CN117663503A - Method and system for intelligently adjusting molten salt heat storage rate - Google Patents
Method and system for intelligently adjusting molten salt heat storage rate Download PDFInfo
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Abstract
The application discloses a method and a system for intelligently adjusting molten salt heat storage rate, which relate to the technical field of molten salt heat storage, and the method comprises the following steps: collecting sunlight prediction data and expected sunlight duration; acquiring power generation parameter information of target photovoltaic equipment, wherein the power generation parameter information comprises photoelectric conversion efficiency; carrying out photovoltaic power generation prediction based on sunlight prediction data, expected sunlight duration and photoelectric conversion efficiency to generate expected photovoltaic power generation; calculating a photovoltaic power generation deviation amount based on the photovoltaic rated power generation amount and the expected photovoltaic power generation amount; acquiring operation parameter information of molten salt energy storage equipment; calculating the generated energy to be stored according to the deviation amount of the photovoltaic power generation and the real-time heat loss coefficient; acquiring a valley electricity period of a target area, judging whether expected energy storage electric quantity can meet the energy to be stored or not under the optimal charging rate in the valley electricity period, and carrying out energy storage charging of the molten salt energy storage equipment according to the optimal charging rate when the expected energy storage electric quantity can meet the energy to be stored. Thereby achieving the technical effects of intelligently controlling the heat storage rate, improving the energy conversion rate and optimizing the energy efficiency of the system.
Description
Technical Field
The invention relates to the technical field of molten salt heat storage, in particular to a method and a system for intelligently adjusting molten salt heat storage rate.
Background
Under the background of global warming and gradual exhaustion of traditional energy, the duty ratio of renewable energy in energy supply is gradually increased, the existing clean energy is greatly influenced by natural factors, the productivity fluctuation is large, and the stability of a power grid is influenced. Therefore, the multi-junction energy storage technology reduces the peak filling of the electric curtailment valley. The existing energy storage method based on the molten salt heat storage technology is often used for passively controlling the heat storage rate, and the technical problems of extensive control of the heat storage rate, low energy conversion rate and low energy efficiency of the system exist.
Disclosure of Invention
The application aims to provide a method and a system for intelligently adjusting molten salt heat storage rate. The method is used for solving the technical problems of extensive heat storage rate control, low energy conversion rate and low system energy efficiency in the prior art.
In view of the technical problems, the application provides a method and a system for intelligently adjusting the heat storage rate of molten salt.
In a first aspect, the present application provides a method for intelligently adjusting a molten salt heat storage rate, wherein the method comprises:
collecting sunshine forecast data and expected sunshine duration of a target area in a preset time period; acquiring power generation parameter information of target photovoltaic equipment, wherein the power generation parameter information comprises photoelectric conversion efficiency; carrying out photovoltaic power generation prediction based on the sunlight prediction data, the expected sunlight duration and the photoelectric conversion efficiency to generate expected photovoltaic power generation; calculating to obtain photovoltaic power generation deviation based on photovoltaic rated power generation capacity of a target area and the expected photovoltaic power generation capacity in a preset time period; acquiring operation parameter information of molten salt energy storage equipment, wherein the operation parameter information comprises a charging rate threshold value, an optimal charging rate and a real-time heat loss coefficient; calculating to obtain the power generation capacity to be stored according to the deviation amount of the photovoltaic power generation and the real-time heat loss coefficient; and acquiring a valley electricity period of a target area, judging whether the expected energy storage electric quantity can meet the to-be-stored generating capacity under the optimal charging rate in the valley electricity period, and carrying out energy storage charging of the molten salt energy storage equipment according to the optimal charging rate when the expected energy storage electric quantity meets the to-be-stored generating capacity.
