Methods, devices, electronic equipment and storage media for assessing the peak-shaving potential of power grids

Abstract

This invention discloses a method, device, electronic equipment, and storage medium for assessing the peak-shaving potential of a power grid, addressing the problem of insufficient comprehensiveness in the scheduling evaluation of existing power grid peak-shaving methods. The method includes: acquiring output prediction data of a microgrid system and constructing peak-shaving effect indices for summer and winter respectively; constructing an objective function corresponding to the summer indoor cooling scenario and constructing a summer multi-objective optimization model based on the summer peak-shaving effect indices; optimizing the summer multi-objective optimization model to obtain the summer peak-shaving solution set and solving for the summer optimized scheduling result; constructing an objective function corresponding to the winter indoor cooling scenario and constructing a winter multi-objective optimization model based on the winter peak-shaving effect indices; optimizing the winter multi-objective optimization model to obtain the winter peak-shaving solution set and solving for the winter optimized scheduling result; and conducting a comprehensive evaluation based on the summer and winter optimized scheduling results to obtain a comprehensive annual evaluation result of the participation of large-scale cooling equipment in peak shaving within the microgrid system.

Description

Technical Field

[0001] This invention relates to the field of electricity market and demand-side response technology, and in particular to a method, apparatus, electronic device and storage medium for assessing the peak-shaving potential of a power grid. Background Technology

[0002] With the continuous growth of electricity demand, the load pressure on the power grid during peak hours is increasing. Refrigeration equipment, as a large-scale power load, significantly impacts the safe and stable operation of the power grid due to its fluctuations. Therefore, including refrigeration equipment in the power grid's peak-shaving scope is of great significance for improving the reliability and economy of power supply. Large-scale refrigeration equipment, due to its inherent adjustability, can effectively reduce the load pressure on the power grid during peak hours through reasonable control strategies, while ensuring that the needs of daily life and production are met.

[0003] However, existing methods for assessing the peak-shaving capacity of refrigeration equipment have significant shortcomings. First, traditional methods typically rely on historical data and static models for assessment, lacking a deep understanding and comprehensive consideration of the load characteristics of refrigeration equipment. This results in an inability to accurately reflect the dynamic changes and uncertainties in the load of refrigeration equipment. Second, existing methods ignore the complexity and diversity of refrigeration equipment systems. In reality, different types of refrigeration equipment differ in performance, energy consumption, and control capabilities, leading to varying effectiveness in grid peak shaving. Furthermore, factors such as climate conditions, building structure, user habits, and the intended use of refrigeration equipment also affect its load characteristics, limiting the universality and accuracy of the assessment results. In addition, existing methods do not fully explore the peak-shaving potential of large-scale refrigeration equipment. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and storage medium for assessing the peak-shaving potential of a power grid, which solves or partially solves the technical problem that the scheduling assessment of existing power grid peak-shaving methods is not comprehensive enough.

[0005] This invention provides a method for assessing the peak-shaving potential of a power grid, applicable to microgrid systems with large-scale refrigeration equipment participating in peak shaving. The method includes:

[0006] Obtain the power output prediction data of the microgrid system, and construct summer peak shaving effect index and winter peak shaving effect index based on the power output prediction data;

[0007] Construct a first objective function corresponding to the summer indoor cooling scenario, and construct a summer multi-objective optimization model based on the summer peak-shaving effect index and the first objective function;

[0008] The summer multi-objective optimization model is optimized to obtain the summer peak shaving solution set, and the summer optimization scheduling result of the summer peak shaving solution set is solved.

[0009] A second objective function is constructed for the indoor cooling scenario in winter. Based on the winter peak-shaving effect index and the second objective function, a multi-objective optimization model for winter is constructed.

[0010] The winter multi-objective optimization model is optimized to obtain the winter peak shaving solution set, and the winter optimization scheduling result of the winter peak shaving solution set is solved.

[0011] Based on the summer and winter optimized scheduling results, a comprehensive evaluation is conducted to obtain the annual comprehensive evaluation result of the participation of large-scale refrigeration equipment in peak shaving in the microgrid system.

[0012] Optionally, the power output forecast data includes summer load forecast data and winter load forecast data. The summer load forecast data includes a first maximum load value and a first minimum load value, and the winter load forecast data includes a second maximum load value and a second minimum load value. Constructing summer peak-shaving effect indicators and winter peak-shaving effect indicators based on the power output forecast data includes:

[0013] Calculate the first net load peak-to-valley difference based on the first maximum load value and the first minimum load value;

[0014] Based on the summer load forecast data, calculate the first load change per hour, and based on the first load change, calculate the first load fluctuation information;

[0015] After normalizing the first net load peak-valley difference and the first load waveform information, a summer peak-shaving effect index is constructed.

[0016] Calculate the second net load peak-to-valley difference based on the second maximum load value and the second minimum load value;

[0017] Based on the winter load forecast data, calculate the second load change between hourly loads, and based on the second load change, calculate the second load fluctuation information;

[0018] After normalizing the second net load peak-valley difference and the second load waveform information, a winter peak-shaving effect index is constructed.

[0019] Optionally, the first objective function for constructing the indoor cooling scenario in summer includes:

[0020] Initialize and build the system operation mode for a summer indoor cooling scenario;

[0021] Construct a user cooling satisfaction index and jointly constrain the indoor temperature and the user cooling satisfaction index to adjust the output of large-scale refrigeration equipment.

[0022] Based on the user cooling satisfaction index, a first objective function corresponding to the summer indoor cooling scenario is constructed.

[0023] Optionally, constructing a summer multi-objective optimization model based on the summer peak-shaving effect index and the first objective function includes:

[0024] Simultaneously considering user satisfaction with indoor cooling in summer and the peak-shaving effect of large-scale refrigeration equipment, a multi-objective optimization model for summer is constructed based on the summer peak-shaving effect index and the first objective function.

[0025] Optionally, optimizing the summer multi-objective optimization model to obtain the summer peak-shaving solution set includes:

[0026] The summer target optimization model is solved iteratively, and during the iterative solution process:

[0027] Determine whether the preset convergence threshold has been reached at the current iteration number. If yes, obtain the optimal solution with the current weight. If no, increment the iteration number by 1 and continue iterating until convergence is reached, or the total number of iterations is reached.

[0028] Determine if the current iteration number is greater than or equal to the total iteration number. If yes, output the first Pareto solution set of user cooling satisfaction and the peak-shaving effect of large-scale refrigeration equipment under all weights, as the summer peak-shaving solution set. If no, increment the iteration number by 1 and continue iterating until convergence is reached, or the total iteration number is reached.

[0029] Optionally, the step of solving for the summer peak shaving solution set and obtaining the summer optimal scheduling result includes:

[0030] The solution set curve of the summer peak shaving solution set is fitted by the least squares method, and then solved simultaneously with the first straight line equation. The number of solutions is made unique, and the solution with the highest satisfaction is obtained as the summer optimal scheduling result under the summer optimal scheme.

[0031] Optionally, the construction of the second objective function corresponding to the winter indoor cooling scenario includes:

[0032] Initialize and build the system operation mode for indoor cooling in winter;

[0033] Determine the storage temperature range for frozen products in cold storage and construct logistics pressure indicators;

[0034] Based on the aforementioned logistics pressure index and combined with the frozen product sales weighting coefficient, a second objective function corresponding to the indoor cooling scenario in winter is constructed.

[0035] Optionally, the step of constructing a multi-objective optimization model for winter based on the winter peak-shaving effect index and the second objective function includes:

[0036] Simultaneously considering the sales pressure index of frozen products and the peak-shaving effect of large-scale refrigeration equipment in the indoor cooling scenario during winter, a multi-objective optimization model for winter is constructed based on the winter peak-shaving effect index and the second objective function.

[0037] Optionally, optimizing the winter multi-objective optimization model to obtain the winter peak-shaving solution set includes:

[0038] The winter target optimization model is solved iteratively, and during the iterative solution process:

[0039] Determine whether the preset convergence threshold has been reached at the current iteration number. If yes, obtain the optimal solution with the current weight. If no, increment the iteration number by 1 and continue iterating until convergence is reached, or the total number of iterations is reached.

[0040] Determine if the current iteration number is greater than or equal to the total iteration number. If yes, output the second Pareto solution set with all weights of frozen product sales pressure index and the peak-shaving effect of large refrigeration equipment as the winter peak-shaving solution set. If no, increment the iteration number by 1 and continue iterating until convergence is reached, or the total iteration number is reached.

[0041] Optionally, the step of solving for the winter peak-shaving solution set and obtaining the winter optimal scheduling result includes:

[0042] The solution set curve of the winter peak shaving solution set is fitted by the least squares method, and then solved simultaneously with the second linear equation. The number of solutions is made unique, and the solution with the highest satisfaction is obtained as the winter optimal scheduling result under the winter optimal scheme.

