A control method for rural power supply system

Abstract

The present application provides a control method for a rural power supply system, which belongs to the field of power grid dispatching and is used to solve the problem in related technologies that power grid dispatching strategies are difficult to continuously and reliably meet power demand. The method introduces obtainable influencing factors that affect power consumption and power generation on the basis of traditional trend prediction of power consumption and power generation, so as to achieve more accurate prediction of power consumption and power generation, and there is no limit on the number of influencing factors. The more influencing factors are obtained, the more accurate the prediction results are in theory, which is conducive to overcoming the uncertainty of power generation resources and power demand in complex situations, and is conducive to more reliably ensuring the continuous and reliable satisfaction of power demand, as well as reducing the dependence of rural power supply systems on the public grid as much as possible, with the ultimate goal of achieving self-sufficiency of rural power supply systems.

Description

Technical Field

[0001] The present application relates to the field of power supply systems, and in particular to a control method for rural power supply systems. Background Art

[0002] Rural power supply systems (or rural microgrids) refer to small, independent power systems established in rural areas. They typically include distributed generation resources (such as solar photovoltaic, wind power, and small hydropower), energy storage systems, and loads, enabling self-power supply and management. Rural power supply systems aim to improve the reliability of power supply in rural areas, reduce reliance on long-distance transmission, promote the use of renewable energy, improve energy efficiency, and reduce carbon emissions. Both the generation resources and power demand of rural power supply systems are subject to uncertainty. Ensuring that power demand is met within this uncertainty has long been a challenge for control solutions used in rural power supply systems. Summary of the Invention

[0003] The present application provides a control method for a rural power supply system, which is helpful in overcoming the uncertainty of power generation resources and power demand, so as to ensure that power demand is continuously and reliably met.

[0004] This application provides a control method for a rural power supply system. The method includes:

[0005] Obtaining historical electricity consumption data, historical power generation data, and information on several influencing factors within a first preset time period before the current moment;

[0006] Determining predicted power consumption data within a second preset time period after the current moment based on the power consumption history data and factor information;

[0007] Determining predicted power generation data within a second preset time period after the current moment based on the power generation history data and factor situation information;

[0008] The scheduling strategy information is determined based on the pre-acquired current power storage data and the predicted power consumption data and the predicted power generation data.

[0009] By adopting the above technical solution, it is possible to combine influencing factors as well as historical electricity consumption data and historical power generation data to determine predicted electricity consumption data and predicted power generation data, making the predicted electricity consumption data and predicted power generation data more reliable, so as to facilitate more reasonable determination of scheduling strategy information, which is beneficial to customer-facing power generation resources and the uncertainty of power demand, and is beneficial to ensuring that power demand is continuously and reliably met.

[0010] Furthermore, determining the predicted power consumption data within a second preset time period after the current moment based on the power consumption history data and factor situation information includes:

[0011] Performing trend analysis on the historical electricity consumption data to obtain trend electricity consumption data;

[0012] Determine the electricity consumption impact coefficient based on the factor information;

[0013] The predicted power consumption data is determined according to the trend power consumption data and the power consumption impact coefficient.

[0014] Furthermore, determining the electricity consumption impact coefficient according to the factor situation information includes:

[0015] Substituting factor situation information into the electricity consumption scoring rule of the influencing factor to determine the factor electricity consumption side score of the influencing factor;

[0016] Calculate the electricity consumption impact coefficient based on the electricity consumption score of the factors, and set is the electricity consumption score of the i-th influencing factor, Calculate the weight of electricity consumption for the i-th influencing factor and , n is the number of influencing factors for which corresponding factor information has been obtained, is the electricity consumption influence coefficient, then

[0017]

[0018] Where p is a preset positive integer, is a preset constant, Indicates taking The maximum value in Not greater than 1.

[0019] Furthermore, performing trend analysis on the historical electricity usage data to obtain trend electricity usage data includes:

[0020] Dividing the first preset time into a plurality of third preset time periods, and determining a power consumption component data for each third preset time period;

[0021] Assume that there are m electricity consumption component data, and the i-th electricity consumption component data is , 、 ,..., The time tags carried are from near to far from the current time, and the trend coefficient results are ;

[0022] According to the m power consumption component data, m-1 power consumption change data are obtained. The i-th power consumption change data is ,but ;

[0023] Determine whether the power consumption change data are all greater than zero or not greater than zero;

[0024] If so, then ,otherwise, , where is the number of electricity consumption change data greater than zero, is the amount of electricity consumption change that is not greater than zero, and They are all preset constants greater than 0 and less than 1;

[0025] Trend electricity consumption data .

