A comprehensive energy system capacity planning optimization method for zero-carbon park

Abstract

The present application relates to the technical field of energy planning utilization, and particularly relates to a comprehensive energy system capacity planning optimization method for a zero-carbon park, the present application obtains an output power curve and a consumption power curve, divides a plurality of explicit time periods on the output power curve and the consumption power curve respectively, calculates explicit characteristic coefficients of the explicit time periods according to performance parameters of the explicit time periods, screens first profit and loss fluctuation time periods and second profit and loss fluctuation time periods according to analysis results of the output power curve and the consumption power curve under a first division ratio matching mechanism and a second division ratio matching mechanism, determines profit and loss fluctuation characteristic aggregation time periods, and determines an optimized calling mode of an energy storage end device in each profit and loss fluctuation characteristic aggregation time period, thereby, a complex energy supply and demand relationship is captured according to a multi-matching mechanism, and energy storage optimization calling is combined with physical constraints such as device capacity and geographical distribution, so that the overall performance of the energy system is improved.

Description

A Comprehensive Energy System Capacity Planning Optimization Method for Zero-Carbon Industrial Parks Technical Field

[0001] This invention relates to the field of energy planning and utilization technology, and in particular to a method for optimizing the capacity planning of integrated energy systems for zero-carbon industrial parks. Background Technology

[0002] Zero-carbon industrial parks rely primarily on renewable energy sources for energy supply. The intermittent and fluctuating output of energy production capacity, coupled with the temporal differences in energy demand, leads to a significant imbalance between energy supply and demand. Energy storage systems are urgently needed for power balancing. However, traditional integrated energy system capacity planning methods have many shortcomings, resulting in inefficient dispatch schemes and a lack of engineering feasibility. Furthermore, the differences in charging and discharging efficiency and temperature rise characteristics of energy storage devices with different capacities make it difficult for existing energy planning methods to meet the system's multi-objective requirements for efficiency, reliability, and economy.

[0003] For example, Chinese Patent Publication No. CN116432817A discloses a method for optimizing the configuration of an integrated energy system in a park. The method includes the following steps: constructing a construction time series set for the integrated energy system in the park; based on the established construction time series set, using the optimal construction time series method and cloud energy storage mechanism, constructing a two-layer optimized configuration model for the integrated energy system in the park; this invention establishes a power optimization configuration system through configuration models of equipment such as CHP units, electric boilers, and gas boilers, acquires data information within the park, sets an objective function, determines the park's comprehensive optimization objective function based on the objective function and the park's optimization constraints, inputs the comprehensive optimization objective function into a pre-constructed simulated annealing model to seek a scheduling scheme, and during the scheduling process, determines the system's reliable power supply probability through simulation calculations, ultimately determining the planned capacity and operational scheduling values ​​of the integrated energy equipment within the park, and performing integrated energy optimization configuration for the park.

[0004] The following problems still exist in the existing technology:

[0005] Existing technologies cannot accurately identify the key fluctuation phases of production capacity and energy consumption curves, cannot capture complex energy supply and demand relationships based on multi-matching mechanisms, and cannot optimize energy storage deployment by combining physical constraints such as equipment capacity and geographical distribution, thus affecting the overall performance of the energy system. Summary of the Invention

[0006] To address this, the present invention provides a comprehensive energy system capacity planning and optimization method for zero-carbon parks, which overcomes the problems of existing technologies being unable to accurately identify the key fluctuation stages of capacity and energy consumption curves, unable to capture complex energy supply and demand relationships based on multi-matching mechanisms, and unable to optimize energy storage deployment in conjunction with physical constraints such as equipment capacity and geographical distribution.

[0007] To achieve the above objectives, this invention provides a method for comprehensive energy system capacity planning and optimization in zero-carbon industrial parks, comprising:

[0008] Obtain the output power curve of the production-end equipment and the power consumption curve of the energy-consuming equipment;

[0009] Several dominant points are marked on the output power curve and the power consumption curve respectively. Based on the dominant points arranged in time sequence, several dominant time periods are divided on the output power curve and the power consumption curve respectively. The performance parameters of each dominant time period are weighted and calculated to determine the dominant characteristic coefficient of each dominant time period.

[0010] The output power curve and the power consumption curve are analyzed according to the first division comparison mechanism and the second division comparison mechanism, respectively. Based on the analysis results, the first profit and loss fluctuation period and the second profit and loss fluctuation period are selected respectively.

[0011] Among them, the first division comparison mechanism and the second division comparison mechanism have different methods for dividing the output power curve and the power consumption curve;

[0012] Based on the first profit and loss fluctuation period and the second profit and loss fluctuation period, a profit and loss fluctuation feature aggregation period is determined, and within each profit and loss fluctuation feature aggregation period, an optimized calling method for energy storage devices is determined. The optimized calling method includes determining the energy storage devices to be called based on the capacity limit of a single energy storage device and the interval distance between energy storage devices.