[0043] Optionally, the summer optimized scheduling result includes the first optimized net load peak-to-valley difference and the first optimized load fluctuation, and the winter optimized scheduling result includes the second optimized net load peak-to-valley difference and the second optimized load fluctuation. The step of comprehensively evaluating the summer and winter optimized scheduling results to obtain the annual comprehensive evaluation result of the participation of large-scale refrigeration equipment in peak shaving within the microgrid system includes:

[0044] Obtain the net load peak-to-valley difference and load fluctuation before the first optimization in the summer indoor cooling scenario;

[0045] Based on the first optimized net load peak-valley difference, the first optimized load fluctuation, the first optimized net load peak-valley difference and the first optimized load fluctuation, the summer peak shaving effect is quantified to obtain the summer peak shaving quantification result.

[0046] Obtain the net load peak-to-valley difference and load fluctuation before the second optimization in the winter indoor cooling scenario;

[0047] Based on the second optimized net load peak-valley difference, the second optimized load fluctuation, the second optimized net load peak-valley difference before the second optimization, and the second optimized load fluctuation, the winter peak-shaving effect is quantified to obtain the winter peak-shaving quantification result.

[0048] Taking into account the summer and winter cooling load usage scenarios of the microgrid system, and based on the summer peak shaving quantification results and the winter peak shaving quantification results, a comprehensive evaluation of the effect of large-scale refrigeration equipment participating in peak shaving throughout the year is conducted to obtain the comprehensive evaluation result for the whole year.

[0049] Optionally, the method further includes:

[0050] Based on the summer and winter day scenarios of the designated scheduling area, a microgrid system with large-scale refrigeration equipment participating in peak shaving is constructed, and a large amount of day-ahead scenario data of the microgrid system is collected.

[0051] Typical days and corresponding power output prediction data are selected from the current day scenario data using a forward iterative search method. The typical days include summer typical days and winter typical days.

[0052] Optionally, the method further includes:

[0053] Obtain the indoor set temperature and the outdoor measured temperature corresponding to the large-scale refrigeration equipment, and calculate the power consumption of the large-scale refrigeration equipment based on the indoor set temperature and the outdoor measured temperature.

[0054] Based on the power consumption and the energy efficiency ratio of the large-scale refrigeration equipment, the output cooling power is calculated, and the cooling power output constraint corresponding to the output cooling power is constructed.

[0055] The interactive power constraints between the microgrid and the distribution network are obtained, and the unit output of each unit in the microgrid system and the electricity load of each user are extracted from the output prediction data.

[0056] Based on the unit output, the electrical load, the power consumption, and the interactive power constraints, an electrical power balance constraint is constructed.

[0057] Obtain the total cooling power of large-scale refrigeration equipment in the microgrid system, and construct cooling power constraints within the microgrid based on the total cooling power;

[0058] The cold power output constraint, the electric power balance constraint, and the cold power constraint within the microgrid are used as multi-objective constraints for the microgrid system. These multi-objective constraints are used to apply power balance constraints to the microgrid system when optimizing the summer multi-objective optimization model and the winter multi-objective optimization model.

[0059] This invention also provides a power grid peak-shaving potential assessment device, applied to a microgrid system in which large-scale refrigeration equipment participates in peak shaving, the device comprising:

[0060] The peak shaving effect index construction module is used to obtain the power output prediction data of the microgrid system and construct the summer peak shaving effect index and the winter peak shaving effect index based on the power output prediction data.

[0061] The summer multi-objective optimization model construction module is used to construct the first objective function corresponding to the summer indoor cooling scenario, and to construct the summer multi-objective optimization model based on the summer peak-shaving effect index and the first objective function.

[0062] The summer optimization scheduling solution module is used to optimize the summer multi-objective optimization model, obtain the summer peak shaving solution set, and solve the summer optimization scheduling result of the summer peak shaving solution set.

[0063] The winter multi-objective optimization model construction module is used to construct the second objective function corresponding to the winter indoor cooling scenario, and construct the winter multi-objective optimization model based on the winter peak-shaving effect index and the second objective function.

[0064] The winter optimization scheduling solution module is used to optimize the winter multi-objective optimization model, obtain the winter peak shaving solution set, and solve the winter optimization scheduling result of the winter peak shaving solution set.

[0065] The comprehensive evaluation module is used to conduct a comprehensive evaluation based on the summer optimization scheduling results and the winter optimization scheduling results to obtain the annual comprehensive evaluation results of the participation of large-scale refrigeration equipment in peak shaving in the microgrid system.

[0066] The present invention also provides an electronic device, the device comprising a processor and a memory:

[0067] The memory is used to store program code and transmit the program code to the processor;

[0068] The processor is used to execute the power grid peak-shaving potential assessment method as described above, according to the instructions in the program code.

[0069] The present invention also provides a computer-readable storage medium for storing program code for executing the power grid peak-shaving potential assessment method as described in any of the preceding claims.

[0070] As can be seen from the above technical solutions, the present invention has the following advantages:

[0071] This paper presents a method for assessing the peak-shaving potential of large-scale cooling equipment in microgrid systems. The method involves acquiring power output forecast data of the microgrid system and constructing peak-shaving performance indicators for summer and winter. An objective function for the summer indoor cooling scenario is constructed, and a summer multi-objective optimization model is built based on the summer peak-shaving performance indicators. The summer multi-objective optimization model is optimized to obtain the summer peak-shaving solution set, and the summer optimal scheduling result is solved. Similarly, an objective function for the winter indoor cooling scenario is constructed, and a winter multi-objective optimization model is built based on the winter peak-shaving performance indicators. The winter multi-objective optimization model is optimized to obtain the winter peak-shaving solution set, and the winter optimal scheduling result is solved. A comprehensive evaluation is performed based on the summer and winter optimal scheduling results to obtain a comprehensive annual evaluation result for the participation of large-scale cooling equipment in peak shaving within the microgrid system. This method comprehensively considers the comprehensive evaluation indicators under both summer and winter indoor cooling scenarios, constructs corresponding multi-optimization models, obtains the optimal scheduling results under the best schemes in summer and winter through optimization calculations, and further combines the two optimal scheduling results for a more comprehensive and accurate annual comprehensive evaluation result for the participation of large-scale cooling equipment in peak shaving within the microgrid system. Attached Figure Description

[0072] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0073] Figure 1 A flowchart illustrating the steps of a method for assessing the peak-shaving potential of a power grid;

[0074] Figure 2 A schematic diagram of the basic architecture of a microgrid system dominated by a refrigeration-storage device;

[0075] Figure 3(a) shows a summer photovoltaic and wind power output prediction curve;

[0076] Figure 3(b) shows a prediction curve of photovoltaic and wind power output in winter;

[0077] Figure 4 This is a schematic diagram comparing temperature curves in various environments in southern China during the summer.

[0078] Figure 5 A schematic diagram of Pareto frontier curves showing the relationship between summer user satisfaction with cooling and the degree of fluctuation.

[0079] Figure 6(a) is a schematic diagram of the optimized scheduling power balance under a summer optimal scheme;

[0080] Figure 6(b) is a schematic diagram of the optimized scheduling peak shaving and valley filling results under an optimal summer scheme;

[0081] Figure 7 This is a schematic diagram comparing the average winter temperatures in southern and northern China.

[0082] Figure 8 A schematic diagram of Pareto front fitting between a winter logistics pressure index and its fluctuation degree;

[0083] Figure 9(a) is a schematic diagram of the optimized scheduling power balance under an optimal winter scheme;

[0084] Figure 9(b) is a schematic diagram of the results of optimized scheduling for peak shaving and valley filling under an optimal winter scheme;

[0085] Figure 10 This is a structural block diagram of a power grid peak-shaving potential assessment device. Detailed Implementation

[0086] This invention provides a method, apparatus, electronic device, and storage medium for assessing the peak-shaving potential of a power grid, which addresses or partially addresses the technical problem that existing power grid peak-shaving methods are not comprehensive enough in their scheduling assessment.

[0087] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0088] As an example, traditional methods for assessing the peak-shaving capacity of refrigeration equipment typically rely on historical data and static models. However, this approach lacks a deep understanding and comprehensive consideration of the load characteristics of refrigeration equipment, resulting in an inability to accurately reflect the dynamic changes and uncertainties in the load. Existing methods also ignore the complexity and diversity of refrigeration equipment systems. In reality, different types of refrigeration equipment vary in performance, energy consumption, and control capabilities, leading to varying effectiveness in grid peak shaving. Furthermore, factors such as climate conditions, building structure, user habits, and the intended use of refrigeration equipment also affect its load characteristics, limiting the universality and accuracy of the assessment results. In addition, existing methods do not fully explore the peak-shaving potential of large-scale refrigeration equipment.

[0089] This invention, through further analysis, argues that in practical applications, the reduction or transfer of load from refrigeration equipment, as a controllable load, can effectively alleviate grid pressure. However, existing methods primarily focus on the amount of load reduction, neglecting the possibility of further tapping peak-shaving potential through optimized control strategies and improved energy efficiency. Therefore, existing methods for assessing the peak-shaving potential and capability of refrigeration equipment urgently need improvement. To fully leverage the role of refrigeration equipment in grid peak shaving, it is necessary to establish a more accurate and comprehensive assessment system and methodology.

