Comprehensive energy storage scheduling system optimization method and device

Abstract

The invention discloses a method and a device for optimizing a comprehensive energy storage scheduling system, relates to the technical field of energy scheduling optimization, and solves the technical problems that the conditions of energy waste and unstable supply occur occasionally due to the fact that a system analysis and scheduling method is lacked and energy storage resources cannot be fully utilized to optimize energy supply. The power generation energy is accurately classified into the balanced power generation energy and the unbalanced power generation energy, the overload power generation energy is deeply analyzed, the high-load time period is obtained, the overload analysis information is generated, and meanwhile the underload analysis information is generated by combining the residual energy storage electric quantity of the underload power generation energy in the high-load time period. The load condition and the energy storage condition are comprehensively considered, energy storage resources can be more effectively utilized, energy scheduling is optimized, and the energy utilization efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy scheduling optimization, and specifically to an optimization method and device for an integrated energy storage scheduling system. Background Art

[0002] In today's energy field, with the continuous growth of energy demand and the increasing complexity of energy structure, integrated energy storage and scheduling systems face many challenges.

[0003] Patent application publication number CN112163968B discloses a method for optimizing the scheduling of an integrated energy storage system. First, the system's structural framework and equipment model are developed. Secondly, considering the impact of time-of-use prices for energy storage purchases and sales, and from the perspective of investor profitability, a multi-objective optimization scheduling model for the energy storage system is established, with the goals of maximizing the economic benefits of energy storage, minimizing energy losses during storage charging and discharging, and minimizing power fluctuations in the microgrid-mainline interconnection line when energy storage is dispatched. Finally, the model is solved using a multi-objective particle swarm optimization algorithm and a fuzzy membership function.

[0004] Traditional power stations lack precision and efficiency in energy scheduling and management during the power generation process. The acquisition and analysis of power generation data is incomplete, making it difficult to accurately determine the energy balance constraints of power generation sources. Furthermore, when dealing with different types of power generation sources, there is a lack of systematic analysis and scheduling methods, making it impossible to fully utilize energy storage resources to optimize energy supply. This leads to frequent energy waste and unstable supply. Furthermore, existing energy scheduling systems often fail to fully consider the distribution and correlation of different power generation sources during peak hours, making it difficult to make reasonable decisions during energy scheduling and affecting the operational efficiency and stability of the entire energy system. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a comprehensive energy storage and scheduling system optimization method and device, which solves the problem of lack of systematic analysis and scheduling methods, inability to fully utilize energy storage resources to optimize energy supply, resulting in frequent energy waste and unstable supply.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for optimizing a comprehensive energy storage scheduling system, the method specifically comprising the following steps:

[0007] Step 1: Obtain power generation data from the power station, and based on the power generation data, determine the energy balance constraints of the power generation energy, generating balanced power generation energy and unbalanced power generation energy;

[0008] Step 2: Analyze the obtained overloaded power generation energy. By obtaining and analyzing the load conditions at different times, overloaded analysis information is generated for high-load periods. At the same time, the remaining energy storage power of the underloaded power generation energy during high-load periods is sorted to generate underloaded analysis information;

[0009] Step 3: Obtain the obtained overloaded analysis information and underloaded analysis information, and combine the two for energy storage scheduling analysis. By analyzing the high-load periods corresponding to different unbalanced power generation energies and performing scheduling analysis based on the distribution of high-load periods;

[0010] Step 4: Obtain non-adjacent underloaded power generation sources, and perform scheduling optimization processing based on the peak time periods and remaining stored power corresponding to the non-adjacent underloaded power sources to generate scheduling optimization information.

