Simulation and real-time decision system of incentive strategy for orderly charging facing extreme weather emergency regulation

Abstract

This invention relates to a simulation and real-time decision-making system for orderly charging incentive strategies in response to extreme weather emergencies. Belonging to the field of intelligent power system control technology, its key technical features include: a tag extraction module: classifying and aggregating load clusters based on residents' electricity consumption habits, economic levels, and energy conservation, emission reduction, and environmental attitudes at various times during extreme weather conditions. This yields the number of electric vehicle users in each cluster, the 15-minute average load for each cluster, and the average values ​​of each questionnaire data item, extracting three-dimensional feature tags for each type of electric vehicle user; a characterization module: using a logarithmic function to characterize the load of electric vehicle users under different real-time electricity prices; and an analysis and electricity price decision module: setting peak shaving, valley filling, and peak-shaving-valley-filling optimization objectives, and analyzing the optimal real-time electricity price mechanism under different objectives.

Description

Technical Field

[0001] This invention belongs to the field of intelligent power system control technology, specifically relating to an emergency control system for optimizing the charging load management of electric vehicle users under extreme weather conditions. By integrating user behavior clustering analysis, dynamic demand curve modeling, and multi-objective optimization algorithms, a real-time electricity price incentive strategy and decision-making platform are constructed to resolve peak load contradictions in the power grid and improve the efficiency of power supply and demand matching under extreme weather conditions. Background Technology

[0002] With the popularization of new energy electric vehicles and the advancement of energy internet construction, the periodic peak load problem caused by extreme weather (such as typhoons and high temperatures) is becoming increasingly prominent, mainly manifested in: inefficient supply-side regulation: traditional power generation schemes rely on thermal power capacity expansion, which is costly, has large carbon emissions, and is difficult to respond quickly to short-term power shortages; unquantified user-side potential: electric vehicle users exhibit significant heterogeneity in their electricity consumption behavior, and their adjustable load characteristics lack systematic modeling; rigid electricity pricing mechanisms: existing time-of-use pricing strategies cannot accurately incentivize users to smooth peak and valley loads, and the compensation mechanism lacks dynamism and fairness. While existing technologies have attempted to achieve source-load interaction through V2G (Vehicle-to-Grid) technology, problems remain, such as coarse user classification and simplistic demand response modeling. Therefore, there is an urgent need for an adaptive control system based on multi-dimensional user behavior characteristics to achieve refined scheduling of power resources under extreme weather conditions. Summary of the Invention

[0003] To address the aforementioned problems, this application provides a simulation and real-time decision-making system for an ordered charging incentive strategy for extreme weather emergency control, comprising, according to some embodiments thereof.

[0004] Tag extraction module: The load clusters of residents’ electricity consumption habits, economic level and energy conservation, emission reduction and environmental protection attitudes at different times under extreme weather conditions are classified and aggregated to obtain the number of electric vehicle users in each cluster, the 15-minute load average of each cluster and the average of each questionnaire data item, and extract the three-dimensional feature tags of various types of electric vehicle users.

[0005] Characterization module: The logarithmic function is used to characterize the load of electric vehicle users under different real-time electricity prices;

[0006] Analysis and Electricity Price Decision Module: Set peak shaving, valley filling, and peak shaving and valley filling optimization objectives, and analyze the optimal real-time electricity price mechanism under different objectives.

[0007] According to some embodiments of this application, a simulation and real-time decision-making system for an ordered charging incentive strategy for emergency regulation in extreme weather is provided. In the characterization module, the demand curves of electric vehicle users in each cluster at different time periods are characterized as follows:

[0008]

[0009] In the formula, i represents the electric vehicle user group category, j = 1, 2, 3, 4, representing off-peak, flat, peak and peak periods respectively; Qij represents the total load demand of the i-th type of electric vehicle user group during the period when the electricity price is pj.

[0010] Parameter a i j Assignment

[0011]

[0012] Parameter b i j Assignment

[0013]

[0014] According to some embodiments of this application, a simulation and real-time decision-making system for an ordered charging incentive strategy for emergency regulation in extreme weather is proposed. The analysis and electricity price decision module, under the objective of peak shaving and valley filling, sets the following two objective functions:

[0015] Objective 1: Objective 2:

[0016] Among them, Q max Q represents the maximum total charging amount among 96 time points on a typical day with extreme weather for electric vehicle users in the region, indicating the peak charging amount; min Q represents the minimum total charging amount among 96 time points on a typical day with extreme weather for electric vehicle users in the region; i Let I be the total charging amount of electric vehicle users in this area at time i; i The total output at a typical time point on day i during extreme weather in the region.

