CN117595517B - Intelligent cluster control method and system based on distributed photovoltaic - Google Patents
Intelligent cluster control method and system based on distributed photovoltaic Download PDFInfo
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Abstract
The invention discloses an intelligent cluster control method and system based on distributed photovoltaic, and particularly relates to the field of power control. By dynamically adjusting the supply and demand coefficients, the power supply and demand can be matched better, waste and shortage are reduced, and the stability of the system is ensured. According to the comparison of the supply and demand coefficient and the threshold value, the system can intelligently identify the demand party and the supply party, and intelligent supply and demand switching is realized. The solar radiation, balance capacity and loss trend are comprehensively considered, the system can be better adapted to the power fluctuation condition, and the stability of the power system is improved. By the iterative matching method, the power requirement is met, the available power resources are fully utilized, the external power dependence is reduced, and the reliability and stability of the system are improved. And further, the fluctuation of the power demand is effectively treated, and the efficient and intelligent management of the power supply and demand is realized.
Description
Technical Field
The invention relates to the field of power control, in particular to an intelligent cluster control method and system based on distributed photovoltaic.
Background
The world is continuously advancing energy structure transformation, and a new energy power station is built vigorously to relieve the current situation of energy exhaustion and environmental problems. The distributed photovoltaic has the advantages of high safety and reliability, flexible installation, low construction cost, quick period, convenient maintenance, no environmental pollution and the like, and is widely focused on the development of the current new energy technology.
The existing photovoltaic power station is mainly centralized, but the construction of the centralized photovoltaic power station needs to occupy a large area of land, so that the site selection is generally far away from areas with shortage of land such as cities; the distributed power station is flexible in site selection, is installed close to a user side, is short in construction period, can be supported by a building, and utilizes illumination resources to the greatest extent.
The existing photovoltaic micro-power stations are mostly independent micro-power stations, unified layout, planning and scheduling are lacking among the micro-power stations, so that the concept of clustering appears subsequently, distributed photovoltaic power stations in a certain area are planned and managed uniformly, each micro-power station is connected with a power distribution network when supplying power for a certain area, power grid interconnection and intercommunication are realized between the micro-power stations, electric energy scheduling in the area is realized, the flexibility of the distributed photovoltaic power stations is further enhanced, on-site consumption is realized, the power consumption quota of the area is reduced, and meanwhile, the stable and economic operation of the power distribution network in the area is ensured.
However, the existing clustered micro power station has some problems in scheduling:
The traditional method often adopts a fixed and static scheduling strategy, and is difficult to flexibly adapt to dynamic power requirements. The power transmission loss and the future weather condition are not fully considered, and the power scheduling requirements inside the micro power station cluster cannot be effectively met, the self-healing capacity of the system is lacking, and comprehensive resource cooperation and coordination are lacking. These problems tend to lead to challenges such as wasted, insufficient, inefficient power distribution, and sub-optimal utilization of resources.
In order to solve the above problems, a technical solution is now provided.
Disclosure of Invention
In order to overcome the defects in the prior art, the embodiment of the invention provides that the invention comprehensively considers the fluctuation index of the junction capacity, the conversion loss trend index and the sunlight influence index, constructs a supply and demand model and generates a supply and demand coefficient so as to realize the optimization of power supply and demand. By dynamically adjusting the supply and demand coefficients, the power supply and demand can be matched better, the efficiency of the power system is improved, the waste and shortage are reduced, and the stability of the system is ensured. And the automatic supply and demand switching enables the system to intelligently identify the demand party and the supply party according to the comparison of the supply and demand coefficient and the threshold value, and the intelligent supply and demand switching is realized. This helps to achieve flexible power scheduling, meeting diverse needs. The solar radiation, balance capacity and loss trend are comprehensively considered, the system can be better adapted to the power fluctuation condition, and the stability of the power system is improved. By the iterative matching method, the power requirement is met, the available power resources are fully utilized, the external power dependence is reduced, and the reliability and stability of the system are improved. The method effectively aims at power demand fluctuation, and achieves efficient and intelligent management of power supply and demand, so that the problems in the background technology are solved.