[0013] Furthermore, the process of obtaining the output power curve and the power consumption curve includes:

[0014] Obtain the output power of each device on the production side and the power consumption of each device on the energy consumption side at several consecutive moments within a preset time period;

[0015] Plot a fluctuation curve of the sum of the output power of the production capacity end equipment as the vertical axis and time as the horizontal axis, and determine the fluctuation curve as the output power curve.

[0016] Plot a fluctuation curve of the sum of power consumption of energy-consuming devices as the vertical axis and time as the horizontal axis, and determine the fluctuation curve as the power consumption curve.

[0017] Furthermore, the dominant points are marked on the output power curve and the power consumption curve respectively as follows:

[0018] On the output power curve, select several data points whose power values ​​on the vertical axis exceed the preset output power reference value and whose curvature meets the curvature selection criteria, and mark these data points as output dominant points.

[0019] On the power consumption curve, select several data points whose power values ​​on the vertical axis exceed the preset power consumption reference value and whose curvature meets the curvature selection criteria, and mark these data points as power consumption explicit points.

[0020] Furthermore, the process of determining the output dominance characteristic coefficient during the output dominance period on the output power curve includes:

[0021] Sort all output dominant points according to time sequence;

[0022] The time interval between adjacent dominant output points in the sorting is defined as the dominant output time interval;

[0023] Obtain the performance parameters of the output power curve during each output dominance period. The performance parameters include the minimum output power of the power curve on the vertical axis during a single output dominance period and the duration of that output dominance period.

[0024] The result obtained by weighting the minimum output power and the duration is determined as the output dominance characteristic coefficient for the period of output dominance.

[0025] Furthermore, the process of determining the consumption dominance characteristic coefficient during the consumption dominance period on the power consumption curve includes:

[0026] Sort all consumption explicit points in chronological order;

[0027] The time interval between adjacent dominant output points in the sorting is defined as the consumption dominant time interval;

[0028] Obtain the performance parameters of the power consumption curve in each dominant consumption period. The performance parameters include the minimum power consumption of the power curve on the vertical axis in a single dominant consumption period and the duration of that dominant consumption period.

[0029] The result obtained by weighting the minimum power consumption and the duration is determined as the power consumption characteristic coefficient of the power consumption period.

[0030] Furthermore, the process of determining the dominant characteristic coefficients by comparing the output power curve and the power consumption curve according to the first division comparison mechanism includes:

[0031] The output power curve and the power consumption curve are divided into several time periods by a preset time interval, so that the starting point of an output power curve segment and a power consumption curve segment in each time period are the same, and the ending point of an output power curve segment and a power consumption curve segment in each time period are the same.

[0032] Determine the output power curve segment of a single time period, including several output dominant periods and the output dominant characteristic coefficient of each output dominant period; and determine the consumption power curve segment of each time period, including several consumption dominant periods and the consumption dominant characteristic coefficient of each consumption dominant period.

[0033] Calculate the average difference of the output dominant characteristic coefficients for the output power curve segment of a single time period and the average difference of the consumption dominant characteristic coefficients for the consumption power curve segment of that time period.

[0034] Furthermore, the process of selecting the first profit and loss fluctuation period under the first segmentation comparison mechanism includes:

[0035] If the average difference of the output dominant characteristic coefficients in a single time period exceeds the preset threshold for the difference of output dominant characteristic coefficients, or the average difference of the consumption dominant characteristic coefficients exceeds the preset threshold for the difference of consumption characteristic coefficients, then that time period is selected as the first profit and loss fluctuation time period.

[0036] Furthermore, the process of selecting the second profit and loss fluctuation period under the second segmentation comparison mechanism includes:

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

[0038] Furthermore, the time period in which the first profit and loss fluctuation period overlaps with the second profit and loss fluctuation period is determined as the profit and loss fluctuation feature aggregation period.

[0039] Furthermore, the process of optimizing the deployment of energy storage equipment during the period of aggregated profit and loss fluctuations includes:

[0040] Obtain the upper limit of energy storage capacity for each device in the energy storage terminal;

[0041] Energy storage devices whose upper limit of energy storage capacity is less than the reference value of the upper limit of energy storage capacity within a single time period of profit and loss fluctuation characteristics are selected to construct a set of calling devices;

[0042] Call energy storage devices within the set of calling devices whose interval distance between calling devices meets the calling distance determination condition;

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

[0044] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention obtains the output power curve and the power consumption curve, divides the output power curve and the power consumption curve into several manifest time periods, calculates the manifest characteristic coefficient of each manifest time period based on the performance parameters of each manifest time period, and selects the first profit and loss fluctuation time period and the second profit and loss fluctuation time period based on the analysis results of the output power curve and the power consumption curve under the first division comparison mechanism and the second profit and loss fluctuation time period. Based on the first profit and loss fluctuation time period and the second profit and loss fluctuation time period, the profit and loss fluctuation characteristic aggregation time period is determined, and the optimized call method for energy storage equipment is determined within each profit and loss fluctuation characteristic aggregation time period. Thus, the key fluctuation stages of the production capacity and energy consumption curves are accurately identified, complex energy supply and demand relationships are captured based on the multi-matching mechanism, and energy storage is optimized and called in combination with physical constraints such as equipment capacity and geographical distribution, thereby improving the overall performance of the energy system.

