Comprehensive energy system capacity planning optimization method for zero-carbon park

Abstract

The invention relates to the technical field of energy planning and utilization, in particular to an integrated energy system capacity planning optimization method for a zero-carbon park, and the method comprises the steps: obtaining an output power curve and a consumption power curve, and dividing a plurality of dominant time periods on the output power curve and the consumption power curve respectively; a dominant characteristic coefficient of each dominant time period is calculated according to the performance parameter of each dominant time period, and a first profit and loss fluctuation time period and a second profit and loss fluctuation time period are screened according to analysis results of the output power curve and the consumption power curve under the first division comparison mechanism and the second division comparison mechanism; according to the method and the device, the energy storage end equipment is selected to determine the profit and loss fluctuation feature aggregation time periods, and the optimal calling mode of the energy storage end equipment is determined in each profit and loss fluctuation feature aggregation time period, so that the complex energy supply and demand relationship is captured according to a multi-matching mechanism, and energy storage optimal calling is performed in combination with physical constraints such as equipment capacity and geographical distribution. And the overall performance of the energy system is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy planning and utilization, and particularly relates to a comprehensive energy system capacity planning optimization method for a zero-carbon park. BACKGROUND

[0002] The zero-carbon park mainly uses renewable energy as the main energy source. The intermittency and volatility of the energy production end output and the time difference of the energy consumption end demand result in a prominent energy supply and demand imbalance problem. Energy storage systems are urgently needed for power balance. However, the traditional comprehensive energy system capacity planning method has many deficiencies, resulting in an inefficient scheduling scheme and a lack of engineering feasibility. In addition, the differences in charging and discharging efficiency and temperature rise characteristics of different capacity energy storage devices make it difficult for the existing energy planning method to meet the multi-objective demand of the system for efficiency, reliability and economy.

[0003] For example, Chinese Patent Publication No. CN116432817A discloses a park comprehensive energy system optimization configuration method. The method includes the following steps: constructing a construction time sequence set of the park comprehensive energy system, based on the established construction time sequence set, using the optimal construction time sequence method and the cloud energy storage mechanism, constructing a park comprehensive energy system double-layer optimization configuration model; the invention establishes an electric power optimization configuration system through the configuration model of the CHP unit, the electric boiler and the gas boiler and other devices, obtains the data information in the park, sets the objective function at the same time, determines the park optimization comprehensive objective function according to the objective function and the park optimization constraint condition, inputs the optimization comprehensive objective function into the pre-constructed simulated annealing model to seek the scheduling scheme, and determines the reliable power supply probability of the system through simulation calculation in the scheduling process. Finally, the park comprehensive energy optimization configuration is carried out by obtaining the park comprehensive energy equipment planning capacity and the operation scheduling value.

[0004] The existing technology also has the following problems:

[0005] The existing technology cannot accurately identify the key fluctuation stage of the energy production and consumption curve, cannot capture the complex energy supply and demand relationship according to the multi-matching mechanism, and cannot optimize the calling of energy storage in combination with the physical constraints such as device capacity and geographical distribution, thereby affecting the overall performance of the energy system. SUMMARY

[0006] Therefore, the present application provides a comprehensive energy system capacity planning optimization method for a zero-carbon park, which overcomes the problems that the existing technology cannot accurately identify the key fluctuation stage of the energy production and consumption curve, cannot capture the complex energy supply and demand relationship according to the multi-matching mechanism, and cannot optimize the calling of energy storage in combination with the physical constraints such as device capacity and geographical distribution.

[0007] To achieve the above object, the application provides a comprehensive energy system capacity planning optimization method for zero-carbon park, comprising:

[0008] Obtaining the output power curve of the energy production end device and the consumption power curve of the energy consumption end device;

[0009] Marking a plurality of dominant points on the output power curve and the consumption power curve respectively, and dividing a plurality of dominant time periods on the output power curve and the consumption power curve respectively based on the time-sequenced dominant points, and calculating the performance parameters of each dominant time period to determine the dominant feature coefficients of each dominant time period;

[0010] Analyzing the output power curve and the consumption power curve respectively according to the first division ratio mechanism and the second division ratio mechanism, and selecting the first profit and loss fluctuation time period and the second profit and loss fluctuation time period according to the analysis results;

[0011] The first division ratio mechanism and the second division ratio mechanism have different division methods for the output power curve and the consumption power curve;

[0012] According to the first profit and loss fluctuation time period and the second profit and loss fluctuation time period, the profit and loss fluctuation feature aggregation time period is determined, and the optimization calling mode of the energy storage end device is determined in each profit and loss fluctuation feature aggregation time period, wherein the optimization calling mode comprises determining the energy storage end device to be called according to the capacity upper limit of a single energy storage end device and the interval distance between the energy storage end devices.

[0013] Further, the process of obtaining the output power curve and the consumption power curve comprises:

[0014] Obtaining the output power of each device of the energy production end and the consumption power of each device of the energy consumption end at a plurality of continuous time points within a preset time period;

[0015] Drawing the fluctuation curve of the sum of the output power of the energy production end device with time as the horizontal axis and the sum of the output power of the energy production end device as the vertical axis, and determining the fluctuation curve as the output power curve;

[0016] Drawing the fluctuation curve of the sum of the consumption power of the energy consumption end device with time as the horizontal axis and the sum of the consumption power of the energy consumption end device as the vertical axis, and determining the fluctuation curve as the consumption power curve.