In order to achieve the above purpose, the present invention provides the following technical solutions: step S1, updating a micro power station cluster, counting on-line micro power stations, acquiring energy storage information and environment information of each micro power station of a distributed cluster to construct a supply and demand model, and generating a supply and demand coefficient;
step S2, dividing the micro power station into two types of suppliers and demanders according to supply and demand coefficients, and respectively sequencing the suppliers and demanders to obtain corresponding lists;
Step S3, matching the suppliers and the demander, listing all possible combinations by using an iterative algorithm, and counting the demander which does not meet the demand and the supplier which does not use up the resource again in each iteration until the demander list does not have any more or the supplier list does not have any more.
In a preferred embodiment, step S1 specifically includes the following:
Determining the micro power station in the online state by using an online state monitoring system, confirming the position of the micro power station which is not online and dispatching personnel for maintenance; and collecting energy storage information and environment information of the micro power station in an online state, wherein the energy storage information comprises balance capacity fluctuation indexes and conversion loss trend indexes, and the environment information comprises sunlight influence indexes.
In a preferred embodiment, the balance capacity fluctuation index is obtained by the following steps:
For each micro power station, a daily power balance is calculated using the following formula: daily electricity balance= (daily electricity generation amount-daily electricity demand) + (daily charge amount-daily discharge amount); and (3) aggregating the electric quantity balance data of each micro power station according to time, calculating an average value and a standard deviation of the electric quantity balance data of each unit time period, and dividing the standard deviation by the average value to obtain a difference coefficient, namely a balance capacity fluctuation index.
In a preferred embodiment, the conversion loss trend index is obtained by:
for each micro power station, recording daily conversion loss rate data, recording time stamps corresponding to the conversion loss rate which is greater than or equal to the standard conversion loss rate, wherein the time stamps represent time points when the change of the power loss rate is not in accordance with the requirement, and sequencing according to the sequence of the time stamp records; calculating a time difference between each time stamp, i.e., a time interval of adjacent time stamps; according to the calculated time interval data, the corresponding time stamp is regarded as an independent variable, and the time interval is regarded as a dependent variable; and (3) performing linear regression analysis to obtain a conversion loss trend index by fitting a linear model of time interval change with time.
In a preferred embodiment, the solar impact index is obtained by:
And collecting the illumination intensity data of each day in the future unit time of the position of the micro power station, calculating an average value and a standard deviation according to the illumination intensity data of each day, marking the illumination intensity data of each day as ideal weather, so as to obtain ideal days, calculating the ratio of the ideal days to the total days to obtain the duty ratio of the ideal days, counting the illumination intensity data of each day, calculating the illumination intensity standard deviation, dividing the duty ratio of the ideal days by the illumination intensity standard deviation, and multiplying the illumination intensity average value by the illumination intensity standard deviation to obtain the sunshine influence index.
In a preferred embodiment, the solar impact index is obtained by:
And collecting the illumination intensity data of each day in the future unit time of the position of the micro power station, calculating an average value and a standard deviation according to the illumination intensity data of each day, marking the illumination intensity data of each day as ideal weather, so as to obtain ideal days, calculating the ratio of the ideal days to the total days to obtain the duty ratio of the ideal days, dividing the duty ratio of the ideal days by the illumination intensity standard deviation, and multiplying the illumination intensity standard deviation by the illumination intensity average value to obtain the sunlight influence index.
In a preferred embodiment, step S2 specifically includes the following:
And comparing the supply and demand coefficient with a first classification threshold value and a second classification threshold value respectively: if the supply and demand coefficient is greater than or equal to the second classification threshold value, an up-switching signal is generated, and the supply and demand relationship is switched to a supplier; if the supply and demand coefficient is smaller than the second classification threshold value and larger than or equal to the first classification threshold value, no signal is generated, and the supply and demand relationship is not switched; if the supply and demand coefficient is smaller than the first classification threshold value, generating a downward switching signal, and switching the supply and demand relation to a demand party; for suppliers, sorting the suppliers according to the supply and demand coefficients from large to small to obtain a supplier list; and for the demander, sequencing the demander according to the supply and demand coefficients from small to large to obtain a demander list.