[0011] As a further solution of the present invention, the specific method for generating balanced power generation energy and unbalanced power generation energy in Step 1 is as follows:

[0012] Obtain the power generation data corresponding to the power generation station. At the same time, obtain the power generation energy corresponding to the power generation station and label it as i, and i = 1, 2,..., j, where j represents the number of power generation energies. Then, taking time t as the unit, obtain the power generation power Pgen corresponding to power generation energy i i , the energy storage charging power Pch i , the energy storage discharging power Pdis i , and the power load Pload corresponding to the corresponding power generation energy i i , and at the same time, according to the formula Pgen i +Pdis i -Pch i =Pload i Judge whether the corresponding energy balance constraint is satisfied;

[0013] Substitute the actual value corresponding to power generation energy i into the above formula. If the above formula can be satisfied, mark the corresponding power generation energy i as balanced power generation energy. Conversely, if the above formula cannot be satisfied, mark the corresponding power generation energy i as unbalanced power generation energy, and perform secondary classification on the unbalanced power generation energy according to the magnitude of the values on both sides of the formula to obtain underloaded power generation energy and overloaded power generation energy.

[0014] As a further solution of the present invention, the specific method for generating underloaded information in Step 2 is as follows:

[0015] Obtain all unbalanced power generation energy sources, and at the same time obtain the overloaded power generation energy sources among the unbalanced power generation energy sources. Take one group of overloaded power generation energy sources as the analysis object for analysis, obtain the historical data of the target object, and based on the historical data, obtain the high-load time corresponding to the target object. At the same time, organize the obtained high-load time to obtain the high-load period. Then, obtain the total power consumption load corresponding to the target object during the high-load period, and calculate the corresponding power to be adjusted based on the energy storage situation of the target object. And so on, perform the same processing on all overloaded power generation energy sources, and generate overloaded analysis information;

[0016] Then obtain the underloaded power generation energy sources among the unbalanced power generation energy sources, and at the same time obtain the total power consumption load corresponding to the high-load period, and calculate the remaining energy storage power of the underloaded power generation energy sources, and generate underloaded analysis information.

[0017] As a further solution of the present invention, the specific method for performing scheduling analysis in step three based on the distribution of the high-load period is:

[0018] Obtain the overloaded analysis information and the underloaded analysis information. At the same time, obtain the overloaded power generation energy sources corresponding to the overloaded analysis information, and obtain the peak periods corresponding to different overloaded power generation energy sources. Then sort them in the chronological order of the peak periods, and at the same time obtain the overloaded power generation energy source corresponding to the peak period with the smallest sorting order and record it as the energy source to be analyzed;

[0019] Then obtain all the underloaded power generation energy sources, and obtain the peak periods corresponding to the underloaded power generation energy sources. At the same time, judge the adjacency between the high-load period of the energy source to be analyzed and the peak periods of the underloaded energy sources. If the peak period of the underloaded power generation energy source is an adjacent period to the high-load period of the energy source to be analyzed, then record the corresponding underloaded power generation energy source as the adjacent underloaded power generation energy source. Conversely, if the peak period of the underloaded power generation energy source is a non-adjacent period to the high-load period of the energy source to be analyzed, then record the corresponding underloaded power generation energy source as the non-adjacent underloaded power generation energy source.

[0020] As a further solution of the present invention, the specific method for generating scheduling optimization information in step four is:

[0021] Obtain all the non-adjacent underloaded power generation power sources and label them as a, and a = 1, 2,..., b, where b represents the number of non-adjacent underloaded power generation power sources. At the same time, obtain the peak period information and the remaining stored power of the non-adjacent underloaded power generation power sources, and sort them from large to small according to the remaining stored power. Then obtain the peak periods corresponding to the non-adjacent underloaded power generation power sources, and at the same time compare the obtained peak periods with the time line of the high-load period of the energy source to be analyzed;

[0022] If the peak period of non - adjacent under - load power generation sources is longer than the high - load period of the energy to be analyzed, the corresponding non - adjacent under - load power generation sources are classified as non - over - period power sources. Conversely, if the peak period of non - adjacent under - load power generation sources is shorter than the high - load period of the energy to be analyzed, the corresponding non - adjacent under - load power generation sources are classified as over - period power sources, and the two are analyzed separately.

