New energy system capacity configuration method and device, and optimization equipment

Abstract

The application discloses a new energy system capacity configuration method and device, and an optimization equipment. The new energy system capacity configuration method comprises the following steps: according to operation information of a new energy system, dividing a preset period into multiple time periods, and taking configuration capacity in one time period as one single body; classifying each time period into multiple scene time periods; all single bodies in one scene time period form one group; respectively performing single body configuration capacity optimization on each single body in each group according to environmental constraint conditions and group stop conditions, to obtain single body optimization results and group optimization results; determining group configuration capacity in each scene time period according to the group optimization results, the single body optimization results, and time proportions of each time period in the scene time periods; and determining configuration capacity in the preset period according to the group configuration capacity and time proportions of each scene time period in the preset period. The application can improve the adaptability of the new energy system capacity configuration result to the system operation environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of new energy technology, and in particular to a new energy system capacity configuration method and device, and an optimization device. BACKGROUND

[0002] At present, global energy shortage and environmental pollution problems are increasingly serious, and with the development of energy revolution, the proportion of clean energy (such as wind power and photovoltaic power) used by load is becoming increasingly important. The large-scale grid connection of new energy installed capacity will bring great challenges to regional power grids, and how to smoothly supply clean power to loads becomes a key problem in new energy system planning. However, the characteristics of the new energy system are greatly affected by the operating environment, and due to changes in regional climate conditions and loads, the generation and consumption of new energy systems in different time periods are not the same. The load in different time periods may present the following differences: large peak-valley power consumption difference, large power consumption dynamic fluctuation, power consumption periodic fluctuation, large power consumption demand difference in different time periods, and uncertainty of original load capacity expansion.

[0003] The existing new energy system is usually constructed based on the overall economic benefits of the power station, and only the characteristics of the existing characteristic time points are used to represent the generation and consumption characteristics of the new energy system, which is difficult to analyze the influence of the difference in new energy output characteristics caused by time period factors on the grid connection of new energy, so that the planning result of the capacity of the new energy system is relatively general and wide, and the adaptability to the system operating environment is poor. SUMMARY

[0004] The present application provides a new energy system capacity configuration method and device, and an optimization device, which finely optimizes the capacity of the new energy system by simulating the operating characteristics of the new energy system, and improves the adaptability of the capacity configuration result to the system operating environment.

[0005] In a first aspect, an embodiment of the present application provides a new energy system capacity configuration method, comprising:

[0006] According to the operating information of the new energy system in a preset period, the preset period is divided into a plurality of time periods, and the configuration capacity of the new energy system in one time period is taken as an individual for optimization;

[0007] Each of the time periods is classified into a plurality of scene periods, and all the individuals in one scene period form a group;

[0008] According to the environmental constraint condition and the group stop condition, the individual configuration capacity optimization is performed on each individual in each group, and the individual optimization result and the group optimization result are obtained;

[0009] determine a group configuration capacity of the new energy system in each of the scenario time periods according to the population optimization result, the single optimization result, and a time proportion of a time period of each of the single in a time period of the population;

[0010] determine a configuration capacity of the new energy system in the preset period according to the group configuration capacity of each of the group and a time proportion of a time period of each group in the preset period.

[0011] Optionally, the single configuration capacity optimization is performed on each of the single in each of the group according to an environmental constraint condition and a group stop condition, including:

[0012] set an initial configuration capacity for the group, and take the initial configuration capacity as a group basic configuration capacity of the group and a single basic configuration capacity of the single in the group;

[0013] perform an optimization calculation on the single basic configuration capacity based on the environmental constraint condition to obtain a single optimal capacity of the single and a credibility value corresponding to the single optimal capacity of the single;

[0014] update the single basic configuration capacity according to the single optimal capacity of the single, and update the group basic configuration capacity according to the single optimal capacity of the single and the credibility value of each of the single in the group;

[0015] perform an iteration process of the single basic configuration capacity optimization, the single basic configuration capacity update and the group basic configuration capacity update until the group stop condition is met, take the single optimal capacity of each of the single obtained in the last iteration process as the single optimization result, and take the group basic configuration capacity updated in the last iteration process as the population optimization result.

[0016] Optionally, the group stop condition includes that a ratio of a number of the single in the group satisfying the environmental constraint condition to a total number of the single in the group reaches a preset ratio.

[0017] Optionally, the configuration capacity of the new energy system in the preset period is determined, including:

[0018] calculate a group configuration capacity statistical value according to the group configuration capacity of each of the group in the preset period;

[0019] calculate the configuration capacity of the new energy system in the preset period according to the group configuration capacity statistical value, a difference between the group configuration capacity of each of the group and the group configuration capacity statistical value, and a time proportion of a time period of each of the group in the preset period.

[0020] Optionally, a group configuration capacity statistical value is calculated according to the group configuration capacity of each of the groups in the preset period, including:

[0021] A group of scenes is selected as a feature group, wherein the capacity value of each of the scenes is within a preset configuration capacity range, and the sum of the time lengths of the scenes whose time length proportion in the preset period reaches a time proportion threshold.

[0022] An average value of the group configuration capacity of the feature group is calculated as the group configuration capacity statistical value.

[0023] Optionally, the configuration capacity of the new energy system includes a new energy power generation system installed capacity and an energy storage system capacity.

[0024] Optionally, a double closed loop strategy is used for monomer configuration capacity optimization.

[0025] The double closed loop strategy includes:

[0026] According to the grid system limit condition of the power grid to which the new energy system is connected, the load information of the new energy system, and the energy storage information of the energy storage system, feedback adjustment is performed on the new energy power generation system installed capacity in the time period.

[0027] According to the power generation information of the new energy system and the load information of the new energy system, feedback adjustment is performed on the energy storage system capacity in the time period.

[0028] Optionally, the double closed loop strategy further includes:

[0029] According to the power generation information of the new energy system, the load information of the new energy system, and the characteristic information of the energy storage system battery, the energy storage capacity of the energy storage system battery is calculated; and the energy storage capacity of the energy storage system battery acts on the feedback adjustment of the energy storage system capacity.