[0090] Therefore, one of the core inventive points of this invention is to provide a method for evaluating the annual peak-shaving potential of large-scale refrigeration equipment, addressing the shortcomings of existing technologies. First, a forward iterative search method is used to select typical summer and winter days from a large number of day-ahead scenarios, and peak-shaving effect indices for summer and winter are constructed based on net load peak-to-valley difference and load fluctuation information, respectively. Second, unit constraints, power constraints, and cooling power-related constraints after the large-scale refrigeration equipment is connected to the power grid are listed. Then, a cooling satisfaction index is constructed based on summer meteorological data, and the Pareto fronts of the summer peak-shaving effect index and cooling satisfaction index are obtained and fitted using the least squares method. These are then solved simultaneously with the linear equation to obtain the optimal scheduling result under the optimal summer scheme. Simultaneously, a logistics pressure index is constructed based on winter meteorological data, and the Pareto fronts of the winter peak-shaving effect index and logistics pressure index are obtained and fitted using the least squares method. These are then solved simultaneously with the linear equation to obtain the optimal scheduling result under the optimal winter scheme. Finally, considering the cooling load usage scenarios in summer and winter, and based on the summer and winter optimal scheduling results, a comprehensive evaluation of the annual peak-shaving effect of large-scale refrigeration equipment is conducted to obtain the overall annual evaluation result. By adopting the technical solution of this invention, the peak-shaving potential of large-scale refrigeration equipment can be comprehensively evaluated from multiple aspects, overcoming the problem that traditional methods ignore the complexity and diversity of refrigeration equipment systems. At the same time, this invention also comprehensively considers four factors: climate conditions, building structure, user habits, and the purpose of the refrigeration equipment, providing more accurate calculation results of the peak-shaving potential of refrigeration equipment and providing a more accurate backup source for the grid's emergency demand response.

[0091] Reference Figure 1 This document illustrates a flowchart of a method for assessing the peak-shaving potential of a power grid, provided by an embodiment of the present invention. The method is applied to a microgrid system where large-scale refrigeration equipment participates in peak shaving, and specifically includes the following steps:

[0092] Step 101: Obtain the power output prediction data of the microgrid system, and construct summer peak shaving effect index and winter peak shaving effect index based on the power output prediction data.

[0093] This invention uses the southern region of China as an example to illustrate the peak-shaving assessment. Before proceeding with the actual assessment steps, a microgrid system is first constructed based on summer and winter day scenarios in southern China. Then, a forward search method is used to select typical days from a large number of collected day-ahead scenarios as the predicted values ​​for solar photovoltaic, wind power output, and base load. Finally, a comprehensive evaluation index is established to assess the peak-shaving and valley-filling effects of large-scale cooling equipment.

[0094] In the specific implementation, firstly, based on the summer and winter day scenarios of the designated scheduling area, a microgrid system with large-scale cooling equipment participating in peak shaving is constructed, and a large amount of day-ahead scenario data of the microgrid system is collected.

[0095] For example, Figure 2 A schematic diagram of the basic architecture of a microgrid system dominated by refrigeration and cold storage equipment is shown. Here, PCC (Point of Common Coupling) represents the common connection point, and P... G P represents the prime mover power of a distribution transformer. WT P represents the mechanical power of the wind turbine rotor. PV P represents the output power of the photovoltaic array. AC P represents the aggregate power of an AC-load chiller. load This indicates the power load of the users. (Combined with...) Figure 2 The microgrid divides the load into two parts: one part is the aggregated power P of the chiller load participating in peak shaving and valley filling. AC The total power of the remaining loads is denoted as P. load Ignore line losses.

[0096] By using a forward iterative search method, typical days are selected from a large amount of day-ahead scenario data, along with the corresponding power output forecast data (which mainly includes the predicted values ​​of solar photovoltaic, wind power output, and base load within the day).

[0097] Given the differences in climate between summer and winter, a typical day can include both typical summer days and typical winter days. The predicted output of photovoltaic (PV) and wind power also differs between summer and winter scenarios. For example, the predicted output curves for summer PV and wind power are shown in Figure 3(a), and those for winter are shown in Figure 3(b). In both Figure 3(a) and Figure 3(b), time (in hours, h) is used as the horizontal axis, and power (in megawatts, MW) is used as the vertical axis. The blue dashed line represents the predicted wind power output, and the red solid line represents the predicted PV output.

[0098] The following section describes the specific construction process of the comprehensive evaluation index for peak shaving effect (referred to as the peak shaving effect index in this embodiment of the invention).

[0099] First, the peak-to-valley difference in net load needs to be calculated:

[0100]

[0101] Among them, P max P represents the maximum load value. min The minimum load; the absolute peak-to-valley difference P of the load. pvd It reflects the difference between the peak and valley extreme values ​​of the load. The smaller the absolute peak-valley difference, the smaller the maximum absolute deviation of the local load.

[0102] Next, it is necessary to calculate load fluctuation information (also known as load fluctuation degree information). First, calculate the hourly load change ΔP, and based on the load change, calculate the load fluctuation information:

[0103]

[0104] Where T is the total scheduling time; This indicates the search for the first and second norms of ΔP; P l.t The load value for the t-th sample; load fluctuation information P var Reflecting the overall fluctuation of the load, this information includes the total amount, overall amplitude, standard deviation, and maximum amplitude of the net load change. w1, w2, and w3 are the weighting coefficients for each fluctuation information in formula (3), which can be set according to actual needs. When setting them, the following formula should be met:

[0105] w1+w2+w3=1 (4)

[0106] The absolute peak-to-valley difference is used to evaluate the local peak-shaving and valley-filling effect based on the extreme values ​​of peak and valley loads. The load fluctuation information is used to evaluate the overall peak-shaving and valley-filling effect based on the smoothness of the global load.

[0107] To comprehensively consider both the peak-valley difference of user-side load and the reduction of overall fluctuation, the sum of the normalized absolute peak-valley difference and the load fluctuation is used as the target value for peak shaving and valley filling, referred to as the peak-shaving effect, and denoted as the peak-shaving effect index F1:

[0108] F1=m×P pvd +n×P var (5)

[0109] Where m and n are the absolute peak-to-valley difference P of the load. pvd Information on load fluctuation level P varThe weighting coefficients can be set to 0.5 and 0.5 respectively. The smaller the F1 value, the better the peak-shaving effect.

[0110] It is understood that for the values ​​of the two weighting coefficients m and n mentioned above, it is sufficient to satisfy m + n = 1. The larger m is, the more the peak-shaving focuses on the absolute peak-to-valley difference of the load. The larger n is, the more the peak-shaving focuses on adjusting the variance of load fluctuations. Those skilled in the art can choose according to the actual situation, and this invention does not impose any restrictions on this.

[0111] Based on the foregoing, the output forecast data can include summer load forecast data. This summer load forecast data can include the first maximum load value and the first minimum load value. Therefore, considering the aforementioned method for constructing peak-shaving effect indicators, for the summer indoor cooling scenario, the construction of summer peak-shaving effect indicators can be achieved by executing the following sub-steps S01 to S03:

[0112] Step S01: Calculate the first net load peak-to-valley difference based on the first maximum load value and the first minimum load value;

[0113] Step S02: Based on the summer load forecast data, calculate the first load change between hours, and based on the first load change, calculate the first load fluctuation information;

[0114] Step S03: After normalizing the peak-valley difference of the first net load and the waveform information of the first load, construct the summer peak-shaving effect index.

[0115] The output forecast data may also include winter load forecast data. This winter load forecast data may include the second maximum load value and the second minimum load value. For indoor cooling scenarios in winter, the construction of the winter peak-shaving effect index can be achieved by executing the following sub-steps S11 to S13:

[0116] Step S11: Calculate the second net load peak-to-valley difference based on the second maximum load value and the second minimum load value;

[0117] Step S12: Based on the winter load forecast data, calculate the second load change between hours, and based on the second load change, calculate the second load fluctuation information;

[0118] Step S13: After normalizing the second net load peak-valley difference and the second load waveform information, construct the winter peak-shaving effect index.

[0119] Understandably, in subsequent calculations, although the peak-shaving effect index parameter symbol used in both summer and winter indoor cooling scenarios is F1, the F1 used in the summer indoor cooling scenario represents the summer peak-shaving effect index calculated based on summer daily data, while the F1 used in the winter indoor cooling scenario represents the winter peak-shaving effect index calculated based on winter daily data.

[0120] As an optional embodiment, when evaluating and calculating the microgrid system, it is necessary to consider the normal operation of each unit in the microgrid system at the same time, that is, it is necessary to maintain the power balance state in the microgrid system as much as possible.

[0121] Therefore, based on this purpose, several related constraints can be established as follows:

[0122] (1) Constraints of large-scale refrigeration equipment (cooling power output constraints)

[0123] First, the indoor set temperature and the outdoor measured temperature corresponding to the large-scale refrigeration equipment can be obtained, and the power consumption of the large-scale refrigeration equipment can be calculated based on the indoor set temperature and the outdoor measured temperature.