[0045] Furthermore, by acquiring power data of each device on the production and consumption sides within a preset time period and plotting output power curves and consumption power curves respectively, this invention can intuitively and accurately present the dynamic changes in energy supply and consumption over time. By marking prominent points on the power curves, key information in power changes can be highlighted. These prominent points represent important states in the energy supply or consumption process, thereby enabling targeted analysis of the characteristics of the energy system at different times.

[0046] Furthermore, this invention sorts the output dominance points and determines the output dominance time periods, then obtains the performance parameters of the output power curve within the time period. The minimum output power and duration in the performance parameters can accurately capture the fluctuation characteristics and the degree of influence of the output power of the production capacity equipment. The output dominance characteristic coefficient is obtained by weighting the minimum output power and the duration, thereby realizing the quantification of the characteristics of the output dominance time period, which facilitates the comparison between different output dominance time periods. In this way, a comprehensive and objective evaluation of the output performance of the production capacity equipment is achieved.

[0047] Furthermore, by determining the explicit consumption periods and their characteristic coefficients, this invention can accurately characterize the load characteristics of energy-consuming equipment in the integrated energy system of a zero-carbon park. By sorting the explicit consumption points by time sequence and dividing them into explicit consumption periods, the complex power consumption curve can be decomposed into multiple time periods with specific characteristics. The two performance parameters, minimum power consumption and duration, describe the energy consumption of each period from the perspectives of power level and time, respectively. Thus, a deeper understanding of the energy demand status of energy-consuming equipment can be achieved.

[0048] Furthermore, this invention divides the output power curve and the consumption power curve into preset time intervals, calculates the average difference of the output dominant characteristic coefficient and the average difference of the consumption dominant characteristic coefficient within each time period, quantifies the degree of power fluctuation, accurately identifies the first profit and loss fluctuation period with large power fluctuations, and determines the time period where the output dominant period on the output power curve and the consumption dominant period on the consumption power curve overlap as the second profit and loss fluctuation period. This intuitively presents the direct conflict between energy supply and consumption in time, thereby achieving accurate identification of the key fluctuation stages of the production capacity and energy consumption curves, and capturing complex energy supply and demand relationships based on a multi-matching mechanism.

[0049] Furthermore, by determining the aggregation time period of profit and loss fluctuation characteristics and optimizing the scheduling of energy storage devices based on this, the present invention can better match the charging and discharging needs of large-capacity and small-capacity energy storage batteries. For large-capacity energy storage batteries, if intermittent charging and discharging occurs during the aggregation time period of profit and loss fluctuation characteristics, the charging and discharging intervals can be used to avoid prolonged continuous heating of the battery, thereby improving its charging and discharging efficiency. For small-capacity energy storage batteries, since the capacity is released before the battery temperature reaches a level that affects efficiency during long-term charging and discharging, the reduction in charging and discharging efficiency caused by prolonged heating can also be avoided. This allows batteries of different capacities to work in a relatively efficient state. By limiting the interval distance between devices within the scheduling device set, the layout of energy storage devices can be optimized, avoiding local overheating caused by devices being too close together during charging and discharging, thereby improving the stability and reliability of the entire energy storage system and ensuring that energy storage batteries of different capacities operate stably in their respective suitable charging and discharging modes. Attached Figure Description

[0050] Figure 1 is a flowchart illustrating the steps of the integrated energy system capacity planning and optimization method for zero-carbon parks according to an embodiment of the present invention;

[0051] Figure 2 is a schematic diagram of marking the output dominance point on the output power curve according to an embodiment of the present invention;

[0052] Figure 3 is a flowchart illustrating the steps for determining the output dominant characteristic coefficients in an embodiment of the present invention;

[0053] Figure 4 is a flowchart illustrating the steps for determining the consumption dominant characteristic coefficient in an embodiment of the present invention;

[0054] Figure 5 is a flowchart illustrating the steps of optimizing and utilizing energy storage devices according to an embodiment of the present invention. Detailed Implementation

[0055] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0056] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0057] It should be noted that in the description of this invention, the terms "upper," "lower," "inner," "outer," etc., which indicate the direction or positional relationship, are based on the direction or positional relationship shown in the drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0058] Please refer to Figure 1, which is a flowchart illustrating the steps of the integrated energy system capacity planning and optimization method for zero-carbon parks according to an embodiment of the present invention. The integrated energy system capacity planning and optimization method for zero-carbon parks according to the present invention includes:

[0059] Step S100: Obtain the output power curve of the production capacity equipment and the power consumption curve of the energy consumption equipment;

[0060] Specifically, the power generation equipment in this invention includes photovoltaic power generation equipment, wind power generation equipment and other new energy power generation equipment, while the energy consumption equipment includes industrial production equipment, building electrical equipment and server equipment, etc., which will not be elaborated here.