[0030] Optionally, after determining the configuration capacity of the new energy system in the preset period, the method further includes:

[0031] According to the configuration capacity of the new energy system and the device parameter table of the new energy system, device selection of the new energy system is performed with the lowest cost as the target.

[0032] Optionally, the environmental constraint condition includes a new energy consumption rate constraint and a load green power rate constraint.

[0033] Optionally, the operation information includes weather information and load information.

[0034] According to the operation information of the location of the new energy system in a preset period, the preset period is divided into multiple time periods, including:

[0035] obtain power generation prediction information of the new energy system according to the weather information;

[0036] obtain power consumption prediction information of the new energy system according to the load information;

[0037] divide the preset period into multiple time periods according to the power generation prediction information and the power consumption prediction information.

[0038] In a second aspect, an embodiment of the present application provides a new energy system capacity configuration device, comprising:

[0039] a single-body division module configured to divide a preset period into multiple time periods according to operation information of a new energy system in the preset period, and configure capacity of the new energy system in one time period as an optimization of one single body;

[0040] a group classification module configured to classify each of the time periods into multiple scenario time periods, and all the single bodies in one scenario time period form a group;

[0041] a single-body optimization module configured to perform single-body capacity optimization on each of the single bodies in each of the groups according to environmental constraints and group stop conditions, and obtain single-body optimization results and group optimization results;

[0042] a group capacity determination module configured to determine group capacity of the new energy system in each of the scenario time periods according to the group optimization results, the single-body optimization results, and time proportion of the time period of each of the single bodies in the scenario time period of the group;

[0043] a system capacity determination module configured to determine capacity of the new energy system in the preset period according to each of the group capacities and time proportion of the scenario time period of each of the groups in the preset period.

[0044] In a third aspect, an embodiment of the present application further provides an optimization device, comprising:

[0045] a data processing storage unit, an optimization algorithm processing unit, and a computer program stored on the data processing storage unit and executable on the optimization algorithm processing unit; the optimization algorithm processing unit implements the new energy system capacity configuration method provided by any embodiment of the present application when executing the program.

[0046] The new energy system capacity configuration method provided by the embodiment of the application is based on the operation information of the region where the new energy system is located in a preset period, subdivides the preset period into multiple time periods, and forms various typical scene periods according to the operation characteristics of different time periods. This division method fully considers the complex power generation and consumption situation of a region. In the optimization process, the system evolution process of each time period is included in the consideration range, according to the single optimization result, the group configuration capacity under each scene period is considered first, and then the system configuration capacity under the complete period is considered based on the proportion of each scene period, which can effectively improve the accuracy of optimization. Moreover, the optimization groups of different scene periods are independent of each other, and the optimization individuals of different time periods in the same scene period are independent of each other. In this way, conditions are provided for independent parallel optimization of each individual and independent parallel optimization of each group. That is, the embodiment of the application not only considers the change process of the operation characteristics of the new energy system in the time domain full cycle, but also takes into account the reduction of optimization complexity and the improvement of optimization speed. The embodiment of the application optimizes the capacity of the new energy system by simulating the operation characteristics of the new energy system itself, which can effectively improve the adaptability of the capacity configuration result to the system operation environment.

[0047] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the application, nor is it used to limit the scope of the application. Other features of the application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0049] Figure 1 is a flowchart of a new energy system capacity configuration method provided by the embodiment of the application;

[0050] Figure 2 is a flowchart of single optimization in a group provided by the embodiment of the application;

[0051] Figure 3 is a framework diagram of a double closed loop strategy provided by the embodiment of the application;

[0052] Figure 4 is a framework diagram of another double closed loop strategy provided by the embodiment of the application;

[0053] Figure 5 is a distribution diagram of group configuration capacity provided by the embodiment of the application;

[0054] Figure 6 is a flow diagram of another new energy system capacity configuration method provided by the embodiment of the present application.

[0055] Figure 7 is a structural diagram of a new energy system capacity configuration device provided by the embodiment of the present application.

[0056] Figure 8 is a structural diagram of an optimization device provided by the embodiment of the present application. DETAILED DESCRIPTION

[0057] In order for those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0058] It should be noted that the terms “first”, “second”, and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein.

[0059] The embodiment of the present application provides a new energy system capacity configuration method, which is suitable for planning and designing the capacity of a new energy system based on regional geography and climate conditions and considering the operation characteristics of the new energy system. The method can be executed by a new energy system capacity configuration device, which can be realized in the form of hardware and / or software. The device can be integrated into a device of a power grid system to realize local optimization, or deployed on a cloud platform of a network to realize remote optimization. Figure 1 is a flow diagram of a new energy system capacity configuration method provided by the embodiment of the present application. Referring to Figure 1 The new energy system capacity configuration method comprises the following steps:

[0060] S110, according to the operation information of the new energy system in a preset period, dividing the preset period into multiple time periods, and taking the new energy system configuration capacity in a time period as a single optimization.

[0061] The operation information of the new energy system in a preset period can be obtained through the prediction data of a daily wind and light prediction unit. Exemplarily, the preset period is a typical period that can reflect the change trend of the power generation and power consumption characteristics of the new energy system during operation, for example, one year. The operation information is working condition information that affects the operation state of the new energy system, for example, weather information, geographical information, load information, equipment aging information, and power flow information of the power grid in which the new energy system is incorporated. In terms of time scale, the operation information can include overall change trends of each type of information in a typical period, for example, annual change curves, or can include change trends of each type of information in a smaller time scale, for example, daily change curves and weekly change curves.

[0062] Specifically, the operation information can be divided into the preset period according to the weather information to obtain power generation prediction information of the new energy system, according to the load information to obtain power consumption prediction information of the new energy system, and according to curve analysis of the power generation prediction information and the power consumption prediction information in a small time scale to find a minimum basic period of information change. The preset period is divided into a plurality of time periods based on the minimum basic period as a division basis, thereby determining the optimization unit. Each time period can include at least one minimum basic period, and a plurality of continuous and similar minimum basic periods can be combined into one time period.

[0063] In S120, each time period is classified into a plurality of scene periods, and all units in one scene period form a group.