In a second aspect, the present application also provides a system for intelligently adjusting molten salt heat storage rate, wherein the system comprises:
the environment information acquisition module is used for acquiring sunlight prediction data and expected sunlight duration of the target area in a preset time period; the device parameter acquisition module is used for acquiring power generation parameter information of the target photovoltaic device, wherein the power generation parameter information comprises photoelectric conversion efficiency; the power generation prediction module is used for carrying out photovoltaic power generation prediction based on the sunlight prediction data, the expected sunlight duration and the photoelectric conversion efficiency to generate expected photovoltaic power generation capacity; the deviation calculation module is used for calculating and obtaining photovoltaic power generation deviation based on photovoltaic rated power generation capacity of a target area and the expected photovoltaic power generation capacity in a preset time period; the energy storage parameter acquisition module is used for acquiring operation parameter information of the molten salt energy storage equipment, wherein the operation parameter information comprises a charging rate threshold value, an optimal charging rate and a real-time heat loss coefficient; the electricity to be stored quantity calculating module is used for calculating and obtaining electricity to be stored according to the photovoltaic electricity generation deviation and the real-time heat loss coefficient; and the judging control module is used for acquiring the valley electricity period of the target area, judging whether the expected energy storage electric quantity can meet the to-be-stored electric quantity under the optimal charging rate in the valley electricity period, and carrying out energy storage charging of the molten salt energy storage equipment according to the optimal charging rate when the expected energy storage electric quantity meets the to-be-stored electric quantity.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
collecting sunshine forecast data and expected sunshine duration of a target area in a preset time period; acquiring power generation parameter information of target photovoltaic equipment, wherein the power generation parameter information comprises photoelectric conversion efficiency; carrying out photovoltaic power generation prediction based on sunlight prediction data, expected sunlight duration and photoelectric conversion efficiency to generate expected photovoltaic power generation; calculating to obtain photovoltaic power generation deviation based on photovoltaic rated power generation capacity and expected photovoltaic power generation capacity of a target area in a preset time period; acquiring operation parameter information of molten salt energy storage equipment, wherein the operation parameter information comprises a charging rate threshold value, an optimal charging rate and a real-time heat loss coefficient; calculating according to the deviation amount of the photovoltaic power generation and the real-time heat loss coefficient to obtain the power generation amount to be stored; and acquiring the valley electricity period of the target area, judging whether the expected energy storage electric quantity can meet the requirement of the to-be-stored generating capacity under the optimal charging rate in the valley electricity period, and carrying out energy storage charging of the molten salt energy storage equipment according to the optimal charging rate when the expected energy storage electric quantity meets the requirement. Thereby achieving the technical effects of intelligently controlling the heat storage rate, improving the energy conversion rate and optimizing the energy efficiency of the system.
The foregoing description is merely an overview of the technical solutions of the present application, and may be implemented according to the content of the specification, so that the technical means of the present application can be more clearly explained, and the following specific embodiments of the present application are given for more understanding of the above and other objects, features and advantages of the present application.
Drawings
Embodiments of the invention and the following brief description are described with reference to the drawings, in which:
FIG. 1 is a schematic flow chart of a method for intelligently adjusting molten salt heat storage rate;
FIG. 2 is a schematic flow chart of a method for intelligently adjusting the heat storage rate of molten salt for acquiring expected sunshine duration;
fig. 3 is a schematic structural diagram of a system for intelligently adjusting molten salt heat storage rate.
Reference numerals illustrate: the device comprises an environment information acquisition module 11, a device parameter acquisition module 12, a power generation prediction module 13, a deviation calculation module 14, an energy storage parameter acquisition module 15, a to-be-stored electricity calculation module 16 and a discrimination control module 17.
Detailed Description
The method and the system for intelligently adjusting the molten salt heat storage rate solve the technical problems of rough heat storage rate control, low energy conversion rate and low energy efficiency of the system in the prior art.
In order to solve the above problems, the technical embodiment adopts the following overall concept:
first, solar radiation prediction data and expected solar radiation duration of a target area are collected in a preset time period. Subsequently, power generation parameter information of the target photovoltaic device including photoelectric conversion efficiency and the like is acquired. And carrying out photovoltaic power generation prediction based on the sunlight prediction data, the expected sunlight duration and the photoelectric conversion efficiency, and generating expected photovoltaic power generation. Next, the photovoltaic power generation deviation amount is calculated using the difference between the target area photovoltaic rated power generation amount and the expected photovoltaic power generation amount within the preset time period. Operating parameter information of the molten salt energy storage device is acquired, including a charge rate threshold, an optimal charge rate, a real-time heat loss coefficient, and the like. And calculating the generated energy to be stored according to the deviation amount of the photovoltaic power generation and the real-time heat loss coefficient. And finally, obtaining the valley electricity time periods of the target area, and judging whether the expected energy storage electric quantity is enough to meet the requirement of the electric quantity to be stored under the optimal charging rate in the time periods. If the condition is met, the energy storage charging of the molten salt energy storage device may be performed at an optimal charging rate. Thereby achieving the technical effects of intelligently controlling the heat storage rate, improving the energy conversion rate and optimizing the energy efficiency of the system.
In order to better understand the foregoing technical solutions, the following detailed description will be given with reference to the accompanying drawings and specific embodiments, and it should be noted that the described embodiments are only some examples of the present application, and not all examples of the present application, and it should be understood that the present application is not limited by the example embodiments described herein. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to fall within the scope of the invention. It should be further noted that, for convenience of description, only some, but not all of the drawings related to the present invention are shown.
Example 1
As shown in fig. 1, the present application provides a method for intelligently adjusting a molten salt heat storage rate, the method comprising:
s100: collecting sunshine forecast data and expected sunshine duration of a target area in a preset time period;
optionally, for the same target area, the sun shine characteristics of different time periods are different, and for example, natural days in different seasons have different sun shine characteristics, and taking northern hemisphere as an example, the sun shine time in winter is short, the solar altitude angle is small, and the solar radiation intensity is low; the solar energy is long in summer, the solar altitude angle is large, and the radiation intensity is high.
