovoltaic capacity, and determining a reference heat storage capacity according to the installed photothermal capacity.
Wherein optionally, carry out and confirm benchmark energy storage capacity according to wind-powered electricity generation installed capacity and photovoltaic installed capacity, confirm the step of benchmark heat-retaining capacity according to light and heat installed capacity, specifically can include: and determining the reference energy storage capacity according to the product of the sum of the installed wind power capacity and the installed photovoltaic capacity and a preset coefficient, and determining the reference heat storage capacity according to the installed photothermal capacity and the heat storage time.
Optionally, the preset coefficient may be 10%, and 10% of the sum of the installed wind power capacity and the installed photovoltaic capacity is used as the reference energy storage capacity. The product of the photo-thermal installed capacity and the heat storage time is used as the reference heat storage capacity, wherein the heat storage time can be 6 hours, namely the reference heat storage capacity is determined by the fact that the photo-thermal power station internal heat storage capacity can meet the requirement that all photo-thermal units independently operate for 6 hours under rated output.
And step 104, constructing a time sequence production simulation model by taking the optimal economy as a target function and taking power balance constraint, new energy power generation output constraint, photo-thermal power station constraint and electrochemical energy storage battery constraint as constraint conditions on the basis of the wind power output time sequence, the photovoltaic output time sequence and the load time sequence.
Optionally, the objective function is:
in the formula (I), the compound is shown in the specification,
in order to reduce the power generation cost of the photo-thermal power station,
for the overall discharge cost, lambda, of an electrochemical energy storage cell RC A penalty coefficient for the new energy power abandonment,
abandoning electricity for new energy, lambda LC In order to make the load-shedding penalty factor,
for load shedding, C is the cost of construction of the electrochemical energy storage cell per unit, S OC_max For reference energy storage capacity, xi bat The energy storage capacity coefficient is T, and the production simulation duration is T.
Wherein, the electricity generation cost of the photo-thermal power station is as follows:
in the formula (I), the compound is shown in the specification,
is a function of the operation and maintenance cost and the output of the photo-thermal unit, and
and
binary state variables for startup and shutdown of the ith photothermal unit respectively,
and
the startup and shutdown costs of the ith photothermal unit, C csp The unit photo-thermal unit power generation cost is high,
and scheduling real-time output for the ith photo-thermal unit of the photo-thermal power station in the t time period.
The overall discharge cost of the electrochemical energy storage battery is as follows:
in the formula (I), the compound is shown in the specification,
for electrochemical energy storage cellsRunning cost function during discharge, C bat For the cost of discharge per electrochemical energy storage cell,
and discharging power of the mth electrochemical energy storage battery for the t time period.
The electric quantity abandoned by the new energy is as follows:
in the formula (I), the compound is shown in the specification,
the maximum output of the wind power is obtained in the time period of t,
real-time output is scheduled for the wind power in the time period t,
the maximum photovoltaic output is obtained in the period of t,
and (5) dispatching real-time output for the photovoltaic in the t time period.
The load shedding amount is as follows:
in the formula (I), the compound is shown in the specification,
participate in scheduling balanced loads for a period t, an
Real-time loading for a period t.
Optionally, the power balance constraint is:
in the formula (I), the compound is shown in the specification,
charging power, N, for the mth electrochemical energy storage cell in the t period csp Number of photothermal units in photothermal power station, N bat The number of the electrochemical energy storage batteries is.
The new energy power generation output constraint is as follows:
in the formula (I), the compound is shown in the specification,
in order to obtain the installed capacity of wind power,
is the photovoltaic installed capacity.
Optionally, the photo-thermal power station constraint includes a heat storage capacity constraint, a heat collection field heat dynamic balance constraint, a thermal tank heat dynamic balance constraint, a photo-thermal power station power generation output constraint and a photo-thermal power station climbing constraint.
The heat storage capacity constraint is:
in the formula (I), the compound is shown in the specification,
the heat storage capacity is taken as a reference,
heat quantity stored for the hot pot in the period t, xi csp The coefficient of the heat storage capacity is the variable to be optimized.
The heat dynamic balance constraint of the heat collection field is as follows:
in the formula (I), the compound is shown in the specification,
the heat for generating electricity of the ith photo-thermal unit of the photo-thermal power station in the t time period,
and
the total solar heat absorbed by the heat collection field in the period of t, the heat stored in the heat tank by the photothermal power station and the waste heat of the photothermal power station,
the heat efficiency of absorbing heat to the heat collecting field and transferring the heat to the heat tank.