[0026] Furthermore, determining the predicted power generation data within a second preset time period after the current moment based on the power generation history data and factor situation information includes:

[0027] Performing trend analysis on the historical power generation data to obtain trend power generation data;

[0028] Determining a power generation impact coefficient based on the factor situation information;

[0029] The predicted power generation data is determined according to the trend power generation data and the power generation influence coefficient.

[0030] Furthermore, determining the power generation impact coefficient according to the factor situation information includes:

[0031] Substituting the factor situation information into the power generation scoring rule of the influencing factor to determine the power generation side score of the influencing factor;

[0032] Calculate the power generation impact coefficient based on the power generation side score of the factors, and set is the power generation side score of the i-th influencing factor, The power generation calculation weight of the i-th influencing factor is , n is the number of influencing factors for which corresponding factor information has been obtained, is the power generation influence coefficient, then

[0033]

[0034] Where, Indicates taking The maximum value in Not greater than 1.

[0035] Furthermore, the performing trend analysis on the historical power generation data to obtain trend power generation data includes:

[0036] Dividing the first preset time into a plurality of third preset time periods, and determining a power generation component data for each third preset time period;

[0037] Assume that there are m power generation component data and the i-th power consumption component data is , 、 ,..., The time tags carried are from near to far from the current time, and the trend coefficient results are ;

[0038] According to the m power generation component data, m-1 power generation change data are obtained. The i-th power generation change data is ,but ;

[0039] Determine whether the power generation change data are all greater than zero or not greater than zero;

[0040] If so, then ,otherwise, , where is the number of power generation change data greater than zero, is the number of changes in power generation that are not greater than zero, and They are all preset constants greater than 0 and less than 1;

[0041] Trend power generation data .

[0042] Furthermore, the determining of the scheduling strategy information based on the pre-acquired current power storage data and the predicted power consumption data and the predicted power generation data includes:

[0043] If the predicted power generation data is higher than the predicted power consumption data, the dispatch strategy information includes a recommendation to increase power consumption;

[0044] If the predicted power consumption data is not lower than the predicted power generation data and not higher than the sum of the predicted power generation data and the current storage data, the dispatch strategy information includes a recommendation to use power under the premise of ensuring that the energy storage system storage data is higher than the storage threshold;

[0045] If the predicted electricity consumption data is higher than the sum of the predicted power generation data and the current storage data, the dispatch strategy information includes a suggestion to save electricity.

[0046] Furthermore, the influencing factors include one or more of light intensity, wind level, water conditions, season, and agricultural activities.

[0047] In summary, this application has at least the following beneficial effects:

[0048] A control method for a rural power supply system is provided, which is conducive to more accurate prediction of power generation and consumption of the rural power supply system, and further conducive to determining a more reasonable scheduling strategy.

[0049] It should be understood that the contents described in the Summary of the Invention are not intended to limit the key or important features of the embodiments of the present application, nor are they intended to limit the scope of the present application. Other features of the present application will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0051] Figure 1 A schematic diagram showing an exemplary operating environment in which embodiments of the present application can be implemented;

[0052] Figure 2 A flow chart of a control method for a rural power supply system in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0053] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0054] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0055] The present application provides a control method for a rural power supply system, which is conducive to more accurate prediction of power generation and power consumption of the rural power supply system, and further conducive to determining a more reasonable scheduling strategy.

[0056] Figure 1 A schematic diagram of an exemplary operating environment in which embodiments of the present application can be implemented is shown.

[0057] Reference Figure 1 The operating environment includes a power generation system, a power consumption system and an energy storage system. The power generation system and the power consumption system are electrically connected to the energy storage system so that the electricity generated by the power generation system is transmitted to the energy storage system and the power consumption system uses the electricity stored in the energy storage system.