[0017] Under the objective of peak reduction, the objective function is set as follows:

[0018] Objective 3:

[0019] Under the objective of filling the valley, the objective function is set as follows:

[0020] Objective 4:

[0021] According to some embodiments of this application, a simulation and real-time decision-making system for an ordered charging incentive strategy for emergency regulation in extreme weather includes an analysis and electricity price decision module that sets the real-time electricity price decision set to range from 0.24 to 1.24 yuan and performs traversal optimization with a step size of 0.02, including the following steps:

[0022] Step 1: Traverse the real-time electricity price decision set; calculate the proportion of charging volume at each time point to the total charging volume during different time periods for different types of electric vehicle user groups.

[0023] Step 2: Substitute the real-time electricity price into the electric vehicle charging demand function for different types of electric vehicle users and at different times; calculate the total charging amount for different types of electric vehicles at different times under the real-time electricity price.

[0024] Step 3: Calculate the difference between the initial charging amount of various types of electric vehicle users during off-peak, flat, peak, and peak periods and the charging amount under the current real-time electricity price; allocate the load change at each time point in each period according to the proportion calculated in Step 2;

[0025] Step 4: Save the charging volume data of various electric vehicle user groups at 96 time points under the real-time electricity price; find the optimal real-time electricity price under the current objective based on the optimization objective.

[0026] A simulation and real-time decision-making method for an ordered charging incentive strategy for extreme weather emergency control, according to some embodiments of this application, includes...

[0027] The load clusters of residents' electricity consumption habits, economic level, and energy conservation, emission reduction and environmental protection attitudes at different times under extreme weather conditions are classified and aggregated to obtain the number of electric vehicle users in each cluster, the 15-minute average load of each cluster and the average of each questionnaire data item, and the three-dimensional feature labels of various types of electric vehicle users are extracted.

[0028] A logarithmic function is used to characterize the load of electric vehicle users under different real-time electricity prices;

[0029] Set peak shaving, valley filling, and peak shaving and valley filling optimization objectives, and analyze the optimal real-time electricity pricing mechanism under different objectives.

[0030] According to a simulation and real-time decision-making method for an ordered charging incentive strategy for extreme weather emergency control based on some embodiments of this application, the demand curves of electric vehicle users in each cluster at different time periods are characterized as follows:

[0031]

[0032] In the formula, i represents the electric vehicle user group category, j = 1, 2, 3, 4, representing off-peak, flat, peak and peak periods respectively; Qij represents the total load demand of the i-th type of electric vehicle user group during the period when the electricity price is pj.

[0033] Parameter a i j Assignment

[0034]

[0035] Parameter b i j Assignment

[0036]

[0037] According to some embodiments of this application, a simulation and real-time decision-making method for an ordered charging incentive strategy for extreme weather emergency regulation is proposed. The analysis and electricity price decision module, under the objective of peak shaving and valley filling, sets the following two objective functions:

[0038] Objective 1: Objective 2:

[0039] Among them, Q max Q represents the maximum total charging amount among 96 time points on a typical day with extreme weather for electric vehicle users in the region, indicating the peak charging amount; min Q represents the minimum total charging amount among 96 time points on a typical day with extreme weather for electric vehicle users in the region; i Let I be the total charging amount of electric vehicle users in this area at time i; i The total output at a typical time point on day i during extreme weather in the region.

[0040] Under the objective of peak reduction, the objective function is set as follows:

[0041] Objective 3:

[0042] Under the objective of filling the valley, the objective function is set as follows:

[0043] Objective 4:

[0044] According to some embodiments of this application, a simulation and real-time decision-making method for an ordered charging incentive strategy for emergency regulation in extreme weather conditions is proposed. The analysis and electricity price decision module sets the real-time electricity price decision set to range from 0.24 to 1.24 yuan, and performs traversal optimization with a step size of 0.02, including the following steps:

[0045] Step 1: Traverse the real-time electricity price decision set; calculate the proportion of charging volume at each time point to the total charging volume during different time periods for different types of electric vehicle user groups.