[0017] Further, the way of marking the dominant points on the output power curve and the consumption power curve respectively is:

[0018] Selecting a plurality of data points on the output power curve, wherein the power value of the vertical axis of the data point exceeds the preset output power reference value and the curvature meets the curvature screening condition, and marking the data point as an output dominant point;

[0019] Screening data points on the consumption power curve, whose power values of the longitudinal axis exceed a preset consumption power reference value and whose curvatures meet a curvature screening condition, as consumption dominant points.

[0020] Further, the process of determining the output dominant feature coefficient of the output dominant period on the output power curve comprises:

[0021] sequencing all the output dominant points in time sequence;

[0022] determining a time period between adjacent output dominant points on the sequencing as an output dominant period;

[0023] acquiring a performance parameter of the output power curve in each output dominant period, the performance parameter comprising an output power minimum value of the power curve on the longitudinal axis in a single output dominant period and a duration of the output dominant period;

[0024] determining a result of weighted calculation of the output power minimum value and the duration as the output dominant feature coefficient of the output dominant period.

[0025] Further, the process of determining the consumption dominant feature coefficient of the consumption dominant period on the consumption power curve comprises:

[0026] sequencing all the consumption dominant points in time sequence;

[0027] determining a time period between adjacent output dominant points on the sequencing as a consumption dominant period;

[0028] acquiring a performance parameter of the consumption power curve in each consumption dominant period, the performance parameter comprising a consumption power minimum value of the power curve on the longitudinal axis in a single consumption dominant period and a duration of the consumption dominant period;

[0029] determining a result of weighted calculation of the consumption power minimum value and the duration as the consumption dominant feature coefficient of the consumption dominant period.

[0030] Further, the process of determining the output dominant feature coefficient of the output power curve and the consumption dominant feature coefficient of the consumption power curve respectively according to the first division ratio mechanism comprises:

[0031] dividing the output power curve and the consumption power curve into time periods in time dimension at preset time interval, so that a segment of the output power curve and a segment of the consumption power curve in each time period have the same time start point and the same time end point;

[0032] determining a number of output dominant periods contained in the output power curve segment of the single time period and an output dominant characteristic coefficient of each output dominant period, and determining a number of consumption dominant periods contained in the consumption power curve segment of the single time period and a consumption dominant characteristic coefficient of each consumption dominant period;

[0033] calculating an average value of the difference between the output dominant characteristic coefficients of the output power curve segment of the single time period and an average value of the difference between the consumption dominant characteristic coefficients of the consumption power curve segment of the single time period.

[0034] Further, the process of screening the first profit and loss fluctuation time period under the first division and comparison mechanism comprises:

[0035] If the average value of the difference between the output dominant characteristic coefficients of the single time period exceeds a preset output characteristic coefficient difference threshold value, or the average value of the difference between the consumption dominant characteristic coefficients exceeds a preset consumption characteristic coefficient difference threshold value, the time period is screened as the first profit and loss fluctuation time period.

[0036] Further, the process of screening the second profit and loss fluctuation time period under the second division and comparison mechanism comprises:

[0037] The time period overlapping between the output dominant period on the output power curve and the consumption dominant period on the consumption power curve is determined as the second profit and loss fluctuation time period.

[0038] Further, the time period overlapping between the first profit and loss fluctuation time period and the second profit and loss fluctuation time period is determined as the profit and loss fluctuation characteristic aggregation time period.

[0039] Further, the process of optimizing the call of the energy storage end device in the profit and loss fluctuation characteristic aggregation time period comprises:

[0040] obtaining an upper limit of the energy storage capacity of each energy storage end device;

[0041] screening the energy storage end device whose energy storage capacity upper limit is less than an energy storage capacity upper limit reference value in the single profit and loss fluctuation characteristic aggregation time period to construct a call device set;

[0042] calling the energy storage end device whose interval distance between the call devices in the call device set meets a call distance determination condition;

[0043] The energy storage capacity upper limit reference value is negatively correlated with the duration of the single profit and loss fluctuation characteristic aggregation time period, and the call distance determination condition is that the interval distance is greater than a preset interval distance threshold value.

[0044] Compared with the prior art, the beneficial effects of the present application are that, by acquiring the output power curve and the consumption power curve, the present application divides a plurality of dominant periods on the output power curve and the consumption power curve respectively, calculates the dominant characteristic coefficients of each dominant period according to the performance parameters of each dominant period, screens the first profit and loss fluctuation time period and the second profit and loss fluctuation time period according to the analysis results of the output power curve and the consumption power curve under the first division ratio matching mechanism and the second division ratio matching mechanism, determines the profit and loss fluctuation characteristic aggregation time period according to the first profit and loss fluctuation time period and the second profit and loss fluctuation time period, and determines the optimized calling mode of the energy storage end device in each profit and loss fluctuation characteristic aggregation time period, thereby realizing accurate identification of the key fluctuation stage of the energy production and consumption curve, capturing the complex energy supply and demand relationship according to the multi-matching mechanism, and combining the physical constraints such as device capacity and geographical distribution to optimize the calling of the energy storage, thereby improving the overall performance of the energy system.

[0045] Further, the present application can intuitively and accurately present the dynamic change of energy supply and consumption over time by acquiring the power data of each device of the energy production end and the energy consumption end within a preset time period and drawing the output power curve and the consumption power curve respectively, and by marking the dominant points on the power curve, the key information in the power change can be highlighted, and these dominant points represent important states in the energy supply or consumption process, thereby realizing targeted analysis of the characteristics of the energy system at different time periods.