[0061] Step S200: Mark several dominant points on the output power curve and the power consumption curve respectively, and divide the output power curve and the power consumption curve into several dominant time periods based on the dominant points arranged in time sequence. Calculate the performance parameters of each dominant time period by weighting to determine the dominant characteristic coefficient of each dominant time period.

[0062] Step S300: Analyze the output power curve and the power consumption curve according to the first division comparison mechanism and the second division comparison mechanism respectively, and select 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 have different division methods for the output power curve and the power consumption curve;

[0064] Step S400: Determine the profit and loss fluctuation feature aggregation time period based on the first profit and loss fluctuation time period and the second profit and loss fluctuation time period, and determine the optimized calling method for energy storage terminal equipment within each profit and loss fluctuation feature aggregation time period. The optimized calling method includes determining the energy storage terminal equipment to be called based on the capacity limit of a single energy storage terminal equipment and the interval distance between energy storage terminal equipment.

[0065] Specifically, the energy storage device in this invention can be an energy storage battery, used to store the electrical energy output by the power generation device and to discharge the energy consumption device during peak electricity consumption periods. The upper limit of the capacity of the energy storage device is the rated capacity of the battery, in kWh.

[0066] It is understandable that zero-carbon industrial parks rely primarily on renewable energy sources, such as solar and wind power. However, energy consumption demands vary over time, such as changes in industrial workloads and building electricity consumption. This dynamic mismatch between supply and demand necessitates power balancing in the energy system. Energy storage devices, acting as intermediaries between power generation and consumption equipment, are frequently used for power balancing. Therefore, accurate dynamic adjustment of energy storage devices in response to dynamic changes in supply and demand is crucial to improving the overall performance of the energy system.

[0067] Large-capacity energy storage batteries generate heat due to their large electrode material volume, high electrolyte storage capacity, and internal resistance (Q=I). 2 Rt increases exponentially with charging and discharging current and duration. If high-frequency charging and discharging is carried out for a long time, heat accumulation will lead to a significant increase in battery temperature, triggering side reactions such as electrolyte decomposition, which will reduce charging and discharging efficiency. However, short-term high-frequency charging and discharging processes provide a heat dissipation window for the battery due to the charging and discharging interval, avoiding a continuous rise in temperature. Small-capacity energy storage batteries have low capacity, and even if they are continuously discharged at the rated current, the total discharge time is short, the energy release cycle is short, and the total amount of heat generated by internal resistance is limited (total heat generation Q ∝ capacity × current square × time). The battery temperature rise is small and the discharge is completed before it reaches a level that affects efficiency.

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

[0069] Obtain the output power of each device on the production side and the power consumption of each device on the energy consumption side at several consecutive moments within a preset time period;

[0070] Specifically, the preset duration can be 24 hours. In practice, the output power of each device on the production side and the power consumption of each device on the energy consumption side can be obtained every 5 minutes.

[0071] Plot a fluctuation curve of the sum of the output power of the production capacity end equipment as the vertical axis and time as the horizontal axis, and determine the fluctuation curve as the output power curve.

[0072] Plot a fluctuation curve of the sum of power consumption of energy-consuming devices as the vertical axis and time as the horizontal axis, and determine the fluctuation curve as the power consumption curve.

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

[0074] On the output power curve, select several data points whose power values ​​on the vertical axis exceed the preset output power reference value and whose curvature meets the curvature selection criteria, and mark these data points as output dominant points.

[0075] On the power consumption curve, select several data points whose power values ​​on the vertical axis exceed the preset power consumption reference value and whose curvature meets the curvature selection criteria, and mark these data points as power consumption explicit points.

[0076] In implementation, the preset output power reference value can be determined based on the maximum total power of the production capacity equipment in the park. The preset output power reference value can be the maximum total power of the production capacity equipment multiplied by the output power factor. In order to ensure that the power value points of the output power curve can be selected based on the preset output power reference value as the points with larger power on the output power curve, the range of the output power factor can be [0.7, 0.9]. Preferably, the output power 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 is selected as the output dominant point.

[0077] Please refer to Figure 2, which is a schematic diagram of marking the output dominance point on the output power curve according to an embodiment of the present invention. This is based on the maximum total power P of the production-side equipment. a The preset output power reference value P is calculated. b =0.8×P a The curve segments on the output power curve whose power values ​​exceed the preset output power reference value are selected, including curve segments with time periods T1, T2, T3, T4 and T5. The points with the maximum curvature within the curve segments with time periods T1, T2, T3, T4 and T5 are the output dominant points, including output dominant points a, a1, a2, a3 and a4.

[0078] Similarly, the preset output power reference value can be determined based on the maximum total power of the energy-consuming equipment in the park. The preset power consumption reference value can be the maximum total power of the energy-consuming equipment multiplied by the power consumption factor. In order to ensure that the power value points of the power consumption curve can be selected based on the preset power consumption reference value, the power consumption factor is set to [0.7, 0.9]. Preferably, the power consumption factor can be 0.8, and the point with the maximum curvature on the curve segment where the power value exceeds the preset power consumption reference value is selected as the power consumption explicit point.

