Intelligent power plant integrated optimization scheduling method and system

Abstract

The invention provides an integrated optimal scheduling method and system for a smart power plant, and belongs to the technical field of unit scheduling, and the method specifically comprises the steps: taking weather data as a basis, and when determining that the matching coefficient of the frequency modulation response data of the smart power plant in the remaining time period of the current date and the electric energy output of a new energy output device meets the requirement, determining that the new energy output device outputs the new energy output device; and obtaining frequency modulation response data of the smart power plant in the current time period of the current date and historical matching data of electric energy output of the new energy output device, and dynamically adjusting an energy storage utilization form of the new energy output device of the smart power plant in the current time period of the current date in combination with the residual electric quantity of an electric energy storage device of the smart power plant. Therefore, the flexibility of the unit is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of unit dispatching, and in particular relates to an integrated optimization dispatching method and system for a smart power plant. Background Art

[0002] With the rapid development of new energy technologies, coal-fired power plants have gradually transformed from single coal-fired boiler power generation to energy output using multiple energy forms such as photovoltaics, wind turbines, and energy storage devices. Therefore, how to achieve joint scheduling and processing of energy forms of different dimensions has become a technical problem that needs to be solved urgently.

[0003] In specific existing technical solutions, for example, CN202320178057.5 "A Thermal Power Plant Molten Salt Heat Storage New Energy Power Station Integrated Auxiliary Frequency Regulation System" builds the molten salt energy storage system required for the new energy power station in the thermal power plant, and uses the molten salt electric heater to respond to the AGC command issued by the power grid to the new energy power station. The heat stored in the molten salt is supplied to the outside in the form of steam through the heating pipeline, or directly sent to the steam turbine of the coal-fired power unit to work, thereby improving the flexibility of the unit. However, the above technical solution has the following technical problems:

[0004] The existing technical solutions ignore the adjustment of the utilization method of new energy output in combination with the electricity price in the electricity spot market and the production of differentiated grid-side loads. Compared with direct output as electric energy, the energy utilization efficiency of the molten salt electric heater is lower. If the energy utilization method cannot be adjusted, the overall energy utilization efficiency cannot be improved.

[0005] In response to the above technical problems, the present invention provides an integrated optimization and scheduling method and system for a smart power plant. Summary of the invention

[0006] A smart power plant integrated optimization scheduling method, characterized by specifically including:

[0007] S1 determines the matching dispatching strategy of different forms of energy output modes of the smart power plant at different time periods on the current date based on the weather data of the current date and the remaining power of the electric energy storage device, and performs the dispatching process of the smart power plant on the current date according to the matching dispatching strategy;

[0008] S2 uses the electric energy storage device of the smart power plant to perform frequency modulation response processing of the smart power plant. When the change of the remaining power of the electric energy storage device of the smart power plant in different time periods meets the requirements, enter the next step;

[0009] S3 determines, based on the weather data, that the frequency modulation response data of the smart power plant in the remaining period of the current date and the matching coefficient of the electric energy output of the new energy output device meet the requirements, and then proceeds to the next step;

[0010] S4 obtains the frequency modulation response data of the smart power plant in the current time period on the current date and the historical matching data of the electric energy output of the new energy output device, and combines the remaining power of the electric energy storage device of the smart power plant to dynamically adjust the energy storage utilization form of the new energy output device of the smart power plant in the current time period on the current date.

[0011] A further technical solution is that the weather data of the current date includes weather data of the current date at different time periods, specifically including wind speed, light and temperature.

[0012] A further technical solution is that the remaining power of the electric energy storage device is determined based on the analysis results of monitoring data of a power monitoring device of the electric energy storage device.

[0013] A further technical solution is that the method for determining the matching scheduling strategy is:

[0014] Based on the weather data of the current date, determine the weather data of the current date at different time periods, and determine the weather matching coefficient with the preset scheduling strategy through the weather data at different time periods;

[0015] Based on the remaining power of the electric energy storage device, determining a power matching coefficient between the remaining power and the preset scheduling strategy;

[0016] The strategy matching coefficient between the remaining power and the preset scheduling strategy is determined based on the weather matching coefficient and the power matching coefficient, and the matching scheduling strategy in the preset scheduling strategy is determined using the strategy matching coefficient.

[0017] A further technical solution is that the matching scheduling strategy is a preset matching scheduling strategy with the largest strategy matching coefficient.