[0046] Further, the present application can accurately capture the fluctuation characteristics and fluctuation influence degree of the output power of the energy production end device by sorting the output dominant points and determining the output dominant period, and acquiring the performance parameters of the output power curve in the time period, and the minimum output power and the duration in the performance parameters can accurately capture the fluctuation characteristics and fluctuation influence degree of the output power of the energy production end device, and the minimum output power and the duration are weighted to obtain the output dominant characteristic coefficient, thereby realizing quantification of the characteristics of the output dominant period, facilitating comparison between different output dominant periods, and thereby realizing comprehensive and objective evaluation of the output performance of the energy production end device.

[0047] Further, the present application can accurately depict the load characteristics of the energy consumption end device in the zero-carbon park comprehensive energy system by determining the consumption dominant period and its characteristic coefficient, and the consumption dominant points are sorted in time sequence and divided into consumption dominant periods, which can decompose the complex consumption power curve into a plurality of time periods with specific characteristics, and the two performance parameters of the minimum consumption power and the duration describe the energy consumption of each period from the power level and the time dimension, thereby realizing in-depth understanding of the energy demand state of the energy consumption end device.

[0048] Further, the present application quantifies the power fluctuation degree by dividing the output power curve and the consumption power curve according to the preset time interval, calculates the average value of the output dominant feature coefficient difference and the average value of the consumption dominant feature coefficient difference in each time period, accurately finds out the first profit and loss fluctuation period with large power fluctuation, determines the time period overlapping between the output dominant period on the output power curve and the consumption dominant period on the consumption power curve as the second profit and loss fluctuation period, and intuitively presents the direct conflict of energy supply and consumption in time, thereby accurately identifying the key fluctuation stage of the energy production and consumption curve, and capturing the complex energy supply and demand relationship according to the multi-matching mechanism.

[0049] Further, the present application can better match the charge and discharge requirements of large-capacity and small-capacity energy storage batteries by determining the profit and loss fluctuation feature aggregation time period and optimizing the call of the energy storage device based on the same. For large-capacity energy storage batteries, if the charge and discharge is intermittent, the charge and discharge interval can be used to avoid long-term continuous heating of the battery, thereby improving the charge and discharge efficiency. For small-capacity energy storage batteries, since the capacity release is completed before the battery temperature reaches the efficiency-affected temperature during long-term charge and discharge, the reduction of charge and discharge efficiency caused by long-term heating can also be avoided, so that batteries of different capacities can work in a relatively efficient state. The interval distance between devices in the device set is limited to optimize the layout of the energy storage device and avoid local overheating caused by too close distance between devices during charge and discharge, thereby improving the stability and reliability of the entire energy storage system and ensuring stable operation of energy storage batteries of different capacities in their appropriate charge and discharge modes. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 A step diagram of the comprehensive energy system capacity planning optimization method for zero-carbon park of the embodiment of the present application;

[0051] Figure 2 A schematic diagram of marking the output dominant point on the output power curve of the embodiment of the present application;

[0052] Figure 3 A step diagram of determining the output dominant feature coefficient of the embodiment of the present application;

[0053] Figure 4 A step diagram of determining the consumption dominant feature coefficient of the embodiment of the present application;

[0054] Figure 5 A step diagram of optimizing the call of the energy storage end device of the embodiment of the present application. DETAILED DESCRIPTION

[0055] In order to make the objects, technical schemes and advantages of the present application clearer, the following further describes the present application with reference to the embodiments; it should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0056] The preferred embodiments of the present application are described below with reference to the drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application and are not intended to limit the protection scope of the present application.

[0057] It should be noted that, in the description of the present application, the terms indicating the direction or positional relationship of "upper", "lower", "inner", "outer" and the like are based on the direction or positional relationship shown in the drawings, which is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application.

[0058] Please refer to Figure 1 As shown in the figure, it is a step diagram of the capacity planning optimization method for the comprehensive energy system of the zero-carbon park according to the embodiment of the present application, the capacity planning optimization method for the comprehensive energy system of the zero-carbon park according to the present application comprises:

[0059] Step S100, obtaining the output power curve of the energy-producing end device and the consumption power curve of the energy-consuming end device;

[0060] Specifically, the energy-producing end device in the present application includes photovoltaic power generation equipment, wind power generation equipment and other new energy power generation equipment, and the energy-consuming end device includes industrial production equipment, building power equipment and server equipment, etc., which will not be described here.

[0061] Step S200, marking a plurality of explicit points on the output power curve and the consumption power curve respectively, and dividing a plurality of explicit time periods on the output power curve and the consumption power curve respectively based on the explicit points arranged in time sequence, and calculating the performance parameters of each explicit time period to determine the explicit characteristic coefficients of each explicit time period;

[0062] Step S300, analyzing the output power curve and the consumption power curve respectively according to the first division comparison mechanism and the second division comparison mechanism, and screening the first profit and loss fluctuation time period and the second profit and loss fluctuation time period according to the analysis results;

[0063] Among them, the first division comparison mechanism and the second division comparison mechanism are different in the division mode of the output power curve and the consumption power curve;

[0064] Step S400, determining a profit and loss fluctuation feature aggregation time period according to the first profit and loss fluctuation time period and the second profit and loss fluctuation time period, and determining an optimized calling mode of the energy storage end device in each profit and loss fluctuation feature aggregation time period, the optimized calling mode comprising determining a called energy storage end device according to a capacity upper limit of a single energy storage end device and an interval distance between energy storage end devices.

[0065] Specifically, the energy storage end device in the present application can be an energy storage battery, used to store the electric energy output by the energy production end device and discharge the energy consumption end device during the power consumption peak period. The capacity upper limit of the energy storage end device is the rated capacity of the battery, in kWh.