[0064] The classification of the unit can be performed according to the characteristics of the operation information of each time period. Exemplarily, weather factors affecting wind and light power generation and time factors strongly related to load changes can be considered in the system optimization model, and different scene periods can be classified based on weather types and load change types. For example, taking one year as the preset period: for power generation, for example, for wind power generation, one year can be divided into monsoon period and windless period; for photovoltaic power generation, one year can be divided into rainy period, cloudy period and sunny period; for other types of clean energy, one year can be divided into summer high temperature period and winter cold season according to the demand. For load, one year can be divided into production peak period and production trough period. The new energy power generation period and the load power consumption period can be combined in time sequence, and one year can be divided into several typical scene periods, and each day or week in the same type of scene period can be used as a unit in the group. Taking wind power generation as an example, the scene periods in one year can include: monsoon period production peak segment, monsoon period production trough segment, windless period production peak segment, and windless period production trough segment.

[0065] After the scene periods are divided, the sequence relationship of each group in time sequence, the sequence relationship of each unit in each group in time sequence, and the number of units can be recorded to facilitate data processing in the subsequent optimization process.

[0066] S130, respectively perform individual configuration capacity optimization on each individual in each group according to the environmental constraint condition and the group stop condition, to obtain individual optimization results and group optimization results.

[0067] Among them, for a single group and each individual in it, the optimization process can be realized based on swarm intelligence optimization algorithms such as ant colony algorithm, bee colony algorithm or particle swarm algorithm; that is, based on the evolution process of each individual in a time period, the optimal solution of the individual is found, and the optimal solution of each individual is followed to find the optimal solution of the group. Among them, there is no overlap between the time periods of each individual in the same group, so the optimization process of each individual in the same group can be independently parallel to improve the optimization speed.

[0068] Among them, the environmental constraint condition can include: new energy consumption rate constraint and load green electricity rate constraint; the whole optimization process is to select the direction of optimization with large new energy installed capacity, large power generation and high consumption. The group stop condition can include: the ratio of the number of individuals in the group that meet the environmental constraint condition to the total number of individuals in the group reaches a preset ratio, that is, the convergence of each individual to the global optimal direction of the group reaches a certain degree as the overall stop criterion for the evolution of each individual in the group.

[0069] S140, determine the group configuration capacity of the new energy system in each scene time period according to the group optimization result, the individual optimization result, and the time proportion of each individual time period in the scene time period of the group.

[0070] Among them, the group optimization result can be used as the reference value of the group configuration capacity, and then the individual optimization result is used to correct the reference value to obtain the group configuration capacity according to the contribution rate of each individual to the group.

[0071] S150, determine the configuration capacity of the new energy system in the preset period according to the group configuration capacity of each group and the time proportion of the scene time period of each group in the preset period.

[0072] Among them, each type of group is independent of each other, and the group configuration capacity under each type of scene time period is obtained by independent optimization through the above steps. When determining the final capacity configuration of the system, the consumption rate and green electricity rate of the system under different scene time periods need to be considered comprehensively, so as to configure the optimal configuration capacity in a complete preset period (such as N years). Exemplarily, the reference value of the system configuration capacity can be determined according to the group configuration capacity, and then the system reference value is corrected according to the group configuration capacity to obtain the system configuration capacity according to the contribution rate of the group to the whole preset period of the system.

[0073] The new energy system capacity configuration method provided by the embodiment of the application is based on the operation information of the region where the new energy system is located within a preset period, subdivides the preset period into multiple time periods, and forms various typical scene periods according to the operation characteristics of different time periods. This division method fully considers the complex power generation and consumption situation of a region. In the optimization process, the system evolution process of each time period is included in the consideration range, and according to the single optimization result, the group configuration capacity under each scene period is considered first, and then the system configuration capacity under the complete period is considered based on the proportion of each scene period, which can effectively improve the accuracy of optimization. Moreover, the optimization groups of different scene periods are independent of each other, and the optimization units of different time periods within the same scene period are independent of each other. In this way, conditions are provided for independent parallel optimization of each unit and independent parallel optimization of each group. That is, the embodiment of the application not only considers the change process of the operation characteristics of the new energy system in the time domain full cycle, but also takes into account the reduction of optimization complexity and the improvement of optimization speed. The embodiment of the application optimizes the capacity of the new energy system by simulating the operation characteristics of the new energy system itself, which can effectively improve the adaptability of the capacity configuration result to the system operation environment.

[0074] The single optimization process, the group configuration capacity obtaining process and the system configuration capacity obtaining process are described below respectively.

[0075] Figure 2 is a flowchart of the single optimization in a group provided by the embodiment of the application. Referring to Figure 2 In an implementation manner, optionally, the flow of the single optimization in a group includes:

[0076] S210, set an initial configuration capacity to the group, and take the initial configuration capacity as a group basic configuration capacity of the group and a single basic configuration capacity of a single in the group.

[0077] The consumption rate and green electricity rate targets of the power grid system in each region in the region can be determined based on the consumption rate and green electricity rate requirements of the entire regional power grid system on the new energy. From the perspective of the entire region, the energy interconnection relationship between different regions is considered, and the planning of the new energy system in each region in the region is performed, so as to obtain an initial configuration capacity range of the new energy system in each region. The initial configuration capacity of the new energy system in the region can be selected from the planned initial configuration capacity range.

[0078] S220, based on the environmental constraint condition, optimize and calculate the single basic configuration capacity to obtain a single optimal capacity and a credible value corresponding to the single optimal capacity.

[0079] Wherein, each monomer is optimized in the direction of high new energy consumption rate and high load green electricity rate in the time period. After the monomer finds the monomer single optimal capacity, the actual consumption rate and green electricity rate of the monomer can be calculated based on the capacity, and then the credible value corresponding to the monomer single optimal capacity can be obtained. The credible value can be understood as the closeness of the monomer single optimal capacity to the group optimal capacity, and the credible value determines the degree to which the monomer single optimal capacity can be adopted by other monomers in the group.

[0080] S230, updating the monomer basic configuration capacity according to the monomer single optimal capacity.