[0018] A further technical solution is that the method for dynamically adjusting the energy storage utilization form in the current period is:

[0019] Using the frequency modulation response data of the current time period on the current date and the historical matching data of the electric energy output of the new energy output device, determine the proportion of the number of electric energy output vacancy time periods in the similar historical time periods corresponding to the current time period under the remaining power of the electric energy storage device of the smart power plant, and use the proportion of the number of electric energy output vacancy time periods to determine the electric energy vacancy probability coefficient of the current time period;

[0020] A strategy for dynamically adjusting the energy storage utilization form in the current period is determined based on the power shortage probability.

[0021] A further technical solution is to determine a strategy for dynamically adjusting the energy storage utilization form in the current period based on the power shortage probability, specifically:

[0022] When the power shortage probability is greater than a preset probability threshold, the power output of the new energy output device is directly stored and managed through the power storage device;

[0023] When the power shortage probability is not greater than the preset probability threshold, no strategy is temporarily implemented for the energy storage utilization form in the current period.

[0024] A further technical solution is that the energy storage utilization form includes molten salt heat storage and electric energy storage device.

[0025] On the other hand, the present application provides a smart power plant integrated optimization dispatching system, which adopts the above-mentioned smart power plant integrated optimization dispatching method, specifically including:

[0026] Dispatch strategy screening module, power change assessment module, matching coefficient assessment module, dispatch strategy adjustment module;

[0027] The scheduling strategy screening module is responsible for determining the matching scheduling strategy of different forms of energy output modes of the smart power plant at different time periods on the current date based on the weather data on the current date and the remaining power of the electric energy storage device, and performing the scheduling processing of the smart power plant on the current date according to the matching scheduling strategy;

[0028] The power change assessment module is responsible for using the electric energy storage device of the smart power plant to perform frequency modulation response processing of the smart power plant, and determining whether the change of the remaining power of the electric energy storage device of the smart power plant in different time periods meets the requirements;

[0029] The matching coefficient evaluation module is responsible for determining whether the matching coefficient between the frequency modulation response data of the smart power plant in the remaining period of the current date and the power output of the new energy output device meets the requirements based on the weather data;

[0030] The dispatching strategy adjustment module is responsible for obtaining the frequency modulation response data of the smart power plant in the current time period on the current date and the historical matching data of the electric energy output of the new energy output device, and combining the remaining power of the electric energy storage device of the smart power plant to dynamically adjust the energy storage utilization form of the new energy output device of the smart power plant in the current time period on the current date.

[0031] On the other hand, a computer-readable storage medium is provided in an embodiment of the present application, on which a computer program is stored. When the computer program is executed in a computer, the computer is caused to execute the above-mentioned integrated optimization and scheduling method for a smart power plant.

[0032] On the other hand, a computer program product is provided in an embodiment of the present application, characterized in that the computer program product stores instructions, and when the instructions are executed by a computer, the computer implements the above-mentioned integrated optimization and scheduling method for a smart power plant.

[0033] The beneficial effects of the present invention are:

[0034] Based on the weather data of the current date and the remaining power of the electric energy storage device, the matching dispatching strategy of the smart power plant in different forms of energy output modes in different time periods of the current date is determined. This takes into account the differences in the frequency regulation needs of the smart power plant caused by the differences in weather data, as well as the differences in the reliability of the electric energy storage device in responding to the frequency regulation needs due to the differences in the remaining power. This realizes the determination of matching dispatching strategies from multiple dimensions, thereby improving the efficiency and reliability of energy utilization.

[0035] By utilizing the frequency modulation response data, the historical matching data of the electric energy output of the new energy output device, and the remaining power of the electric energy storage device of the smart power plant, dynamic adjustment of the energy storage utilization form of the new energy output device of the smart power plant in the current period of the current date is carried out. Dynamic adjustment of the energy storage utilization form is achieved from two angles: the change of the remaining power of the electric energy storage device and the matching with the electric energy output. On the basis of ensuring the efficiency of energy utilization, the reliability of frequency modulation processing is further guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0037] Figure 1 It is a flow chart of an integrated optimization dispatching method for a smart power plant;

[0038] Figure 2 is a flow chart of a method for determining a matching scheduling strategy;

[0039] Figure 3 It is a flow chart of a method for dynamically adjusting the form of energy storage utilization in the current period. DETAILED DESCRIPTION

[0040] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of ways and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that the present invention will be comprehensive and complete and fully convey the concepts of the example embodiments to those skilled in the art. The same reference numerals in the figures represent the same or similar structures, and thus their detailed description will be omitted.