The thermal dynamic balance constraint of the hot tank is as follows:
in the formula (I), the compound is shown in the specification,
the amount of heat stored in the hot tank for the period t +1,
the amount of heat transferred from the hot pot to the power generation side for the period t,
the heat efficiency of the heat tank transferred to the power generation side.
The thermoelectric conversion efficiency constraints are:
in the formula (I), the compound is shown in the specification,
for the generating efficiency of the ith photo-thermal unit of the photo-thermal power station,
and (4) scheduling real-time output of the ith photo-thermal unit of the photo-thermal power station at the t time interval.
The power generation output constraint of the photo-thermal power station is as follows:
in the formula (I), the compound is shown in the specification,
and
the minimum technical output and the maximum technical output of the ith photothermal unit of the photothermal power station are respectively.
The climbing constraint of the photo-thermal power station is as follows:
in the formula (I), the compound is shown in the specification,
the scheduling real-time output R of the ith photo-thermal unit of the photo-thermal power station in the t-1 time period U And R D The maximum climbing capacity and the maximum descending capacity of the photo-thermal unit are respectively.
Optionally, the constraints of the electrochemical energy storage battery include energy storage capacity constraints, energy storage charge-discharge power constraints, and energy storage capacity balance constraints.
The energy storage capacity constraint is:
in the formula, S OC_max For the reference energy storage capacity, S OC,m,t The charge quantity, xi, of the mth electrochemical energy storage battery in the t period bat The energy storage capacity coefficient and the variable to be optimized.
The energy storage capacity balance constraint is:
in the formula, S OC,m,t-1 The charge amount of the mth electrochemical energy storage battery is in the t-1 period,
and
the operation discharge state and the charge state of the mth electrochemical energy storage battery are respectively in the t time period eta c And η d The discharging efficiency and the charging efficiency of the electrochemical energy storage battery are respectively, and delta t is the time difference between the t time period and the t-1 time period.
Wherein, the first and the second end of the pipe are connected with each other,
and
is a variable of 0 to 1, e.g.) "
And is
"represents a charged state"
And is
"represents a discharge state.
The energy storage charge and discharge power constraint is as follows:
in the formula (I), the compound is shown in the specification,
and
respectively the minimum power and the maximum power in the discharging process of the electrochemical energy storage battery,
and with
Respectively the minimum power and the maximum power in the charging process of the electrochemical energy storage battery.
And 105, solving the time sequence production simulation model by using a CPLEX solver, and determining the optimal energy storage capacity and the optimal heat storage capacity according to the solving result, the reference energy storage capacity and the reference heat storage capacity.
Optionally, the step of solving the time sequence production simulation model by using a CPLEX solver, and determining the optimal energy storage capacity and the optimal heat storage capacity according to the solution result, the reference energy storage capacity, and the reference heat storage capacity may specifically include: solving the time sequence production simulation model by using a CPLEX solver to determine an optimal energy storage capacity coefficient and an optimal heat storage capacity coefficient; and determining the optimal energy storage capacity according to the reference energy storage capacity and the optimal energy storage capacity coefficient, and determining the optimal heat storage capacity according to the reference heat storage capacity and the optimal heat storage capacity coefficient.
Optionally, the product operation is performed on the reference energy storage capacity and the optimal energy storage capacity coefficient to obtain the optimal energy storage capacity, and the product operation is performed on the reference heat storage capacity and the optimal heat storage capacity coefficient to obtain the optimal heat storage capacity.
The method for configuring the energy storage capacity and the heat storage capacity provided by the embodiment of the application determines a wind power output time sequence, a photovoltaic output time sequence and a load time sequence by performing time sequence production simulation processing on the acquired historical data, determines a reference energy storage capacity and a reference heat storage capacity according to the wind power installed capacity, the photovoltaic installed capacity and the photo-thermal installed capacity, constructs a time sequence production simulation model by taking the optimal economical efficiency as a target function and taking power balance constraint, new energy power generation output constraint, photo-thermal power station constraint and electrochemical energy storage battery constraint as constraint conditions based on the data, solves the time sequence production simulation model by using a CPLEX solver, determines the optimal energy storage capacity and the optimal heat storage capacity according to the solution result, the reference energy storage capacity and the reference heat storage capacity, and can accurately and effectively configure the energy storage capacity and the heat storage capacity, thereby improving the utilization rate of new energy.