[0124] Assuming the chiller (AC) maintains heat equilibrium with the building for a short period, when the outdoor temperature is constant, the electrical power P consumed by the chiller... AC for:

[0125]

[0126] Among them, T in Set the indoor temperature; T out λ is the measured outdoor temperature; λ is the energy efficiency ratio coefficient of the refrigeration unit; R1 is the equivalent thermal resistance of the large refrigeration equipment-building system.

[0127] Next, based on the power consumption P AC Based on the energy efficiency ratio of large-scale refrigeration equipment, the output cooling power is calculated, and the corresponding cooling power output constraint is constructed.

[0128] The cooling capacity of the refrigeration unit can be controlled by adjusting the AC load, thereby changing the ambient temperature. The specific cooling power output constraint is shown in equation (8) below:

[0129] Q AC,t =η AC P AC,t (7)

[0130]

[0131] Among them, P AC,t Let η be the electrical power consumed by the electric chiller at time t; ACQ is the energy efficiency ratio of an electric chiller. AC,t Let t be the output cooling power of the large refrigeration equipment. These represent the lower and upper limits of the cooling power output of the electric chiller, respectively.

[0132] (2) Power balance constraint

[0133] First, the interactive power constraints between the microgrid and the distribution network are obtained, and then the unit output of each unit in the microgrid system and the electricity load of each user are extracted from the output prediction data.

[0134] Next, based on the unit output, electrical load, power consumption, and interactive power constraints, power balance constraints are constructed. The specific power balance constraints are shown in equation (9) below:

[0135]

[0136] in, P represents the output of unit i at time t; in this embodiment of the invention, the units in the microgrid system are photovoltaic power plants and wind power plants; L,t P represents the user's electrical load at time t. M,t Let represent the interaction power between the microgrid and the distribution network. A positive value indicates that the microgrid purchases electricity from the distribution network, while a negative value indicates that the microgrid sells surplus electricity back to the distribution network. The corresponding interaction power constraint is shown in equation (10):

[0137]

[0138] In the formula, U represents the maximum power exchanged between the microgrid and the distribution network. M,t This represents the microgrid's power purchase and sale status. A value of 1 indicates the microgrid purchases power from the distribution network, while a value of 0 indicates the microgrid sells power to the distribution network. The superscript "buy" indicates... This refers to the power purchased by the microgrid from the distribution network; the superscript "sell" indicates the power purchased. This refers to the power output of a microgrid when it sells electricity to the distribution network.

[0139] Based on the power purchase / sale status, the interaction power between the microgrid and the distribution network can be further expressed as follows:

[0140]

[0141] (3) Constraints related to cooling power in microgrid

[0142] Specifically, the total cooling power of large-scale refrigeration equipment in the microgrid system can be obtained, and based on the total cooling power, a cooling power constraint within the microgrid can be constructed.

[0143] Unlike the power system, which generates and uses power immediately and maintains real-time balance, heating systems have significant thermal inertia. The heat transfer rate in heating systems is slow, and energy transfer and transformation can span multiple time segments, thus exhibiting effects similar to energy storage charging and discharging behavior. In other words, it has a certain capacity to "store" heat energy. The effectiveness of this storage depends on the magnitude of its thermal inertia.

[0144] Similarly, distributed cooling systems composed of large-scale refrigeration equipment and cooling buildings also possess such "energy storage" capabilities. The cooling power-related constraints within the microgrid can be constructed based on the temperature dynamic characteristics corresponding to this "energy storage" capability. The temperature dynamic characteristics can be described using a room model. The room model is a mathematical model describing the relationship between the accumulation of indoor and outdoor heat sources and changes in room temperature. Its temperature dynamic characteristics can be described using an equivalent thermal parameter (ETP) model, as shown in equation (12) below:

[0145]

[0146] Among them, Q L,t Let t be the total cooling power of the electric chiller; R and C are the equivalent thermal resistance and equivalent heat capacity of the indoor space of the refrigerated building, respectively; and Δt represents the indoor and outdoor temperatures of the building at time t; Δt is the sampling time interval.

[0147] Finally, the cold power output constraint, the electric power balance constraint, and the cold power constraint within the microgrid can be used as multi-objective constraints for the microgrid system. Among them, the multi-objective constraints are mainly used to impose power balance constraints on the microgrid system when optimizing the summer multi-objective optimization model and the winter multi-objective optimization model.

[0148] Step 102: Construct a first objective function corresponding to the summer indoor cooling scenario; and construct a summer multi-objective optimization model based on the summer peak-shaving effect index and the first objective function.

[0149] The core idea for scheduling optimization in summer indoor cooling scenarios is as follows: First, construct the system operation mode under summer indoor cooling scenarios; then, based on summer meteorological data, establish the objective function F2 considering customer satisfaction with cooling service (CSWC); then perform multi-objective optimization of F1 and F2 and obtain the Pareto solution set; finally, use the least squares method to obtain the scheduling result required in Pareto.

[0150] Specifically, the process of constructing the first objective function F2 corresponding to the summer indoor cooling scenario can be achieved by executing the following sub-steps S21 to S23:

[0151] Step S21: Initialize and build the system operation mode for indoor cooling in summer;

[0152] Southern China primarily experiences a subtropical monsoon climate, with relatively warm temperatures year-round. Summer temperatures are particularly high, generally between 28°C and 36°C. Therefore, large-scale refrigeration equipment experiences heavy loads and high inertia in summer, resulting in significant peak-shaving potential. First, we can determine the indoor and outdoor temperatures over a 24-hour period in southern China during summer. For example... Figure 4 A schematic diagram showing a temperature comparison curve for various environments in southern China during summer is presented.

[0153] Step S22: Construct a user cooling satisfaction index and jointly constrain the indoor temperature and the user cooling satisfaction index to adjust the output of large-scale refrigeration equipment.

[0154] Combination Figure 4 It can be seen that when large-scale cooling equipment is turned on indoors in summer, the temperature can be maintained at a relatively stable level. However, if only simple temperature control is used without considering the inertia of the cooling load and the human body's perception of temperature, it will not only waste cooling load resources, but also result in a poor cooling experience for users.

[0155] Based on this, in this embodiment of the invention, an indoor user cooling satisfaction index is constructed to drive and adjust the output of large-scale refrigeration equipment. The user cooling satisfaction index I... CSWC As shown in equation (13):

[0156]

[0157] In the formula, M represents the human body's energy metabolism rate; W represents the mechanical work done by the human body; P a The ambient water vapor partial pressure is Ta; the air temperature around the human body is Ta; the thermal resistance of the clothing is fcl; the surface temperature of the clothing is Tcl; the mean radiant temperature is Tr; the convective heat transfer coefficient is hc; the age factor is amplified, which can amplify the influence of age on this indicator; and the user's work type is jt, such as day shift or night shift.

[0158] The output of large-scale refrigeration equipment is adjusted by combining indoor temperature and user cooling satisfaction indicators:

[0159]

[0160] In the formula, T in (t) represents the ambient temperature of the indoor user at time t; T min With T max The minimum and maximum ambient temperatures acceptable to indoor users; and The satisfaction level of indoor cooling users (I) CSWC The upper / lower limit.

[0161] Step S23: Based on the user cooling satisfaction index, construct the first objective function corresponding to the summer indoor cooling scenario.

[0162] The first objective function F2 is constructed as shown in equation (15):

[0163]

[0164] Among them, T day T night These refer to the sum of the corresponding time periods during the day and night, respectively.

[0165] Furthermore, based on the summer peak-shaving effect index and the first objective function, a summer multi-objective optimization model is constructed. Specifically, it can be: simultaneously considering user cooling satisfaction and the peak-shaving effect of large-scale refrigeration equipment in the summer indoor cooling scenario, and constructing a summer multi-objective optimization model based on the summer peak-shaving effect index and the first objective function.

[0166] The construction of the summer multi-objective optimization model is actually the transformation of the multi-objective function into a single-objective function for optimization. The formula of the summer multi-objective optimization model is shown in equation (16) below:

[0167]

[0168] In the formula, minF summer This represents a multi-objective optimization model for summer; min indicates finding the minimum value; N is the current iteration number; N rep This represents the total number of iterations.

[0169] Step 103: Optimize the summer multi-objective optimization model to obtain the summer peak shaving solution set, and solve the summer optimization scheduling result of the summer peak shaving solution set;

[0170] Specifically, by optimizing the multi-objective optimization model for summer to obtain the summer peak-shaving solution set, it can be:

[0171] The summer target optimization model is solved iteratively, and during the iterative solution process:

[0172] Determine whether the preset convergence threshold has been reached at the current iteration number. If so, obtain the optimal solution (F1(N), F2(N)) under the current weight. If not, increment the iteration number by 1, N = N + 1, and continue iterating until convergence is reached, or the total number of iterations is reached.

[0173] Determine if the current iteration number N is greater than or equal to the total iteration number N. repIf yes, output the first Pareto solution set of user cooling satisfaction and the peak-shaving effect of large-scale refrigeration equipment under all weights, as the summer peak-shaving solution set; if no, increment the iteration count by 1, N = N + 1, and continue iterating until convergence is reached, or the total number of iterations is reached.