[0079] Specifically, this invention does not limit the method for obtaining the maximum total power of the production capacity equipment and the maximum total power of the energy consumption equipment. These can be determined by those skilled in the art within a preset time period. This invention also does not limit the method for determining the curvature of the curve. The curvature of any point on the curve can be determined by the difference method. This is prior art and will not be elaborated here.

[0080] Specifically, this invention acquires power data from each device on the production and consumption sides within a preset time period and plots output power curves and consumption power curves respectively. This allows for an intuitive and accurate presentation of the dynamic changes in energy supply and consumption over time. By marking prominent points on the power curves, key information in power changes can be highlighted. These prominent points represent important states in the energy supply or consumption process, thereby enabling targeted analysis of the characteristics of the energy system at different times.

[0081] Specifically, please refer to Figure 3, which is a flowchart of the steps for determining the output dominance characteristic coefficient in an embodiment of the present invention. The process of determining the output dominance characteristic coefficient during the output dominance period on the output power curve includes:

[0082] Step S201: Sort all output dominant points according to time sequence;

[0083] For example, the output explicit points can be sorted according to time sequence.

[0084] Step S202: The time period between adjacent output dominant points in the sorting is determined as the output dominant time period;

[0085] Step S203: Obtain the performance parameters of the output power curve during each output dominance period. The performance parameters include the minimum output power of the power curve on the vertical axis during a single output dominance period and the duration of that output dominance period.

[0086] Step S204: The result obtained by weighting the minimum output power and the duration is determined as the output dominance characteristic coefficient for the output dominance period.

[0087] It is understandable that by weighting the minimum output power within a single dominant output period with the duration of that period, the supply capacity of that output range can be comprehensively reflected. The minimum output power reflects the lowest power supply level at the production end during that period, while the duration reflects the duration of the supply status. The weighted result of the two quantifies the supply capacity of the energy system during that period.

[0088] For example, the output dominant characteristic coefficient R1 can be calculated using the formula R1=α×(P 1min The calculation is performed using P(t1 / t0) + β × (t0 / t0), where P1min P0 is the minimum output power, P1 is the duration of the output dominant period, t0 is the duration reference value, α is the power weighting factor, and β is the duration weighting factor. Preferably, the preset output power reference value is the maximum total power of the production end equipment multiplied by the output power value factor. The duration reference value t0 can be 30 min, and α + β = 1. Here, we provide a set of values ​​for the power weighting factor and the duration weighting factor: power weighting factor α = 0.4 and duration weighting factor β = 0.6.

[0089] It is understandable that sorting the output manifest points by time sequence and dividing them into manifest periods is a dynamic processing of the original power curve data. In the integrated energy system of a zero-carbon park, since the output power curves of the production capacity equipment often exhibit irregular fluctuations, the manifest points mark the key nodes in the power change. The time period between adjacent manifest points is a relatively independent dynamic stage in the system operation. Through this division, the continuous power curve can be decomposed into multiple analyzable discrete units, reflecting the dynamic characteristics of the production capacity in the continuous output stage. Selecting the minimum output power and duration as performance parameters is to extract the power change characteristics within the manifest period. The minimum output power can intuitively reflect the minimum output capacity of the production capacity in that period, while the duration reflects the degree of persistence of this power state. When the minimum value is low and the duration is short, it indicates that there is a power value point with a significantly smaller power value between two manifest points with larger power values ​​within a short period of time, that is, the energy supply stability is poor in a short period of time.

[0090] Specifically, this invention sorts the output dominance points and determines the output dominance period, then obtains the performance parameters of the output power curve within the period. The minimum output power and duration in the performance parameters can accurately capture the fluctuation characteristics and the degree of influence of the output power of the production capacity equipment. The minimum output power and duration are weighted to obtain the output dominance characteristic coefficient, which realizes the quantification of the characteristics of the output dominance period, facilitates the comparison between different output dominance periods, and thus enables a comprehensive and objective evaluation of the output performance of the production capacity equipment.

[0091] Specifically, please refer to Figure 4, which is a flowchart of the steps for determining the consumption dominance characteristic coefficient in an embodiment of the present invention. The process of determining the consumption dominance characteristic coefficient during the consumption dominance period on the consumption power curve includes:

[0092] Step S211: Sort all the explicit consumption points in time order;

[0093] Step S212: The time interval between adjacent output dominant points in the sorting is determined as the consumption dominant time interval;

[0094] Step S213: Obtain the performance parameters of the power consumption curve in each power consumption period. The performance parameters include the minimum power consumption of the power curve on the vertical axis in a single power consumption period and the duration of the power consumption period.

[0095] Step S214: The result obtained by weighting the minimum power consumption and the duration is determined as the power consumption characteristic coefficient of the power consumption period.