In a preferred embodiment, step S3 specifically includes the following:
for the micro power stations belonging to the same micro power station cluster, matching a demand party and a delivery party by using an iteration method so as to meet the demand of the demand party, wherein the iteration process is as follows:
a, selecting a first-order demand party in a demand party list;
c. matching the demand side with each provider in the provider list, and calculating to obtain matching degree;
c. Selecting a supplier with highest matching degree, and performing power dispatching;
d. Updating supply and demand coefficients of a supplier and a demander;
e. refreshing respective lists of the supplier and the demander according to the newly obtained supply and demand coefficients;
f. repeating steps a through e until there are no more requesters in the requester list or no more suppliers in the supplier list;
g. If the demand party exists in the demand party list, generating an intervention signal, requiring an external power grid to supply power for the micro power station cluster, and if the supply party exists in the supply party list, generating an output signal, and transmitting redundant electric energy to the outside.
The intelligent cluster control method and the intelligent cluster control system based on the distributed photovoltaic have the technical effects and advantages that:
1. According to the invention, the balance capacity fluctuation index, the conversion loss trend index and the sunshine influence index are comprehensively considered, the supply and demand model is constructed, the supply and demand coefficient is generated, and the supply and demand relation of the power supply and demand of each micro power station under the current and future conditions is defined through the supply and demand coefficient, so that the power supply and demand optimization is realized, and the power supply and demand can be better matched through the dynamic adjustment of the supply and demand coefficient, so that the power utilization efficiency is improved. This helps to reduce power waste and shortage, ensuring stability of the power system. And through the comparison result of the supply and demand coefficient and the corresponding threshold value, the demand party and the supply party can be automatically identified, and intelligent supply and demand switching is realized. The method is beneficial to realizing more flexible power scheduling in the micro power station cluster so as to meet different requirements. By considering sunlight, balance capacity and loss trend, the system can better cope with different power fluctuation conditions, so that the stability of the power system is improved;
2. The invention is favorable for fully meeting the power demand by the iterative matching method, maximally utilizes the available power resource, and can flexibly adapt to the situation of continuously changing power demand. And also helps to reduce the frequency of external power intervention, reducing dependence on external power, thereby improving the usability and stability of the power system. The fluctuation of the power demand is effectively treated, and the efficient and intelligent management of the power supply and demand is ensured.
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FIG. 1 is a schematic flow chart of a distributed photovoltaic-based intelligent cluster control method and system of the present invention;
Fig. 2 is a schematic structural diagram of the intelligent cluster control method and system based on distributed photovoltaic according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Fig. 1 shows an intelligent cluster control method based on distributed photovoltaic, which comprises the following steps:
Step S1, updating a micro power station cluster, counting on-line micro power stations, acquiring energy storage information and environment information of each micro power station of a distributed cluster to construct a supply and demand model, and generating a supply and demand coefficient;
step S2, dividing the micro power station into two types of suppliers and demanders according to supply and demand coefficients, and respectively sequencing the suppliers and demanders to obtain corresponding lists;
Step S3, matching the suppliers and the demander, listing all possible combinations by using an iterative algorithm, and counting the demander which does not meet the demand and the supplier which does not use up the resource again in each iteration until the demander list does not have any more or the supplier list does not have any more.
The step S1 specifically comprises the following steps:
The data acquisition system is deployed to acquire real-time data from each micro-power station, which includes installing sensors to monitor battery energy storage capacity, state of charge, power generation, and monitoring electricity demand, etc. The sensors are connected to a micro-plant control system, which transmits data in real time to a central database via a communication network, where data processing and analysis software is used to process the micro-plant data, including energy storage information, electricity demand, etc. Analysis includes data cleaning, energy storage state estimation, demand prediction, and the like.
An on-line monitoring system is used to determine the micro-power station that is on-line, confirm its location for the micro-power station that is not on-line and dispatch personnel for maintenance.
And collecting energy storage information and environment information of the micro power station in an online state, wherein the energy storage information comprises balance capacity fluctuation indexes and conversion loss trend indexes, and the environment information comprises sunlight influence indexes.
The balance capacity fluctuation index is obtained by the following steps:
For each micro power station, a daily power balance is calculated using the following formula: daily electricity balance= (daily electricity generation amount-daily electricity demand) + (daily charge amount-daily discharge amount);
Daily power generation refers to the daily solar photovoltaic power generation of a micro power station, and can be estimated based on the rated capacity of a solar panel and the illumination condition of the day in kilowatt-hours (kWh).
Daily electricity demand refers to the actual electricity demand within the micro-plant jurisdiction and may be estimated based on historical electricity data or load curves.