Optionally, the solar radiation prediction data refers to a predicted value of a weather solar radiation characteristic of the target area within a preset time period, and the predicted value includes a solar altitude angle, a solar radiation intensity time curve and the like. The data sources of the solar forecast data comprise a weather station, a weather forecast service provider, satellite data and the like. And extracting sunlight prediction data of the target area in a preset time period from the data source through proper query parameters and geographic position information. Illustratively, the predetermined period of time is a sun day.
Further, as shown in fig. 2, predicted sunshine data and expected sunshine duration of the target area within a preset time period are collected. The step S100 includes:
acquiring historical sunshine duration of a near-N-year target area, and dividing the historical sunshine duration according to the preset time period to obtain a plurality of sunshine duration sets, wherein the sunshine duration sets and nodes of the preset time period have a corresponding relationship, each sunshine duration set comprises N historical sunshine duration, and N is an integer greater than 5;
sequentially carrying out average value calculation on the plurality of sunshine duration to obtain average values of the plurality of sunshine duration;
taking a node with a preset time period as a child node, taking the average value of the sunshine duration as a leaf node of the child node, and constructing a time node-sunshine duration comparison table;
and acquiring a real-time node of a preset time period, inputting the real-time node into the time node-sunlight duration comparison table, and matching to obtain the expected sunlight duration.
Alternatively, the expected solar time period refers to the solar time of photovoltaic power generation (peak solar time), which is defined as the total irradiance integrated over a period of time corresponding to 1kW/m irradiance 2 The time in hours (h) for which the light source is continuously illuminated.
Optionally, the expected solar duration is calculated and obtained based on solar prediction data, wherein a calculation formula of the expected solar duration is as follows:
wherein PSH (Peak Sun Hours) refers to the peak solar hours, i.e., the number of hours the photovoltaic system receives enough solar radiation in a day; total irradiation (total irradiance) is the total integrated solar irradiance in kilowatt-hours per square meter (kWh/m) 2 ) Is a unit; standard Srradiance (Standard irradiance) irradiance of 1kW/m 2 。
Wherein Total irradiation (total dose) is obtained by integrating the irradiation intensity in time based on the solar light intensity time curve.
S200: acquiring power generation parameter information of target photovoltaic equipment, wherein the power generation parameter information comprises photoelectric conversion efficiency;
optionally, the power generation parameter information acquisition path of the target photovoltaic device includes: consult product documentation, specification sheets and user manuals of the target photovoltaic device. These documents contain detailed information about the device performance and parameters, including the photoelectric conversion efficiency; accessing the manufacturer's website, the manufacturer typically provides information about its product specifications and performance; contacting a photovoltaic device manufacturer or a technical support team thereof to obtain information about device parameters, including photoelectric conversion efficiency; and querying the photovoltaic database and the resources.
The photoelectric conversion efficiency refers to the efficiency of converting light energy into electric energy of target photovoltaic equipment, and is one of important parameters for evaluating the performance of a photovoltaic system, and the photoelectric conversion efficiency is expressed as a percentage. A higher photoelectric conversion efficiency represents that the device is able to generate more electrical energy under the same solar radiation conditions.
Optionally, based on the power generation parameter information of the target photovoltaic device, the photoelectric conversion efficiency is obtained according to a calculation method or a calculation formula in the related design specification. Illustratively, calculating the photoelectric conversion efficiency involves parameters including:
photovoltaic module type correction factor: the power station of the same standard normal A-level crystal silicon assembly is generally subjected to correction-free treatment, and 100 percent is taken; considering that the same specification component has parameter deviation, which causes matching loss, the correction coefficient is preferably 98%.
Photovoltaic power generation system availability: considering the influences of power station maintenance shutdown, equipment faults and the like on photovoltaic power generation, if the operation and maintenance profession and the equipment faults are found and overhauled timely, the correction coefficient can be taken to be 98% according to about 2% loss.
Light utilization rate: according to experience, the total radiation quantity can be counted when the irradiance of most photovoltaic power stations is more than 50W/m < 2 >, but solar radiation with irradiance lower than 50W/m < 2 > is not too much, but is lost, the loss is about 3% according to the part, and the correction coefficient is 97%;
inverter efficiency: the inverter weighting efficiency is typically around 97.5%, and this factor takes 97.5%.
Current collecting line loss: according to recommended standards, a general centralized inverter photovoltaic power station requires that the direct current line loss is not more than 2% and the alternating current line loss is not more than 1%; for the string inverter photovoltaic power station, the direct current line loss and the alternating current line loss are required to be not more than 1%; thus, the correction factor is 0.98×0.99=0.97, taking 97%.