[0041] The terms "a", "an", "the", and "said" are used to indicate the presence of one or more elements / components / etc.; the terms "comprising" and "having" are used to express an open-ended inclusive meaning and mean that additional elements / components / etc. may be present in addition to the listed elements / components / etc.

[0042] Example 1

[0043] To solve the above problems, according to one aspect of the present invention, Figure 1 As shown, a smart power plant integrated optimization scheduling method is provided, which is characterized by specifically including:

[0044] S1 determines the matching dispatching strategy of different forms of energy output modes of the smart power plant at different time periods on the current date based on the weather data of the current date and the remaining power of the electric energy storage device, and performs the dispatching process of the smart power plant on the current date according to the matching dispatching strategy;

[0045] S2 uses the electric energy storage device of the smart power plant to perform frequency modulation response processing of the smart power plant. When the change of the remaining power of the electric energy storage device of the smart power plant in different time periods meets the requirements, enter the next step;

[0046] S3 determines, based on the weather data, that the frequency modulation response data of the smart power plant in the remaining period of the current date and the matching coefficient of the electric energy output of the new energy output device meet the requirements, and then proceeds to the next step;

[0047] S4 obtains the frequency modulation response data of the smart power plant in the current time period on the current date and the historical matching data of the electric energy output of the new energy output device, and combines the remaining power of the electric energy storage device of the smart power plant to dynamically adjust the energy storage utilization form of the new energy output device of the smart power plant in the current time period on the current date.

[0048] Furthermore, the weather data of the current date includes weather data of the current date at different time periods, specifically including wind speed, light and temperature.

[0049] Optionally, the remaining power of the electric energy storage device is determined based on an analysis result of monitoring data of a power monitoring device of the electric energy storage device.

[0050] Specifically, Figure 2 As shown, the method for determining the matching scheduling strategy is:

[0051] Based on the weather data of the current date, determine the weather data of the current date at different time periods, and determine the weather matching coefficient with the preset scheduling strategy through the weather data at different time periods;

[0052] Based on the remaining power of the electric energy storage device, determining a power matching coefficient between the remaining power and the preset scheduling strategy;

[0053] The strategy matching coefficient between the remaining power and the preset scheduling strategy is determined based on the weather matching coefficient and the power matching coefficient, and the matching scheduling strategy in the preset scheduling strategy is determined using the strategy matching coefficient.

[0054] Furthermore, the matching scheduling strategy is a preset matching scheduling strategy with the largest strategy matching coefficient.

[0055] Optionally, the determination of the matching scheduling strategy includes steps S11-S13, specifically including:

[0056] S11 determines, based on the remaining power of the electric energy storage device, a deviation between the remaining power and an endpoint of a remaining power interval corresponding to the preset scheduling strategy;

[0057] S12 determines the weather data of the current date at different time periods based on the weather data of the current date, and determines the weather time period matching the preset scheduling strategy through the weather data of the different time periods;

[0058] S13 determines a strategy matching coefficient between the remaining power and the preset scheduling strategy based on the number of matching weather periods and the deviation of the endpoints of the remaining power interval, and uses the strategy matching coefficient to determine a matching scheduling strategy in the preset scheduling strategy.

[0059] Optionally, the above step S11 includes step S111 and step S112, which are specifically:

[0060] S111: based on the remaining power of the electric energy storage device, when it is determined that the remaining power of the electric energy storage device is less than the preset power threshold, the matching scheduling strategy is determined by using the preset scheduling strategy, and when it is determined that the remaining power of the electric energy storage device is not less than the preset power threshold, the process proceeds to step S112;

[0061] S112 determines the deviation between the remaining power and the endpoints of the remaining power interval corresponding to the preset scheduling strategy. When the deviation between the endpoints of the remaining power interval corresponding to the preset scheduling strategy does not meet the requirements, it is determined that the preset scheduling strategy does not belong to the matching scheduling strategy. If and only if the deviation between the endpoints of the remaining power interval corresponding to the preset scheduling strategy meets the requirements, enter step S12.