[0066] It can be understood that the zero-carbon park takes renewable energy as the main energy source, such as solar energy and wind energy, and the demand of the energy consumption end also has periodical differences, such as industrial task load changes and building electricity consumption. The dynamic mismatch of the two sides of supply and demand leads to the need for power balance of the energy system. The energy storage end device is commonly used to balance the power between the energy production end device and the energy consumption end device. Therefore, it is crucial to accurately adjust the energy storage end device in the dynamic change of the state of the two sides of supply and demand to improve the overall performance of the energy system.

[0067] The large-capacity energy storage battery has a large electrode material volume and a high electrolyte storage capacity, and the internal resistance heat (Q = I 2 Rt) increases exponentially with the charging and discharging current and duration. If the high-frequency charging and discharging is continued for a long time, the heat accumulation will cause the battery temperature to rise significantly, triggering side reactions such as electrolyte decomposition, and reducing the charging and discharging efficiency. During the short-time high-frequency charging and discharging process, there is an interval between charging and discharging, which provides a heat dissipation window period for the battery, avoiding continuous temperature rise. The small-capacity energy storage battery has a low capacity. Even if it is discharged at a rated current for a long time, the total discharge time is short, the energy release period is short, and the total internal resistance heat is limited (total heat Q ∝ capacity x current square x time). The battery temperature rises less, and the efficiency is not affected.

[0068] Specifically, the process of obtaining the output power curve and the consumption power curve includes:

[0069] Obtaining the output power of each device of the energy production end and the consumption power of each device of the energy consumption end at a plurality of continuous time points within a preset time period;

[0070] Specifically, the value of the preset time period can be 24h. In the implementation, the output power of each device of the energy production end and the consumption power of each device of the energy consumption end at the corresponding time point can be obtained every 5min.

[0071] With the sum of the output powers of the energy-producing end devices as the vertical axis and time as the horizontal axis, a fluctuation curve of the sum of the output powers of the energy-producing end devices over time is plotted, and the fluctuation curve is determined as the output power curve;

[0072] A fluctuation curve of the sum of the power consumption of the energy-consuming end devices over time is drawn with the sum of the power consumption of the energy-consuming end devices as the vertical axis and time as the horizontal axis, and the fluctuation curve is determined as the power consumption curve.

[0073] Specifically, the method of marking dominant points on the output power curve and the power consumption curve is as follows:

[0074] Screening a number of data points on the output power curve whose power values ​​on the vertical axis exceed a preset output power reference value and whose curvature meets a curvature screening condition, and marking the data points as output dominant points;

[0075] On the power consumption curve, a plurality of data points whose power values ​​on the vertical axis exceed a preset power consumption reference value and whose curvature meets the curvature screening condition are screened, and the data points are marked as power consumption dominant points.

[0076] In implementation, the preset output power reference value can be determined based on the total maximum power of the production-end equipment in the park. The preset output power reference value can be the total maximum power of the production-end equipment multiplied by the output power value factor. In order to ensure that the power value point of the output power curve can be screened according to the preset output power reference value as a point with larger power on the output power curve, the value range of the output power value factor can be [0.7, 0.9]. Preferably, the value of the output power value factor can be 0.8, and the point with the maximum curvature on the curve segment where the power value exceeds the preset output power reference value on the output power curve is screened as the output dominant point.

[0077] See also Figure 2 As shown, it is a schematic diagram of marking the output dominant point on the output power curve according to the embodiment of the present invention, according to the total maximum power P of the power generation end device a , calculate the preset output power reference value P b =0.8×P a , filter out the curve segments on the output power curve whose power values ​​exceed the preset output power reference values, including the curve segments with time periods of T1, T2, T3, T4 and T5, and take the points with maximum curvature in the curve segments with time periods of T1, T2, T3, T4 and T5 as the output dominant points, including the output dominant points a, a1, a2, a3 and a4.

[0078] Similarly, the preset output power reference value can be determined according to the total power maximum value of the energy-consuming end device in the park, and the preset consumption power reference value can be obtained by multiplying the total power maximum value of the energy-consuming end device by a consumption power value factor. In order to ensure that the power value point of the consumption power curve selected according to the preset consumption power reference value is a point with relatively large power on the consumption power curve, the value range of the consumption power value factor is [0.7, 0.9], preferably, the value of the consumption power value factor can be 0.8, and the point with the maximum curvature on the curve segment with power value exceeding the preset consumption power reference value on the consumption power curve is the consumption dominant point.

[0079] Specifically, the present application does not limit the acquisition method of the total power maximum value of the energy-producing end device and the total power maximum value of the energy-consuming end device, which can be determined by the power acquisition result within a preset time length by a person skilled in the art. The present application also does not limit the determination method of the curve curvature, which can be determined according to the difference method. This is a prior art and will not be described here.

[0080] Specifically, the present application can intuitively and accurately present the dynamic change of energy supply and consumption with time by acquiring the power data of each device of the energy-producing end and the energy-consuming end within a preset time length and drawing the output power curve and the consumption power curve, respectively. By marking the dominant points on the power curve, the key information in the power change can be highlighted. These dominant points represent important states in the energy supply or consumption process, and then the characteristics of the energy system in different time periods are analyzed.

[0081] Specifically, please refer to Figure 3 The process of determining the output dominant feature coefficient of the output dominant time period on the output power curve includes:

[0082] Step S201, sort all output dominant points in time sequence;

[0083] For example, the output dominant points can be sorted in time sequence.