[0023] As a further solution of the present invention, the specific method for analyzing the non - over - period power sources and over - period power sources in step four is as follows:

[0024] Obtain all non - over - period power sources and over - period power sources, and label them as o and f respectively, where o = 1, 2, …, g, f = 1, 2, …, d. Here, g represents the number of non - over - period power sources, and d represents the number of over - period power sources. At the same time, obtain the remaining stored energy of non - over - period power source o and over - period power source f, denoted as Lo and Lf respectively. Then obtain all the energy to be analyzed. At the same time, calculate the corresponding amount to be dispatched according to the total load and total power generation corresponding to the peak period of the energy to be analyzed, and calculate the total dispatching amount corresponding to all the energy to be analyzed. Compare the total dispatching amount with the total remaining stored energy corresponding to over - period power source f;

[0025] If the total dispatching amount is greater than the total remaining stored energy, calculate the numerical difference between the two as the supplementary power. Then obtain the number of all non - over - period power sources, and evenly divide the supplementary power according to the number to get the evenly - divided supplementary power. At the same time, perform dispatching processing on the non - over - period power sources based on the evenly - divided supplementary power to generate dispatching optimization information;

[0026] If the total dispatching amount is less than the total remaining stored energy, obtain the number of over - period power sources, and evenly divide the total dispatching amount according to the number of over - period power sources. At the same time, perform dispatching processing on the over - period power sources to generate dispatching optimization information.

[0027] An optimization device for a comprehensive energy storage dispatching system includes an information acquisition module, a power generation data analysis module, an optimized dispatching analysis module, and an optimized information output module, and the above - mentioned functional modules are connected in a one - way electrical connection.

[0028] The information acquisition module is used to obtain the power generation data of the power station and transmit the obtained power generation data to the power generation data analysis module.

[0029] Power generation data analysis module, which is used to judge the energy balance constraint situation of power generation energy according to the acquired power generation data, generate balanced power generation energy and unbalanced power generation energy, analyze the overloaded power generation energy in the obtained unbalanced power generation energy, obtain the high load period by acquiring and analyzing the load conditions in different periods to generate overload analysis information, and at the same time sort the remaining energy storage power of the underloaded power generation energy corresponding to the high load period to generate underload analysis information, and transmit the generated underload analysis information and overload analysis information to the optimal scheduling analysis module

[0030] Optimal scheduling analysis module, which is used to analyze the acquired overload analysis information and underload analysis information, and combine the two for energy storage scheduling analysis. By analyzing the high load periods corresponding to different unbalanced power generation energies and conducting scheduling analysis based on the distribution of high load periods, and the specific analysis method is the same as the processing process in step three of Embodiment 1. Obtain non-adjacent underloaded power generation power sources, and conduct scheduling optimization processing according to the peak time periods and remaining stored power corresponding to the non-adjacent underloaded power sources to generate scheduling optimization information, and transmit the scheduling optimization information to the optimization information output module;

[0031] Optimization information output module, which is used to display the acquired scheduling optimization information to the corresponding operators.

[0032] Beneficial effects

[0033] The present invention provides an optimization method and device for an integrated energy storage scheduling system. Compared with the prior art, it has the following beneficial effects:

[0034] By comprehensively acquiring the power generation data of the power station, accurately judging the energy balance constraint situation of power generation energy based on these data, accurately classifying the power generation energy into balanced power generation energy and unbalanced power generation energy, through in-depth analysis of the overloaded power generation energy, obtaining the high load period and generating overload analysis information, and at the same time generating underload analysis information by combining the remaining energy storage power of the underloaded power generation energy during the high load period, the comprehensive consideration of the load situation and energy storage situation is realized, which can make more effective use of energy storage resources, optimize energy scheduling, improve energy utilization efficiency, fully consider the distribution of high load periods corresponding to different unbalanced power generation energies, classify them into non-overtime power sources and overtime power sources by analyzing the peak time periods and remaining stored power of non-adjacent underloaded power generation power sources, and formulate targeted scheduling strategies accordingly. Scientific scheduling based on the total scheduling volume and relevant parameters of overtime power sources and non-overtime power sources can allocate energy more reasonably, improve the stability and reliability of energy supply, and meet the actual energy demand. Description of the drawings

[0035] Figure 1 It is a flowchart of the method steps of the present invention;

[0036] Figure 2 This is the principle block diagram of the device system of the present invention. Specific implementation manners

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0038] Example 1. Please refer to Figure 1 This application provides an optimization method for an integrated energy storage and dispatching system, and the method specifically includes the following steps:

[0039] Step 1: Obtain the power generation data of the power station, and judge the energy balance constraint situation of the power generation energy based on the power generation data to generate balanced power generation energy and unbalanced power generation energy.