[0062] Optionally, the above step S12 includes step S121 and step S122, which are specifically:

[0063] S121 determines the weather data of the current date at different time periods based on the weather data of the current date, and when it is determined through the weather data of the different time periods that there is a matching weather time period with the preset scheduling strategy, enters step S121, and when it is determined through the weather data of the different time periods that there is no matching weather time period with the preset scheduling strategy, it is determined that the preset scheduling strategy does not belong to the matching scheduling strategy;

[0064] S122 obtains the number of the matching time periods. When the ratio of the number of the matching time periods does not meet the requirement, it is determined that the preset scheduling strategy does not belong to the matching scheduling strategy. When the ratio of the number of the matching time periods meets the requirement, the process proceeds to step S13.

[0065] It should be noted that determining whether the change in the remaining power meets the requirements specifically includes:

[0066] Based on the change of the remaining power of the electric energy storage device of the smart power plant in different time periods, the time period is divided into a remaining power increase time period and a remaining power decrease time period, and the distribution clustering coefficient of the remaining power decrease time period is determined according to the number and distribution data of the remaining power decrease time period;

[0067] Determine the power variation reliability coefficient of the electric energy storage device based on the variation of the remaining power in the remaining power reduction period and the variation of the remaining power of the electric energy storage device at the current moment, and determine the use reliability of the electric energy storage device based on the distribution dispersion coefficient and the power variation reliability coefficient;

[0068] The usage reliability is used to determine whether the change in the remaining power of the electric energy storage device meets the requirements.

[0069] Furthermore, when the change in the remaining power of the electric energy storage device does not meet the requirements, the electric energy output of the new energy output device is directly stored and managed through the electric energy storage device.

[0070] Optionally, determining whether the change in the remaining power meets the requirement includes steps S21-S23, specifically:

[0071] S21 determines a basic variation abnormal coefficient of the remaining power of the electric energy storage device according to the variation of the remaining power of the electric energy storage device at the current moment;

[0072] S22 divides the time period into a time period of increasing residual power and a time period of decreasing residual power based on the change of the residual power of the electric energy storage device of the smart power plant in different time periods, and determines the abnormal coefficient of the electric power change of the electric energy storage device according to the proportion of the number of the time periods of decreasing residual power;

[0073] S23 determines the use reliability of the electric energy storage device based on the basic variation abnormal coefficient and the power variation abnormal coefficient, and uses the use reliability to determine whether the variation of the remaining power of the electric energy storage device meets the requirements.

[0074] Optionally, before entering step S22, it is also necessary to determine whether the basic variation abnormal coefficient of the remaining power of the electric energy storage device meets the requirements. When the basic variation abnormal coefficient of the remaining power of the electric energy storage device is not within the preset interval, it can be directly determined that the change of the remaining power of the electric energy storage device does not meet the requirements. When the basic variation abnormal coefficient of the remaining power of the electric energy storage device is within the preset interval, enter step S22 for further determination.

[0075] Optionally, the above step S22 includes sub-steps S221-S223, specifically:

[0076] S221 divides the time period into a period of increased remaining power and a period of decreased remaining power based on the change in the remaining power of the electric energy storage device of the smart power plant in different time periods. When the proportion of the number of time periods with decreased remaining power does not meet the requirements, it can be directly determined that the change in the remaining power of the electric energy storage device does not meet the requirements. When the proportion of the number of time periods with decreased remaining power meets the requirements, enter step S22 for further determination.

[0077] S222 obtains the remaining power change amount in the remaining power reduction period. When there is a remaining power reduction period in which the remaining power change amount does not meet the requirement, it can be directly determined that the change of the remaining power of the electric energy storage device does not meet the requirement. When there is no remaining power reduction period in which the remaining power change amount does not meet the requirement, step S223 is entered;

[0078] S223 obtains distribution data of different remaining power reduction time periods, and determines the longest duration of the remaining power reduction time period based on the distribution data. When the longest duration does not meet the requirements, it can be directly determined that the change of the remaining power of the electric energy storage device does not meet the requirements. When the longest duration meets the requirements, the power change abnormal coefficient of the electric energy storage device is determined by the proportion of the number of remaining power reduction time periods, and proceeds to step S23.