The daily charge is the amount of electricity charged by the battery system in kilowatt-hours (kWh). The daily discharge is the amount of electricity released by the battery system, again in kilowatt-hours.
Daily balance of electricity, positive number represents balance of electricity, negative number represents shortage of electricity, and zero represents balance of supply and demand.
The daily balance of electricity represents the electricity adequacy of the daily energy of the micro power station. If the balance of the electric quantity is positive, indicating that the electric power is abundant; if often negative, it indicates an insufficient power supply; if the fluctuation is small and near zero, this indicates that the power supply and demand is substantially balanced.
The power balance data for each micro-plant is aggregated over time, e.g., may be counted in months or quarters. The average value and standard deviation are calculated for the electric quantity balance data for each unit time period. Dividing the standard deviation by the average value to obtain a difference coefficient, namely balance capacity fluctuation index.
The balance capacity fluctuation index is used for representing the stability and fluctuation of the supply and demand of the electric quantity, a larger balance capacity fluctuation index represents that the electric quantity balance fluctuates more in a unit time period, and a smaller balance capacity fluctuation index represents that the electric quantity balance fluctuates less.
The importance of collecting the conversion loss trend index of a micro power station is that this index provides information about the dynamic trend of the power loss rate. By monitoring and analyzing this index, the effectiveness of power management and stability of power quality can be better understood. A larger trend index indicates a gradual decrease in the power consumption rate of the micro-plant, which is an effective implementation of power management measures, implying a higher power quality and resource utilization efficiency. Conversely, a smaller trend index may mean a fluctuation or rise in the power loss rate, at which time corrective action needs to be taken to maintain the stability of the power supply. Therefore, the index is helpful for monitoring and identifying potential problems in real time, and helping the micro power station to take timely improvement measures so as to ensure the reliability and the high efficiency of the power system.
The conversion loss trend index is obtained by the following steps:
For each micro power station, recording daily conversion loss rate data, recording time stamps corresponding to the conversion loss rate which is greater than or equal to the standard conversion loss rate, wherein the time stamps represent time points when the change of the power loss rate is not in accordance with the requirement, and sequencing according to the sequence of the time stamp records;
calculating a time difference between each time stamp, i.e., a time interval of adjacent time stamps;
according to the calculated time interval data, the corresponding time stamp is regarded as an independent variable, and the time interval is regarded as a dependent variable;
Linear regression analysis is performed to fit a linear model of the time interval over time, which can be expressed as: time interval = β0+ β1 x time stamp;
wherein, beta 0 is intercept, beta 1 is slope, namely conversion loss trend index, which represents the change trend of time interval along with time;
The smaller the conversion loss trend index is, the smaller the time interval is, i.e., the trend of the change in the power loss rate is toward the increasing direction, i.e., the time interval data is changed with time, the smaller the time interval is, the more frequently the number of times of not conforming to the standard conversion loss rate is, and the larger the conversion loss trend index is, the larger the time interval is, i.e., the trend of the change in the power loss rate is toward the decreasing direction.
The ideal degree of photovoltaic power generation and the meteorological environment are closely related, so that the solar condition of the photovoltaic power generation area of the micro power station in the unit time in the future can be estimated through the meteorological prediction result. The prediction enables the micro power station to know future illumination conditions in advance, and then corresponding power management strategies are adopted. If the predictions show that future insolation will be sufficient, the micro-power station may better accumulate electrical energy, thereby improving the reliability of the power supply. Especially in case that the current micro power station has accumulated a sufficient amount of electric energy reserve, the micro power station can be regarded as a potential electric power supplier with good sunshine conditions to supply electric energy to the outside, thereby increasing its economic benefit. Thus, weather forecast has important strategic value for power planning and operational decisions for micro-plants.
The acquisition process of the sunlight influence index comprises the following steps:
And acquiring the daily illumination intensity data in the future unit time of the position of the micro power station from a meteorological unit, calculating an average value and a standard deviation according to the daily illumination intensity data, marking the daily illumination intensity data as ideal weather by using the standard value of the illumination intensity or more, so as to obtain ideal days, calculating the ratio of the ideal days to the total days to obtain the duty ratio of the ideal days, dividing the duty ratio of the ideal days by the standard deviation of the illumination intensity, and multiplying the duty ratio by the average value of the illumination intensity to obtain the sunlight influence index.