A simple example is that wind speed historical data, illumination intensity historical data and load historical data of each month and month length of 1-12 months in a certain place are selected, the historical data are processed by the energy storage capacity and heat storage capacity configuration method provided by the embodiment of the application, and the optimal energy storage capacity and the optimal heat storage capacity are determined. The method specifically comprises the following steps: and performing time sequence production simulation processing on the historical data to obtain a wind power output time sequence, a photovoltaic output time sequence and a load time sequence with production simulation duration of one year. And constructing a time sequence production simulation model by taking the optimal economy as an objective function based on the time sequence. The ground is configured with wind power installed capacity 600MW, photovoltaic installed capacity 400MW and light and heat installed capacity 300 MW. And setting a load shedding penalty coefficient to be 3000 yuan/MW & h, a new energy power abandon penalty coefficient to be 300 yuan/MW & h, and the overall discharge cost of the electrochemical energy storage battery to be 240 yuan/MW & h. The startup and shutdown cost of the photothermal unit is 6000 yuan/time, and the construction cost of the unit electrochemical energy storage battery is 750000 yuan/MW & h. And solving the time sequence production simulation model by using a CPLEX solver, setting a solving clearance to be 0.005%, and obtaining the optimal energy storage capacity of 391MW & h and the optimal heat storage capacity of 3000MW & h.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Fig. 3 is a schematic structural diagram of an arrangement of energy storage capacity and heat storage capacity according to an embodiment of the present application. As shown in fig. 3, the energy storage capacity and heat storage capacity configuration apparatus provided in this embodiment may include: an acquisition module 301, a first determination module 302, a second determination module 303, a construction module 304, and a configuration module 305.
The acquisition module 301 is used for acquiring historical data, wind power installed capacity, photovoltaic installed capacity and photo-thermal installed capacity;
the first determining module 302 is configured to perform time sequence production simulation processing on the historical data, and determine a wind power output time sequence, a photovoltaic output time sequence and a load time sequence;
the second determining module 303 is configured to determine a reference energy storage capacity according to the wind power installed capacity and the photovoltaic installed capacity, and determine a reference heat storage capacity according to the photo-thermal installed capacity;
the building module 304 is used for building a time sequence production simulation model based on the wind power output time sequence, the photovoltaic output time sequence and the load time sequence, with the optimal economy as a target function and with the power balance constraint, the new energy power generation output constraint, the photo-thermal power station constraint and the electrochemical energy storage battery constraint as constraint conditions;
the configuration module 305 is configured to solve the time sequence production simulation model by using a CPLEX solver, and determine an optimal energy storage capacity and an optimal heat storage capacity according to a solution result, the reference energy storage capacity, and the reference heat storage capacity.
Optionally, the historical data is historical data of a unit time length, the historical data includes historical wind speed data, historical light intensity data, and historical load data, and the first determining module 302 is specifically configured to:
sampling and clustering the wind speed historical data, the illumination intensity historical data and the load historical data by adopting a Latin cube sampling method and a central point clustering algorithm, and determining a wind speed time sequence, an illumination intensity time sequence and a load time sequence of a unit time length;
determining a wind power output time sequence of unit time length according to the wind speed time sequence of unit time length, and determining a photovoltaic output time sequence of unit time length according to the illumination intensity time sequence of unit time length;
and acquiring a plurality of unit time length historical data, repeating the steps, and respectively determining a wind power output time sequence, a photovoltaic output time sequence and a load time sequence according to the wind power output time sequence, the photovoltaic output time sequence and the load time sequence of the unit time lengths.
Optionally, the second determining module 303 is specifically configured to:
determining a reference energy storage capacity according to the product of the sum of the installed wind power capacity and the installed photovoltaic capacity and a preset coefficient;
and determining the reference heat storage capacity according to the photo-thermal installed capacity and the heat storage time.
Optionally, the configuration module 305 is specifically configured to:
solving the time sequence production simulation model by using a CPLEX solver to determine an optimal energy storage capacity coefficient and an optimal heat storage capacity coefficient;
and determining the optimal energy storage capacity according to the reference energy storage capacity and the optimal energy storage capacity coefficient, and determining the optimal heat storage capacity according to the reference heat storage capacity and the optimal heat storage capacity coefficient.
It should be noted that, for the information interaction, execution process, and other contents between the above-mentioned devices/units, the specific functions and technical effects thereof are based on the same concept as those of the embodiment of the method of the present application, and specific reference may be made to the part of the embodiment of the method, which is not described herein again.
Fig. 4 is a schematic structural diagram of a terminal device according to an embodiment of the present application. As shown in fig. 4, the terminal device 400 of this embodiment includes: a processor 410, a memory 420, wherein the memory 420 stores a computer program 421 that can be executed on the processor 410. The steps in any of the various method embodiments described above, such as steps 101 to 105 shown in fig. 1, are implemented when the computer program 421 is executed by the processor 410. Alternatively, the processor 410, when executing the computer program 421, implements the functions of the modules/units in the above-described device embodiments, such as the functions of the modules 301 to 305 shown in fig. 3.