[0040] Obtain the power generation data corresponding to the power station, and the power generation data includes power generation power and conversion efficiency. The specific power generation power and conversion efficiency are both expressed as the power generation data corresponding to different energy sources. At the same time, obtain the power generation energy corresponding to the power station and label it as i, and i = 1, 2,..., j, where j represents the number of power generation energy sources. Then, taking time t as the unit, obtain the power generation power Pgen of the power generation energy source i i the energy storage charging power Pch i the energy storage discharging power Pdis i and the power consumption load Pload corresponding to the power generation energy source i i At the same time, according to the formula Pgen i +Pdis i -Pch i =Pload i judge whether the corresponding energy balance constraint is satisfied;

[0041] Substitute the actual value of the power generation energy source i into the above formula. If the above formula can be satisfied, specifically, if the values on both sides are equal, it means it is satisfied. Then, label the corresponding power generation energy source i as balanced power generation energy. On the contrary, if the above formula cannot be satisfied, specifically, if the values on both sides are not equal, it means it is satisfied. Then, label the corresponding power generation energy source i as unbalanced power generation energy, and perform secondary classification on the unbalanced power generation energy according to the magnitude of the values on both sides of the formula to obtain underloaded power generation energy and overloaded power generation energy. Among them, the underloaded power generation energy means that the current power consumption load is small, and the formula relationship is Pgen i +Pdis i -Pchi > Pload i while the overloaded power generation energy indicates high current electricity consumption, and the formula relationship is Pgen i +Pdis i -Pch i <Pload i .

[0042] Step 2: Analyze the obtained overloaded power generation energy. By obtaining and analyzing the load conditions in different time periods, overloaded analysis information is generated for high-load periods. At the same time, the remaining energy storage power of the underloaded power generation energy corresponding to the high-load period is sorted to generate underloaded analysis information.

[0043] Obtain all unbalanced power generation energy, and at the same time obtain the overloaded power generation energy in the unbalanced power generation energy. Take one group of overloaded power generation energy as the analysis object for analysis, obtain the historical data of the target object, and obtain the high-load time corresponding to the target object according to the historical data. The specific high-load time is expressed as the corresponding high-load duration. At the same time, the high-load period is sorted out according to the obtained high-load time, and the high-load period is expressed as the corresponding time period obtained by integrating the high-load time. Then, obtain the total electricity load corresponding to the target object during the high-load period, and calculate the corresponding adjustable power based on the energy storage situation of the target object. And so on, the same processing is carried out for all overloaded power generation energy, and overloaded analysis information is generated;

[0044] Then obtain the underloaded power generation energy in the unbalanced power generation energy, and at the same time obtain the total electricity load corresponding to the high-load period. And here, the high-load period corresponding to the underloaded power generation energy is the same set of data as the high-load period corresponding to the overloaded power generation energy. Specifically, it is the overloaded power generation energy and the underloaded power generation energy corresponding to the same high-load period, and calculate the remaining energy storage power of the underloaded power generation energy to generate underloaded analysis information.

[0045] Step 3: Obtain the obtained overloaded analysis information and underloaded analysis information, and combine the two for energy storage scheduling analysis. By analyzing the high-load periods corresponding to different unbalanced power generation energies, scheduling analysis is carried out based on the distribution of the high-load periods.