[0046] Step 2: Substitute the real-time electricity price into the electric vehicle charging demand function for different types of electric vehicle users and at different times; calculate the total charging amount for different types of electric vehicles at different times under the real-time electricity price.

[0047] Step 3: Calculate the difference between the initial charging amount of various types of electric vehicle users during off-peak, flat, peak, and peak periods and the charging amount under the current real-time electricity price; allocate the load change at each time point in each period according to the proportion calculated in Step 2;

[0048] Step 4: Save the charging volume data of various electric vehicle user groups at 96 time points under the real-time electricity price; find the optimal real-time electricity price under the current objective based on the optimization objective.

[0049] This application also provides an electronic device, which includes: one or more processors, a memory, and one or more programs; wherein the one or more programs are stored in the memory, and the one or more programs include instructions that, when executed by the electronic device, cause the electronic device to perform a first aspect and any possible technical solution of the first aspect.

[0050] This application also provides a computer-readable storage medium, which includes a computer program that, when run on an electronic device, causes the electronic device to perform the first aspect and any possible technical solution of the first aspect.

[0051] Beneficial effects:

[0052] (1) Positive optimization effect:

[0053] This invention significantly improves the matching of supply and demand, reducing the total amount of supply-demand mismatch under extreme weather conditions by 50.8%, reflecting that the electricity pricing mechanism effectively guides electricity consumption behavior to match the output curve of new energy sources; the 3.5 times price difference between peak and off-peak electricity prices and the 48.6% premium during peak hours form a strong economic incentive signal.

[0054] This invention has outstanding peak shaving and valley filling effects, reducing peak power shortages by 87.8% and effectively alleviating the risk of power overload; reducing power surplus by 30.7% and improving energy consumption efficiency; and realizing spatiotemporal load transfer through price signals, reducing grid regulation costs.

[0055] The invention enhances system security, alleviates the pressure on traditional power generation reserve capacity, and smooths out the impact of the steep output curve (normal function characteristics) of new energy sources.

[0056] (2) Dynamic equilibrium characteristics

[0057] The present invention focuses on the coordination and trade-off between objectives. Objective 1 (peak-valley difference optimization) is inversely correlated with other objectives. The increase in the maximum peak-valley difference originates from the load curve actively tracking the output pattern of new energy sources.

[0058] This invention is a novel system interaction mode, in which the user-side response forms a new paradigm of "load following power generation"; it changes the traditional "source follows load" dispatch logic and reflects the flexibility of the new power system.

[0059] (3) Value of Innovation Mechanism

[0060] This invention represents a breakthrough in price signal design, constructing a refined regulation system across a multi-dimensional electricity price range (0.24-1.248); and enabling precise demand guidance through time-based differentiated pricing strategies.

[0061] This invention promotes the consumption of new energy sources by adapting to the fluctuating output of wind and solar power through demand-side management; thus alleviating the contradiction between the coexistence of wind and solar curtailment and power shortage.

[0062] This invention reveals the dual role of real-time electricity pricing in novel power systems: it improves short-term operational indicators through economic incentives and promotes the coordinated evolution of power generation, grid, load, and storage at the system morphology level. However, attention should be paid to the trade-off effect between different optimization objectives. It is recommended that future research explore multi-objective collaborative optimization models and dynamic electricity price adjustment mechanisms. Attached Figure Description

[0063] Figure 1 These are the load clustering data, median load data, and mean data of questionnaire data items for each user group; a. is the first category, b. is the second category, c. is the third category, d. is the fourth category, e. is the fifth category, and f. is the sixth category.

[0064] Figure 2 This is a comparison chart of the power supply and demand gap under different optimization objectives; a. is objective 1, b. is objective 2, c. is objective 3, and d. is objective 4.

[0065] Figure 3 These are comparison charts of output-load curves under different optimization objectives; a. is objective 1, b. is objective 2, c. is objective 3, and d. is objective 4. Detailed Implementation

[0066] The embodiments of this application are described in detail below with reference to the accompanying drawings, examples of which are shown in the drawings. This application provides a method and an electronic device. The method and device are based on the same technical concept. Since the principles by which the method and device solve the problem are similar, the implementations of the device and method can be referred to each other, and repeated details will not be repeated.