Illustratively, the computer program 421 may be partitioned into one or more modules/units, which are stored in the memory 420 and executed by the processor 410 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 421 in the terminal device 400.
Those skilled in the art will appreciate that fig. 4 is merely an example of a terminal device and is not meant to be limiting and may include more or fewer components than those shown, or some of the components may be combined, or different components such as input output devices, network access devices, buses, etc.
The Processor 410 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 420 may be an internal storage unit of the terminal device, such as a hard disk or a memory of the terminal device, or an external storage device of the terminal device, such as a plug-in hard disk provided on the terminal device, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and so on. The memory 420 may also include both an internal storage unit of the terminal device and an external storage device. The memory 420 is used for storing computer programs and other programs and data required by the terminal device. The memory 420 may also be used to temporarily store data that has been output or is to be output.
It should be clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional units and modules is only used for illustration, and in practical applications, the above function distribution may be performed by different functional units and modules as needed, that is, the internal structure of the apparatus may be divided into different functional units or modules to perform all or part of the above described functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the description of each embodiment has its own emphasis, and reference may be made to the related description of other embodiments for parts that are not described or recited in any embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the technical solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated module/unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. . Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A method of configuring energy storage capacity and heat storage capacity, comprising:
acquiring historical data, wind power installed capacity, photovoltaic installed capacity and photo-thermal installed capacity; performing time sequence production simulation processing on the historical data, and determining a wind power output time sequence, a photovoltaic output time sequence and a load time sequence; determining a reference energy storage capacity according to the installed wind power capacity and the installed photovoltaic capacity, and determining a reference heat storage capacity according to the installed photothermal capacity;
constructing a time sequence production simulation model based on the wind power output time sequence, the photovoltaic output time sequence and the load time sequence, taking the optimal economy as a target function, and taking power balance constraint, new energy power generation output constraint, photo-thermal power station constraint and electrochemical energy storage battery constraint as constraint conditions;
and solving the time sequence production simulation model by using a CPLEX solver, and determining the optimal energy storage capacity and the optimal heat storage capacity according to a solving result, the reference energy storage capacity and the reference heat storage capacity.
2. The method of configuring energy storage capacity and heat storage capacity of claim 1, wherein the historical data is historical data per unit time length, the historical data comprising wind speed historical data, light intensity historical data, and load historical data;
the time sequence production simulation processing is carried out on the historical data, and the wind power output time sequence, the photovoltaic output time sequence and the load time sequence are determined, and the method comprises the following steps:
sampling and clustering the wind speed historical data, the illumination intensity historical data and the load historical data by adopting a Latin cube sampling method and a central point clustering algorithm, and determining a wind speed time sequence, an illumination intensity time sequence and a load time sequence of a unit time length;
determining a wind power output time sequence of unit time length according to the wind speed time sequence of unit time length, and determining a photovoltaic output time sequence of unit time length according to the illumination intensity time sequence of unit time length;
and acquiring a plurality of unit time length historical data, repeating the steps, and respectively determining a wind power output time sequence, a photovoltaic output time sequence and a load time sequence according to the wind power output time sequence, the photovoltaic output time sequence and the load time sequence of the unit time lengths.
3. The method of claim 1, wherein the determining a reference energy storage capacity according to the installed wind power capacity and the installed photovoltaic capacity and a reference heat storage capacity according to the installed photothermal capacity comprises:
determining a reference energy storage capacity according to the product of the sum of the wind power installed capacity and the photovoltaic installed capacity and a preset coefficient;
and determining the reference heat storage capacity according to the photo-thermal installed capacity and the heat storage time.
4. The method of claim 1, wherein the objective function is:
in the formula (I), the compound is shown in the specification,
in order to reduce the power generation cost of the photo-thermal power station,
for the overall discharge cost, lambda, of an electrochemical energy storage cell RC A penalty coefficient for the new energy power abandonment,
abandoning electricity for new energy, lambda LC In order to make the load-shedding penalty factor,
in order to cut the load, C is the construction cost of the unit electrochemical energy storage battery, S OC_max Is a reference energy storage capacity, ξ bat The energy storage capacity coefficient is T, and the production simulation duration is T.