[0046] Obtain the overloaded analysis information and the underloaded analysis information. At the same time, obtain the overloaded power generation energy corresponding to the overloaded analysis information, and obtain the peak periods corresponding to different overloaded power generation energies. Then sort them in the chronological order of the peak periods. And here, the peak periods are sorted from small to large. Specifically, the sorting starts from 0:00 and ends at 24:00. At the same time, obtain the overloaded power generation energy corresponding to the peak period with the smallest sorting order and record it as the energy to be analyzed;

[0047] Next, obtain all underloaded power generation energy sources, and obtain the peak periods corresponding to the underloaded power generation energy sources. At the same time, judge the adjacency between the high-load period of the energy source to be analyzed and the peak periods of the underloaded energy sources. If the peak period of the underloaded power generation energy source is adjacent to the high-load period of the energy source to be analyzed, then record the corresponding underloaded power generation energy source as an adjacent underloaded power generation energy source. Conversely, if the peak period of the underloaded power generation energy source is not adjacent to the high-load period of the energy source to be analyzed, then record the corresponding underloaded power generation energy source as a non-adjacent underloaded power generation energy source;

[0048] For example, there are four underloaded hydroelectric generators for power generation. The peak period of hydroelectric generator 1 is from 9:30 to 10:30, the peak period of hydroelectric generator 2 is from 13:00 to 14:00, the peak period of hydroelectric generator 3 is from 17:00 to 18:00, and the peak period of hydroelectric generator 4 is from 7:00 to 8:00. Judge the adjacency between the high-load period (8:00 - 9:00) of the energy source to be analyzed (gas turbine C) and the peak periods of each underloaded power generation energy source. If two periods are closely connected in time and there is no other obvious non-peak period in between, they are judged as adjacent periods. For hydroelectric generator 1, its peak period from 9:30 to 10:30 is adjacent to the high-load period of gas turbine C from 8:00 to 9:00, and record hydroelectric generator 1 as an adjacent underloaded power generation energy source; while the peak period of hydroelectric generator 2 from 13:00 to 14:00 is not adjacent to the high-load period of gas turbine C, and record it as a non-adjacent underloaded power generation energy source. And so on, judge and label all underloaded power generation energy sources.

[0049] Step Four: Obtain non-adjacent underloaded power generation power sources, and perform scheduling optimization processing according to the peak time periods corresponding to the non-adjacent underloaded power sources and the remaining stored power, and generate scheduling optimization information.

[0050] Obtain all non-adjacent underloaded power generation power sources and label them as a, and a = 1, 2, …, b, where b represents the number of non-adjacent underloaded power generation power sources. At the same time, obtain the peak period information and the remaining stored power corresponding to the non-adjacent underloaded power generation power sources. Here, the remaining stored power is the remaining stored power corresponding to the peak period of the current overloaded power generation energy source, and sort them from largest to smallest according to the remaining stored power. Then, obtain the peak time periods corresponding to the non-adjacent underloaded power generation power sources, and at the same time compare the obtained peak time periods with the time line of the high-load period of the energy source to be analyzed. Here, the time line is represented as the corresponding time moment;

[0051] If the peak periods of non - adjacent under - load power generation sources are longer than the high - load period of the energy to be analyzed, specifically, if the time sequence of the high - load period of the energy to be analyzed is before the peak periods of non - adjacent under - load power generation sources, then the corresponding non - adjacent under - load power generation sources are classified as non - over - period power sources. Conversely, if the peak periods of non - adjacent under - load power generation sources are shorter than the high - load period of the energy to be analyzed, specifically, if the time sequence of the high - load period of the energy to be analyzed is after the peak periods of non - adjacent under - load power generation sources, then the corresponding non - adjacent under - load power generation sources are classified as over - period power sources;

[0052] For example, it is known that the high - load period of the energy to be analyzed is determined to be 8:00 - 9:00 through data analysis. There are two non - adjacent under - load power generation sources, namely a small - scale hydro - generator with a peak period of 10:00 - 11:00 and a biomass generator with a peak period of 7:00 - 7:30. For the small - scale hydro - generator, since its peak period of 10:00 - 11:00 is after the high - load period of 8:00 - 9:00 of the energy to be analyzed, that is, the time sequence of the high - load period of the energy to be analyzed is before the peak period of the small - scale hydro - generator, this small - scale hydro - generator is classified as a non - over - period power source. For the biomass generator, because its peak period of 7:00 - 7:30 is before the high - load period of 8:00 - 9:00 of the energy to be analyzed, that is, the time sequence of the high - load period of the energy to be analyzed is after the peak period of the biomass generator, this biomass generator is classified as an over - period power source.