[0058] The operating environment also includes a management system, which is used to manage the power generation system, the power consumption system, and the energy storage system to facilitate the scheduling optimization of the rural power supply system. The management system can push the results of the scheduling optimization (i.e., subsequent scheduling strategy information) to the manager's mobile terminal, such as a mobile phone, to facilitate the manager to make specific adjustments to the rural power supply system. Alternatively, the management system can be connected to the corresponding industrial control software to provide a reference basis for the industrial control software to intelligently control the scheduling optimization of the rural power supply system. The control method for the rural power supply system provided in this application is executed by the management system.

[0059] Figure 2 A flow chart of a control method for a rural power supply system in an embodiment of the present application is shown.

[0060] Reference Figure 2 , the method specifically comprises the following steps:

[0061] S210: Obtaining historical electricity consumption data, historical power generation data, and information on several influencing factors within a first preset time period before the current moment.

[0062] Specifically, electricity consumption history data reflects the specific electricity consumption of the rural power supply system within the first preset duration before the current moment, while power generation history data reflects the specific power generation of the rural power supply system within the preset duration before the current moment. Influencing factors represent factors that specifically affect electricity consumption and power generation, such as sunlight intensity, wind level, water conditions, season, agricultural activities, etc. Each influencing factor has specific factor information. For example, sunlight intensity has specific light intensity values ​​and duration of sunlight, while wind level has specific wind level data and duration of wind. These factors are not listed here one by one.

[0063] S220: Determine predicted power consumption data within a second preset time period after the current moment based on the power consumption history data and factor situation information.

[0064] The method of this step specifically includes: performing trend analysis on the historical electricity consumption data to obtain trend electricity consumption data; determining the electricity consumption impact coefficient based on the factor situation information; and determining the predicted electricity consumption data based on the trend electricity consumption data and the electricity consumption impact coefficient.

[0065] Specifically, the power consumption impact coefficient is determined based on the factor situation information, which includes: substituting the factor situation information into the power consumption scoring rule of the influencing factor to determine the power consumption side score of the influencing factor; calculating the power consumption impact coefficient based on the power consumption side score of the factor, setting is the electricity consumption score of the i-th influencing factor, Calculate the weight of electricity consumption for the i-th influencing factor and , n is the number of influencing factors for which corresponding factor information has been obtained, is the electricity consumption influence coefficient, then

[0066]

[0067] Where p is a preset positive integer, is a preset constant, Indicates taking The maximum value in Not greater than 1.

[0068] The electricity consumption scoring rule can be specifically configured in the form of a comparison table. After the specific factor situation information of the influencing factor is determined, the factor situation information is substituted into the electricity consumption scoring rule to obtain the factor electricity consumption side score of the influencing factor. The factor electricity consumption side score is not less than 0 and not greater than 1.

[0069] In the method of this step, the trend analysis of the power consumption history data to obtain the trend power consumption data includes: dividing the first preset time length into multiple third preset time lengths, and determining a power consumption component data relative to each third preset time length; assuming that there are m power consumption component data, the i-th power consumption component data is , 、 ,..., The time tags carried are from near to far from the current time, and the trend coefficient results are ; According to the m power consumption component data, m-1 power consumption change data are obtained, and the i-th power consumption change data is ,but ; Determine whether the power consumption change data are all greater than zero or not greater than zero; if so, then ,otherwise, , where is the number of electricity consumption change data greater than zero, is the amount of electricity consumption change that is not greater than zero, and are all preset constants greater than 0 and less than 1; the trend power consumption data .

[0070] S230: Determine predicted power generation data within a second preset time period after the current moment based on the power generation history data and factor situation information.

[0071] The method of this step specifically includes: performing trend analysis on the historical power generation data to obtain trend power generation data; determining a power generation influence coefficient based on the factor situation information; and determining the predicted power generation data based on the trend power generation data and the power generation influence coefficient.

[0072] Specifically, determining the power generation impact coefficient according to the factor situation information includes: substituting the factor situation information into the power generation scoring rule of the influencing factor to determine the power generation side score of the influencing factor; calculating the power generation impact coefficient according to the power generation side score of the factor, setting is the power generation side score of the i-th influencing factor, The power generation calculation weight of the i-th influencing factor is , n is the number of influencing factors for which corresponding factor information has been obtained, is the power generation influence coefficient, then

[0073]

[0074] Where, Indicates taking The maximum value in Not greater than 1. The scoring rules for power generation are the same as those for power consumption, and are also in the form of a comparison table. The only difference between the two is that different influencing factors have different effects on power consumption and power generation, that is, the specific score values ​​under the scoring rules are different, so they are not disclosed repeatedly here.