[0084] Step S202, determine the time period between adjacent output dominant points in the sorting as an output dominant time period;

[0085] Step S203, acquire the performance parameter of the output power curve in each output dominant time period, the performance parameter including the minimum output power of the power curve on the vertical axis in a single output dominant time period and the duration of the output dominant time period;

[0086] Step S204, determine the output dominant feature coefficient of the output dominant time period by weighted calculation of the minimum output power and the duration, respectively.

[0087] It can be understood that by weighting the output power minimum value in the single output explicit period and the duration of the period, the supply capacity of the output interval is comprehensively reflected, the output power minimum value reflects the lowest power supply level of the energy production end in the period, and the duration reflects the duration of the supply state, and the weighted result of the two quantifies the supply capacity of the period to the energy system.

[0088] For example, the calculation method of the output explicit feature coefficient R1 can be calculated according to the formula R1=α×(P 1min / P0)+β×(t1 / t0), wherein P 1min is the output power minimum value, P0 is a preset output power reference value, t1 is the duration of the output explicit period, t0 is a duration reference value, α is a power weight factor, and β is a duration weight factor, preferably, the preset output power reference value is the total power maximum value of the energy production end device multiplied by an output power value factor, the duration reference value t0 can be 30 min, and α+β=1, here, a power weight factor and a duration weight factor are provided, the power weight factor α=0.4, and the duration weight factor β=0.6.

[0089] It can be understood that the output explicit points are sorted in time sequence and divided into explicit periods, which is a dynamic processing of the original power curve data, in the zero-carbon park comprehensive energy system, since the output power curve of the energy production end device often presents irregular fluctuations, the explicit points mark the key nodes in the power change, and the period between adjacent explicit points is a relatively independent dynamic stage in the system operation, by this division, the continuous power curve can be disassembled into multiple analyzable discrete units, reflecting the dynamic characteristics of the energy production end in the continuous output stage, selecting the output power minimum value and the duration as performance parameters is the extraction of the power change characteristics in the explicit period, the output power minimum value can intuitively reflect the lowest output capacity of the energy production end in the period, and the duration reflects the duration of the power state, when the minimum value is low and the duration is short, it is indicated that there is a power value point with a significantly smaller power value between two explicit points with a larger power value in a short time, that is, the energy supply stability is poor in a short time.

[0090] Specifically, the output explicit points are sorted and the output explicit period is determined, then the performance parameters of the output power curve in the period are obtained, the output power minimum value and the duration in the performance parameters can accurately capture the fluctuation characteristics and fluctuation influence degree of the output power of the energy production end device, the output power minimum value and the duration are weighted to obtain the output explicit feature coefficient, the quantification of the characteristics of the output explicit period is realized, comparison between different output explicit periods is facilitated, and then, the output performance of the energy production end device is comprehensively and objectively evaluated.

[0091] Specifically, see Figure 4 As shown in FIG. , which is a step diagram of determining a consumption dominant characteristic coefficient according to an embodiment of the present invention, the process of determining the consumption dominant characteristic coefficient of a consumption dominant period on a consumption power curve includes:

[0092] Step S211, sorting all the consumption dominant points in time sequence;

[0093] Step S212, determining the time period between the adjacent output dominant points in the order as the consumption dominant time period;

[0094] Step S213, obtaining performance parameters of the power consumption curve in each dominant consumption period, wherein the performance parameters include the minimum power consumption value on the vertical axis of the power curve in a single dominant consumption period and the duration of the dominant consumption period;

[0095] Step S214 : Determine the result obtained by weighted calculation of the minimum power consumption value and the duration as the consumption dominant characteristic coefficient of the consumption dominant period.

[0096] It can be understood that the consumption explicit characteristic coefficient is a quantitative indicator of the energy demand characteristics of the energy-consuming end equipment during the consumption explicit period. Its physical meaning is that it comprehensively reflects the demand intensity of the high-load power consumption interval by weighted calculation of the minimum power consumption value in a single consumption explicit period and the duration of the period. The minimum power consumption value reflects the lowest power demand level of the energy-consuming end during the period, and the duration reflects the duration of the demand state. The weighted result of the two quantifies the degree of energy demand during the period.

[0097] For example, the consumption dominant characteristic coefficient R2 can be calculated according to the formula R2=α×(P 2min / P0')+β×(t2 / t0), where P 2min is the minimum power consumption value, P0' is the preset power consumption reference value, t2 is the duration of the explicit consumption period, t0 is the duration reference value, α is the power weight factor, β is the duration weight factor. Preferably, the preset power consumption reference value is the total maximum power of the energy-consuming end device multiplied by the power consumption value factor.

[0098] It can be understood that in the zero-carbon park comprehensive energy system, the consumption power curve of the energy-consuming end device is usually continuous and complex, the entire consumption explicit points are sorted in time sequence, and the time period between adjacent consumption explicit points is determined as the consumption explicit time period, which is a way of discretizing continuous data, and the minimum consumption power of the power curve in a single consumption explicit time period and the duration of the time period are selected as the performance parameters, which are based on the consideration of the key elements of the energy consumption process. The minimum consumption power can reflect the lowest power demand of the energy-consuming end device in the time period, which represents the lowest energy consumption level of the system in the time period, and the duration represents the duration of the specific energy consumption state. When the minimum consumption power is low and the duration is short, there is a power value point between the two explicit points with high power value in a short time, that is, the stability of energy demand in a short time is poor.