The solar impact index is used for evaluating potential impact of illumination conditions on power supply of the micro power station in unit time in the future, and comprehensively considers the level of average illumination intensity, variability of illumination intensity data and the proportion of days meeting ideal illumination conditions to total days. A smaller solar impact index indicates a higher uncertainty and variability of the lighting conditions, while a larger solar impact index indicates a relatively more reliable and stable lighting conditions. The micro power station can formulate a power management strategy according to the sunlight influence index so as to better adapt to future illumination conditions and improve the reliability of power supply.
The supply and demand coefficient is generated by analyzing a supply and demand model through a balance capacity fluctuation index, a conversion loss trend index and a sunlight influence index framework, and can be calculated by the following formula, for example, the expression is as follows:
Wherein pdc represents supply and demand coefficients, scfi, cti and ssi are balance capacity fluctuation indexes, conversion loss trend indexes and sunlight influence indexes respectively, and f 1、f2 and f 3 are preset proportional coefficients of the balance capacity fluctuation indexes, the conversion loss trend indexes and the sunlight influence indexes respectively and are all larger than 0.
The supply and demand coefficients are used for representing and evaluating the power supply and demand relation of the micro power station and the state of power management of the micro power station. In particular, the supply and demand coefficients reflect whether the micro-plant is in the role of a power supplier or a consumer, and the extent of its power abundance, the supply and demand coefficients determining whether the micro-plant is a supplier or a consumer.
The step S2 specifically includes the following:
and comparing the supply and demand coefficient with a first classification threshold value and a second classification threshold value respectively:
if the supply and demand coefficient is greater than or equal to the classification threshold value II, the micro power station is in the role of an electric power supplier, and the electric power supply is very sufficient. The micro power station has the capability of transmitting electric energy to the outside, and the electric power adequacy is very high, so that not only can the self demand be met, but also additional electric energy can be provided, an up-switching signal is generated, and the supply-demand relationship is switched to a supply side;
If the supply and demand coefficient is smaller than the classification threshold value II and larger than or equal to the classification threshold value I, the micro power station is in a self-sufficient state, the micro power station cannot transmit power to the outside and does not need external power supply, no signal is generated, and the supply and demand relationship is not switched;
if the supply-demand coefficient is less than the classification threshold one, it indicates that the micro-power station is in the role of a power demand party, i.e. the power demand exceeds the power supply. The power reserve of the micro power station is insufficient to meet the demand, so that external energy is required to supplement power to meet the demand and maintain operation, a down-switching signal is generated, and the supply-demand relationship is switched to a demand side;
for suppliers, sorting the suppliers according to the supply and demand coefficients from large to small to obtain a supplier list;
and for the demander, sequencing the demander according to the supply and demand coefficients from small to large to obtain a demander list.
According to the invention, the balance capacity fluctuation index, the conversion loss trend index and the sunshine influence index are comprehensively considered, the supply and demand model is constructed, the supply and demand coefficient is generated, and the supply and demand relation of the power supply and demand of each micro power station under the current and future conditions is defined through the supply and demand coefficient, so that the power supply and demand optimization is realized, and the power supply and demand can be better matched through the dynamic adjustment of the supply and demand coefficient, so that the power utilization efficiency is improved. This helps to reduce power waste and shortage, ensuring stability of the power system. And through the comparison result of the supply and demand coefficient and the corresponding threshold value, the demand party and the supply party can be automatically identified, and intelligent supply and demand switching is realized. The method is beneficial to realizing more flexible power scheduling in the micro power station cluster so as to meet different requirements. By considering sunlight, balance capacity and loss trend, the system can better cope with different power fluctuation conditions, so that the stability of the power system is improved.
The step S3 specifically comprises the following steps:
for the micro power stations belonging to the same micro power station cluster, matching a demand party and a delivery party by using an iteration method so as to meet the demand of the demand party, wherein the iteration process is as follows:
a, selecting a first-order demand party in a demand party list;
b, matching the demand side with each provider in the provider list, and calculating to obtain matching degree, wherein the calculation process is as follows:
obtaining supply and demand coefficients corresponding to a supplier and a supplier, the available power quantity of the supplier, the required power quantity of a demander and the transmission power loss between the supplier and the demander;
The matching degree is calculated, for example, by the following formula:
Wherein M represents matching degree, pdc G and pdc X are supply and demand coefficients of a supplier and a demander respectively, P G is the available power quantity of the supplier, P X is the required power quantity of the demander, I 2.R represents power transmission loss of the supplier and the demander, I and R are current and resistance, and k is And greater than 0.