[0096] Understandably, the consumption visibility characteristic coefficient is a quantitative indicator of the energy demand characteristics of energy-consuming equipment during a consumption visibility period. Its physical meaning lies in the fact that by weighting the minimum power consumption in a single consumption visibility period with the duration of that period, it comprehensively reflects the demand intensity of that high-load electricity consumption range. The minimum power consumption reflects the minimum electricity demand level of the energy-consuming end during that period, and the duration reflects the duration of the demand state. The weighted result of the two quantifies the degree of energy demand during that period.

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

[0098] Understandably, in a zero-carbon industrial park's integrated energy system, the power consumption curves of energy-consuming equipment are typically continuous and complex. Sort all visible consumption points chronologically and define the time intervals between adjacent visible consumption points as visible consumption periods. This is a method of discretizing continuous data. Selecting the minimum power consumption value and duration of the power curve within a single visible consumption period as performance parameters is based on consideration of key elements in the energy consumption process. The minimum power consumption value reflects the lowest power demand of the energy-consuming equipment within that period, embodying the system's lowest energy consumption level during that time. The duration represents the duration of this specific energy consumption state. When the minimum power consumption value is low and the duration is short, it indicates that there is a significantly lower power value point between two visible points with higher power values ​​within a short period, meaning that the stability of energy demand is poor in the short term.

[0099] Specifically, by determining the explicit consumption periods and their characteristic coefficients, this invention can accurately characterize the load characteristics of energy-consuming equipment in a zero-carbon park integrated energy system. By sorting the explicit consumption points by time sequence and dividing them into explicit consumption periods, the complex power consumption curve can be decomposed into multiple time periods with specific characteristics. The two performance parameters, minimum power consumption and duration, describe the energy consumption of each period from the perspectives of power level and time, respectively. Thus, a deeper understanding of the energy demand status of energy-consuming equipment can be achieved.

[0100] Specifically, the process of determining the dominant characteristic coefficients of the output power curve and the power consumption curve according to the first division comparison mechanism includes:

[0101] The output power curve and the power consumption curve are divided into several time periods by a preset time interval, so that the starting point of an output power curve segment and a power consumption curve segment in each time period are the same, and the ending point of an output power curve segment and a power consumption curve segment in each time period are the same.

[0102] Specifically, the starting point of an output power curve segment and the starting point of a power consumption curve segment within each time period are the same, and the ending point of an output power curve segment and the starting point of a power consumption curve segment within each time period are the same. That is, an output power curve segment and a power consumption curve segment within a single time period are synchronized in the time dimension.

[0103] For example, the preset time interval in this invention can be set by those skilled in the art. In order to avoid insufficient monitoring accuracy due to setting the preset time interval too long and lack of data characterization due to setting the preset time interval too short, preferably, the value of the preset time interval can be 1 hour.

[0104] Determine the output power curve segment of a single time period, including several output dominant periods and the output dominant characteristic coefficient of each output dominant period; and determine the consumption power curve segment of each time period, including several consumption dominant periods and the consumption dominant characteristic coefficient of each consumption dominant period.

[0105] Calculate the average difference of the output dominant characteristic coefficients for the output power curve segment of a single time period and the average difference of the consumption dominant characteristic coefficients for the consumption power curve segment of that time period.

[0106] For example, the output dominance characteristic coefficients of each output dominance period included in the output power curve segment of a single time period are obtained, and the average difference between the output dominance characteristic coefficients of the output dominance period is calculated as the average difference of the output dominance characteristic coefficients. The consumption dominance characteristic coefficients of each consumption dominance period included in the consumption power curve segment of a single time period are obtained, and the average difference between the consumption dominance characteristic coefficients of the consumption dominance period is calculated as the average difference of the consumption dominance characteristic coefficients.

[0107] Specifically, the process of selecting the first profit and loss fluctuation period under the first segmentation comparison mechanism includes:

[0108] If the average difference of the output dominant characteristic coefficients in a single time period exceeds the preset threshold for the difference of output dominant characteristic coefficients, or the average difference of the consumption dominant characteristic coefficients exceeds the preset threshold for the difference of consumption characteristic coefficients, then that time period is selected as the first profit and loss fluctuation time period.

[0109] For example, the preset output feature coefficient difference threshold ranges from [0.1, 0.2], and the preset consumption feature coefficient difference threshold ranges from [0.1, 0.2]. Here, we provide a set of values ​​for the output feature coefficient difference threshold and the consumption feature coefficient difference threshold. The preset consumption feature coefficient difference threshold can be 0.15.

[0110] Specifically, the process of selecting the second profit and loss fluctuation period under the second segmentation comparison mechanism includes:

[0111] The time period that overlaps between the output dominant period on the output power curve and the consumption dominant period on the consumption power curve is defined as the second profit and loss fluctuation period.

[0112] In practice, the method for determining the overlapping time period between the output dominant period on the output power curve and the consumption dominant period on the consumption power curve in this invention can be based on the start and end times of the output dominant period and the consumption dominant period. The start and end times of the overlapping period are then used to determine the time period constructed from the start and end times of the overlapping period as the second profit and loss fluctuation time period.