[0099] Specifically, by determining the consumption explicit time period and its characteristic coefficients, the load characteristics of the energy-consuming end device in the zero-carbon park comprehensive energy system can be accurately described. The consumption explicit points are sorted in time sequence and divided into consumption explicit time periods, which can decompose the complex consumption power curve into multiple time periods with specific characteristics. The two performance parameters, the minimum consumption power and the duration, describe the energy consumption of each time period from the power level and the time dimension, respectively, and further realize the in-depth understanding of the energy demand state of the energy-consuming end device.

[0100] Specifically, the process of determining the explicit characteristic coefficients of the output power curve and the consumption power curve respectively by the first division ratio mechanism includes:

[0101] The output power curve and the consumption power curve are divided into time periods in the time dimension at a preset time interval, so that the time start point of a segment of the output power curve and a segment of the consumption power curve in each time period is the same, and the time end point of a segment of the output power curve and a segment of the consumption power curve in each time period is the same.

[0102] Specifically, the time start point of a segment of the output power curve and a segment of the consumption power curve in each time period is the same, and the time end point of a segment of the output power curve and a segment of the consumption power curve in each time period is the same, that is, a segment of the output power curve and a segment of the consumption power curve in a single time period are synchronous in the time dimension.

[0103] For example, the preset time interval in the present application can be set by those skilled in the art. In order to avoid the lack of monitoring accuracy caused by setting the preset time interval too long and the lack of data representation caused by setting the preset time interval too short, preferably, the value of the preset time interval can be 1h.

[0104] determining a number of output dominant periods included in the output power curve segment of the single time period and an output dominant feature coefficient of each output dominant period, and determining a number of consumption dominant periods included in the consumption power curve segment of the single time period and a consumption dominant feature coefficient of each consumption dominant period;

[0105] calculating an average value of the difference between the output dominant feature coefficients of the output dominant periods in the output power curve segment of the single time period as the average value of the difference between the output dominant feature coefficients, and calculating an average value of the difference between the consumption dominant feature coefficients of the consumption dominant periods in the consumption power curve segment of the single time period as the average value of the difference between the consumption dominant feature coefficients.

[0106] For example, the output dominant feature coefficients of the output dominant periods included in the output power curve segment of the single time period are obtained, an average value of the difference between the output dominant feature coefficients of the output dominant periods is calculated as the average value of the difference between the output dominant feature coefficients, the consumption dominant feature coefficients of the consumption dominant periods included in the consumption power curve segment of the single time period are obtained, and an average value of the difference between the consumption dominant feature coefficients of the consumption dominant periods is calculated as the average value of the difference between the consumption dominant feature coefficients.

[0107] Specifically, the process of screening the first profit and loss fluctuation time period under the first division and comparison mechanism includes:

[0108] If the average value of the difference between the output dominant feature coefficients of the single time period exceeds a preset threshold value of the difference between the output feature coefficients, or the average value of the difference between the consumption dominant feature coefficients exceeds a preset threshold value of the difference between the consumption feature coefficients, the time period is screened as the first profit and loss fluctuation time period.

[0109] For example, the threshold value of the difference between the output feature coefficients has a value range of [0.1, 0.2], and the threshold value of the difference between the consumption feature coefficients has a value range of [0.1, 0.2]. Here, a value of the threshold value of the difference between the output feature coefficients and the threshold value of the difference between the consumption feature coefficients is provided. The threshold value of the difference between the consumption feature coefficients can be 0.15, and the threshold value of the difference between the output feature coefficients can be 0.15.

[0110] Specifically, the process of screening the second profit and loss fluctuation time period under the second division and comparison mechanism includes:

[0111] The time period in which the output dominant period on the output power curve coincides with the consumption dominant period on the consumption power curve is determined as the second profit and loss fluctuation time period.

[0112] In implementation, the determination of the time period in which the output dominant period on the output power curve coincides with the consumption dominant period on the consumption power curve can be performed according to the start time and the end time of the output dominant period and the consumption dominant period, the start time and the end time of the coinciding time period are determined, and the time period constructed by the start time and the end time of the coinciding time period is determined as the second profit and loss fluctuation time period.

[0113] It can be understood that the first division and comparison mechanism divides the output power curve and the consumption power curve in the time dimension at preset time interval, and divides the output power curve and the consumption power curve into a plurality of time periods, and the principle of this division manner is based on the consideration of the time scale of the energy system operation, and the second division and comparison mechanism determines the time period coinciding between the output dominant period on the output power curve and the consumption dominant period on the consumption power curve as the second profit and loss fluctuation time period, and this division manner starts from the direct correspondence relationship between energy supply and demand, and then accurately locates the time period of energy supply and demand fluctuation.

[0114] Specifically, by dividing the output power curve and the consumption power curve at preset time interval, the average value of the output dominant feature coefficient difference and the average value of the consumption dominant feature coefficient difference in each time period are calculated, the power fluctuation degree is quantified, the first profit and loss fluctuation time period with large power fluctuation is accurately found out, the time period coinciding between the output dominant period on the output power curve and the consumption dominant period on the consumption power curve is determined as the second profit and loss fluctuation time period, the direct conflict between energy supply and consumption in time is directly presented, and then the key fluctuation stage of the energy production and consumption curve is accurately identified, and the complex energy supply and demand relationship is captured according to the multi-matching mechanism.

[0115] Specifically, the time period coinciding between the first profit and loss fluctuation time period and the second profit and loss fluctuation time period is determined as the profit and loss fluctuation feature aggregation time period.

[0116] Specifically, the coinciding time period of the two time periods is the prior art, which will not be described here.