The matching degree is used for showing the power matching degree between the power supply and the demand party, and is used for assessing the power matching degree between the power supply and the demand party, in particular, if the matching degree is large, the power matching degree between the power supply and the demand party is high, and the power supplied by the power supply party is enough to meet the power demand of the demand party. Indicating that there is sufficient power match between the suppliers and the demand is satisfied; if the match is small, it means that the match of power between the power suppliers and the power suppliers is low, the power supplied by the power suppliers is insufficient to meet the power demands of the power suppliers, which means that the power suppliers may face the situation that the power suppliers are short or cannot meet the demands.
C. Selecting a supplier with highest matching degree, and performing power dispatching;
d. Updating supply and demand coefficients of a supplier and a demander;
e. refreshing respective lists of the supplier and the demander according to the newly obtained supply and demand coefficients;
f. repeating steps a through e until there are no more requesters in the requester list or no more suppliers in the supplier list;
g. If the demand party exists in the demand party list, generating an intervention signal, requiring an external power grid to supply power for the micro power station cluster, and if the supply party exists in the supply party list, generating an output signal, and transmitting redundant electric energy to the outside.
The invention is favorable for fully meeting the power demand by the iterative matching method, maximally utilizes the available power resource, and can flexibly adapt to the situation of continuously changing power demand. And also helps to reduce the frequency of external power intervention, reducing dependence on external power, thereby improving the usability and stability of the power system. The fluctuation of the power demand is effectively treated, and the efficient and intelligent management of the power supply and demand is ensured.
Example 2
Fig. 2 shows an intelligent cluster control system based on distributed photovoltaic according to the invention, comprising: the system comprises a collection analysis module, a supply and demand classification module and a matching scheduling module;
The collection analysis module is used for updating the micro power station cluster, counting the online micro power stations, acquiring energy storage information and environment information of each micro power station of the distributed cluster to construct a supply and demand model, generating supply and demand coefficients, and sending the supply and demand coefficients to the supply and demand classification module;
the supply and demand classification module divides the micro power station into two types of suppliers and demanders according to supply and demand coefficients, sorts the suppliers and demanders respectively to obtain corresponding lists, and sends the lists to the matching scheduling module;
the matching scheduling module is used for matching the suppliers and the demander, and enumerating all possible combinations by using an iterative algorithm, and re-counting the suppliers of the demander and the unspent resource which do not meet the demand in each iteration until the demander is no longer in the demand list or the suppliers are no longer in the supplier list.
The above formulas are all formulas with dimensionality removed and numerical calculation, the formulas are formulas with the latest real situation obtained by software simulation through collecting a large amount of data, and preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
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 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 application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (2)
1. The intelligent cluster control method based on the distributed photovoltaic is characterized by comprising the following steps of:
Step S1, updating a micro power station cluster, counting on-line micro power stations, acquiring energy storage information and environment information of each micro power station of a distributed cluster to construct a supply and demand model, and generating a supply and demand coefficient;
step S2, dividing the micro power station into two types of suppliers and demanders according to supply and demand coefficients, and respectively sequencing the suppliers and demanders to obtain corresponding lists;
Step S3, matching the suppliers and the demander, listing all possible combinations by using an iterative algorithm, and counting the demander which does not meet the demand and the supplier which does not use up the resource again in each iteration until the demander list does not have any more or the supplier list does not have any more;
the step S1 specifically comprises the following steps:
Determining the micro power station in the online state by using an online state monitoring system, confirming the position of the micro power station which is not online and dispatching personnel for maintenance; collecting energy storage information and environment information of a micro power station in an online state, wherein the energy storage information comprises balance capacity fluctuation indexes and conversion loss trend indexes, and the environment information comprises sunlight influence indexes;
the balance capacity fluctuation index is obtained by the following steps:
For each micro power station, a daily power balance is calculated using the following formula: daily electricity balance= (daily electricity generation amount-daily electricity demand) + (daily charge amount-daily discharge amount); the electric quantity balance data of each micro power station are aggregated according to time, average value and standard deviation are calculated for the electric quantity balance data of each unit time period, and the standard deviation is divided by the average value to obtain a difference coefficient, namely balance capacity fluctuation index;