[0075] In the method of this step, the trend analysis of the power generation history data to obtain the trend power generation data includes: dividing the first preset time length into multiple third preset time lengths, and determining a power generation component data relative to each third preset time length; assuming that there are m power generation component data, and the i-th power consumption component data is , 、 ,..., The time tags carried are from near to far from the current time, and the trend coefficient results are ; According to the m power generation component data, m-1 power generation change data are obtained, and the i-th power generation change data is ,but ; Determine whether the power generation change data are all greater than zero or not; if so, then ,otherwise, , where is the number of power generation change data greater than zero, is the number of changes in power generation that are not greater than zero, and are all preset constants greater than 0 and less than 1; then the trend power generation data .

[0076] S240: Determine scheduling strategy information based on the pre-acquired current power storage data and the predicted power consumption data and the predicted power generation data.

[0077] The method of this step specifically includes: if the predicted power generation data is higher than the predicted power consumption data, the scheduling strategy information includes a recommendation to increase power consumption; if the predicted power consumption data is not lower than the predicted power generation data and not higher than the sum of the predicted power generation data and the current power storage data, the scheduling strategy information includes a recommendation to use electricity under the premise of ensuring that the energy storage system power storage data is higher than the power storage threshold; if the predicted power consumption data is higher than the sum of the predicted power generation data and the current power storage data, the scheduling strategy information includes a recommendation to save electricity.

[0078] Of course, the power consumption system and the energy storage system can also be directly electrically connected to the public grid, and power exchange can be achieved through the public grid and the energy storage system, or the public grid can directly supply power to the power consumption system. In this case, the method of this step can be adjusted as follows: if the predicted power generation data is higher than the predicted power consumption data, the scheduling strategy information is to give priority to the use of stored power; if the predicted power consumption data is not lower than the predicted power generation data and not higher than the sum of the predicted power generation data and the current power storage data, the scheduling strategy information is to ensure that the energy storage system power storage data is higher than the power storage threshold and to use power flexibly; if the predicted power consumption data is higher than the sum of the predicted power generation data and the current power storage data, the scheduling strategy information is to ensure that the energy storage system power storage data is higher than the power storage threshold and to give priority to the use of public grid power.

[0079] In the method of this step, flexible electricity use means that if the storage capacity data of the energy storage system is higher than the storage capacity threshold, the electricity stored in the energy storage system will be used first. If the storage capacity data of the energy storage system is not higher than the storage capacity threshold, the public grid electricity will be used first, leaving the energy storage system to gradually supplement the electricity using the power generation system.

[0080] In this step, the method prioritizes using public grid power, which can also be used to charge the energy storage system. Of course, when the energy stored in the energy storage system reaches the maximum energy storage capacity allowed by the energy storage system, the energy storage system can also be fed back to the public grid in exchange for currency or contribution.

[0081] In the embodiment of the present application, the first preset duration, the second preset duration, and the third preset duration can be specifically configured according to specific forecast requirements and specific parameters of the energy storage system, the power generation system, and the power consumption system.

[0082] In summary, this method introduces the influencing factors that can be obtained to affect electricity consumption and power generation on the basis of traditional trend prediction of electricity consumption and power generation, so as to achieve more accurate prediction of electricity consumption and power generation, and there is no limit on the number of influencing factors. The more influencing factors are obtained, the more accurate the prediction results are in theory, which is conducive to overcoming the uncertainty of power generation resources and power demand in complex situations, and is conducive to more reliably ensuring the continuous and reliable satisfaction of electricity demand, as well as reducing the dependence of rural power supply systems on the public grid as much as possible, with the ultimate goal of achieving self-sufficiency of rural power supply systems.

[0083] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to the embodiments of this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required for this application.

[0084] In summary, this application has at least the following beneficial effects:

[0085] A control method for a rural power supply system is provided, which is conducive to more accurate prediction of power generation and consumption of the rural power supply system, and further conducive to determining a more reasonable scheduling strategy.