5. The energy and heat storage capacity configuration method of claim 4, wherein the power balance constraint is:
in the formula (I), the compound is shown in the specification,
real-time output is scheduled for the ith photo-thermal unit of the photo-thermal power station in a t time period,
for t time interval photovoltaic schedulingThe force is exerted when the electric heating furnace is used,
real-time output is scheduled for the wind power in the time period t,
for the time period t to participate in scheduling the balanced load,
for the discharge power of the mth electrochemical energy storage battery in the period t,
charging power of mth electrochemical energy storage battery for t time period, N csp Number of photothermal units in photothermal power station, N bat The number of the electrochemical energy storage batteries is;
the new energy power generation output constraint is as follows:
in the formula (I), the compound is shown in the specification,
the maximum output of the wind power is obtained in the time period of t,
the maximum photovoltaic output is obtained in the period of t,
in order to obtain the installed capacity of wind power,
is the photovoltaic installed capacity.
6. The method of claim 4, wherein the photothermal power station constraints comprise a thermal storage capacity constraint, a thermal collection field heat dynamic balance constraint, a thermal tank heat dynamic balance constraint, a photothermal power station power generation output constraint, and a photothermal power station ramp constraint;
the constraint of the electrochemical energy storage battery comprises energy storage capacity constraint, energy storage charging and discharging power constraint and energy storage capacity balance constraint.
7. The method for configuring energy storage capacity and heat storage capacity according to any one of claims 1 to 6, wherein the solving of the time series production simulation model by using a CPLEX solver and the determining of the optimal energy storage capacity and the optimal heat storage capacity according to the solving result, the reference energy storage capacity and the reference heat storage capacity comprise:
solving the time sequence production simulation model by using a CPLEX solver to determine an optimal energy storage capacity coefficient and an optimal heat storage capacity coefficient;
and determining the optimal energy storage capacity according to the reference energy storage capacity and the optimal energy storage capacity coefficient, and determining the optimal heat storage capacity according to the reference heat storage capacity and the optimal heat storage capacity coefficient.
8. An energy storage capacity and heat storage capacity deployment apparatus, comprising:
the acquisition module is used for acquiring historical data, wind power installed capacity, photovoltaic installed capacity and photo-thermal installed capacity;
the first determining module is used for performing time sequence production simulation processing on the historical data and determining a wind power output time sequence, a photovoltaic output time sequence and a load time sequence;
the second determining module is used for determining a reference energy storage capacity according to the installed wind power capacity and the installed photovoltaic capacity and determining a reference heat storage capacity according to the installed photo-thermal capacity;
the construction module is used for constructing a time sequence production simulation model based on the wind power output time sequence, the photovoltaic output time sequence and the load time sequence, with optimal economy as a target function and with power balance constraint, new energy power generation output constraint, photo-thermal power station constraint and electrochemical energy storage battery constraint as constraint conditions;
and the configuration module is used for solving the time sequence production simulation model by using a CPLEX solver, and determining the optimal energy storage capacity and the optimal heat storage capacity according to the solving result, the reference energy storage capacity and the reference heat storage capacity.
9. A terminal device comprising a memory and a processor, the memory having stored thereon a computer program operable on the processor, wherein the processor when executing the computer program is adapted to implement the energy storage capacity and heat storage capacity configuration method as claimed in any of claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out a method for configuring an energy storage capacity and a heat storage capacity according to any one of claims 1 to 7.
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| CN114156920A (en) * | 2021-11-29 | 2022-03-08 | 国网宁夏电力有限公司经济技术研究院 | Capacity allocation method for electricity-heat energy storage in multi-energy complementary comprehensive energy system |
| CN115528712A (en) * | 2022-11-23 | 2022-12-27 | 国网天津市电力公司滨海供电分公司 | Energy storage capacity balancing method and system for different source network charge storage areas |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114156920A (en) * | 2021-11-29 | 2022-03-08 | 国网宁夏电力有限公司经济技术研究院 | Capacity allocation method for electricity-heat energy storage in multi-energy complementary comprehensive energy system |
| CN114156920B (en) * | 2021-11-29 | 2023-12-29 | 国网宁夏电力有限公司经济技术研究院 | Capacity configuration method for electric-thermal energy storage in multi-energy complementary comprehensive energy system |
| CN115528712A (en) * | 2022-11-23 | 2022-12-27 | 国网天津市电力公司滨海供电分公司 | Energy storage capacity balancing method and system for different source network charge storage areas |
| CN115528712B (en) * | 2022-11-23 | 2023-06-20 | 国网天津市电力公司滨海供电分公司 | Method and system for balancing energy storage capacities of different areas of source network charge storage |
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