The conversion loss trend index is obtained by the following steps:
For each micro power station, recording daily conversion loss rate data, recording time stamps corresponding to the conversion loss rate which is greater than or equal to the standard conversion loss rate, and sequencing according to the sequence of the time stamp records; calculating a time difference between each time stamp, i.e., a time interval of adjacent time stamps; according to the calculated time interval data, the corresponding time stamp is regarded as an independent variable, and the time interval is regarded as a dependent variable; performing linear regression analysis to obtain a conversion loss trend index by fitting a linear model of time interval change along with time;
The acquisition process of the sunlight influence index comprises the following steps:
collecting illumination intensity data of each day in a future unit time of a position of a micro power station, calculating an average value and a standard deviation according to the illumination intensity data of each day, marking the illumination intensity data of each day as ideal weather, so as to obtain ideal days, calculating the ratio of the ideal days to the total days to obtain the duty ratio of the ideal days, dividing the duty ratio of the ideal days by the illumination intensity standard deviation, and multiplying the illumination intensity standard deviation by the illumination intensity average value to obtain a sunlight influence index;
Analyzing a supply and demand model through a balance capacity fluctuation index, a conversion loss trend index and a sunlight influence index framework to generate a supply and demand coefficient, and calculating through the following formula:
Wherein pdc represents supply and demand coefficients, scfi, cti and ssi are balance capacity fluctuation indexes, conversion loss trend indexes and sunlight influence indexes respectively, and f 1、f2 and f 3 are preset proportional coefficients of the balance capacity fluctuation indexes, the conversion loss trend indexes and the sunlight influence indexes respectively and are all larger than 0;
the step S2 specifically includes the following:
And comparing the supply and demand coefficient with a first classification threshold value and a second classification threshold value respectively: if the supply and demand coefficient is greater than or equal to the second classification threshold value, an up-switching signal is generated, and the supply and demand relationship is switched to a supplier; if the supply and demand coefficient is smaller than the second classification threshold value and larger than or equal to the first classification threshold value, no signal is generated, and the supply and demand relationship is not switched; if the supply and demand coefficient is smaller than the first classification threshold value, generating a downward switching signal, and switching the supply and demand relation to a demand party; for suppliers, sorting the suppliers according to the supply and demand coefficients from large to small to obtain a supplier list; for the demand party, sequencing the demand party according to the supply and demand coefficients from small to large to obtain a demand party list;
The step S3 specifically comprises the following steps:
for the micro power stations belonging to the same micro power station cluster, matching a demand party and a delivery party by using an iteration method so as to meet the demand of the demand party, wherein the iteration process is as follows:
a, selecting a first-order demand party in a demand party list;
b. Matching the demand side with each provider in the provider list, and calculating to obtain matching degree;
c. Selecting a supplier with highest matching degree, and performing power dispatching;
d. Updating supply and demand coefficients of a supplier and a demander;
e. refreshing respective lists of the supplier and the demander according to the newly obtained supply and demand coefficients;
f. repeating steps a through e until there are no more requesters in the requester list or no more suppliers in the supplier list;
g. If the demand party exists in the demand party list, generating an intervention signal, requiring an external power grid to supply power for the micro power station cluster, and if the supply party exists in the supply party list, generating an output signal, and transmitting redundant electric energy to the outside.
2. A distributed photovoltaic-based intelligent cluster control system for implementing the distributed photovoltaic-based intelligent cluster control method of claim 1, comprising: the system comprises a collection analysis module, a supply and demand classification module and a matching scheduling module;
The collection analysis module is used for updating the micro power station cluster, counting the online micro power stations, acquiring energy storage information and environment information of each micro power station of the distributed cluster to construct a supply and demand model, generating supply and demand coefficients, and sending the supply and demand coefficients to the supply and demand classification module;
The supply and demand classification module divides the micro power station into two types of suppliers and demanders according to the supply and demand coefficients, sorts the suppliers and demanders respectively to obtain corresponding lists, and sends the lists to the matching scheduling module;
the matching scheduling module is used for matching the suppliers and the demander, and enumerating all possible combinations by using an iterative algorithm, and re-counting the suppliers of the demander and the unspent resource which do not meet the demand in each iteration until the demander is no longer in the demand list or the suppliers are no longer in the supplier list.
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