[0113] It is understood that the first division and comparison mechanism in this invention divides the output power curve and the consumption power curve into several time periods by a preset time interval. The principle of this division method is based on the consideration of the time scale of energy system operation. The second division and comparison mechanism determines the time period that overlaps 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. This division method starts from the direct correspondence between energy supply and demand, and thus accurately locates the time period of energy supply and demand fluctuation.

[0114] Specifically, by dividing the output power curve and the consumption power curve into preset time intervals, the average difference of the output dominant characteristic coefficient and the average difference of the consumption dominant characteristic coefficient within each time period are calculated to quantify the degree of power fluctuation. The first profit and loss fluctuation period with larger power fluctuations is accurately identified, and the time period that overlaps 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 period. This intuitively presents the direct conflict between energy supply and consumption in time, thereby achieving accurate identification of the key fluctuation stages of the production capacity and energy consumption curves and capturing complex energy supply and demand relationships based on a multi-matching mechanism.

[0115] Specifically, the time period in which the first profit and loss fluctuation period overlaps with the second profit and loss fluctuation period is determined as the profit and loss fluctuation feature aggregation period.

[0116] Specifically, determining the overlapping time period of the two time periods is an existing technique, which will not be elaborated here.

[0117] Specifically, please refer to Figure 5, which is a flowchart of the steps for optimizing the use of energy storage devices according to an embodiment of the present invention. The process of optimizing the use of energy storage devices during the period of aggregated profit and loss fluctuation characteristics includes:

[0118] Step S401: Obtain the upper limit of energy storage capacity of each device in the energy storage terminal;

[0119] Step S402: Select energy storage devices whose upper limit of energy storage capacity is less than the upper limit reference value of energy storage capacity within a single profit and loss fluctuation characteristic aggregation period to construct a set of calling devices;

[0120] Specifically, the device number is called from the set of devices that include several energy storage devices whose upper limit of energy storage capacity is less than the reference value of the upper limit of energy storage capacity. The device number is prepared in advance by those skilled in the art and will not be described in detail here.

[0121] Step S403: Call the energy storage device whose interval distance between the calling devices in the calling device set meets the calling distance determination condition;

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

[0123] For example, the reference value A for the upper limit of energy storage capacity can be determined based on the maximum upper limit A of the energy storage capacity of the energy storage terminal device. max The upper limit reference value of energy storage capacity can be determined as follows: A = [(t0 / t')-1]×A maxWhere t' is the duration of the profit and loss fluctuation feature aggregation period, and t0 is the reference value of the duration. Since t' is the period when 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 based on the average interval distance between calling devices within the calling device set. The interval distance threshold is the product of the interval distance factor and the average interval distance. The range of the interval distance factor is [0.5, 0.7]. Preferably, the value of the interval distance factor is 0.6.

[0124] Understandably, by obtaining the upper limit of the energy storage capacity of each device at the energy storage end, and setting a negatively correlated reference value for the upper limit of the energy storage capacity based on the duration of the period of profit and loss fluctuation, the set of devices to be built and called can be selected. This allows for the rational allocation of energy storage resources according to actual charging and discharging needs. For periods of profit and loss fluctuation with a longer duration, the reference value for the upper limit of the energy storage capacity is lower, and smaller capacity energy storage batteries will be prioritized for continuous long-term charging and discharging. This avoids the overheating of large capacity energy storage batteries due to continuous long-term charging and discharging, which would reduce efficiency. For shorter periods, large capacity energy storage batteries have intervals for heat dissipation during short-term intermittent charging and discharging, giving full play to their stable high-capacity charging and discharging power characteristics, and enabling energy storage batteries of different capacities to operate stably in their respective suitable charging and discharging modes.

[0125] Specifically, this invention, by determining the aggregation time period of profit and loss fluctuation characteristics and optimizing the scheduling of energy storage devices based on this, can better match the charging and discharging needs of large-capacity and small-capacity energy storage batteries. For large-capacity energy storage batteries, if intermittent charging and discharging occurs within the aggregation time period of profit and loss fluctuation characteristics, the charging and discharging intervals can be used to prevent the battery from continuously heating up for a long time, thereby improving its charging and discharging efficiency. For small-capacity energy storage batteries, since the capacity is released before the battery temperature reaches a level that affects efficiency during long-term charging and discharging, the reduction in charging and discharging efficiency caused by prolonged heating can also be avoided. This allows batteries of different capacities to work in a relatively efficient state. By limiting the interval distance between devices within the scheduling device set, the layout of energy storage devices can be optimized, avoiding local overheating caused by devices being too close together during charging and discharging. This improves the stability and reliability of the entire energy storage system and ensures that energy storage batteries of different capacities operate stably in their respective suitable charging and discharging modes.

[0126] This embodiment also provides a computer-readable storage medium storing computer program code. When the computer program code is run on a computer, the computer executes the above-described related method steps to implement the integrated energy system capacity planning and optimization method for zero-carbon parks described in the above embodiment.