[0081] Specifically, the monomer next time optimization basic configuration capacity = monomer historical basic configuration capacity + initial configuration capacity * monomer group dependency rate + monomer historical basic configuration capacity * monomer independence rate. Wherein, the monomer historical basic configuration capacity is the monomer basic configuration capacity adopted by the monomer in this optimization. The monomer group dependency rate represents the influence degree of the group on the monomer, and the monomer independence rate represents the coupling degree between the monomers; both coefficients can be positive or negative; and the two coefficients are negatively correlated to a certain extent.

[0082] S240, updating the group basic configuration capacity according to the monomer single optimal capacity and the credible value of each monomer in the group.

[0083] In this step, the group basic configuration capacity is updated based on the credible value of each monomer in the group, so that the optimization results of all monomers in the group are fed back to the group. Illustratively, the group basic configuration capacity can be updated after each monomer obtains the optimization result, or it can be updated as a whole after all monomers in the group complete one optimization.

[0084] Illustratively, the new group basic configuration capacity = group historical basic configuration capacity + Σ (monomer participation rate * monomer single optimal capacity * monomer credible deviation rate). Wherein, the monomer participation rate represents the contribution rate of the monomer in the group; for example, the monomer participation rate of the monomer with higher power generation capacity is higher than that of the monomer with the same time length. The monomer credible deviation rate can be understood as the difference between the monomer credible value and the statistical value of all monomer credible values in the group; wherein, the statistical value can be the average value.

[0085] S250, iterative processing of monomer basic configuration capacity optimization, monomer basic configuration capacity update and group basic configuration capacity update.

[0086] That is, repeat the steps of S220-S240.

[0087] S260, judging whether the group stop condition is met; if yes, executing S270; if no, returning to execute S250.

[0088] Wherein, all monomers in the group simultaneously and in parallel optimize, when the number of monomers satisfying the environmental constraint condition according to the single optimal capacity of the monomers meets a certain group proportion, the iteration is stopped, and the configuration capacity of each monomer in the group is determined.

[0089] S270, the single optimal capacity of each monomer obtained in the last iteration process is taken as the optimization result of each monomer, and the group basic configuration capacity updated in the last iteration process is taken as the optimization result of the group.

[0090] The monomer optimization in the group is completed through S210-S270 in this embodiment.

[0091] On the basis of each embodiment described above, optionally, the new energy system is jointly constructed by a new energy power generation system and an energy storage system. Then, the configuration capacity of the new energy system includes: the installed capacity of the new energy power generation system and the capacity of the energy storage system. The single optimal capacity of each monomer obtained by the monomer optimization once includes the single installed capacity and the single energy storage capacity. On this basis, the monomer optimization can adopt a double closed-loop strategy, according to the power flow change of the monomer in the entire time period, based on the target of high consumption and high green rate of the new energy system, the installed capacity and the energy storage capacity of the monomer are continuously feedback adjusted, and finally the time domain optimization curve of the installed capacity of the new energy power generation system and the capacity of the energy storage system in the time period is obtained, and the single installed capacity and the single energy storage capacity can be inferred according to the curve.

[0092] The following will be described in combination with Figure 3 The double closed-loop optimization strategy will be described. Referring to Figure 3 , the double closed-loop strategy includes two key core adjustment feedback loops: one is the adjustment loop for the new energy power generation system, such as the wind-solar system; the new energy power generation system is the source of clean electric power energy, and the adjustment direction of this loop is: the larger the new energy installed capacity, the more power generation and the higher the consumption. Another is the adjustment loop for the energy storage system; in this adjustment process, the capacity, operation charging and discharging time sequence and discharge depth of the equipment in the energy storage system can be considered. Through the two core loops, the wind-solar storage capacity configuration (i.e. the installed capacity of the wind-solar system and the PCS capacity of the energy storage system) meeting the system planning requirements can be found. It should be noted that the capacity configuration of the new energy system needs to meet the constraints of power flow and power grid system, such as voltage stability and frequency stability, throughout the adjustment process, to ensure the stable grid connection of new energy.

[0093] Specifically, taking the new energy system including the wind-solar system and the energy storage system as an example, referring to Figure 3The monomer new energy power generation system adjustment strategy includes: at any moment, the wind and light system installed capacity and the weather information at the moment determine the current moment output of the wind and light system; the output power of the wind and light system is used for supplying the local load system after power flow processing, and the remaining power can be transmitted to the power grid system to other areas after meeting the current load demand; according to the limit value of the power grid system, the wind and light system adjustment is performed on the remaining power data, and the adjustment result is fed back to the wind and light system to determine the output of the wind and light system at the next moment.

[0094] The monomer energy storage system adjustment strategy includes: at any moment, the remaining power is adjusted by the energy storage system, and the adjustment result and the wind and light system installed capacity jointly determine the current moment PCS capacity of the energy storage system. In the operation process of the new energy system, the energy storage system can also perform wind and light charging adjustment on the wind and light system, that is, auxiliary adjustment of peak clipping and valley filling on the new energy power generation system. Therefore, in the double closed loop strategy, the influence of the energy storage system on the wind and light system capacity at each moment can also be introduced into the feedback adjustment, so that the monomer optimization result is more in line with the actual operation process of the monomer.

[0095] Figure 4 It is another double closed loop strategy framework schematic diagram provided by the embodiment of the application. Referring to Figure 4 On the basis of the above-mentioned embodiments, optionally, for the new energy power generation system (wind and light system), at any moment, the wind and light power generation prediction power is obtained according to the wind and light system installed capacity and the weather and other operation information at the moment; the wind and light power generation prediction power and the charging and discharging power of the energy storage system at the moment are used for supplying the local load system after power flow processing. The load system prediction power at the current moment can be obtained from the initially obtained operation information according to the position of the current moment in the preset period. The wind and light system adjustment strategy specifically includes: wind and light self-use rate automatic adjustment is performed according to the wind and light power generation prediction power, the wind and light self-use rate target and the remaining power after load consumption; grid bearing automatic adjustment is performed according to the remaining power after load consumption and the limit value of the power grid system; feedback adjustment of the wind and light system installed capacity is performed according to the grid bearing automatic adjustment result, the wind and light self-use rate automatic adjustment result and the wind and light charging adjustment result of the energy storage system; and the adjusted wind and light system installed capacity determines the wind and light power generation prediction power at the next moment.