[0174] Furthermore, the summer optimal scheduling result of the summer peak shaving solution set can be obtained by fitting the solution set curve of the summer peak shaving solution set by the least squares method (as shown in the following formula (17)), and solving it simultaneously with the first straight line equation (as shown in the following formula (18)). The number of solutions is unique. In this embodiment of the invention, b1 = 1.128 is obtained by solving the solution. The solution with the highest satisfaction is then obtained as the summer optimal scheduling result under the summer optimal scheme.

[0175] For example, the fluctuation degree is plotted on the horizontal axis, and the customer satisfaction rate (CSWC) for cooling is plotted on the vertical axis. Figure 5 A schematic diagram of Pareto front fitting is shown for summer user cooling satisfaction and fluctuation degree.

[0176]

[0177] y = -1.035x + b1 (18)

[0178] The optimized scheduling result is the indoor temperature constraint and CSWC limit constraint obtained from the current most satisfactory solution:

[0179]

[0180] In the formula, and These represent the lowest and highest temperatures that indoor cooling users can tolerate under the optimal summer cooling scheme. and These represent the upper and lower limits of the overall cooling satisfaction index for indoor cooling users throughout the day in summer.

[0181] For example, Figure 6(a) shows a schematic diagram of the optimized scheduling power balance under the summer optimal scheme, and Figure 6(b) shows a schematic diagram of the peak shaving and valley filling results under the summer optimal scheme.

[0182] Step 104: Construct a second objective function corresponding to the indoor cooling scenario in winter; and construct a multi-objective optimization model for winter based on the winter peak-shaving effect index and the second objective function.

[0183] Similar to the summer indoor cooling scenario, the core idea for scheduling optimization in the winter indoor cooling scenario is as follows: First, construct the system operation mode under the winter cold storage cooling scenario; then, based on winter meteorological data, consider the Supply Pressure Index (SPI) to establish the objective function F3; then, perform multi-objective optimization of F1 and F3 and obtain the Pareto solution set; finally, use the least squares method to obtain the scheduling result required in Pareto.

[0184] The Logistics Stress Index (SPI) is typically used to measure supply and demand imbalances, cost pressures, or efficiency bottlenecks caused by internal and external factors in the supply chain or logistics industry.

[0185] Specifically, the process of constructing the second objective function F3 corresponding to the indoor cooling scenario in winter can be achieved by executing the following sub-steps S31 to S33:

[0186] Step S31: Initialize and build the system operation mode for indoor cooling in winter;

[0187] Compared to northern China, southern China experiences warmer winters, which are not particularly cold. In Guangdong, winter temperatures generally range from 10°C to 19°C, with an average temperature of around 15°C. In contrast, the average winter temperature in northern China is below 0°C. Therefore, the demand for cold storage in southern China during winter is significantly greater than in the north, indicating substantial peak-shaving potential. For example, Figure 7 This diagram illustrates a comparison of average winter temperatures in southern and northern China.

[0188] Step S32: Determine the storage temperature range for frozen products in cold storage and construct logistics pressure indicators;

[0189] Specifically, the constructed logistics pressure index SPI is shown in equation (21) below:

[0190]

[0191] In the formula, This represents the logistics pressure index for type j frozen products; T0 and T f These represent the start and end times of the cold storage scheduling cycle, respectively; α j β represents the weighting factor for temperature deviation of frozen products of type j; j δ is the weighting factor for temperature fluctuations of type j frozen products; j For the quality ratio weighting factor of frozen products of type j; Let t be the actual storage temperature of frozen product type j at time t; The ideal optimal storage temperature for type j frozen products at time t; The remaining mass of type j frozen product at the final moment; Let J be the initial mass of the frozen product of type j at the final moment.

[0192] w time (t) is the time weighting function. This function applies over the time interval T0 to T... f The value increases linearly from 0 to 1, meaning the closer we get to the end time T. f The higher the time weight, the better.

[0193] Step S33: Based on the logistics pressure index and combined with the frozen product sales weight coefficient, construct the second objective function corresponding to the indoor cooling scenario in winter.

[0194] The constructed second objective function F3 is shown in equation (23) below:

[0195]

[0196] In the formula, is the sales weighting coefficient for the j-th type of frozen product, used to reflect the importance of different frozen products to sales pressure.

[0197] Furthermore, based on the winter peak-shaving effect index and the second objective function, a multi-objective optimization model for winter is constructed. Specifically, it can be: simultaneously considering the frozen product sales pressure index and the peak-shaving effect of large-scale refrigeration equipment in the winter indoor cooling scenario, and constructing a multi-objective optimization model for winter based on the winter peak-shaving effect index and the second objective function.

[0198] The construction of the multi-objective optimization model in winter is actually a process of transforming the multi-objective function into a single-objective function for optimization. The formula of the multi-objective optimization model in winter is shown in equation (24) below:

[0199]

[0200] In the formula, minF winter This represents a multi-objective optimization model for winter.

[0201] Step 105: Optimize the winter multi-objective optimization model to obtain the winter peak shaving solution set, and solve the winter optimization scheduling result of the winter peak shaving solution set.

[0202] Similar to the indoor cooling scenario in summer, the multi-objective optimization model for winter is optimized to obtain the winter peak-shaving solution set, which can be specifically:

[0203] The winter target optimization model is solved iteratively, and the following steps are taken during the iterative solution process:

[0204] Determine whether the preset convergence threshold has been reached at the current iteration number. If so, obtain the optimal solution (F1(N), F3(N)) under the current weight. If not, increment the iteration number by 1, N = N + 1, and continue iterating until convergence is reached, or the total number of iterations is reached.

[0205] Determine if the current iteration number N is greater than or equal to the total iteration number N. rep If yes, output the second Pareto solution set containing all weights of frozen product sales pressure index and the peak-shaving effect of large refrigeration equipment, as the winter peak-shaving solution set; if no, increment the iteration count by 1, N = N + 1, and continue iterating until convergence is achieved, or the total number of iterations is reached.

[0206] Furthermore, the winter optimal scheduling result of the winter peak shaving solution set can be obtained by fitting the solution set curve of the winter peak shaving solution set by the least squares method (as shown in the following formula (25)), and solving it simultaneously with the second straight line equation (as shown in the following formula (26)). The number of solutions is unique. In this embodiment of the invention, b2 = 0.3027 is obtained by solving the solution, and the solution with the highest satisfaction is obtained as the winter optimal scheduling result under the winter optimal scheme.

[0207] For example, the fluctuation degree is plotted on the horizontal axis, and the logistics stress index (SPI) is plotted on the vertical axis. Figure 8 A schematic diagram of the Pareto front fitting of a winter logistics pressure index and its fluctuation level is shown.

[0208] y = 0.4325e -3.131x -0.01606 (25)

[0209] y = -0.3472x + b² (26)

[0210] The optimized scheduling result is the current most satisfactory solution to the cold storage temperature constraint and SPI limitation:

[0211]

[0212]

[0213] In the formula, and These represent the minimum and maximum storage temperatures of the cold storage facility for type j frozen products under the optimal winter storage conditions. and These represent the upper and lower limits of the overall sales pressure constraints for all frozen products during the winter.

[0214] For example, Figure 9(a) shows a schematic diagram of the optimized scheduling power balance under the winter optimal scheme, and Figure 9(b) shows a schematic diagram of the peak shaving and valley filling results under the winter optimal scheme.

[0215] Step 106: Based on the summer optimization scheduling results and the winter optimization scheduling results, a comprehensive evaluation is conducted to obtain the annual comprehensive evaluation results of the participation of large-scale refrigeration equipment in peak shaving in the microgrid system.

[0216] The summer optimization scheduling results mainly include the first optimized net load peak-to-valley difference and the first optimized load fluctuation, while the winter optimization scheduling results mainly include the second optimized net load peak-to-valley difference and the second optimized load fluctuation. Furthermore, the process of comprehensively evaluating the summer and winter optimization scheduling results to obtain the annual comprehensive evaluation results of large-scale refrigeration equipment participating in peak shaving in the microgrid system can be achieved by executing the following sub-steps S41 to S45:

[0217] Step S41: Obtain the first pre-optimization net load peak-to-valley difference and the first pre-optimization load fluctuation under the summer indoor cooling scenario;

[0218] Step S42: Based on the first optimized net load peak-valley difference, the first optimized load fluctuation, the first optimized net load peak-valley difference and the first optimized load fluctuation, the summer peak-shaving effect is quantified to obtain the summer peak-shaving quantification result.

[0219] The peak-shaving effect after summer quantization is shown in equation (29):

[0220]

[0221] In the formula, Pse_s represents the summer peak shaving quantification result; These represent the peak-to-valley difference of the net load after the first optimization and the load fluctuation after the first optimization, respectively. These represent the peak-to-valley difference in net load before the first optimization and the load fluctuation before the first optimization, respectively.

[0222] Step S43: Obtain the net load peak-to-valley difference and load fluctuation before the second optimization in the winter indoor cooling scenario;

[0223] Step S44: Based on the second optimized net load peak-valley difference, the second optimized load fluctuation, the second optimized net load peak-valley difference and the second optimized load fluctuation, the winter peak-shaving effect is quantified to obtain the winter peak-shaving quantification result.