[0079] Specifically, it is determined that the matching coefficient between the frequency modulation response data and the electric energy output of the new energy output device meets the requirements, including:

[0080] Using the weather data, determine the deviation between the power demand of the frequency modulation response in similar historical periods of different remaining periods and the power output of the new energy device, and use the deviation to determine the power output vacancy period in the similar historical period;

[0081] Determining the power output vacant time periods in different remaining time periods based on the proportion of the number of power output vacant time periods;

[0082] The matching coefficient between the frequency modulation response data and the power output of the new energy output device is determined according to the proportion of the number of power output vacancy periods, and whether the matching coefficient between the frequency modulation response data and the power output of the new energy output device meets the requirements is determined based on a preset matching coefficient threshold.

[0083] It should be noted that when the matching coefficient between the frequency modulation response data and the electric energy output of the new energy output device does not meet the requirements, the electric energy output of the new energy output device is directly stored and managed through the electric energy storage device.

[0084] Optionally, determining whether the matching coefficient between the frequency modulation response data and the electric energy output of the new energy output device meets the requirements specifically includes steps S31-S33, specifically:

[0085] S31 uses the weather data to determine the deviation between the power demand of the frequency modulation response in similar historical periods of different remaining periods and the power output of the new energy device, and uses the deviation to determine the power output redundant period and the power output vacant period in the similar historical period;

[0086] S32 determines the power output matching coefficients of different remaining time periods based on the proportion of the number of power output redundant time periods and the proportion of the number of power output vacant time periods;

[0087] It should be noted that before entering step S33, it is also necessary to determine whether the number of remaining time periods in which the power output matching coefficient does not meet the requirement meets the requirement. When the number of remaining time periods in which the power output matching coefficient does not meet the requirement does not meet the requirement, it is determined that the matching coefficient of the frequency modulation response data and the power output of the new energy output device does not meet the requirement. If and only if the number of remaining time periods in which the power output matching coefficient does not meet the requirement meets the requirement, then enter step S33;

[0088] S33 determines the matching coefficient between the frequency modulation response data and the electric energy output of the new energy output device according to the electric energy output matching coefficients of different remaining time periods, and determines whether the matching coefficient between the frequency modulation response data and the electric energy output of the new energy output device meets the requirements based on a preset matching coefficient threshold.

[0089] Specifically, the matching coefficient is determined according to an average value of the power output matching coefficients in different remaining time periods.

[0090] It should be further explained that the above step S31 includes step S311 and step S312, which are specifically:

[0091] S311 uses the weather data to determine the deviation between the power demand of the frequency modulation response in the similar historical periods of different remaining periods and the power output of the new energy device, and uses the deviation to determine that there is no vacant period of power output in the similar historical periods, then it can be directly determined that the matching coefficient of the frequency modulation response data and the power output of the new energy output device meets the requirements, and when there is a vacant period of power output, enter step S312;

[0092] S312 determines the power output vacant time periods in the remaining time periods by the proportion of the number of power output vacant time periods in similar historical time periods corresponding to different remaining time periods. When the proportion of the number of power output vacant time periods does not meet the requirement, it can be directly determined that the matching coefficient between the frequency modulation response data and the power output of the new energy output device does not meet the requirement. When the proportion of the number of power output vacant time periods meets the requirement, enter step S313;

[0093] S313 determines the power vacancy coefficients of different remaining time periods by utilizing the proportion of the number of power output vacancy time periods in similar historical time periods corresponding to different remaining time periods and the power vacancy amount of the power output vacancy time periods. When there are remaining time periods whose power vacancy coefficients do not meet the requirements, it can be directly determined that the matching coefficient between the frequency modulation response data and the power output of the new energy output device does not meet the requirements. When there are no remaining time periods whose power vacancy coefficients do not meet the requirements, proceed to step S32.

[0094] Specifically, Figure 3 As shown, the method for dynamically adjusting the energy storage utilization form in the current period is:

[0095] Determine the proportion of power output vacancy periods in similar historical periods corresponding to the current period under the remaining power of the power storage device of the smart power plant by using the frequency modulation response data of the current period on the current date and the historical matching data of the power output of the new energy output device;

[0096] Determine the power shortage probability coefficient of the current time period by using the proportion of the number of power output shortage time periods;

[0097] A strategy for dynamically adjusting the energy storage utilization form in the current period is determined based on the power shortage probability.