[0053] Obtain all non - over - period power sources and over - period power sources, and label them as o and f respectively, where o = 1, 2, …, g and f = 1, 2, …, d. Here, g represents the number of non - over - period power sources, and d represents the number of over - period power sources. At the same time, obtain the remaining stored energy of non - over - period power source o and over - period power source f, denoted as Lo and Lf respectively. Then obtain all the energy to be analyzed. Meanwhile, calculate the corresponding amount to be scheduled according to the total load and total power generation corresponding to the peak period of the energy to be analyzed. The specific calculation method is: amount to be scheduled = total load - total power generation, and calculate the total amount to be scheduled for all the energy to be analyzed. Compare the total amount to be scheduled with the total remaining stored energy of over - period power source f;

[0054] If the total amount to be scheduled is greater than the total remaining stored energy, then calculate the numerical difference between the two as the supplementary power. Then obtain the number of all non - over - period power sources, and evenly divide the supplementary power according to the number to get the evenly - divided supplementary power. For example, if the number of non - over - period power sources is three groups, then the supplementary power is evenly divided into three equal parts. At the same time, perform scheduling processing on non - over - period power sources based on the evenly - divided supplementary power to generate scheduling optimization information; specifically, for the total amount to be scheduled, it is evenly divided according to the number of over - period power sources.

[0055] If the total dispatching amount is less than the total remaining stored power, obtain the number of over-time-section power sources, evenly divide the total dispatching amount based on the number of over-time-section power sources, and perform dispatching processing on the over-time-section power sources to generate dispatching optimization information. Here, no dispatching processing is performed on the non-over-time-section power sources.

[0056] Embodiment 2. Please refer to Figure 2 , this application provides an optimization device for an integrated energy storage dispatching system. The device specifically includes an information acquisition module, a power generation data analysis module, an optimization dispatching analysis module, and an optimization information output module. Combining Figure 2 It can be known that there is a one-way electrical connection between the above functional modules.

[0057] The information acquisition module is used to obtain the power generation data of the power station and transmit the obtained power generation data to the power generation data analysis module.

[0058] The power generation data analysis module is used to judge the energy balance constraint situation of the power generation energy according to the obtained power generation data, generate balanced power generation energy and unbalanced power generation energy. Specifically, the processing method is the same as the processing process of step one in Embodiment 1. Analyze the overloaded power generation energy in the obtained unbalanced power generation energy. By obtaining and analyzing the load conditions in different time periods, obtain the high-load time periods to generate overloaded analysis information. At the same time, sort the remaining stored power corresponding to the under-loaded power generation energy during the high-load time periods to generate under-loaded analysis information, and transmit the generated under-loaded analysis information and overloaded analysis information to the optimization dispatching analysis module. Specifically, the processing method is the same as the processing process of step two in Embodiment 1.

[0059] The optimization dispatching analysis module is used to perform energy storage dispatching analysis on the obtained overloaded analysis information and under-loaded analysis information, and combine the two. By analyzing the high-load time periods corresponding to different unbalanced power generation energies and performing dispatching analysis based on the distribution of the high-load time periods. Specifically, the analysis method is the same as the processing process of step three in Embodiment 1. Obtain non-adjacent under-loaded power generation power sources, and perform dispatching optimization processing according to the peak time periods and the remaining stored power corresponding to the non-adjacent under-loaded power sources to generate dispatching optimization information, and transmit the dispatching optimization information to the optimization information output module.

[0060] The optimization information output module is used to display the obtained dispatching optimization information to the corresponding operators.

[0061] At the same time, the content not described in detail in this specification belongs to the well-known prior art in the art.