[0067] This invention combines electric vehicle user questionnaire data and load data to classify and aggregate load clusters based on residents' electricity consumption habits, economic level, and energy conservation, emission reduction, and environmental protection attitudes at different times during extreme weather conditions. It aims to help power grid companies formulate real-time optimized electricity pricing strategies and propose effective methods to help electric vehicle loads participate in grid interaction to alleviate power supply overload in power supply blocks, thereby improving the expected overall benefits of demand response strategies.

[0068] Load data and large-scale questionnaire data of electric vehicle users in a region (e.g., a province) with a high concentration of charging stations during extreme weather conditions were used to perform weighted clustering of 34,400 electric vehicle users based on three dimensions: economic level, electricity consumption habits, and energy conservation, emission reduction, and environmental attitudes. The weights were assigned based on the contribution of each factor to the user's demand response behavior. Questionnaire data were encoded using both one-hot and ordinal codes. The classification was based on whether there was a size relationship between the answer options. If a size relationship existed, such as in question 1) How much electricity bill would you be willing to pay per month? A: 0-60 yuan; B: 60-110 yuan; C: 110-200 yuan; D: Other, then ordinal codes were used; otherwise, one-hot codes were used. Simultaneously, considering the magnitude differences between data items, to prevent larger data items from dominating the clusters, each data item was normalized using the following formula:

[0069]

[0070] Where Xij is the j-th quantity in the data item array Xj. Yij represents the minimum / maximum value in the data item, and Yij is the normalized value of the data item array Xj.

[0071] When using the k-means clustering algorithm, the number of clusters is set to 6. Within a cluster, the Euclidean distance from a sample to the centroid of that cluster is less than its distance to the centroids of other clusters. Electric vehicle users within each cluster are considered to have similar economic levels, energy consumption habits, and attitudes towards energy conservation, emission reduction, and environmental protection. Through cluster analysis, this section obtains the number of electric vehicle users in each cluster, the 15-minute average load for each cluster, and the average values ​​of each questionnaire data item. Due to space limitations, this section only shows the average values ​​of some questionnaire data items. The specific data for the load clustering, median load, and average values ​​of questionnaire data items for each user cluster are as follows: Figure 1 As shown, note: Figure 1 The red line represents the average load for each cluster of electric vehicle users.

[0072] The load clustering analysis of electric vehicle user groups extracts three-dimensional feature labels for each type of electric vehicle user group by analyzing the median clustering data of load clusters on typical days under extreme weather conditions and the median clustering data of questionnaire data items. See Table 1 for details.

[0073] Table 1. Three-dimensional feature labels of electric vehicle user groups (categories 1-6)

[0074]

[0075] Multi-objective optimization of electric vehicle charging and discharging load: Based on the above analysis, this section categorizes electric vehicle users into six clusters according to three dimensions: economic level, electricity consumption habits, and energy conservation, emission reduction, and environmental attitudes. Each cluster corresponds to a different group of electric vehicle users. Currently, Jiangxi Province has not yet implemented real-time electricity pricing for users. The large-scale integration of renewable energy sources such as wind power has led to a high degree of mismatch between power supply and demand, exacerbating the problem of oversupply in power supply areas. How to formulate reasonable real-time electricity prices to incentivize electric vehicle users to engage in reasonable charging and discharging is a crucial issue facing power grid companies' demand response strategy makers.

[0076] Electric Vehicle Electricity Consumption Demand Curve: The demand curve is one of the core tools in economics for studying supply and demand relationships; electricity, as a special market-based commodity, also possesses the basic attributes of a commodity. Under the same external conditions, when the electricity price increases during a certain period, electric vehicle users will reduce or shift their load during that period. Based on this, this section introduces a demand curve to measure load shifting and reduction under different real-time electricity prices. Considering the behavioral heterogeneity among various electric vehicle user groups and between different time periods of the same type of electric vehicle user group, load demand curves are constructed for different real-time electricity prices on the user demand side. The behavioral heterogeneity between time periods refers to the different sensitivities of the same type of electric vehicle user to changes in electricity prices at different time periods. For example, compared to off-peak hours, electric vehicle users are less sensitive to electricity prices during peak hours, which is determined by considerations of the hard demand and preferences of electric vehicle users for electricity. Therefore, referring to existing literature, this section uses a logarithmic function to characterize the load of electric vehicle users under different real-time electricity prices. At this time, the demand curves of each cluster of electric vehicle users in each time period are characterized as follows:

[0077]

[0078] In the formula, i = 1, 2, 3, 4, 5, 6, representing electric vehicle user groups from category 1 to category 6, respectively; j = 1, 2, 3, 4, representing off-peak, flat, peak, and slack periods, respectively. Q i j This indicates that the electricity price during time period j is p. j At that time, the total load demand of the i-th type of electric vehicle user group during that period.