[0096] Still referring to Figure 4 On the basis of the above-mentioned embodiments, optionally, the energy storage system adjustment strategy specifically includes: at any moment, load green power automatic adjustment is performed according to the load system prediction power, the remaining power after load consumption, the load green power target and the wind and light power generation prediction power at the current moment; the PCS capacity of the energy storage system is adjusted according to the load green power automatic adjustment result; and the adjusted PCS capacity of the energy storage system determines the charging and discharging power of the energy storage system at the next moment.

[0097] Further, the dynamic characteristics of the energy storage system battery can be considered in the energy storage system regulation strategy, so that the optimization process is closer to the actual operation process of the new energy system, and the optimization result is more accurate. Therefore, the energy storage system regulation strategy further comprises: at any time, calculating the power storage capacity of the energy storage system battery according to the power generation information of the new energy system, the load information of the new energy system and the characteristic information of the energy storage system battery; and the power storage capacity of the energy storage system battery is used for feedback regulation of the energy storage system capacity, and specifically used for the load green power automatic regulation process.

[0098] Continuing to refer to Figure 4 , the determination process of the power storage capacity of the energy storage system battery comprises: at any time, performing PID regulation (for example, KPI regulation) on the battery discharge depth according to the system life target, performing PID regulation (for example, time sequence KI regulation) on the predicted power of the load system at the current time, and performing PID regulation (for example, time sequence KI regulation) on the predicted power of the wind and light power generation at the current time; and adjusting the power storage capacity of the energy storage system battery according to the PID regulation results. The PID regulation (for example, time sequence KI regulation) result of the power storage capacity of the energy storage system battery is used for the load green power automatic regulation process at the current time.

[0099] The double closed loop strategy provided by the embodiment of the application realizes local optimal adjustment of each configuration capacity through double closed loop automatic regulation in the monomer optimization process of each time period, completes instantaneous optimization based on power flow balance at each time, and determines the monomer single optimal capacity according to the optimal capacity at each time, which is equivalent to considering the evolution process of the monomer in the whole time period and realizes the iteration in the time domain. In the regulation process, the interaction between the new energy power generation system and the energy storage system, the interconnection between the new energy system and the power grid, and the change of the battery characteristics in the energy storage system are considered, and the simulation of the actual structure and operating state of the new energy system is realized. Therefore, the embodiment of the application realizes more realistic simulation of the operating characteristics of the new energy system, and is beneficial to improving the configuration result accuracy.

[0100] For obtaining the group configuration capacity, in an implementation manner, the group configuration capacity is determined according to the following formula: group configuration capacity = group optimization result + Σ[(monomer optimization result-group optimization result)*confidence value*monomer time proportion*monomer participation rate]. In the formula, the monomer time proportion is the proportion of the time period length of the monomer to the scenario time period length of the group. In the embodiment, the group optimization result is corrected in combination with the sequence relationship and time proportion of each monomer in the group in the whole time period, and compared with the group optimization result used as the group configuration capacity, the actual capacity demand of the new energy system in the scenario time period can be more accurately represented.

[0101] Regarding the acquisition of system configuration capacity, in one implementation, optionally, determining the configuration capacity of the new energy system within a preset period may specifically include:

[0102] Calculate the statistical value of the group configuration capacity based on the configuration capacity of each group within a preset period. The statistical value of the group configuration capacity can be used as the benchmark value of the system configuration capacity. This statistical value can be the average, median, or mode of the configuration capacity of each group.

[0103] The configuration capacity of the new energy system within the preset period is calculated based on the group configuration capacity statistics, the difference between the configuration capacity of each group and the group configuration capacity statistics, and the time proportion of each group's scenario time period within the preset period.

[0104] Specifically, the system configuration capacity can be calculated using the following formula: System configuration capacity = Group configuration capacity statistics + Σ[(Group configuration capacity - Group configuration capacity statistics) * Group time percentage]. Wherein, the group time percentage is the percentage of time the group spends in a given scenario within a preset period.

[0105] The following is combined Figure 5 This section explains the calculation method for the statistical values ​​of group configuration capacity, but it is not intended to limit the invention. See also Figure 5 For example, the horizontal axis represents time, and the vertical axis represents the group configuration capacity; by arranging the group configuration capacities of each group according to the time sequence of the group within the preset period, the following can be obtained: Figure 5 A stepped capacity distribution map. The process of calculating the population configuration capacity statistics may include:

[0106] Calculate the percentage of time each group spends in a scene within a complete preset period.

[0107] Select a group of scene time periods whose capacity values ​​are within the preset configuration capacity range and whose sum of scene time periods accounts for a proportion of the preset period's duration that reaches a time proportion threshold as the characteristic group. For example... Figure 5 The groups within the dashed boxes can be considered as feature groups. The preset configuration capacity range can be set to a range close to the maximum value of the group's configuration capacity. This maximizes the statistical value of the group configuration capacity calculated based on the feature group's configuration capacity, resulting in a larger final system configuration capacity. The product of the preset period and the time percentage threshold is the threshold duration. The time percentage threshold can be selected above 50% to ensure a sufficient number of selected feature groups to represent the capacity situation throughout the preset period. The feature groups can be continuous or discontinuous on the time axis.

[0108] The average of the group configuration capacity of the feature group is calculated as a group configuration capacity statistical value, and the group configuration capacity statistical value calculated in this way is defined as the system configuration capacity median.

[0109] Exemplarily, the new energy system includes a wind power generation system, a photovoltaic power generation system and an energy storage system. Correspondingly, the system configuration capacity involved in the optimization process includes a wind power configuration capacity, a photovoltaic configuration capacity and an energy storage configuration capacity. The configuration capacity of each type of system is corrected based on the configuration capacity median, and the correction is based on the overall consumption in each scenario period. When the wind and light configuration capacity in some scenario period is less than the median, the consumption can be improved by increasing a certain energy storage capacity. Specifically, each configuration capacity is determined according to the following formula:

[0110] Wind power configuration capacity = wind power configuration capacity median + Σ [(wind power group configuration capacity - wind power configuration capacity median) * group time proportion];

[0111] Photovoltaic configuration capacity = photovoltaic configuration capacity median + Σ [(photovoltaic group configuration capacity - photovoltaic configuration capacity median) * group time proportion];

[0112] Energy storage configuration capacity = energy storage configuration capacity median + Σ [(energy storage group configuration capacity - energy storage configuration capacity median) * group time proportion].