[0117] Specifically, please refer to Figure 5 The figure is a step diagram of the optimization calling of the energy storage end equipment according to the embodiment of the application, and the process of the optimization calling of the energy storage end equipment in the profit and loss fluctuation feature aggregation time period comprises:

[0118] Step S401, obtaining the upper limit of the energy storage capacity of each device of the energy storage end;

[0119] Step S402, screening out the energy storage end equipment with the energy storage capacity upper limit less than the energy storage capacity upper limit reference value in a single profit and loss fluctuation feature aggregation time period to construct a calling device set;

[0120] Specifically, the calling device set comprises a plurality of device numbers of the energy storage end equipment with the energy storage capacity upper limit less than the energy storage capacity upper limit reference value, and the device numbers are prepared in advance by the person skilled in the art, which will not be described here.

[0121] Step S403, calling the energy storage end equipment with the interval distance between the calling devices in the calling device set meeting the calling distance determination condition;

[0122] The upper limit reference value of the energy storage capacity is negatively correlated with the duration of the single profit and loss fluctuation characteristic aggregation period, and the calling distance determination condition is that the interval distance is greater than a preset interval distance threshold.

[0123] For example, the value of the upper limit reference value A of the energy storage capacity can be determined according to the maximum value A of the upper limit of the energy storage capacity of the energy storage terminal device max It can be determined that the upper limit reference value A of the energy storage capacity is determined according to the following manner A = [(t0 / t')-1]xA max Where t' is the duration of the profit and loss fluctuation characteristic aggregation period, t0 is the duration reference value, since t' is the time period in which the first profit and loss fluctuation period and the second profit and loss fluctuation period overlap, t' < t0, the preset interval distance threshold is determined according to the average interval distance between the calling devices in the calling device set, the interval distance threshold is the value obtained by multiplying the interval distance value factor by the average interval distance, the value range of the interval distance value factor is [0.5, 0.7], and preferably the value of the interval distance value factor is 0.6.

[0124] It can be understood that the upper limit of the energy storage capacity of each device of the energy storage terminal is obtained, and a negatively correlated upper limit reference value of the energy storage capacity is set according to the duration of the profit and loss fluctuation characteristic aggregation period to screen the devices to construct the calling device set, which can reasonably configure the energy storage resources according to the actual charging and discharging demand. For a longer duration of the profit and loss fluctuation characteristic aggregation period, the upper limit reference value of the energy storage capacity is lower, and small-capacity energy storage batteries are preferentially called for continuous long-time charging and discharging, avoiding the temperature overheating of large-capacity energy storage batteries in this case due to continuous long-time charging and discharging, which causes the efficiency to be reduced. For a shorter duration period, the large-capacity energy storage batteries have an interval time for heat dissipation during the short-time intermittent charging and discharging process, fully utilizing the characteristics of stable large-capacity charging and discharging power, and realizing stable operation of energy storage batteries of different capacities in their respective appropriate charging and discharging modes.

[0125] Specifically, the application can better match the charge and discharge requirements of large-capacity and small-capacity energy storage batteries by determining the profit and loss fluctuation characteristic aggregation period and optimizing the calling of the energy storage device based thereon. For large-capacity energy storage batteries, if the charge and discharge is intermittent, the charge and discharge interval can be used to avoid long-term continuous heating of the battery, thereby improving the charge and discharge efficiency. For small-capacity energy storage batteries, since the capacity release is completed before the battery temperature reaches the point that affects the efficiency during long-term charge and discharge, the reduction of charge and discharge efficiency caused by long-term heating can also be avoided, so that batteries of different capacities can work in a relatively efficient state. The interval distance between devices in the device set is limited to optimize the layout of the energy storage device, avoid local overheating caused by too close distance between devices during charge and discharge, and improve the stability and reliability of the entire energy storage system, and ensure that energy storage batteries of different capacities operate stably in their respective appropriate charge and discharge modes.

[0126] The embodiment also provides a computer readable storage medium, which stores computer program codes, and when the computer program codes are run on a computer, the computer is caused to execute the above-mentioned related method steps to realize the comprehensive energy system capacity planning optimization method for a zero-carbon park according to the above-mentioned embodiment.

[0127] So far, the technical solutions of the application have been described in combination with the preferred embodiments shown in the drawings, but those skilled in the art can easily understand that the protection scope of the application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without departing from the principles of the application, and the technical solutions after the changes or replacements will fall within the protection scope of the application.

[0128] The above description is only the preferred embodiments of the application and is not used to limit the application; for those skilled in the art, the application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the application shall be included in the protection scope of the application.

Claims

1. A comprehensive energy system capacity planning optimization method for a zero-carbon park, characterized in that: include: Obtain the output power curve of the energy-producing end equipment and the power consumption curve of the energy-consuming end equipment; Marking a plurality of dominant points on the output power curve and the power consumption curve, respectively, dividing the output power curve and the power consumption curve into a plurality of dominant time periods based on the dominant points arranged in time sequence, and performing weighted calculation on the performance parameters of each dominant time period to determine a dominant characteristic coefficient of each dominant time period; Analyzing the output power curve and the power consumption curve according to the first division and comparison mechanism and the second division and comparison mechanism, respectively, and selecting the first profit and loss fluctuation time period and the second profit and loss fluctuation time period according to the analysis results; The first division and comparison mechanism and the second division and comparison mechanism divide the output power curve and the power consumption curve in different ways; A profit and loss fluctuation feature aggregation time period is determined based on the first profit and loss fluctuation feature aggregation time period, and an optimized calling method for the energy storage terminal device is determined within each profit and loss fluctuation feature aggregation time period. The optimized calling method includes determining the energy storage terminal device to be called based on the capacity upper limit of a single energy storage terminal device and the interval distance between the energy storage terminal devices.