[0079] Obviously, when p j When Q = 0.6, i.e., under the initial capacity price, i j =a i j That is, parameter a i j Let be the initial total load of the electric vehicle user group in the j-th time period, representing the i-th category. As shown in Section 2, parameter a... i j The settings are shown in Table 2:

[0080] Table 2 Parameter a i j Assignment

[0081] parameter' j＝1 j＝2 j＝3 j＝4 i＝1 2175.36985 1619.8697 1080.5794 1575.98065 i＝2 1856.450064 1409.129064 960.150296 1371.430424 i＝3 1393.070448 1085.460662 704.389961 981.459963 i＝4 1672.18969 1300.080289 880.83011 1187.200824 i＝5 2840.019915 2157.97949 1436.529699 2045.410505 i＝6 1077.410382 837.10971 564.750318 826.129992

[0082] Similarly, the parameter bij is assigned a value based on the clustering data of electric vehicle users' charging volume and questionnaire data items in each cluster. Based on practical experience and existing literature, the following assumptions are made:

[0083] S1: The total rigid load of various electric vehicle user groups accounts for 25% of the total load;

[0084] S2: The higher the economic level, the less affected by changes in electricity prices; the stronger the environmental awareness, the greater the impact of changes in electricity prices; the more flexible the energy consumption habits and the lower the energy consumption, the greater the impact of changes in electricity prices.

[0085] S3: The higher the initial total load within a certain period, the stronger the willingness of electric vehicle users to charge during that period, and the lower their sensitivity to electricity prices during that period.

[0086] Therefore, based on the clustering results and the above assumptions, the parameter bij is set as shown in Table 4-3:

[0087] Table 3 Parameter b i j Assignment

[0088] Parameter bij j＝1 j＝2 j＝3 j＝4 i＝1 1048.202941 1363.120353 792.3933185 942.7413916 i＝2 912.4691458 1129.769227 715.6676398 862.0912016 i＝3 704.1443837 946.7930624 545.5856871 604.0176332 i＝4 848.8407731 1090.117322 663.5188569 707.8944279 i＝5 1316.256707 1707.501271 1007.403178 1163.760427 i＝6 486.5213964 627.8322825 384.621274 465.5614794

[0089] Multi-objective optimization and simulation experiments: This section sets up peak shaving, valley filling, and peak shaving and valley filling optimization objectives, and analyzes the optimal real-time electricity pricing mechanism under different objectives. Under the peak shaving and valley filling objective, the following two objective functions are defined:

[0090] Objective 1: Objective 2:

[0091] Among them, Q max The peak charging value represents the maximum total charging amount among 96 time points on a typical day with extreme weather for electric vehicle users in the region; Q.min Q represents the minimum total charging amount among 96 time points on a typical day with extreme weather for electric vehicle users in the region; i Let I be the total charging amount of electric vehicle users in this area at time i; i The total output at a typical time point on day i during extreme weather in the region.

[0092] Under the objective of peak shaving, the objective function is set as follows:

[0093] Objective 3: Under the objective of filling valleys, the objective function is set as follows:

[0094] Objective 4:

[0095] This section sets the real-time electricity price decision set to range from 0.24 to 1.24 yuan. Then, using the Python programming language, iterates and optimizes the price in steps of 0.02. The specific steps are as follows:

[0096] Step 1: Traverse the real-time electricity price decision set; calculate the proportion of charging volume at each time point to the total charging volume during different time periods for different types of electric vehicle user groups.

[0097] Step 2: Substitute the real-time electricity price into the electric vehicle charging demand function for different types of electric vehicle users and different time periods; calculate the total charging amount for electric vehicles of categories 1 to 6 at different time periods under the real-time electricity price;

[0098] Step 3: Calculate the difference between the initial charging amount of various types of electric vehicle users during off-peak, flat, peak, and peak periods and the charging amount under the current real-time electricity price; allocate the load change at each time point in each period according to the proportion calculated in Step 2;

[0099] Step 4: Save the charging volume data of various electric vehicle user groups at 96 time points under the real-time electricity price; find the optimal real-time electricity price under the current objective based on the optimization objective.