[0113] On the basis of the above-mentioned embodiments, optionally, after the configuration capacity of the new energy system in the preset period is determined, the method further includes selecting equipment required for constructing the new energy generation system to realize complete planning of the new energy system. Specifically, the steps of equipment selection include:

[0114] obtaining an equipment parameter table. The equipment parameter table can include parameters of wind and light power generation equipment and energy storage equipment. The parameters of the wind and light power generation equipment can include equipment price, equipment capacity and power generation efficiency, etc. The parameters of the energy storage equipment can include charging and discharging efficiency and battery life, etc. The equipment parameter table can be updated according to the update of equipment on the market.

[0115] According to the configuration capacity of the new energy system and the equipment parameter table of the new energy system, equipment selection of the new energy system is performed with the lowest cost as the target. Exemplarily, the equipment selection process can adopt a knapsack optimization algorithm to optimize the configuration mode that meets the own optimization of the system based on the running characteristics of the system itself.

[0116] The following will be described in combination with Figure 6 The new energy system capacity configuration method is summarized and described. Figure 6 is a flowchart of another new energy system capacity configuration method provided by the embodiment of the present application. Referring to Figure 6 , the new energy system capacity configuration method includes:

[0117] S310, from the system and system relationship, based on global optimization algorithm, from the system level screening to determine the initial capacity of the system.

[0118] Wherein, from the interconnection structure of the whole region different regional power grid system, based on the global optimization algorithm, the initial capacity range of the regional different regional new energy system can be planned. The initial capacity of the local new energy system can be selected according to the initial planning.

[0119] S320, from the system operation and consumption optimization angle, based on the double closed loop optimization strategy, screening the operation strategy of the system, and correcting and optimizing the system capacity.

[0120] This step can be based on the optimization process of the time period-scenario time period-preset period given in the above embodiments. In the single optimization process, the double closed loop adjustment strategy is adopted to fine tune and correct the wind and light capacity, energy storage capacity and charging and discharging strategy in the system, so as to find the optimal configuration capacity of each time period, and then obtain the final capacity configuration scheme.

[0121] S330, from the system cost optimization angle, based on the optimization of each device combination, the device selection in the system is carried out.

[0122] This step can be based on the configuration capacity obtained in S320 and the existing device parameter table.

[0123] S340, judge whether the selected device meets the system and system relationship and the system internal requirement; if yes, execute S350; if not, return to execute S320.

[0124] This step is equivalent to judging whether the selected device combination meets the actual operation demand, so that the selected device combination can not only meet the overall configuration capacity demand, but also meet the stability conditions of power grid and power flow in the operation process, and meet the switching on and off demand of different devices under different scenario time periods.

[0125] S350, determine the devices of the system.

[0126] The embodiment of the application provides a distributed load side wind light storage capacity configuration adjustment method based on the multi-closed loop feedback stable control mechanism of the automatic control principle through S310-S350. First, the coordination relationship between new energy systems in a region is considered, the initial capacity range of wind light storage in each new energy system is found by optimization, then based on the consumption and load green electricity rate optimization of the new energy system, the wind light storage capacity configuration planning meeting the stable power supply is found by optimization through closed loop control adjustment, and then based on each configuration capacity, the device selection is optimized at the lowest cost. When the configuration capacity found after the above steps meets the constraint boundary conditions, it is the optimal configuration of the new energy system itself.

[0127] The embodiment of the present application also provides a new energy system capacity configuration device for realizing the new energy system capacity configuration method provided by any of the embodiments of the present application, and having the corresponding function modules and beneficial effects of the execution method. Figure 7 FIG. 1 is a structural schematic diagram of a new energy system capacity configuration device provided by an embodiment of the present application. Referring to FIG. 1, Figure 7 The new energy system capacity configuration device comprises a single-body division module 710, a group classification module 720, a single-body optimization module 730, a group configuration capacity determination module 740, and a system configuration capacity determination module 750.

[0128] The single-body division module 710 is configured to divide a preset period into multiple time periods according to the operation information of the new energy system in the preset period, and take the new energy system configuration capacity in one time period as an optimization single body. The group classification module 720 is configured to classify each time period into multiple scene time periods, and all the single bodies in one scene time period form a group. The single-body optimization module 730 is configured to perform single-body configuration capacity optimization on each single body in each group according to the environmental constraint condition and the group stop condition, and obtain a single-body optimization result and a group optimization result. The group configuration capacity determination module 740 is configured to determine the group configuration capacity of the new energy system in each scene time period according to the group optimization result, the single-body optimization result, and the time proportion of the time period of each single body in the scene time period of the group. The system configuration capacity determination module 750 is configured to determine the configuration capacity of the new energy system in the preset period according to the group configuration capacity and the time proportion of the scene time period of each group in the preset period.

[0129] Optionally, on the basis of each of the above embodiments, the single-body optimization module 730 comprises an initial configuration unit, a single-body optimization unit, a configuration updating unit, and a result processing unit. The initial configuration unit is configured to set an initial configuration capacity to the group, take the initial configuration capacity as a group basic configuration capacity of the group, and take the initial configuration capacity as a single-body basic configuration capacity of the single body in the group. The single-body optimization unit is configured to perform optimization calculation on the single-body basic configuration capacity based on the environmental constraint condition, and obtain a single-body single-time optimal capacity and a credible value corresponding to the single-body single-time optimal capacity. The configuration updating unit is configured to update the single-body basic configuration capacity according to the single-body single-time optimal capacity, and update the group basic configuration capacity according to the single-body single-time optimal capacity and the credible value of each single body in the group. The result processing unit is configured to perform iterative processing of the single-body basic configuration capacity optimization, the single-body basic configuration capacity updating, and the group basic configuration capacity updating until the group stop condition is met, take each single-body single-time optimal capacity obtained in the last iteration process as each single-body optimization result, and take the group basic configuration capacity updated in the last iteration process as the group optimization result.