Step-up transformer losses: taking a value according to the product specification, and estimating according to 98% if no detailed data exists;
photovoltaic module surface pollution correction coefficient: the surface pollution of the photovoltaic module is mainly dust, and the loss of a general power station is 5%, namely the coefficient is 95%;
the temperature loss, the photovoltaic module has a certain negative temperature coefficient, the module power is usually 25 degrees as the standard temperature, and the higher the temperature is, the lower the generated energy is. The temperature experience coefficient of the current assembly power is-0.35%/DEGC, the partial loss is considered to be about 4%, and the correction coefficient is 96%.
Optionally, the photovoltaic module type correction factor further comprises a module attenuation factor taking into account the module attenuation. Obtained by querying the module manufacturer specification and the like.
S300: carrying out photovoltaic power generation prediction based on the sunlight prediction data, the expected sunlight duration and the photoelectric conversion efficiency to generate expected photovoltaic power generation;
further, photovoltaic power generation prediction is performed based on the solar radiation prediction data, the expected solar radiation time length and the photoelectric conversion efficiency, and an expected photovoltaic power generation amount is generated, and step S300 includes:
based on the electric power big data, taking photovoltaic power generation as a retrieval condition, and acquiring a plurality of historical photovoltaic power generation data, wherein the historical photovoltaic power generation data comprises historical sunlight data, historical sunlight duration, equipment photoelectric conversion efficiency and historical power generation capacity;
taking the plurality of historical photovoltaic power generation data as training data, and performing supervision training on a photovoltaic power generation prediction channel constructed based on the BP neural network to obtain a photovoltaic power generation prediction channel meeting expected standards;
and inputting the sunlight prediction data, the expected sunlight duration and the photoelectric conversion efficiency into the photovoltaic power generation prediction channel to perform power generation prediction, and outputting expected photovoltaic power generation.
Optionally, the generating capacity of the photovoltaic power station in the preset time period, namely the expected photovoltaic generating capacity, can be calculated according to the installed capacity of the photovoltaic power station, the expected sunshine duration, the photoelectric conversion efficiency and the like. Specifically, because of the difference between the sunlight prediction data and the actual meteorological conditions, the expected photovoltaic power generation amount obtained directly through calculation of the mathematical model has deviation, and the expected photovoltaic power generation amount is obtained by adopting a photovoltaic power generation prediction channel constructed based on the BP neural network.
Optionally, the photovoltaic power generation is used as a search condition, and a plurality of historical photovoltaic power generation data are obtained, wherein the historical photovoltaic power generation data comprise photovoltaic power station geographic information (longitude and latitude, altitude and the like), photovoltaic power station meteorological information (annual average precipitation, cloud cover, haze, wind speed, temperature and the like), photovoltaic power station historical photovoltaic power generation capacity, photovoltaic power station historical sunshine duration, photovoltaic panel parameters and the like.
Optionally, the photovoltaic power generation prediction channel constructed based on the BP neural network is supervised and trained, firstly, a plurality of historical photovoltaic power generation data are subjected to data preprocessing to ensure the data quality and consistency, and the preprocessed data are split and divided into a training set and a verification set according to a certain proportion. The training set is used for training the neural network, and the testing set is used for evaluating the performance of the model. And then, designing the structure of the photovoltaic power generation prediction channel based on the BP neural network, wherein the structure comprises the neuron number of an input layer, a hidden layer and an output layer. And performing supervision training on the photovoltaic power generation prediction channel by using the training set data. In each training iteration, data is input, errors between actual observed values and predicted values are calculated based on a preset error function, and weights and deviations of all layers of the photovoltaic power generation prediction channel are updated through counter-propagation and gradient descent algorithms. After completing the monitoring training for a preset number of times, the performance of the photovoltaic power generation prediction channel is monitored by using the verification set data, wherein the evaluation indexes comprise a Mean Square Error (MSE), a Root Mean Square Error (RMSE) and a decision coefficient (R) 2 ) Etc. Once the photovoltaic power generation prediction channel meets the expected standard, the photovoltaic power generation prediction channel is deployed as an actual photovoltaic power generation prediction channel for predicting future photovoltaic power generation in real time.
S400: calculating to obtain photovoltaic power generation deviation based on photovoltaic rated power generation capacity of a target area and the expected photovoltaic power generation capacity in a preset time period;
the photovoltaic rated power generation amount refers to the planned photovoltaic power generation amount in the preset time period of the target area, and the photovoltaic power generation deviation amount is the difference between the photovoltaic rated power generation amount and the expected power generation amount. Calculated by the following formula:
photovoltaic power generation deviation amount = photovoltaic rated power generation amount-expected power generation amount;
wherein the photovoltaic power generation deviation amount may be a positive value or a negative value depending on whether the actual power generation amount is higher or lower than the expected value. Positive values indicate that the expected photovoltaic power generation amount is higher than the rated photovoltaic power generation amount, and energy storage is required for the excess, while negative values indicate that the actual power generation amount is lower than the expected power generation amount, and energy storage is not required.