[0100] Optimization results of electricity consumption regulation strategies for electric vehicle users: The above analysis shows that, under different optimization objectives, the optimal real-time electricity price and the electricity supply-demand gap are respectively as follows: Figure 2 As shown in Table 4.

[0101] Under objective 1 (i.e., minimizing the maximum peak-valley difference), the optimal real-time electricity price range is (0.39-1.008), at which point the maximum peak-valley difference decreases by 0.004 compared to the initial capacity price. Under objective 2 (i.e., maximizing supply-demand matching), the optimal real-time electricity price range is (0.24-1.248), at which point the total supply-demand mismatch on a typical day during extreme weather in the region decreases from 18065.024 to 8879.567, a year-on-year reduction of 50.8%. Under the constraint of objective 3 (peak shaving), the optimal real-time electricity price is (0.24-1.248). At this point, compared with the initial capacity price, the total power shortage at each time point decreases from 6572.056 to 801.899, a year-on-year decrease of 87.8%. Under the constraint of objective 4 (valley filling), the optimal real-time electricity price range is (0.24-1.008). At this point, compared with the initial capacity price, the total remaining power at each time point decreases from 11492.968 to 7908.306, a year-on-year decrease of 30.7%.

[0102] Table 4 Optimal Real-Time Electricity Price under Different Objectives

[0103] Electricity price f1(x) f2(x) f3(x) f4(x) initial 0.6 0.601 17991.514 6572.056 11419.458 Japanese Standard 1 (0.39,0.6,0.84,1.008) 0.597 12155.103 2080.763 10074.340 Target 2 (0.24,0.6,0.84,1.248) 0.669 8879.567 958.286 7921.281 Japanese Standard 3 (0.24,0.6,0.93,1.248) 0.707 9265.546 801.899 8463.647 Japanese Standard 4 (0.24,0.6,0.84,1.008) 0.669 9989.069 2080.763 7908.306

[0104] Clearly, compared to the initial capacity price, the optimal real-time prices under different optimization objectives all alleviated the power supply-demand mismatch to varying degrees, positively impacting energy utilization, peak shaving and valley filling, reducing grid dispatching costs, ensuring grid safety, and alleviating overload in power supply areas. However, as shown in Table 4, the maximum peak-valley difference under the optimal real-time prices corresponding to objectives 2-4 is increased compared to the initial capacity price. This is because, under the influence of the real-time price, electric vehicle users spontaneously shift their electricity consumption from peak and off-peak periods to off-peak and flat periods. Meanwhile, from... Figure 3 It is known that, unlike traditional power generation models, the output curve under large-scale grid connection of renewable energy exhibits a steep normal function curve shape. Therefore, under the objectives of peak shaving, valley filling, and peak-valley shaving, the load curve after electric vehicle users participate in demand response does not tend to flatten, but rather tends to converge with the output curve, thus potentially widening the maximum peak-valley difference. To more intuitively illustrate the changing trend of the load curve of the electric vehicle user group after demand response, a comparison chart of the output curve and the load curve under different real-time electricity prices is presented, as shown below. Figure 3 As shown.

[0105] Depend on Figure 3 It can be seen that the optimal real-time electricity price under objectives 1 to 4 can guide electric vehicle users to adjust their electricity consumption structure and improve the power supply and demand situation to varying degrees. In particular, the optimal real-time electricity price under objective 2 can maximize the convergence of the total load curve of electric vehicle users to the output curve. At this time, the peak-valley electricity price difference is 3.5 times, and the peak-hour electricity price is increased by 48.6% on the basis of the high-peak-hour electricity price.

[0106] Therefore, based on the above analysis, to adapt to the new situation and requirements such as the large-scale development of new energy sources, changes in the peak-valley characteristics of the power system, and the intensification of supply-demand imbalances, power grid companies should consider factors such as the local installed capacity of new energy sources, the load characteristics of urban and rural residents, and the power supply-demand gap, and rationally improve the local real-time electricity pricing mechanism. To fully leverage the signaling role of market-based real-time electricity pricing, it is necessary not only to increase the price difference between peak and valley periods but also to further widen the price gap during peak and low-peak periods. A reasonable real-time electricity pricing mechanism can incentivize households to spontaneously reduce and shift loads, thereby adjusting the current electricity consumption structure.