[0062] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An optimization method for an integrated energy storage scheduling system, characterized in that, The method specifically includes the following steps: Step 1: Obtain the power generation data of the power station, and judge the energy balance constraint situation of the power generation energy based on the power generation data to generate balanced power generation energy and unbalanced power generation energy; Step 2: Analyze the obtained overloaded power generation energy. By obtaining and analyzing the load conditions in different time periods, obtain the high-load time periods to generate overloading analysis information. At the same time, sort the remaining energy storage power of the underloaded power generation energy corresponding to the high-load time periods to generate underloading analysis information; Step 3: Obtain the obtained overloading analysis information and underloading analysis information, and combine the two for energy storage scheduling analysis. By analyzing the high-load time periods corresponding to different unbalanced power generation energies, and perform scheduling analysis based on the distribution of the high-load time periods; Step 4: Obtain non-adjacent underloaded power generation sources, and perform scheduling optimization processing according to the peak time periods and the remaining stored power corresponding to the non-adjacent underloaded power sources to generate scheduling optimization information.

2. The optimization method for an integrated energy storage scheduling system according to claim 1, wherein The specific method for generating balanced power generation energy and unbalanced power generation energy in Step 1 is as follows: Obtain the power generation data corresponding to the power station. At the same time, obtain the power generation energy corresponding to the power station and label it as i, where i = 1, 2, …, j, and j represents the number of power generation energy types. Then, taking time t as the unit, obtain the power generation power Pgen corresponding to power generation energy i i , the energy storage charging power Pch i , the energy storage discharging power Pdis i , and the power load Pload corresponding to the power generation energy i i . At the same time, according to the formula Pgen i + Pdis i - Pch i = Pload i Judge whether the corresponding energy balance constraint is satisfied; Substitute the actual value corresponding to the power generation energy i into the above formula. If the above formula can be satisfied, mark the corresponding power generation energy i as balanced power generation energy. Conversely, if the above formula cannot be satisfied, mark the corresponding power generation energy i as unbalanced power generation energy, and perform secondary classification on the unbalanced power generation energy according to the numerical size on both sides of the formula to obtain underloaded power generation energy and overloaded power generation energy.

3. The optimization method of an integrated energy storage and dispatching system according to claim 1, characterized in that The specific method for generating underloading information in Step 2 is as follows: Obtain all unbalanced power generation energies, and at the same time obtain the overloaded power generation energies among the unbalanced power generation energies. Take one group of overloaded power generation energies as the analysis object for analysis, obtain the historical data of the target object, and obtain the high-load time corresponding to the target object according to the historical data. At the same time, organize the obtained high-load time to obtain high-load time periods. Then obtain the total power consumption load corresponding to the target object within the high-load time periods, and calculate the corresponding power to be adjusted based on the energy storage situation of the target object. And so on, perform the same processing on all overloaded power generation energies, and generate overloading analysis information; Then obtain the underloaded power generation energies among the unbalanced power generation energies, and at the same time obtain the total power consumption load corresponding to the high-load time periods, and calculate the remaining energy storage power corresponding to the underloaded power generation energies to generate underloading analysis information.

4. An optimization method for an integrated energy storage and scheduling system according to claim 1, characterized in that, The specific method for performing scheduling analysis based on the distribution of high-load time periods in Step 3 is as follows: Obtain the overloading analysis information and underloading analysis information, and at the same time obtain the overloaded power generation energies corresponding to the overloading analysis information, and obtain the peak time periods corresponding to different overloaded power generation energies. Then sort them in the time order of the peak time periods, and at the same time obtain the overloaded power generation energy corresponding to the peak time period with the smallest sorting order as the energy to be analyzed; Next, obtain all underloaded power generation energy sources, obtain the peak periods corresponding to the underloaded power generation energy sources, and at the same time determine the adjacency between the high load periods of the energy source to be analyzed and the peak periods of the underloaded energy sources. If the peak period of the underloaded power generation energy source and the high load period of the energy source to be analyzed are adjacent periods, then record the corresponding underloaded power generation energy source as an adjacent underloaded power generation energy source. Conversely, if the peak period of the underloaded power generation energy source and the high load period of the energy source to be analyzed are non - adjacent periods, then record the corresponding underloaded power generation energy source as a non - adjacent underloaded power generation energy source.