S500: acquiring operation parameter information of molten salt energy storage equipment, wherein the operation parameter information comprises a charging rate threshold value, an optimal charging rate and a real-time heat loss coefficient;
wherein, the charging rate threshold value refers to the maximum rate at which the molten salt energy storage device receives energy, the charging rate threshold value depends on the upper power limit of the molten salt heating assembly and the electrothermal conversion rate, and the charging rate unit is kW.
The optimal charging rate refers to the molten salt energy storage and power receiving capacity with the highest energy conversion rate in the molten salt energy storage system. The optimal charge rate is determined based on the design and performance characteristics of the molten salt energy storage system. The influencing factors of the optimal charge rate include: salt storage material characteristics, different materials can bear different charging rates, and have different heat capacities and heat conductivities; design parameters of the molten salt energy storage system, such as the capacity and structure of a salt storage tank; heat conduction and transfer characteristics, too fast a charge rate may result in increased heat loss, while too slow a charge rate may waste time and energy.
Optionally, the operation parameter information of the molten salt energy storage device further includes: fused salt parameters (fused salt flow, high temperature fused salt temperature, low temperature fused salt temperature and the like), unit efficiency (steam production efficiency, power generation efficiency, main steam pressure, main steam temperature, steam turbine extraction amount, steam turbine extraction pressure, steam turbine extraction temperature, steam turbine exhaust pressure, steam turbine exhaust temperature, steam supply pressure, steam supply temperature and the like).
Further, acquiring operation parameter information of the molten salt energy storage device, and step S500 includes:
the historical energy storage log of the molten salt energy storage equipment is called, a plurality of historical heat loss coefficients are obtained based on the historical energy storage log, and the historical heat loss coefficients have time node identifications;
constructing a heat loss dynamic curve based on the time node and the historical heat loss coefficient;
and carrying out heat loss prediction on a real-time node of a preset time period according to the heat loss dynamic curve to generate the real-time heat loss coefficient.
Alternatively, heat loss refers to the energy lost during storage and release of salt storage due to heat transfer and conduction. These losses are caused by factors such as heat conduction, radiation and convection. The heat loss coefficient is typically expressed in terms of a percentage or fraction reflecting the difference between the actual energy loss and the theoretical energy storage or release at a particular moment. The heat loss coefficient may vary with different operating conditions. Therefore, real-time monitoring and recording under different workloads, temperature ranges and charge/discharge rates is required to obtain real-time heat loss coefficients.
S600: calculating to obtain the power generation capacity to be stored according to the deviation amount of the photovoltaic power generation and the real-time heat loss coefficient;
alternatively, first, the energy lost by the energy storage system due to heat loss during a specific period of time is calculated based on the real-time heat loss coefficient. Calculated by the following formula:
real-time heat loss = real-time heat loss coefficient x energy storage system capacity;
and then, calculating the power generation to be stored, wherein the power generation to be stored refers to the electric quantity required to be stored in the molten salt energy storage system. Calculated by the following formula:
to-be-stored generating capacity = actual photovoltaic generating deviation-real-time heat loss;
and calculating and obtaining the generated energy to be stored through the deviation amount of the photovoltaic power generation and the real-time heat loss coefficient. The unification of measurement points between the deviation amount of photovoltaic power generation and the charging rate threshold value and between the photovoltaic power generation deviation amount and the optimal charging rate is realized, so that the deviation amount and the charging rate threshold value and the optimal charging rate are subjected to association analysis, and the accuracy and the confidence of subsequent discrimination are ensured.
S700: and acquiring a valley electricity period of a target area, judging whether the expected energy storage electric quantity can meet the to-be-stored generating capacity under the optimal charging rate in the valley electricity period, and carrying out energy storage charging of the molten salt energy storage equipment according to the optimal charging rate when the expected energy storage electric quantity meets the to-be-stored generating capacity.
Alternatively, the expected stored energy power refers to the net power that can be input to the molten salt energy storage device during the off-peak period of time when energy is stored at an optimal charge rate. And if the expected energy storage electric quantity is larger than or equal to the to-be-stored electric quantity, the expected energy storage electric quantity is considered to meet the to-be-stored electric quantity, namely, under the optimal charging rate, the fused salt energy storage equipment can finish electric energy storage of the to-be-stored electric quantity value in the valley electric period. At this time, the energy utilization rate of the molten salt energy storage device is highest. The electricity consumption period of the power grid is a period of electricity consumption valley, the required electric quantity level of the power grid is low, and the power generation capacity of the photovoltaic power station is surplus.
Further, determining whether the expected stored energy power can meet the to-be-stored power generation amount at the optimal charging rate, step S700 further includes:
when the expected energy storage electric quantity cannot meet the generated energy to be stored, calculating according to the generated energy to be stored and the off-peak electric period to obtain an expected charging rate;
judging whether the expected charging rate meets the charging rate threshold, and when the expected charging rate meets the charging rate threshold, carrying out energy storage charging on the molten salt energy storage equipment according to the expected charging rate;
and when the energy storage and charging of the molten salt energy storage equipment are not met, carrying out energy storage and charging of the molten salt energy storage equipment according to the maximum charging rate in the charging rate threshold, and generating an electric quantity warning signal to carry out energy storage deficiency early warning.