[0127] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0128] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for comprehensive energy system capacity planning and optimization in zero-carbon industrial parks, characterized in that, include: Obtain the output power curve of the production capacity equipment and the power consumption curve of the energy consumption equipment; mark several dominant points on the output power curve and the power consumption curve respectively, and divide the output power curve and the power consumption curve into several dominant time periods based on the dominant points arranged in time sequence; calculate the performance parameters of each dominant time period by weighting to determine the dominant characteristic coefficient of each dominant time period; the method of marking dominant points on the output power curve and the power consumption curve respectively is as follows: select several data points on the output power curve whose power values ​​on the vertical axis exceed the preset output power reference value and whose curvature meets the curvature screening conditions, and mark the data points as output dominant points; On the power consumption curve, select several data points whose power values ​​on the vertical axis exceed the preset power consumption reference value and whose curvature meets the curvature selection criteria, and mark these data points as power consumption explicit points. The process of determining the output dominance characteristic coefficient of the output dominance period on the output power curve includes: sorting all output dominance points in chronological order; determining the time interval between adjacent output dominance points in the sort as the output dominance period; obtaining the performance parameters of the output power curve within each output dominance period, the performance parameters including the minimum output power of the power curve on the vertical axis and the duration of the output dominance period; determining the output dominance characteristic coefficient of the output dominance period by weighting the minimum output power and the duration; and comparing the output power curve and the power consumption curve according to a first division comparison mechanism and a second division comparison mechanism, respectively. Analysis, based on the analysis results, select the first profit and loss fluctuation period and the second profit and loss fluctuation period respectively; determine the dominant feature coefficients of the output power curve and the power consumption curve according to the first division comparison mechanism, and select the first profit and loss fluctuation period based on the dominant feature coefficients under the first division comparison mechanism. The process includes: dividing the output power curve and the power consumption curve into several time periods by a preset time interval, so that the starting point of an output power curve segment and a power consumption curve segment in each time period are the same, and the ending point of an output power curve segment and a power consumption curve segment in each time period are the same; determining the several output power curve segments contained in a single time period. The system identifies the dominant output period and the output dominant characteristic coefficients for each dominant output period, as well as the dominant consumption period and its characteristic coefficients for each dominant consumption period within the power consumption curve segment of each time period. It calculates the average difference in the output dominant characteristic coefficients of the output power curve segment for a single time period and the average difference in the dominant consumption characteristic coefficients of the power consumption curve segment for that time period. If the average difference in the output dominant characteristic coefficients of a single time period exceeds a preset threshold for output characteristic coefficient differences, or if the average difference in the dominant consumption characteristic coefficients exceeds a preset threshold for the dominant consumption characteristic coefficients, then that time period is selected as the first profit and loss fluctuation period. The system then considers the first profit and loss fluctuation period and the second profit and loss fluctuation period... The process involves determining the period for aggregated profitability fluctuation characteristics, and within each aggregated period, determining the optimal deployment method for energy storage devices. This optimal deployment method includes determining the deployed energy storage devices based on the capacity limit of a single device and the distance between devices. The process of optimizing the deployment of energy storage devices within the aggregated profitability fluctuation characteristic period includes: obtaining the upper limit of the energy storage capacity of each device; filtering out energy storage devices whose upper limit is less than a reference value within a single aggregated profitability fluctuation characteristic period to construct a set of deployed devices; and deploying energy storage devices within the set whose distance between deployed devices meets the deployment distance criteria.The upper limit reference value of the energy storage capacity is negatively correlated with the duration of the aggregated period of the single profit and loss fluctuation characteristic. The call distance determination condition is that the interval distance is greater than a preset interval distance threshold. The process of selecting the second profit and loss fluctuation period under the second division and comparison mechanism includes: determining the time period that overlaps 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 determining the time period that overlaps between the first profit and loss fluctuation period and the second profit and loss fluctuation period as the aggregated period of the profit and loss fluctuation characteristic.

2. The integrated energy system capacity planning and optimization method for zero-carbon industrial parks according to claim 1, characterized in that, The process of obtaining the output power curve and the power consumption curve includes: obtaining the output power of each device on the production end and the power consumption of each device on the energy consumption end at several consecutive moments within a preset time period; plotting the fluctuation curve of the sum of the output power of the production end devices as the vertical axis and time as the horizontal axis, and determining the fluctuation curve as the output power curve; plotting the fluctuation curve of the sum of the power consumption of the energy consumption end devices as the vertical axis and time as the horizontal axis, and determining the fluctuation curve as the power consumption curve.

3. The integrated energy system capacity planning and optimization method for zero-carbon industrial parks according to claim 2, characterized in that, The process of determining the consumption dominance characteristic coefficient of the consumption dominance period on the consumption power curve includes: sorting all consumption dominance points in chronological order; determining the time period between adjacent consumption dominance points in the sort as the consumption dominance period; obtaining the performance parameters of the consumption power curve in each consumption dominance period, the performance parameters including the minimum consumption power of the power curve on the vertical axis in a single consumption dominance period and the duration of the consumption dominance period; and determining the consumption dominance characteristic coefficient of the consumption dominance period by weighting the minimum consumption power and the duration.

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