5. An optimization method for a comprehensive energy storage scheduling system according to claim 1, characterized in that, The specific method for generating scheduling optimization information in step four is as follows: Obtain all non - adjacent underloaded power generation power sources and label them as a, and a = 1, 2, …, b, where b represents the number of non - adjacent underloaded power generation power sources. At the same time, obtain the peak period information and remaining stored power of the non - adjacent underloaded power generation power sources, and sort them in descending order according to the remaining stored power. Then, obtain the peak periods corresponding to the non - adjacent underloaded power generation power sources, and compare the obtained peak periods with the time line of the high load period of the energy source to be analyzed; If the peak period of the non - adjacent underloaded power generation power source is greater than the high load period of the energy source to be analyzed, then classify the corresponding non - adjacent underloaded power generation power source as a power source in the non - passed period. Conversely, if the peak period of the non - adjacent underloaded power generation power source is less than the high load period of the energy source to be analyzed, then classify the corresponding non - adjacent underloaded power generation power source as a power source in the passed period, and analyze the two respectively.

6. The optimization method for an integrated energy storage scheduling system according to claim 5, characterized in that The specific method for analyzing the power sources in the non - passed period and the power sources in the passed period in step four is as follows: Obtain all power sources in the non - passed period and power sources in the passed period, and label them as o and f respectively, and o = 1, 2, …, g, f = 1, 2, …, d, where g represents the number of power sources in the non - passed period, and d represents the number of power sources in the passed period. At the same time, obtain the remaining stored energy of the power sources in the non - passed period o and the power sources in the passed period f, denoted as Lo and Lf respectively. Then, obtain all the energy sources to be analyzed, and calculate the corresponding amount to be scheduled according to the total load and total power generation corresponding to the peak period of the energy source to be analyzed, and calculate the total amount to be scheduled corresponding to all the energy sources to be analyzed. Compare the total amount to be scheduled with the total remaining stored power of the power sources in the passed period f; If the total amount to be scheduled is greater than the total remaining stored power, then calculate the numerical difference between the two as the supplementary power. Then, obtain the number of all power sources in the non - passed period, and evenly divide the supplementary power according to the number to obtain the evenly divided supplementary power. At the same time, schedule the power sources in the non - passed period according to the evenly divided supplementary power to generate scheduling optimization information; If the total amount to be scheduled is less than the total remaining stored power, then obtain the number of power sources in the passed period, and evenly divide the total amount to be scheduled according to the number of power sources in the passed period, and schedule the power sources in the passed period to generate scheduling optimization information.

7. An optimization device for a comprehensive energy storage scheduling system, which is used to execute the optimization method for a comprehensive energy storage scheduling system according to any one of claims 1-6, and is characterized in that, It includes an information collection module, a power generation data analysis module, an optimal scheduling analysis module, and an optimal information output module, and there is a one - way electrical connection between the above - mentioned functional modules.

8. An optimization device for an integrated energy storage scheduling system according to claim 7, characterized in that The information collection module is used to obtain the power generation data of the power station and transmit the obtained power generation data to the power generation data analysis module; Power generation data analysis module, which is used to judge the energy balance constraint situation of power generation energy according to the acquired power generation data, generate balanced power generation energy and unbalanced power generation energy, analyze the overloaded power generation energy in the obtained unbalanced power generation energy, obtain and analyze the load conditions in different time periods to generate high load period and overloading analysis information, and at the same time sort the remaining energy storage power of the underloaded power generation energy in the high load period to generate underload analysis information, and transmit the generated underload analysis information and overloading analysis information to the optimal scheduling analysis module Optimal scheduling analysis module, which is used to analyze the overloading analysis information and underload analysis information obtained, and combine the two for energy storage scheduling analysis. By analyzing the high load periods corresponding to different unbalanced power generation energies and based on the distribution of the high load periods for scheduling analysis, and the specific analysis method is the same as the processing process in step three of Embodiment 1, obtain non-adjacent underloaded power generation power supplies, and perform scheduling optimization processing according to the peak time periods and remaining stored power corresponding to the non-adjacent underloaded power supplies, generate scheduling optimization information, and transmit the scheduling optimization information to the optimal information output module; Optimal information output module, which is used to display the obtained scheduling optimization information to the corresponding operator.

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