Optionally, when the expected charging rate is less than or equal to the charging rate threshold, the electric energy receiving capability of the target molten salt energy storage device meets the requirement of the target energy storage task, and the molten salt energy storage device performs energy storage charging at the expected charging rate. Otherwise, if the expected charging rate is greater than the charging rate threshold, it indicates that the electric energy receiving capability of the target molten salt energy storage device is insufficient, and phenomena such as electricity discarding and heat discarding are caused. Generating an electric quantity warning signal to remind operators and related personnel to take corresponding measures, wherein the measures comprise adjusting operation parameters of molten salt energy storage equipment, equipment capacity expansion and the like.
Further, generating an electric quantity warning signal for energy storage deficiency early warning, and the steps further comprise:
when an electric quantity warning signal is generated, acquiring the maximum energy storage electric quantity in the valley time period based on the maximum charging rate;
calculating to obtain an energy storage electric quantity deviation based on the maximum energy storage electric quantity and the to-be-stored electric energy generation quantity;
and optimally controlling the molten salt energy storage equipment according to the energy storage electric quantity deviation.
The energy storage electric quantity deviation refers to an energy storage capacity gap of the molten salt energy storage equipment, and the molten salt energy storage equipment is optimized through the energy storage electric quantity deviation. Optionally, the method comprises the following steps: when the difference value is smaller, energy storage charging is carried out in a non-valley time period; and when the difference value is larger, adjusting the technological parameters of the molten salt energy storage equipment to increase the energy storage rate or perform equipment replacement expansion and the like.
In summary, the method for intelligently adjusting the molten salt heat storage rate provided by the invention has the following technical effects:
collecting sunshine forecast data and expected sunshine duration of a target area in a preset time period; acquiring power generation parameter information of target photovoltaic equipment, wherein the power generation parameter information comprises photoelectric conversion efficiency; carrying out photovoltaic power generation prediction based on sunlight prediction data, expected sunlight duration and photoelectric conversion efficiency to generate expected photovoltaic power generation; calculating to obtain photovoltaic power generation deviation based on photovoltaic rated power generation capacity and expected photovoltaic power generation capacity of a target area in a preset time period; acquiring operation parameter information of molten salt energy storage equipment, wherein the operation parameter information comprises a charging rate threshold value, an optimal charging rate and a real-time heat loss coefficient; calculating according to the deviation amount of the photovoltaic power generation and the real-time heat loss coefficient to obtain the power generation amount to be stored; and acquiring the valley electricity period of the target area, judging whether the expected energy storage electric quantity can meet the requirement of the to-be-stored generating capacity under the optimal charging rate in the valley electricity period, and carrying out energy storage charging of the molten salt energy storage equipment according to the optimal charging rate when the expected energy storage electric quantity meets the requirement. Thereby achieving the technical effects of intelligently controlling the heat storage rate, improving the energy conversion rate and optimizing the energy efficiency of the system.
Example two
Based on the same concept as the method for intelligently adjusting the molten salt heat storage rate in the embodiment, as shown in fig. 3, the application further provides a system for intelligently adjusting the molten salt heat storage rate, where the system includes:
the environment information acquisition module 11 is used for acquiring sunshine forecast data and expected sunshine duration of the target area in a preset time period;
an equipment parameter acquisition module 12, configured to acquire power generation parameter information of a target photovoltaic equipment, where the power generation parameter information includes photoelectric conversion efficiency;
a power generation prediction module 13, configured to perform photovoltaic power generation prediction based on the solar radiation prediction data, the expected solar radiation time period, and the photoelectric conversion efficiency, and generate an expected photovoltaic power generation amount;
a deviation calculation module 14 for calculating a photovoltaic power generation deviation amount based on the photovoltaic rated power generation amount of the target area and the expected photovoltaic power generation amount within a preset time period;
the energy storage parameter acquisition module 15 is used for acquiring operation parameter information of the molten salt energy storage device, wherein the operation parameter information comprises a charging rate threshold value, an optimal charging rate and a real-time heat loss coefficient;
the electricity to be stored calculation module 16 is configured to calculate and obtain electricity to be stored according to the deviation amount of the photovoltaic power generation and the real-time heat loss coefficient;
the judging control module 17 is configured to obtain a valley electricity period of the target area, and determine whether the expected energy storage capacity can meet the to-be-stored power generation amount at the optimal charging rate in the valley electricity period, and when the expected energy storage capacity is met, perform energy storage charging of the molten salt energy storage device according to the optimal charging rate.