[0086] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the aforementioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A control method for a rural power supply system, characterized in that: include: Obtaining historical electricity consumption data, historical power generation data, and information on several influencing factors within a first preset time period before the current moment; Determining predicted power consumption data within a second preset time period after the current moment based on the power consumption history data and factor information; Determining predicted power generation data within a second preset time period after the current moment based on the power generation history data and factor situation information; Determine the scheduling strategy information based on the pre-acquired current power storage data and the predicted power consumption data and the predicted power generation data; The step of determining the predicted power consumption data within a second preset time period after the current moment based on the power consumption history data and the factor situation information includes: Performing trend analysis on the historical electricity consumption data to obtain trend electricity consumption data; Determine the electricity consumption impact coefficient based on the factor information; Determining the predicted power consumption data based on the trend power consumption data and the power consumption impact coefficient; Determining the electricity consumption impact coefficient according to the factor situation information includes: Substituting factor situation information into the electricity consumption scoring rule of the influencing factor to determine the factor electricity consumption side score of the influencing factor; Calculate the electricity consumption impact coefficient based on the electricity consumption score of the factors, and set is the electricity consumption score of the i-th influencing factor, Calculate the weight of electricity consumption for the i-th influencing factor and , n is the number of influencing factors for which corresponding factor information has been obtained, is the electricity consumption influence coefficient, then Where p is a preset positive integer, is a preset constant, Indicates taking The maximum value in Not greater than 1; The performing trend analysis on the historical electricity consumption data to obtain trend electricity consumption data includes: Dividing the first preset time into a plurality of third preset time periods, and determining a power consumption component data for each third preset time period; Assume that there are m electricity consumption component data, and the i-th electricity consumption component data is , 、 ,..., The time tags carried are from near to far from the current time, and the trend coefficient results are ; According to the m power consumption component data, m-1 power consumption change data are obtained. The i-th power consumption change data is ,but ; Determine whether the power consumption change data are all greater than zero or not greater than zero; If so, then ,otherwise, , where is the number of electricity consumption change data greater than zero, is the amount of electricity consumption change that is not greater than zero, and They are all preset constants greater than 0 and less than 1; Trend electricity consumption data .

2. The method according to claim 1, characterized in that The step of determining the predicted power generation data within a second preset time period after the current moment based on the power generation history data and the factor situation information includes: Performing trend analysis on the historical power generation data to obtain trend power generation data; Determining a power generation impact coefficient based on the factor situation information; The predicted power generation data is determined according to the trend power generation data and the power generation influence coefficient.

3. The method according to claim 2, characterized in that Determining the power generation impact coefficient according to the factor situation information includes: Substituting the factor situation information into the power generation scoring rule of the influencing factor to determine the power generation side score of the influencing factor; Calculate the power generation impact coefficient based on the power generation side score of the factors, and set is the power generation side score of the i-th influencing factor, The power generation calculation weight of the i-th influencing factor is , n is the number of influencing factors for which corresponding factor information has been obtained, is the power generation influence coefficient, then Where, Indicates taking The maximum value in Not greater than 1.

4. The method according to claim 3, characterized in that The trend analysis of the historical power generation data to obtain trend power generation data includes: Dividing the first preset time into a plurality of third preset time periods, and determining a power generation component data for each third preset time period; Assume that there are m power generation component data and the i-th power consumption component data is , 、 ,..., The time tags carried are from near to far from the current time, and the trend coefficient results are ; According to the m power generation component data, m-1 power generation change data are obtained. The i-th power generation change data is ,but ; Determine whether the power generation change data are all greater than zero or not greater than zero; If so, then ,otherwise, , where is the number of power generation change data greater than zero, is the number of changes in power generation that are not greater than zero, and They are all preset constants greater than 0 and less than 1; Trend power generation data .

5. The method according to claim 1, wherein The determining of the scheduling strategy information based on the pre-acquired current power storage data and the predicted power consumption data and the predicted power generation data includes: If the predicted power generation data is higher than the predicted power consumption data, the dispatch strategy information includes a recommendation to increase power consumption; If the predicted power consumption data is not lower than the predicted power generation data and not higher than the sum of the predicted power generation data and the current storage data, the dispatch strategy information includes a recommendation to use power under the premise of ensuring that the energy storage system storage data is higher than the storage threshold; If the predicted electricity consumption data is higher than the sum of the predicted power generation data and the current storage data, the dispatch strategy information includes a suggestion to save electricity.

6. The method according to any one of claims 1 to 5, characterized in that The influencing factors include one or more of light intensity, wind level, water conditions, season, and agricultural activities.

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