[0224] The peak-shaving effect after quantification in winter is shown in the following formula (30):

[0225]

[0226] In the formula, Pse_w represents the result of winter peak shaving quantification; These represent the peak-to-valley difference of the net load after the second optimization and the load fluctuation after the second optimization, respectively. These represent the peak-to-valley difference in net load before the second optimization and the load fluctuation before the second optimization, respectively.

[0227] Step S45: Taking into account the summer and winter cooling load usage scenarios of the microgrid system, and based on the summer peak shaving quantification results and the winter peak shaving quantification results, conduct a comprehensive evaluation of the effect of large-scale refrigeration equipment participating in peak shaving throughout the year, and obtain the comprehensive evaluation results for the whole year.

[0228] The specific formula used in this step is shown in equation (31) below:

[0229]

[0230] In the formula, time s With time w These represent the durations of summer and winter, respectively; Psp α With Psp β These represent the peak-shaving pressure levels of the distribution network connected to the microgrid in summer and winter, respectively; γ s With γ w θ represents the coefficients of variation for summer and winter, respectively, used to indicate the degree of variation or uncertainty of peak-shaving performance indicators in summer and winter; θ is the cross-seasonal variation adjustment parameter, reflecting the combined impact of performance changes between summer and winter.

[0231] For example, the two results obtained from the aforementioned steps (the summer optimization scheduling result corresponding to Figure 6, Figure 8 The corresponding winter optimization scheduling results are compared with the data before participating in peak shaving optimization. The comparison results are shown in Table 1 and Table 2.

[0232] Summer before after Promote (%) <![CDATA[P pvd ]]> 1.4498 0.4172 71.22 <![CDATA[P var ]]> 4.9500 1.6305 67.06 <![CDATA[I CSWC ]]> 1.4445 0.9050 37.35

[0233] Table 1: Comparison of Indoor Scene Data in Summer

[0234] Winter before after Promote (%) <![CDATA[P' pvd ]]> 2.1155 0.2007 90.55 <![CDATA[P' var ]]> 7.0553 1.1943 83.07 <![CDATA[λ SPI ]]> 0.8312 0.6866 17.39

[0235] Table 2: Comparison of Data for Winter Cold Storage Scenarios

[0236] It should be noted that, in order to enable those skilled in the art to better distinguish data of the same type but with different actual meanings, the embodiments of the present invention use "first" and "second" to distinguish and describe some technical features. "First" and "second" are only used to distinguish data and have no other special meaning. It is understood that the present invention does not impose any limitations on them.

[0237] In this embodiment of the invention, a method for assessing the grid peak-shaving potential of large-scale cooling equipment participating in peak shaving in a microgrid system is provided. First, the output prediction data of the microgrid system is obtained, and peak-shaving effect indices for summer and winter are constructed respectively. Second, an objective function corresponding to the summer indoor cooling scenario is constructed, and a summer multi-objective optimization model is constructed in conjunction with the summer peak-shaving effect indices. Then, the summer multi-objective optimization model is optimized to obtain the summer peak-shaving solution set, and the summer optimal scheduling result is solved. Simultaneously, an objective function corresponding to the winter indoor cooling scenario is constructed, and a winter multi-objective optimization model is constructed in conjunction with the winter peak-shaving effect indices. Then, the winter multi-objective optimization model is optimized to obtain the winter peak-shaving solution set, and the winter optimal scheduling result is solved. Finally, a comprehensive evaluation is performed based on the summer and winter optimal scheduling results to obtain the annual comprehensive evaluation result of large-scale cooling equipment participating in peak shaving in the microgrid system. This method comprehensively considers the comprehensive evaluation indices under summer and winter indoor cooling scenarios, constructs corresponding multi-optimization models, obtains the optimal scheduling results under the best schemes in summer and winter through optimization calculations, and further combines the two optimal scheduling results for a comprehensive evaluation, resulting in a more comprehensive and accurate annual comprehensive evaluation result of large-scale cooling equipment participating in peak shaving in the microgrid system.

[0238] Reference Figure 10 This diagram illustrates a structural block diagram of a power grid peak-shaving potential assessment device provided by an embodiment of the present invention. The device is applied to a microgrid system where large-scale refrigeration equipment participates in peak shaving. Specifically, the device may include:

[0239] The peak shaving effect index construction module 1001 is used to obtain the power output prediction data of the microgrid system and construct the summer peak shaving effect index and the winter peak shaving effect index based on the power output prediction data.

[0240] The summer multi-objective optimization model construction module 1002 is used to construct a first objective function corresponding to the summer indoor cooling scenario, and to construct a summer multi-objective optimization model based on the summer peak-shaving effect index and the first objective function.

[0241] The summer optimization scheduling solution module 1003 is used to optimize the summer multi-objective optimization model, obtain the summer peak shaving solution set, and solve the summer optimization scheduling result of the summer peak shaving solution set.

[0242] The winter multi-objective optimization model construction module 1004 is used to construct the second objective function corresponding to the winter indoor cooling scenario, and construct the winter multi-objective optimization model based on the winter peak-shaving effect index and the second objective function.

[0243] The winter optimization scheduling solution module 1005 is used to optimize the winter multi-objective optimization model, obtain the winter peak shaving solution set, and solve the winter optimization scheduling result of the winter peak shaving solution set.

[0244] The comprehensive evaluation module 1006 is used to conduct a comprehensive evaluation based on the summer optimization scheduling results and the winter optimization scheduling results to obtain the annual comprehensive evaluation results of the participation of large-scale refrigeration equipment in peak shaving in the microgrid system.

[0245] In one optional embodiment, the power output forecast data includes summer load forecast data and winter load forecast data. The summer load forecast data includes a first maximum load value and a first minimum load value, and the winter load forecast data includes a second maximum load value and a second minimum load value. The peak-shaving effect index construction module 1001 includes:

[0246] The first net load peak-valley difference calculation module is used to calculate the first net load peak-valley difference based on the first maximum load value and the first minimum load value.

[0247] The first load fluctuation information calculation module is used to calculate the first load change between hours based on the summer load forecast data, and to calculate the first load fluctuation information based on the first load change.

[0248] The summer peak shaving effect index construction module is used to normalize the first net load peak-valley difference and the first load waveform information to construct the summer peak shaving effect index.

[0249] The second net load peak-valley difference calculation module is used to calculate the second net load peak-valley difference based on the second maximum load value and the second minimum load value.

[0250] The second load fluctuation information calculation module is used to calculate the second load change between hours based on the winter load forecast data, and to calculate the second load fluctuation information based on the second load change.

[0251] The winter peak shaving effect index construction module is used to normalize the second net load peak-valley difference and the second load waveform information to construct the winter peak shaving effect index.

[0252] In one optional embodiment, the summer multi-objective optimization model construction module 1002 includes:

[0253] The summer system operation mode construction module is used to initialize and construct the system operation mode under the summer indoor cooling scenario;

[0254] The user cooling satisfaction index construction module is used to construct the user cooling satisfaction index and to jointly constrain the indoor temperature and the user cooling satisfaction index in order to adjust the output of large-scale refrigeration equipment.

[0255] The first objective function construction module is used to construct a first objective function corresponding to the summer indoor cooling scenario based on the user cooling satisfaction index.

[0256] In one optional embodiment, the summer multi-objective optimization model construction module 1002 includes:

[0257] The summer multi-objective optimization model construction submodule is used to simultaneously consider user cooling satisfaction and the peak-shaving effect of large-scale refrigeration equipment in the summer indoor cooling scenario. Based on the summer peak-shaving effect index and the first objective function, a summer multi-objective optimization model is constructed.

[0258] In one optional embodiment, the summer optimization scheduling solution module 1003 includes:

[0259] The first iterative solution module is used to iteratively solve the summer target optimization model, and during the iterative solution process:

[0260] The first convergence judgment module is used to determine whether the preset convergence threshold has been reached at the current number of iterations. If so, the optimal solution under the current weight is obtained; if not, the iteration count is incremented by 1 and the iteration continues until convergence is reached, or the total number of iterations is reached.

[0261] The first iteration count judgment module is used to determine whether the current iteration count is greater than or equal to the total iteration count. If so, it outputs the first Pareto solution set of user cooling satisfaction and the peak-shaving effect of large-scale refrigeration equipment under all weights, as the summer peak-shaving solution set; if not, it increments the iteration count by 1 and continues iterating until convergence is reached, or the total iteration count is reached.

[0262] In one optional embodiment, the summer optimization scheduling solution module 1003 includes:

[0263] The summer optimal scheduling result solution module is used to fit the solution set curve of the summer peak shaving solution set using the least squares method, and solve it simultaneously with the first straight line equation. The number of solutions is unique, and the solution with the highest satisfaction is obtained as the summer optimal scheduling result under the summer optimal scheme.