[0130] On the basis of each of the above embodiments, optionally, the configuration capacity of the new energy system comprises: a new energy power generation system installed capacity and an energy storage system capacity. The single optimization unit is specifically configured to adopt a double closed loop strategy to perform single configuration capacity optimization.

[0131] The double closed loop strategy comprises: according to the grid system limit condition of the grid connected by the new energy system, the load information of the new energy system and the energy storage information of the energy storage system, the feedback regulation of the new energy power generation system installed capacity is performed within a time period; according to the power generation information of the new energy system and the load information of the new energy system, the feedback regulation of the energy storage system capacity is performed within a time period.

[0132] Further, the double closed loop strategy further comprises: calculating the energy storage capacity of the energy storage system battery according to the power generation information of the new energy system, the load information of the new energy system and the characteristic information of the energy storage system battery; the energy storage capacity of the energy storage system battery acts on the feedback regulation of the energy storage system capacity.

[0133] On the basis of each of the above embodiments, optionally, the system configuration capacity determination module 750 comprises: a statistical value determination unit and a system configuration capacity determination unit. The statistical value determination unit is configured to calculate the group configuration capacity statistical value according to each group configuration capacity in a preset period. The system configuration capacity determination unit is configured to calculate the configuration capacity of the new energy system in the preset period according to the group configuration capacity statistical value, the difference between each group configuration capacity and the group configuration capacity statistical value, and the time proportion of the scenario time period of each group in the preset period.

[0134] On the basis of each of the above embodiments, optionally, the statistical value determination unit is specifically configured to select a group of scenario time periods as a characteristic group, wherein the capacity value of the scenario time period is within a preset configuration capacity range, and the sum of the time length of the scenario time period occupies a time length proportion of the preset period that reaches a time proportion threshold; the average value of the group configuration capacity of the characteristic group is calculated as the group configuration capacity statistical value.

[0135] On the basis of each of the above embodiments, optionally, the new energy system capacity configuration device further comprises: a device selection module, configured to, after determining the configuration capacity of the new energy system in the preset period, select the device of the new energy system according to the configuration capacity of the new energy system, the weather information and the load information of the location of the new energy system in the preset period, and the device parameter table of the new energy system, with the lowest cost as the target.

[0136] On the basis of each of the above embodiments, optionally, the operation information comprises weather information and load information. The single division module 710 is specifically configured to: obtain power generation prediction information of the new energy system according to the weather information; obtain power consumption prediction information of the new energy system according to the load information; and divide the preset period into multiple time periods according to the power generation prediction information and the power consumption prediction information.

[0137] This invention also provides an optimization device. Figure 8 This is a schematic diagram of the structure of an optimization device provided in an embodiment of the present invention. Figure 8 As shown, the optimization device 80 includes a data processing and storage unit 82, an optimization algorithm processing unit 81, and a computer program stored on the data processing and storage unit 82 and capable of running on the optimization algorithm processing unit 81. When the optimization algorithm processing unit 81 executes the program, it implements the new energy system capacity configuration method provided in any embodiment of the present invention, which has corresponding beneficial effects.

[0138] The optimization algorithm processing unit 81 can be composed of a processor, and the number of processors can be one or more; the data processing storage unit 82 can be composed of a memory. The optimization algorithm processing unit 81 and the data processing storage unit 82 can be connected through a bus or other means.

[0139] The data processing storage unit 82, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the new energy system capacity configuration method in this embodiment of the invention. The optimization algorithm processing unit 81 executes various functional applications and data processing of the device by running the software programs, instructions, and modules stored in the data processing storage unit 82, thereby realizing the aforementioned new energy system capacity configuration method.

[0140] The data processing storage unit 82 may primarily include a stored program area and a stored data area. The stored program area may store the operating system and at least one application program required for a given function; the stored data area may store data created based on terminal usage. Furthermore, the data processing storage unit 82 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory, or other non-volatile solid-state storage device. In some instances, the data processing storage unit 82 may further include memory remotely located relative to the optimization algorithm processing unit 81, and this remote memory may be connected to the device / terminal / server via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0141] On the basis of the above-mentioned embodiments, the optimization device 80 can be integrated in the local control device. The optimization device 80 can further comprise an information acquisition unit 83, an optimization result processing unit 84 and a communication processing unit 85. The information acquisition unit 83 is configured to receive the operation information of the system, the optimization result processing unit 84 is configured to convert the result output by the optimization algorithm processing unit 81 into a transmission format, and the communication processing unit 85 is configured to transmit the converted result to other functional modules in the local control device. The local control device further comprises a weather monitoring system 10, a wind and light detection system 20, a load detection system 30, a power grid monitoring system 40 and a power supply system 50. The weather monitoring system 10, the wind and light detection system 20, the load detection system 30 and the power grid monitoring system 40 are configured to provide the operation information to the information acquisition unit 83.

[0142] It should be understood that the various forms of flow shown above can be used to reorder, add or delete steps. For example, the steps described in the present application can be executed in parallel, sequentially or in a different order, as long as the desired results of the technical solutions of the present application can be achieved, which are not limited herein.

[0143] The above detailed description does not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A new energy system capacity configuration method, characterized in that, The method comprises the following steps: According to the operation information of the new energy system in the preset period, the preset period is divided into multiple time periods, and the configuration capacity of the new energy system in one time period is taken as an individual for optimization; Classify each time period into multiple scene periods; All individuals in one scene period form a group; wherein there is no overlap between the time periods of individuals in the same group; different scene periods are independent optimization groups, and different time periods in the same scene period are independent optimization individuals; According to the environmental constraints and the group stop condition, the individual configuration capacity optimization is performed on each individual in each group to obtain the individual optimization result and the group optimization result; wherein for a single group and each individual in the group, the optimization process is realized based on a swarm intelligence optimization algorithm; According to the group optimization result, the individual optimization result, and the time proportion of each individual time period in the group scene period, the group configuration capacity in each scene period of the new energy system is determined; According to each group configuration capacity and the time proportion of each group scene period in the preset period, the configuration capacity of the new energy system in the preset period is determined.