[0107] Under objectives 1 through 4, the optimal real-time electricity prices all incentivize households to voluntarily reduce their total electricity consumption. Under objective 1 (minimizing the maximum peak-to-valley difference), households reduce their total electricity consumption the most. At this point, the optimal real-time electricity price range is (0.39-1.008), and total electricity consumption decreases by 1% year-on-year. During off-peak hours, total electricity consumption under objective 1 increases by 24% compared to the initial total, while under objectives 2 through 4 it increases by 51%. During average hours, total electricity consumption remains unchanged under objectives 1 through 4. During peak hours, except for objective 3, total electricity consumption decreases by 27% compared to the initial total under the other objectives, while under objective 3 it decreases by 36%. During peak-peak hours, total electricity consumption decreases by 25% under objectives 1 and 4, while under objectives 2 and 3 it decreases by 35%. Clearly, compared to the initial electricity consumption structure, the optimal real-time electricity prices under objectives 1 through 4 all incentivize households to voluntarily shift their peak and peak-peak electricity consumption to off-peak hours, thereby alleviating the electricity supply and demand imbalance.

[0108] Based on the above embodiments, this application also provides a computer program that, when run on a computer, causes the computer to execute the methods provided in the above embodiments.

[0109] Based on the above embodiments, this application also provides a computer storage medium storing a computer program, which, when executed by a computer, causes the computer to perform the methods provided in the above embodiments.

[0110] The storage medium can be any available medium that a computer can access. For example, but not limited to, a computer-readable medium can include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.

[0111] Based on the above embodiments, this application also provides a chip for reading a computer program stored in a memory to implement the method provided in the above embodiments.

[0112] Based on the above embodiments, this application provides a computer program product that implements the methods provided in the above embodiments when the computer program product is run on an electronic device.

[0113] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0114] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0115] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0116] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0117] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A simulation and real-time decision-making system for an ordered charging incentive strategy for emergency control in extreme weather, characterized in that, include Tag extraction module: The load clusters of residents’ electricity consumption habits, economic level and energy conservation, emission reduction and environmental protection attitudes at different times under extreme weather conditions are classified and aggregated to obtain the number of electric vehicle users in each cluster, the 15-minute load average of each cluster and the average of each questionnaire data item, and extract the three-dimensional feature tags of various types of electric vehicle users. Characterization module: The logarithmic function is used to characterize the load of electric vehicle users under different real-time electricity prices; Analysis and Electricity Price Decision Module: Set peak shaving, valley filling, and peak shaving and valley filling optimization objectives, and analyze the optimal real-time electricity price mechanism under different objectives.

2. The simulation and real-time decision-making system for ordered charging incentive strategies for emergency control in extreme weather as described in claim 1, characterized in that, The characterization module characterizes the demand curves of electric vehicle users in each cluster at different time periods as follows: In the formula, i represents the electric vehicle user group category, j = 1, 2, 3, 4, representing off-peak, flat, peak and peak periods respectively; Qij represents the total load demand of the i-th type of electric vehicle user group during the period when the electricity price is pj. parameter Assignment parameter Assignment 3. The simulation and real-time decision-making system for ordered charging incentive strategies for extreme weather emergency control as described in claim 1, characterized in that, The analysis and electricity pricing decision module sets the following two objective functions under the goal of peak shaving and valley filling: Objective 1: Objective 2: Among them, Q max Q represents the maximum total charging amount among 96 time points on a typical day with extreme weather for electric vehicle users in the region, indicating the peak charging amount; min Q represents the minimum total charging amount among 96 time points on a typical day with extreme weather for electric vehicle users in the region; i Let I be the total charging amount of electric vehicle users in this area at time i; i This represents the total output at a typical time point on day i during extreme weather in the region. Under the objective of peak reduction, the objective function is set as follows: Objective 3: Under the objective of filling the valley, the objective function is set as follows: Objective 4:

4. The simulation and real-time decision-making system for orderly charging incentive strategies for emergency control in extreme weather as described in claim 3, characterized in that, The analysis and electricity price decision module sets the real-time electricity price decision set to range from 0.24 to 1.24 yuan, and performs traversal optimization with a step size of 0.02, including the following steps: Step 1: Traverse the real-time electricity price decision set; calculate the proportion of charging volume at each time point to the total charging volume during different time periods for different types of electric vehicle user groups. Step 2: Substitute the real-time electricity price into the electric vehicle charging demand function for different types of electric vehicle users and at different times; calculate the total charging amount for different types of electric vehicles at different times under the real-time electricity price. Step 3: Calculate the difference between the initial charging amount and the charging amount under the current real-time electricity price for various types of electric vehicle users during off-peak, flat, peak, and peak periods; Distribute the load changes at each time point within each time period according to the proportions calculated in step 2; Step 4: Save the charging volume data of various electric vehicle user groups at 96 points in time under the real-time electricity price; Based on the optimization objective, find the optimal real-time electricity price under the current objective.

5. A simulation and real-time decision-making method for an ordered charging incentive strategy for emergency control in extreme weather, characterized in that, include The load clusters of residents' electricity consumption habits, economic level, and energy conservation, emission reduction and environmental protection attitudes at different times under extreme weather conditions are classified and aggregated to obtain the number of electric vehicle users in each cluster, the 15-minute average load of each cluster and the average of each questionnaire data item, and the three-dimensional feature labels of various types of electric vehicle users are extracted. A logarithmic function is used to characterize the load of electric vehicle users under different real-time electricity prices; Set peak shaving, valley filling, and peak shaving and valley filling optimization objectives, and analyze the optimal real-time electricity pricing mechanism under different objectives.

6. The simulation and real-time decision-making method for ordered charging incentive strategy for extreme weather emergency control according to claim 5, characterized in that, The demand curves for electric vehicle users in each cluster at different time periods are depicted as follows: In the formula, i represents the electric vehicle user group category, j = 1, 2, 3, 4, representing off-peak, flat, peak and peak periods respectively; Qij represents the total load demand of the i-th type of electric vehicle user group during the period when the electricity price is pj. parameter Assignment parameter Assignment 7. The simulation and real-time decision-making method for ordered charging incentive strategy for extreme weather emergency control according to claim 5, characterized in that, The analysis and electricity pricing decision module sets the following two objective functions under the goal of peak shaving and valley filling: Objective 1: Objective 2: Among them, Q max Q represents the maximum total charging amount among 96 time points on a typical day with extreme weather for electric vehicle users in the region, indicating the peak charging amount; min Q represents the minimum total charging amount among 96 time points on a typical day with extreme weather for electric vehicle users in the region; i Let I be the total charging amount of electric vehicle users in this area at time i; i This represents the total output at a typical time point on day i during extreme weather in the region. Under the objective of peak reduction, the objective function is set as follows: Objective 3: Under the objective of filling the valley, the objective function is set as follows: Objective 4:

8. The simulation and real-time decision-making method for ordered charging incentive strategy for extreme weather emergency control according to claim 7, characterized in that, The analysis and electricity price decision module sets the real-time electricity price decision set to range from 0.24 to 1.24 yuan, and performs traversal optimization with a step size of 0.02, including the following steps: Step 1: Traverse the real-time electricity price decision set; calculate the proportion of charging volume at each time point to the total charging volume during different time periods for different types of electric vehicle user groups. Step 2: Substitute the real-time electricity price into the electric vehicle charging demand function for different types of electric vehicle users and at different times; calculate the total charging amount for different types of electric vehicles at different times under the real-time electricity price. Step 3: Calculate the difference between the initial charging amount and the charging amount under the current real-time electricity price for various types of electric vehicle users during off-peak, flat, peak, and peak periods; Distribute the load changes at each time point within each time period according to the proportions calculated in step 2; Step 4: Save the charging volume data of various electric vehicle user groups at 96 points in time under the real-time electricity price; Based on the optimization objective, find the optimal real-time electricity price under the current objective.

9. An electronic device, the electronic device comprising: One or more processors, a memory, and one or more programs; wherein the one or more programs are stored in the memory, and the one or more programs include instructions that, when executed by the electronic device, cause the electronic device to perform the method of any one of claims 5-8.

10. A computer-readable storage medium comprising a computer program that, when executed on an electronic device, causes the electronic device to perform the method of any one of claims 5-8.

Images