[0264] In one optional embodiment, the winter multi-objective optimization model construction module 1004 includes:

[0265] The winter system operation mode construction module is used to initialize and construct the system operation mode under the winter indoor cooling scenario;

[0266] The logistics pressure index construction module is used to determine the storage temperature range of frozen products in cold storage and to construct logistics pressure indicators.

[0267] The second objective function construction module is used to construct a second objective function corresponding to the winter indoor cooling scenario based on the logistics pressure index and the frozen product sales weight coefficient.

[0268] In one optional embodiment, the winter multi-objective optimization model construction module 1004 includes:

[0269] The winter multi-objective optimization model construction submodule is used to simultaneously consider the frozen product sales pressure index and the peak-shaving effect of large-scale refrigeration equipment in the winter indoor cooling scenario. Based on the winter peak-shaving effect index and the second objective function, a winter multi-objective optimization model is constructed.

[0270] In one optional embodiment, the winter optimization scheduling solution module 1005 includes:

[0271] The second iterative solution module is used to iteratively solve the winter target optimization model, and during the iterative solution process:

[0272] The second convergence judgment module is used to determine whether the preset convergence threshold has been reached under the current number of iterations. If so, the optimal solution under the current weight is obtained; if not, the iteration count is incremented by 1 and the iteration continues until convergence is reached, or the total number of iterations is reached.

[0273] The second iteration count judgment module is used to determine whether the current iteration count is greater than or equal to the total iteration count. If so, it outputs the second Pareto solution set with all weights of frozen product sales pressure index and the peak-shaving effect of large refrigeration equipment as the winter peak-shaving solution set. If not, it increments the iteration count by 1 and continues iterating until convergence is reached, or the total iteration count is reached.

[0274] In one optional embodiment, the winter optimization scheduling solution module 1005 includes:

[0275] The winter optimization scheduling result solution module is used to fit the solution set curve of the winter peak shaving solution set using the least squares method, and solve it simultaneously with the second straight line equation. The number of solutions is unique, and the solution with the highest satisfaction is obtained as the winter optimization scheduling result under the winter optimal scheme.

[0276] In one optional embodiment, the summer optimized scheduling result includes a first optimized net load peak-to-valley difference and a first optimized load fluctuation, and the winter optimized scheduling result includes a second optimized net load peak-to-valley difference and a second optimized load fluctuation. The comprehensive evaluation module 1006 includes:

[0277] The summer peak shaving optimization data acquisition module is used to acquire the first pre-optimization net load peak-valley difference and the first pre-optimization load fluctuation in the summer indoor cooling scenario.

[0278] The summer peak shaving effect quantification module is used to quantify the summer peak shaving effect based on the first optimized net load peak-valley difference, the first optimized load fluctuation, the first optimized net load peak-valley difference and the first optimized load fluctuation, and to obtain the summer peak shaving quantification result.

[0279] The data acquisition module before winter peak shaving optimization is used to acquire the net load peak-to-valley difference and load fluctuation before the second optimization in the winter indoor cooling scenario.

[0280] The winter peak shaving effect quantification module is used to quantify the winter peak shaving effect based on the second optimized net load peak-valley difference, the second optimized load fluctuation, the second optimized net load peak-valley difference and the second optimized load fluctuation, and to obtain the winter peak shaving quantification result.

[0281] The comprehensive evaluation submodule is used to comprehensively consider the summer and winter cooling load usage scenarios of the microgrid system, and to comprehensively evaluate the effect of large-scale refrigeration equipment participating in peak shaving throughout the year based on the summer peak shaving quantification results and the winter peak shaving quantification results, so as to obtain the comprehensive evaluation results for the whole year.

[0282] In one alternative embodiment, the device further includes:

[0283] The microgrid system construction module is used to construct a microgrid system in which large-scale refrigeration equipment participates in peak shaving based on the summer and winter day scenarios of a specified scheduling area, and to collect a large amount of day-ahead scenario data of the microgrid system.

[0284] The typical day screening module is used to screen typical days from the current day scenario data and the corresponding power output prediction data of the typical days through a forward iterative search method. The typical days include summer typical days and winter typical days.

[0285] In one alternative embodiment, the device further includes:

[0286] The power consumption calculation module is used to obtain the indoor set temperature and the outdoor measured temperature corresponding to the large-scale refrigeration equipment, and to calculate the power consumption of the large-scale refrigeration equipment based on the indoor set temperature and the outdoor measured temperature.

[0287] The cold power output constraint construction module is used to calculate the output cold power based on the consumed electrical power and the energy efficiency ratio of the large-scale refrigeration equipment, and to construct the cold power output constraint corresponding to the output cold power.

[0288] The unit-related data extraction module is used to obtain the interactive power constraints between the microgrid and the distribution network, and to extract the unit output of each unit in the microgrid system and the electricity load of each user from the output prediction data.

[0289] The power balance constraint construction module is used to construct power balance constraints based on the unit output, the electrical load, the power consumption, and the interactive power constraints.

[0290] The microgrid internal cooling power constraint construction module is used to obtain the total cooling power of large-scale refrigeration equipment in the microgrid system and construct the microgrid internal cooling power constraint based on the total cooling power.

[0291] A multi-objective constraint construction module is used to take the cold power output constraint, the electric power balance constraint and the cold power constraint within the microgrid as multi-objective constraints of the microgrid system. The multi-objective constraints are used to apply power balance constraints to the microgrid system when optimizing the summer multi-objective optimization model and the winter multi-objective optimization model.

[0292] As the device embodiment is basically similar to the method embodiment, it is described in a relatively simple way. For relevant details, please refer to the description of the method embodiment above.

[0293] This invention also provides an electronic device, which includes a processor and a memory:

[0294] The memory is used to store program code and transfer the program code to the processor;

[0295] The processor is used to execute the power grid peak-shaving potential assessment method of any embodiment of the present invention according to the instructions in the program code.

[0296] This invention also provides a computer-readable storage medium for storing program code for executing the power grid peak-shaving potential assessment method of any embodiment of this invention.

[0297] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0298] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units through some interfaces, and may be electrical, mechanical, or other forms.

[0299] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0300] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0301] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0302] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for assessing the peak-shaving potential of a power grid, characterized in that, The method, applied to a microgrid system where large-scale refrigeration equipment participates in peak shaving, includes: Obtain the power output prediction data of the microgrid system, and construct summer peak shaving effect index and winter peak shaving effect index based on the power output prediction data; Construct a first objective function corresponding to the summer indoor cooling scenario, and construct a summer multi-objective optimization model based on the summer peak-shaving effect index and the first objective function; The summer multi-objective optimization model is optimized to obtain the summer peak shaving solution set, and the summer optimization scheduling result of the summer peak shaving solution set is solved. A second objective function is constructed for the indoor cooling scenario in winter. Based on the winter peak-shaving effect index and the second objective function, a multi-objective optimization model for winter is constructed. The winter multi-objective optimization model is optimized to obtain the winter peak shaving solution set, and the winter optimization scheduling result of the winter peak shaving solution set is solved. Based on the summer optimization scheduling results and the winter optimization scheduling results, a comprehensive annual evaluation result of the participation of large-scale refrigeration equipment in peak shaving in the microgrid system is obtained. The summer optimized scheduling results include the first optimized net load peak-to-valley difference and the first optimized load fluctuation; the winter optimized scheduling results include the second optimized net load peak-to-valley difference and the second optimized load fluctuation. A comprehensive evaluation is then performed based on the summer and winter optimized scheduling results to obtain the annual comprehensive evaluation results of the participation of large-scale refrigeration equipment in peak shaving within the microgrid system, including: Obtain the net load peak-to-valley difference and load fluctuation before the first optimization in the summer indoor cooling scenario; Based on the first optimized net load peak-valley difference, the first optimized load fluctuation, the first optimized net load peak-valley difference and the first optimized load fluctuation, the summer peak shaving effect is quantified to obtain the summer peak shaving quantification result. Obtain the net load peak-to-valley difference and load fluctuation before the second optimization in the winter indoor cooling scenario; Based on the second optimized net load peak-valley difference, the second optimized load fluctuation, the second optimized net load peak-valley difference before the second optimization, and the second optimized load fluctuation, the winter peak-shaving effect is quantified to obtain the winter peak-shaving quantification result. Taking into account the summer and winter cooling load usage scenarios of the microgrid system, and based on the summer peak shaving quantification results and the winter peak shaving quantification results, a comprehensive evaluation of the effect of large-scale refrigeration equipment participating in peak shaving throughout the year is conducted to obtain the comprehensive evaluation result for the whole year.

2. The method for assessing the peak-shaving potential of a power grid according to claim 1, characterized in that, The power output forecast data includes summer load forecast data and winter load forecast data. The summer load forecast data includes a first maximum load value and a first minimum load value. The winter load forecast data includes a second maximum load value and a second minimum load value. Constructing summer peak-shaving effect indicators and winter peak-shaving effect indicators based on the power output forecast data includes: Calculate the first net load peak-to-valley difference based on the first maximum load value and the first minimum load value; Based on the summer load forecast data, calculate the first load change per hour, and based on the first load change, calculate the first load fluctuation information; After normalizing the first net load peak-to-valley difference and the first load fluctuation information, a summer peak-shaving effect index is constructed. Calculate the second net load peak-to-valley difference based on the second maximum load value and the second minimum load value; Based on the winter load forecast data, calculate the second load change between hourly loads, and based on the second load change, calculate the second load fluctuation information; After normalizing the second net load peak-to-valley difference and the second load fluctuation information, a winter peak-shaving effect index is constructed.