[0098] Furthermore, the strategy for dynamically adjusting the energy storage utilization form in the current period is determined based on the power shortage probability, specifically:

[0099] When the power shortage probability is greater than a preset probability threshold, the power output of the new energy output device is directly stored and managed through the power storage device;

[0100] When the power shortage probability is not greater than the preset probability threshold, no strategy is temporarily implemented for the energy storage utilization form in the current period.

[0101] Optionally, the energy storage utilization forms include molten salt heat storage and electric energy storage devices.

[0102] Example 2

[0103] On the other hand, the present application provides a smart power plant integrated optimization dispatching system, which adopts the above-mentioned smart power plant integrated optimization dispatching method, specifically including:

[0104] Dispatch strategy screening module, power change assessment module, matching coefficient assessment module, dispatch strategy adjustment module;

[0105] The scheduling strategy screening module is responsible for determining the matching scheduling strategy of different forms of energy output modes of the smart power plant at different time periods on the current date based on the weather data on the current date and the remaining power of the electric energy storage device, and performing the scheduling processing of the smart power plant on the current date according to the matching scheduling strategy;

[0106] The power change assessment module is responsible for using the electric energy storage device of the smart power plant to perform frequency modulation response processing of the smart power plant, and determining whether the change of the remaining power of the electric energy storage device of the smart power plant in different time periods meets the requirements;

[0107] The matching coefficient evaluation module is responsible for determining whether the matching coefficient between the frequency modulation response data of the smart power plant in the remaining period of the current date and the power output of the new energy output device meets the requirements based on the weather data;

[0108] The dispatching strategy adjustment module is responsible for obtaining the frequency modulation response data of the smart power plant in the current time period on the current date and the historical matching data of the electric energy output of the new energy output device, and combining the remaining power of the electric energy storage device of the smart power plant to dynamically adjust the energy storage utilization form of the new energy output device of the smart power plant in the current time period on the current date.

[0109] Example 3

[0110] On the other hand, a computer-readable storage medium is provided in an embodiment of the present application, on which a computer program is stored. When the computer program is executed in a computer, the computer is caused to execute the above-mentioned integrated optimization and scheduling method for a smart power plant.

[0111] Example 4

[0112] On the other hand, a computer program product is provided in an embodiment of the present application, characterized in that the computer program product stores instructions, and when the instructions are executed by a computer, the computer implements the above-mentioned integrated optimization and scheduling method for a smart power plant.

[0113] In the embodiments of the present invention, the term "plurality" refers to two or more than two, unless otherwise clearly defined. The terms "installation", "connection", "fixation" and the like should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection. For those skilled in the art, the specific meanings of the above terms in the embodiments of the present invention can be understood according to the specific circumstances.

[0114] In the description of the embodiments of the present invention, it needs to be understood that the directions or positional relationships indicated by the terms "upper" and "lower" are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, rather than indicating or implying that the device or unit referred to must have a specific direction, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the embodiments of the present invention.

[0115] In the description of this specification, the description of the terms "one embodiment", "a preferred embodiment", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0116] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A smart power plant integrated optimization scheduling method, characterized in that: Specifically include: Based on the weather data of the current date and the remaining power of the electric energy storage device, determine the matching scheduling strategy of different forms of energy output modes of the smart power plant at different time periods of the current date, and perform the scheduling processing of the smart power plant on the current date according to the matching scheduling strategy; Using the electric energy storage device of the smart power plant to perform frequency modulation response processing of the smart power plant, when the change of the remaining power of the electric energy storage device of the smart power plant in different time periods meets the requirements, proceed to the next step; When it is determined based on the weather data that the frequency modulation response data of the smart power plant in the remaining period of the current date and the matching coefficient of the electric energy output of the new energy output device meet the requirements, proceed to the next step; The frequency modulation response data of the smart power plant in the current time period on the current date and the historical matching data of the electric energy output of the new energy output device are obtained, and combined with the remaining power of the electric energy storage device of the smart power plant, the energy storage utilization form of the new energy output device of the smart power plant in the current time period on the current date is dynamically adjusted.

2. The integrated optimization scheduling method for a smart power plant according to claim 1, characterized in that: The weather data of the current date includes weather data of the current date at different time periods, specifically including wind speed, light and temperature.