2. The new energy system capacity configuration method according to claim 1, characterized in that, According to the environmental constraints and the group stop condition, the individual configuration capacity optimization is performed on each individual in each group, which comprises: Set an initial configuration capacity for the group, and take the initial configuration capacity as the group basic configuration capacity of the group and the individual basic configuration capacity of the individual in the group; Based on the environmental constraints, the individual basic configuration capacity is optimized and calculated to obtain the individual optimal capacity and the corresponding confidence value of the individual optimal capacity; Update the individual basic configuration capacity according to the individual optimal capacity, and update the group basic configuration capacity according to the individual optimal capacity and the confidence value of each individual in the group; Iteratively process the individual basic configuration capacity optimization, individual basic configuration capacity update and group basic configuration capacity update until the group stop condition is met, and then stop iteration; take the individual optimal capacity obtained in the last iteration process as the individual optimization result, and take the group basic configuration capacity updated in the last iteration process as the group optimization result.

3. The new energy system capacity configuration method according to claim 1 or 2, characterized in that, The group stop condition comprises: the ratio of the number of individuals in the group that meet the environmental constraints to the total number of individuals in the group reaches a preset ratio.

4. The new energy system capacity configuration method according to claim 1, characterized in that, The method for determining the configuration capacity of the new energy system in the preset period comprises: Calculate the group configuration capacity statistical value according to each group configuration capacity in the preset period; According to the group configuration capacity statistical value, the difference between each group configuration capacity and the group configuration capacity statistical value, and the time proportion of each group scene period in the preset period, calculate the configuration capacity of the new energy system in the preset period.

5. The new energy system capacity configuration method according to claim 4, characterized in that, According to each group configuration capacity in the preset period, calculate the group configuration capacity statistical value, which comprises: Select a capacity value within a preset configured capacity range, and a group of several scene time periods whose length sum occupies a time length proportion of the preset period reaching a time proportion threshold as a feature group; Calculate the average value of the group configured capacity of the feature group as the group configured capacity statistical value.

6. The new energy system capacity configuration method according to claim 1, characterized in that, The configured capacity of the new energy system includes: new energy power generation system installed capacity and energy storage system capacity.

7. The new energy system capacity configuration method according to claim 6, characterized in that, Each of the monomers adopts a double closed loop strategy for monomer configured capacity optimization; The double closed loop strategy includes: According to the grid system limit condition of the grid connected to the new energy system, the load information of the new energy system and the energy storage information of the energy storage system, the feedback regulation of the new energy power generation system installed capacity is carried out in the time period; According to the power generation information of the new energy system and the load information of the new energy system, the feedback regulation of the energy storage system capacity is carried out in the time period.

8. The new energy system capacity configuration method according to claim 7, characterized in that, The double closed loop strategy also includes: According to the power generation information of the new energy system, the load information of the new energy system and the characteristic information of the energy storage system battery, the storage capacity of the energy storage system battery is calculated; the storage capacity of the energy storage system battery acts on the feedback regulation of the energy storage system capacity.

9. The new energy system capacity configuration method according to claim 1, wherein, After determining the configured capacity of the new energy system in the preset period, it also includes: According to the configured capacity of the new energy system and the device parameter table of the new energy system, the device selection of the new energy system is carried out with the lowest cost as the target.

10. The new energy system capacity configuration method according to claim 1, wherein, The environmental constraint condition includes: new energy consumption rate constraint and load green electricity rate constraint.

11. The new energy system capacity configuration method according to claim 1, wherein, The operation information includes weather information and load information; According to the operation information of the new energy system in the preset period, the preset period is divided into multiple time periods, including: According to the weather information, the power generation prediction information of the new energy system is obtained; According to the load information, the power consumption prediction information of the new energy system is obtained; According to the power generation prediction information and the power consumption prediction information, the preset period is divided into multiple time periods.

12. A new energy system capacity configuration device, characterized in that, Including: The monomer division module is used for dividing the preset period into multiple time periods according to the operation information of the new energy system in the preset period, and taking the new energy system configured capacity in one time period as the optimization of one monomer; The group classification module is used for classifying each of the time periods into multiple scene time periods; All the monomers in one of the scene time periods form a group; wherein there is no overlap between the time periods of each monomer in the same group; different scene time periods are independent optimization groups, and different time periods in the same scene time period are independent optimization monomers; The monomer optimization module is used for respectively optimizing the monomer configured capacity of each of the monomers in each of the groups according to the environmental constraint condition and the group stop condition, to obtain the monomer optimization result and the group optimization result; wherein for a single group and each monomer inside it, the optimization process is realized based on a group intelligent optimization algorithm; The colony configuration capacity determination module is configured to determine the colony configuration capacity of the new energy system in each scenario time period according to the colony optimization result, the single optimization result, and the time proportion of each single time period in the scenario time period of the colony. The system configuration capacity determination module is configured to determine the configuration capacity of the new energy system in the preset period according to each colony configuration capacity and the time proportion of each colony scenario time period in the preset period.

13. An optimization device, characterized by The system configuration capacity determination module is configured to determine the configuration capacity of the new energy system in the preset period according to each colony configuration capacity and the time proportion of each colony scenario time period in the preset period. The system configuration capacity determination module is configured to determine the configuration capacity of the new energy system in the preset period according to each colony configuration capacity and the time proportion of each colony scenario time period in the preset period. The system configuration capacity determination module is configured to determine the configuration capacity of the new energy system in the preset period according to each colony configuration capacity and the time proportion of each colony scenario time period in the preset period. The system configuration capacity determination module is configured to determine the configuration capacity of the new energy system in the preset period according to each colony configuration capacity and the time proportion of each colony scenario time period in the preset period. The system configuration capacity determination module is configured to determine the configuration capacity of the new energy system in the preset period according to each colony configuration capacity and the time proportion of each colony scenario time period in the preset period. The system configuration capacity determination module is configured to determine the configuration capacity of the new energy system in the preset period according to each colony configuration capacity and the time proportion of each colony scenario time period in the preset period. The system configuration capacity determination module is configured to determine the configuration capacity of the new energy system in the preset period according to each colony configuration capacity and the time proportion of each colony scenario time period in the preset period. The system configuration capacity determination module is configured to determine the configuration capacity of the new energy system in the

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