Further, the environmental information obtaining module 11 further includes:
the historical sunshine duration obtaining unit is used for obtaining historical sunshine duration of a target area of nearly N years, dividing the historical sunshine duration according to the preset time period to obtain a plurality of sunshine duration sets, wherein the sunshine duration sets and nodes of the preset time period have a corresponding relationship, each sunshine duration set comprises N historical sunshine duration, and N is an integer larger than 5;
the average value calculation unit is used for sequentially carrying out average value calculation on the plurality of sunshine duration to obtain average values of the plurality of sunshine duration;
the comparison table unit is used for constructing a time node-sunlight duration comparison table by taking a node with a preset time period as a child node and taking the sunlight duration average value as a leaf node of the child node;
the matching unit is used for acquiring a real-time node of a preset time period, inputting the real-time node into the time node-sunlight duration comparison table, and matching to acquire the expected sunlight duration.
Further, the power generation prediction module 13 further includes:
the system comprises a big data retrieval unit, a photovoltaic power generation unit and a power generation unit, wherein the big data retrieval unit is used for acquiring a plurality of historical photovoltaic power generation data based on electric power big data by taking photovoltaic power generation as a retrieval condition, and the historical photovoltaic power generation data comprise historical sunlight data, historical sunlight duration, equipment photoelectric conversion efficiency and historical power generation;
the monitoring training unit is used for taking the plurality of historical photovoltaic power generation data as training data, and performing monitoring training on the photovoltaic power generation prediction channel constructed based on the BP neural network to obtain a photovoltaic power generation prediction channel meeting expected standards;
the power generation prediction unit is used for inputting the sunlight prediction data, the expected sunlight duration and the photoelectric conversion efficiency into the photovoltaic power generation prediction channel to perform power generation prediction and outputting expected photovoltaic power generation amount.
Further, the energy storage parameter obtaining module 15 further includes:
the log analysis unit is used for calling a historical energy storage log of the molten salt energy storage device, acquiring a plurality of historical heat loss coefficients based on the historical energy storage log, wherein the historical heat loss coefficients have time node identifications;
a curve construction unit for constructing a heat loss dynamic curve based on the time node and the historical heat loss coefficient;
and the heat loss prediction unit is used for predicting heat loss of a real-time node of a preset time period according to the heat loss dynamic curve, and generating the real-time heat loss coefficient.
Further, the discrimination control module 17 further includes:
the charging rate correction unit is used for calculating and obtaining the expected charging rate according to the to-be-stored power generation amount and the off-peak power period when the expected energy storage power cannot meet the to-be-stored power generation amount;
the judging unit is used for judging whether the expected charging rate meets the charging rate threshold, and when the expected charging rate meets the charging rate threshold, the energy storage charging of the molten salt energy storage equipment is carried out according to the expected charging rate;
and the alarm unit is used for carrying out energy storage and charging of the molten salt energy storage equipment according to the maximum charging rate in the charging rate threshold when the energy storage and charging is not met, and generating an electric quantity alarm signal to carry out energy storage deficiency early warning.
Further, the method further comprises the following steps:
the energy storage upper limit calculation unit is used for acquiring the maximum energy storage electric quantity in the valley time period based on the maximum charging rate when the electric quantity alarm signal is generated;
the power discarding notch calculating unit is used for calculating and obtaining energy storage electric quantity deviation based on the maximum energy storage electric quantity and the power generation quantity to be stored;
and the optimization control unit is used for optimally controlling the molten salt energy storage equipment according to the energy storage electric quantity deviation.
It should be understood that the embodiments mentioned in this specification focus on differences from other embodiments, and the specific embodiment in the first embodiment is equally applicable to a system for intelligently adjusting the molten salt heat storage rate described in the second embodiment, which is not further developed herein for brevity of description.
It should be understood that the embodiments disclosed herein and the foregoing description may enable one skilled in the art to utilize the present application. While the present application is not limited to the above-mentioned embodiments, obvious modifications and variations of the embodiments mentioned herein are possible and are within the principles of the present application.
Claims (7)
1. A method of intelligently adjusting molten salt heat storage rate, the method comprising:
collecting sunshine forecast data and expected sunshine duration of a target area in a preset time period;
acquiring power generation parameter information of target photovoltaic equipment, wherein the power generation parameter information comprises photoelectric conversion efficiency;
carrying out photovoltaic power generation prediction based on the sunlight prediction data, the expected sunlight duration and the photoelectric conversion efficiency to generate expected photovoltaic power generation;
calculating to obtain photovoltaic power generation deviation based on photovoltaic rated power generation capacity of a target area and the expected photovoltaic power generation capacity in a preset time period;
acquiring operation parameter information of molten salt energy storage equipment, wherein the operation parameter information comprises a charging rate threshold value, an optimal charging rate and a real-time heat loss coefficient;
calculating to obtain the power generation capacity to be stored according to the deviation amount of the photovoltaic power generation and the real-time heat loss coefficient;
and acquiring a valley electricity period of a target area, judging whether the expected energy storage electric quantity can meet the to-be-stored generating capacity under the optimal charging rate in the valley electricity period, and carrying out energy storage charging of the molten salt energy storage equipment according to the optimal charging rate when the expected energy storage electric quantity meets the to-be-stored generating capacity.