3. The integrated optimization scheduling method for a smart power plant according to claim 1, characterized in that: The remaining power of the electric energy storage device is determined based on the analysis result of the monitoring data of the power monitoring device of the electric energy storage device.

4. The integrated optimization scheduling method for a smart power plant according to claim 1, characterized in that: The method for determining the matching scheduling strategy is: Based on the weather data of the current date, determine the weather data of the current date at different time periods, and determine the weather matching coefficient with the preset scheduling strategy through the weather data at different time periods; Based on the remaining power of the electric energy storage device, determining a power matching coefficient between the remaining power and the preset scheduling strategy; The strategy matching coefficient between the remaining power and the preset scheduling strategy is determined based on the weather matching coefficient and the power matching coefficient, and the matching scheduling strategy in the preset scheduling strategy is determined using the strategy matching coefficient.

5. The integrated optimization dispatching method for a smart power plant according to claim 4, characterized in that: The matching scheduling strategy is a preset matching scheduling strategy with the largest strategy matching coefficient.

6. The integrated optimization dispatching method for a smart power plant according to claim 1, characterized in that: Determine whether the change in remaining power meets the requirements, including: Based on the change of the remaining power of the electric energy storage device of the smart power plant in different time periods, the time period is divided into a remaining power increase time period and a remaining power decrease time period, and the distribution clustering coefficient of the remaining power decrease time period is determined according to the number and distribution data of the remaining power decrease time period; Determine the power variation reliability coefficient of the electric energy storage device based on the variation of the remaining power in the remaining power reduction period and the variation of the remaining power of the electric energy storage device at the current moment, and determine the use reliability of the electric energy storage device based on the distribution dispersion coefficient and the power variation reliability coefficient; The usage reliability is used to determine whether the change in the remaining power of the electric energy storage device meets the requirements.

7. The integrated optimization dispatching method for a smart power plant according to claim 6, characterized in that: When the change in the remaining power of the electric energy storage device does not meet the requirements, the electric energy output of the new energy output device is directly stored and managed through the electric energy storage device.

8. The integrated optimization dispatching method for a smart power plant according to claim 1, characterized in that: The method for dynamically adjusting the energy storage utilization form in the current period is: Using the frequency modulation response data of the current time period on the current date and the historical matching data of the electric energy output of the new energy output device, determine the proportion of the number of electric energy output vacancy time periods in the similar historical time periods corresponding to the current time period under the remaining power of the electric energy storage device of the smart power plant, and use the proportion of the number of electric energy output vacancy time periods to determine the electric energy vacancy probability coefficient of the current time period; A strategy for dynamically adjusting the energy storage utilization form in the current period is determined based on the power shortage probability.

9. The integrated optimization dispatching method for a smart power plant according to claim 8, characterized in that: The strategy for dynamically adjusting the energy storage utilization form in the current period based on the power shortage probability is specifically as follows: When the power shortage probability is greater than a preset probability threshold, the power output of the new energy output device is directly stored and managed through the power storage device; When the power shortage probability is not greater than the preset probability threshold, no strategy is temporarily implemented for the energy storage utilization form in the current period.

10. A smart power plant integrated optimization dispatching system, using a smart power plant integrated optimization dispatching method according to any one of claims 1 to 9, specifically comprising: Dispatch strategy screening module, power change assessment module, matching coefficient assessment module, dispatch strategy adjustment module; The scheduling strategy screening module is responsible for determining the matching scheduling strategy of different forms of energy output modes of the smart power plant at different time periods on the current date based on the weather data on the current date and the remaining power of the electric energy storage device, and performing the scheduling processing of the smart power plant on the current date according to the matching scheduling strategy; The power change assessment module is responsible for using the electric energy storage device of the smart power plant to perform frequency modulation response processing of the smart power plant, and determining whether the change of the remaining power of the electric energy storage device of the smart power plant in different time periods meets the requirements; The matching coefficient evaluation module is responsible for determining whether the matching coefficient between the frequency modulation response data of the smart power plant in the remaining period of the current date and the power output of the new energy output device meets the requirements based on the weather data; The dispatching strategy adjustment module is responsible for obtaining the frequency modulation response data of the smart power plant in the current time period on the current date and the historical matching data of the electric energy output of the new energy output device, and combining the remaining power of the electric energy storage device of the smart power plant to dynamically adjust the energy storage utilization form of the new energy output device of the smart power plant in the current time period on the current date.

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