3. The method for assessing the peak-shaving potential of a power grid according to claim 1, characterized in that, The first objective function for constructing the indoor cooling scenario in summer includes: Initialize and build the system operation mode for a summer indoor cooling scenario; Construct a user cooling satisfaction index and jointly constrain the indoor temperature and the user cooling satisfaction index to adjust the output of large-scale refrigeration equipment. Based on the user cooling satisfaction index, a first objective function corresponding to the summer indoor cooling scenario is constructed.

4. The method for assessing the peak-shaving potential of a power grid according to claim 3, characterized in that, The step of constructing a summer multi-objective optimization model based on the summer peak-shaving effect index and the first objective function includes: Simultaneously considering user satisfaction with indoor cooling in summer and the peak-shaving effect of large-scale refrigeration equipment, a multi-objective optimization model for summer is constructed based on the summer peak-shaving effect index and the first objective function.

5. The method for assessing the peak-shaving potential of a power grid according to claim 4, characterized in that, The optimization of the summer multi-objective optimization model to obtain the summer peak-shaving solution set includes: The summer multi-objective optimization model is solved iteratively, and during the iterative solution process: Determine whether the preset convergence threshold has been reached at the current iteration number. If yes, obtain the optimal solution with the current weight. If no, increment the iteration number by 1 and continue iterating until convergence is achieved. Determine if the current iteration count is greater than or equal to the total iteration count. If yes, output the first Pareto solution set for user cooling satisfaction and the peak-shaving effect of large-scale refrigeration equipment under all weights, as the summer peak-shaving solution set. If no, increment the iteration count by 1 and continue iterating until the total iteration count is reached.

6. The method for assessing the peak-shaving potential of a power grid according to claim 5, characterized in that, The process of solving for the summer peak shaving solution set and obtaining the summer optimal scheduling result includes: The solution set curve of the summer peak shaving solution set is fitted by the least squares method, and then solved simultaneously with the first straight line equation. The number of solutions is made unique, and the solution with the highest satisfaction is obtained as the summer optimal scheduling result under the summer optimal scheme.

7. The method for assessing the peak-shaving potential of a power grid according to claim 1, characterized in that, The construction of the second objective function corresponding to the winter indoor cooling scenario includes: Initialize and build the system operation mode for indoor cooling in winter; Determine the storage temperature range for frozen products in cold storage and construct logistics pressure indicators; Based on the aforementioned logistics pressure index and combined with the frozen product sales weighting coefficient, a second objective function corresponding to the indoor cooling scenario in winter is constructed.

8. The method for assessing the peak-shaving potential of a power grid according to claim 7, characterized in that, The step of constructing a multi-objective optimization model for winter based on the winter peak-shaving effect index and the second objective function includes: Simultaneously considering the sales pressure index of frozen products and the peak-shaving effect of large-scale refrigeration equipment in the indoor cooling scenario during winter, a multi-objective optimization model for winter is constructed based on the winter peak-shaving effect index and the second objective function.

9. The method for assessing the peak-shaving potential of a power grid according to claim 8, characterized in that, The optimization of the winter multi-objective optimization model to obtain the winter peak-shaving solution set includes: The winter multi-objective optimization model is solved iteratively, and during the iterative solution process: Determine whether the preset convergence threshold has been reached at the current iteration number. If yes, obtain the optimal solution with the current weight. If no, increment the iteration number by 1 and continue iterating until convergence is achieved. Determine if the current iteration count is greater than or equal to the total iteration count. If yes, output the second Pareto solution set with all weights of frozen product sales pressure index and the peak-shaving effect of large refrigeration equipment as the winter peak-shaving solution set. If no, increment the iteration count by 1 and continue iterating until the total iteration count is reached.

10. The method for assessing the peak-shaving potential of a power grid according to claim 9, characterized in that, The process of solving for the winter peak-shaving solution set and obtaining the winter optimal scheduling result includes: The solution set curve of the winter peak shaving solution set is fitted by the least squares method, and then solved simultaneously with the second linear equation. The number of solutions is made unique, and the solution with the highest satisfaction is obtained as the winter optimal scheduling result under the winter optimal scheme.

11. The method for assessing the peak-shaving potential of a power grid according to any one of claims 1 to 10, characterized in that, Also includes: Based on the summer and winter day scenarios of the designated scheduling area, a microgrid system with large-scale refrigeration equipment participating in peak shaving is constructed, and a large amount of day-ahead scenario data of the microgrid system is collected. Typical days and corresponding power output prediction data are selected from the current day scenario data using a forward iterative search method. The typical days include summer typical days and winter typical days.

12. The method for assessing the peak-shaving potential of a power grid according to claim 11, characterized in that, Also includes: Obtain the indoor set temperature and the outdoor measured temperature corresponding to the large-scale refrigeration equipment, and calculate the power consumption of the large-scale refrigeration equipment based on the indoor set temperature and the outdoor measured temperature. Based on the power consumption and the energy efficiency ratio of the large-scale refrigeration equipment, the output cooling power is calculated, and the cooling power output constraint corresponding to the output cooling power is constructed. The interactive power constraints between the microgrid and the distribution network are obtained, and the unit output of each unit in the microgrid system and the electricity load of each user are extracted from the output prediction data. Based on the unit output, the electrical load, the power consumption, and the interactive power constraints, an electrical power balance constraint is constructed. Obtain the total cooling power of large-scale refrigeration equipment in the microgrid system, and construct cooling power constraints within the microgrid based on the total cooling power; The cold power output constraint, the electric power balance constraint, and the cold power constraint within the microgrid are used as multi-objective constraints for the microgrid system. These multi-objective constraints are used to apply power balance constraints to the microgrid system when optimizing the summer multi-objective optimization model and the winter multi-objective optimization model.

13. A power grid peak-shaving potential assessment device, characterized in that, A microgrid system for peak shaving in large-scale refrigeration equipment, the device comprising: The peak shaving effect index construction module is used to obtain the power output prediction data of the microgrid system and construct the summer peak shaving effect index and the winter peak shaving effect index based on the power output prediction data. The summer multi-objective optimization model construction module is used to construct the first objective function corresponding to the summer indoor cooling scenario, and to construct the summer multi-objective optimization model based on the summer peak-shaving effect index and the first objective function. The summer optimization scheduling solution module is used to optimize the summer multi-objective optimization model, obtain the summer peak shaving solution set, and solve the summer optimization scheduling result of the summer peak shaving solution set. The winter multi-objective optimization model construction module is used to construct the second objective function corresponding to the winter indoor cooling scenario, and construct the winter multi-objective optimization model based on the winter peak-shaving effect index and the second objective function. The winter optimization scheduling solution module is used to optimize the winter multi-objective optimization model, obtain the winter peak shaving solution set, and solve the winter optimization scheduling result of the winter peak shaving solution set. The comprehensive evaluation module is used to conduct a comprehensive evaluation based on the summer optimization scheduling results and the winter optimization scheduling results to obtain the annual comprehensive evaluation results of the participation of large-scale refrigeration equipment in peak shaving in the microgrid system. The summer optimized scheduling results include the first optimized net load peak-to-valley difference and the first optimized load fluctuation; the winter optimized scheduling results include the second optimized net load peak-to-valley difference and the second optimized load fluctuation; the comprehensive evaluation module includes: The summer peak shaving optimization data acquisition module is used to acquire the first pre-optimization net load peak-valley difference and the first pre-optimization load fluctuation in the summer indoor cooling scenario. The summer peak shaving effect quantification module is used to quantify the summer peak shaving effect based on the first optimized net load peak-valley difference, the first optimized load fluctuation, the first optimized net load peak-valley difference and the first optimized load fluctuation, and to obtain the summer peak shaving quantification result. The data acquisition module before winter peak shaving optimization is used to acquire the net load peak-to-valley difference and load fluctuation before the second optimization in the winter indoor cooling scenario. The winter peak shaving effect quantification module is used to quantify the winter peak shaving effect based on the second optimized net load peak-valley difference, the second optimized load fluctuation, the second optimized net load peak-valley difference and the second optimized load fluctuation, and to obtain the winter peak shaving quantification result. The comprehensive evaluation submodule is used to comprehensively consider the summer and winter cooling load usage scenarios of the microgrid system, and to comprehensively evaluate the effect of large-scale refrigeration equipment participating in peak shaving throughout the year based on the summer peak shaving quantification results and the winter peak shaving quantification results, so as to obtain the comprehensive evaluation results for the whole year.

14. An electronic device, characterized in that, The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the power grid peak-shaving potential assessment method according to any one of claims 1-12 according to the instructions in the program code.

15. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program code for executing the power grid peak-shaving potential assessment method according to any one of claims 1-12.

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