2. The method of claim 1, wherein the method further comprises:
when the expected energy storage electric quantity cannot meet the generated energy to be stored, calculating according to the generated energy to be stored and the off-peak electric period to obtain an expected charging rate;
judging whether the expected charging rate meets the charging rate threshold, and when the expected charging rate meets the charging rate threshold, carrying out energy storage charging on the molten salt energy storage equipment according to the expected charging rate;
and when the energy storage and charging of the molten salt energy storage equipment are not met, carrying out energy storage and charging of the molten salt energy storage equipment according to the maximum charging rate in the charging rate threshold, and generating an electric quantity warning signal to carry out energy storage deficiency early warning.
3. The method of claim 1, wherein the method further comprises:
acquiring historical sunshine duration of a near-N-year target area, and dividing the historical sunshine duration according to the preset time period to obtain a plurality of sunshine duration sets, wherein the sunshine duration sets and nodes of the preset time period have a corresponding relationship, each sunshine duration set comprises N historical sunshine duration, and N is an integer greater than 5;
sequentially carrying out average value calculation on the plurality of sunshine duration to obtain average values of the plurality of sunshine duration;
taking a node with a preset time period as a child node, taking the average value of the sunshine duration as a leaf node of the child node, and constructing a time node-sunshine duration comparison table;
and acquiring a real-time node of a preset time period, inputting the real-time node into the time node-sunlight duration comparison table, and matching to obtain the expected sunlight duration.
4. The method of claim 1, wherein the photovoltaic power generation prediction based on the solar radiation prediction data, the expected solar radiation time period, and the photoelectric conversion efficiency generates an expected photovoltaic power generation amount, further comprising:
based on the electric power big data, taking photovoltaic power generation as a retrieval condition, and acquiring a plurality of historical photovoltaic power generation data, wherein the historical photovoltaic power generation data comprises historical sunlight data, historical sunlight duration, equipment photoelectric conversion efficiency and historical power generation capacity;
taking the plurality of historical photovoltaic power generation data as training data, and performing supervision training on a photovoltaic power generation prediction channel constructed based on the BP neural network to obtain a photovoltaic power generation prediction channel meeting expected standards;
and inputting the sunlight prediction data, the expected sunlight duration and the photoelectric conversion efficiency into the photovoltaic power generation prediction channel to perform power generation prediction, and outputting expected photovoltaic power generation.
5. The method of claim 1, wherein the method further comprises:
the historical energy storage log of the molten salt energy storage equipment is called, a plurality of historical heat loss coefficients are obtained based on the historical energy storage log, and the historical heat loss coefficients have time node identifications;
constructing a heat loss dynamic curve based on the time node and the historical heat loss coefficient;
and carrying out heat loss prediction on a real-time node of a preset time period according to the heat loss dynamic curve to generate the real-time heat loss coefficient.
6. The method of claim 2, wherein the generating and generating the power alert signal for the pre-warning of the energy deficiency further comprises:
when an electric quantity warning signal is generated, acquiring the maximum energy storage electric quantity in the valley time period based on the maximum charging rate;
calculating to obtain an energy storage electric quantity deviation based on the maximum energy storage electric quantity and the to-be-stored electric energy generation quantity;
and optimally controlling the molten salt energy storage equipment according to the energy storage electric quantity deviation.
7. A system for intelligently adjusting molten salt heat storage rate, the system comprising:
the environment information acquisition module is used for acquiring sunlight prediction data and expected sunlight duration of the target area in a preset time period;
the device parameter acquisition module is used for acquiring power generation parameter information of the target photovoltaic device, wherein the power generation parameter information comprises photoelectric conversion efficiency;
the power generation prediction module is used for carrying out photovoltaic power generation prediction based on the sunlight prediction data, the expected sunlight duration and the photoelectric conversion efficiency to generate expected photovoltaic power generation capacity;
the deviation calculation module is used for calculating and obtaining photovoltaic power generation deviation based on photovoltaic rated power generation capacity of a target area and the expected photovoltaic power generation capacity in a preset time period;
the energy storage parameter acquisition module is used for acquiring operation parameter information of the molten salt energy storage equipment, wherein the operation parameter information comprises a charging rate threshold value, an optimal charging rate and a real-time heat loss coefficient;
the electricity to be stored quantity calculating module is used for calculating and obtaining electricity to be stored according to the photovoltaic electricity generation deviation and the real-time heat loss coefficient;
and the judging control module is used for acquiring the valley electricity period of the target area, judging whether the expected energy storage electric quantity can meet the to-be-stored electric quantity under the optimal charging rate in the valley electricity period, and carrying out energy storage charging of the molten salt energy storage equipment according to the optimal charging rate when the expected energy storage electric quantity meets the to-be-stored electric quantity.
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