2. The integrated energy system capacity planning and optimization method for a zero-carbon park according to claim 1 is characterized in that: The process of obtaining the output power curve and the power consumption curve includes: Obtain the output power of each device on the energy-generating side and the power consumption of each device on the energy-consuming side at several consecutive moments within a preset time period; With the sum of the output powers of the energy-producing end devices as the vertical axis and time as the horizontal axis, a fluctuation curve of the sum of the output powers of the energy-producing end devices over time is plotted, and the fluctuation curve is determined as the output power curve; A fluctuation curve of the sum of the power consumption of the energy-consuming end devices over time is drawn with the sum of the power consumption of the energy-consuming end devices as the vertical axis and time as the horizontal axis, and the fluctuation curve is determined as the power consumption curve.

3. The integrated energy system capacity planning and optimization method for a zero-carbon park according to claim 2 is characterized in that: The way of marking dominant points on the output power curve and the power consumption curve is as follows: Screening a number of data points on the output power curve whose power values ​​on the vertical axis exceed a preset output power reference value and whose curvature meets a curvature screening condition, and marking the data points as output dominant points; On the power consumption curve, a plurality of data points whose power values ​​on the vertical axis exceed a preset power consumption reference value and whose curvature meets the curvature screening condition are screened, and the data points are marked as power consumption dominant points.

4. The integrated energy system capacity planning and optimization method for a zero-carbon park according to claim 3 is characterized in that: The process of determining the output dominant characteristic coefficient of the output dominant period on the output power curve includes: Sort all output dominant points in time sequence; Determining a period between adjacent output dominant points in the order as an output dominant period; Obtaining performance parameters of the output power curve in each output dominant period, wherein the performance parameters include the minimum output power value on the vertical axis of the power curve in a single output dominant period and the duration of the output dominant period; The results obtained by weighted calculation of the minimum output power value and the duration are determined as the output dominant characteristic coefficient of the output dominant period.

5. The integrated energy system capacity planning and optimization method for a zero-carbon park according to claim 4 is characterized in that: The process of determining the consumption dominant characteristic coefficient of the consumption dominant period on the consumption power curve includes: Sort all consumption dominant points in chronological order; Determining a period between adjacent output dominant points in the order as a consumption dominant period; Obtaining performance parameters of the power consumption curve in each dominant consumption period, wherein the performance parameters include a minimum power consumption value on the vertical axis of the power curve in a single dominant consumption period and a duration of the dominant consumption period; The results obtained by weighted calculation of the minimum power consumption value and the duration are determined as the consumption dominant characteristic coefficient of the consumption dominant period.

6. The integrated energy system capacity planning and optimization method for a zero-carbon park according to claim 5 is characterized in that: The process of determining the dominant characteristic coefficient by comparing the output power curve and the power consumption curve according to the first division and comparison mechanism includes: Dividing the output power curve and the power consumption curve into a plurality of time periods based on a time dimension at preset time intervals, so that a segment of the output power curve and a segment of the power consumption curve in each time period have the same starting time, and a segment of the output power curve and a segment of the power consumption curve in each time period have the same ending time; Determining a number of output dominant time periods included in an output power curve segment of a single time period and an output dominant characteristic coefficient of each output dominant time period, and determining a number of consumption dominant time periods included in a consumption power curve segment of each time period and a consumption dominant characteristic coefficient of each consumption dominant time period; The average value of the output dominant characteristic coefficient difference of the output power curve segment in a single time period and the average value of the consumption dominant characteristic coefficient difference of the consumption power curve segment in the time period are calculated.

7. The integrated energy system capacity planning and optimization method for a zero-carbon park according to claim 6 is characterized in that: The process of screening the first profit and loss fluctuation time period under the first division and comparison mechanism includes: If the average value of the output dominant characteristic coefficient difference in a single time period exceeds the preset output characteristic coefficient difference threshold, or the average value of the consumption dominant characteristic coefficient difference exceeds the preset consumption characteristic coefficient difference threshold, the time period is screened as the first profit and loss fluctuation time period.

8. The integrated energy system capacity planning and optimization method for a zero-carbon park according to claim 7 is characterized in that: The process of screening the second profit and loss fluctuation time period under the second division and comparison mechanism includes: The time period that overlaps between the output dominant time period on the output power curve and the consumption dominant time period on the consumption power curve is determined as the second profit and loss fluctuation time period.

9. The integrated energy system capacity planning and optimization method for a zero-carbon park according to claim 8, characterized in that: A time period in which the first profit and loss fluctuation time period overlaps with the second profit and loss fluctuation time period is determined as the profit and loss fluctuation feature aggregation time period.

10. The integrated energy system capacity planning and optimization method for a zero-carbon park according to claim 9, characterized in that: The process of optimizing the call of energy storage devices during the period of aggregated profit and loss fluctuation characteristics includes: Obtain the upper limit of energy storage capacity of each device at the energy storage end; Filter out energy storage devices whose energy storage capacity upper limit is less than the energy storage capacity upper limit reference value within a single profit and loss fluctuation feature aggregation time period to construct a calling device set; Calling the energy storage terminal devices whose interval distances between the calling devices in the calling device set meet the calling distance determination conditions; The energy storage capacity upper limit reference value is negatively correlated with the duration of the single profit and loss fluctuation characteristic aggregation time period, and the calling distance determination condition is that the interval distance is greater than a preset interval distance threshold.

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