A load aggregation regulation method based on improved archimedes algorithm

Abstract

The application discloses a load aggregation regulation and control method based on an improved Archimedes algorithm, and comprises the following steps: according to the physical characteristics and electricity utilization law of resident load, load aggregation models of air conditioner load, electric water heater and electric vehicle are respectively established; considering the power grid peak regulation scene, a load regulation and control optimization model with the lowest user electricity purchase cost as the target is established; in view of the weak local search capability of the Archimedes optimization algorithm, the simulated annealing algorithm is integrated into the Archimedes optimization algorithm to generate an improved Archimedes optimization algorithm; combined with the time-of-use electricity price information, the improved Archimedes optimization algorithm is used to optimize and solve the load aggregation regulation and control model, and the optimal regulation and control strategy of the load in the calculation period is obtained. The load regulation and control strategy provided by the application can reasonably arrange the electricity utilization of load resources, guide users to participate in the power grid peak regulation on the basis of reducing the load electricity purchase cost, and smooth the power grid load curve.

Description

Technical Field

[0001] This invention belongs to the field of power system load control technology, and in particular, it is a load aggregation control method based on an improved Archimedes algorithm. Background Technology

[0002] With the development of energy internet technology, large-scale power grid interconnection has become a major development trend in power systems. Consequently, power network topologies have become increasingly complex and diverse, and the power source structure and load composition of the power grid are gradually becoming more diversified. This poses a significant challenge to maintaining a constant supply-demand balance in the power system. Exploring the controllable potential of load-side resources has become one of the important methods for solving power grid supply and demand problems. Initially, load regulation was mainly focused on large industrial and commercial users because these loads have large adjustability margins, high levels of informatization, and can quickly complete received control commands. Residential loads, due to their low automation, dispersed distribution, and small individual capacity, were less often considered as controllable load-side resources.

[0003] With the improvement of residents' living standards, the electricity demand of residential users is increasing. Due to the high flexibility and fast response speed of residential load regulation, and the relatively small impact of short-term regulation on user electricity consumption, its regulation potential is gradually gaining attention. However, residential load resources face challenges such as low individual load power consumption, dispersed distribution within the regional power grid, and the inability of the control center to directly issue control commands. Therefore, load aggregation technology has emerged. This technology can not only aggregate and quantify dispersed residential load resources to meet the minimum capacity requirements of power grid regulation, but also rationally allocate load resources to meet the power grid's regulation needs, thus alleviating the supply and demand pressure on the power grid. Summary of the Invention

[0004] The purpose of this invention is to provide a load aggregation and control method based on an improved Archimedes algorithm, which guides users to participate in grid peak shaving and smooths the grid load curve while reducing the cost of purchasing electricity.

[0005] The technical solution to achieve the objective of this invention is: a load aggregation control method based on an improved Archimedes algorithm, the method comprising the following steps:

[0006] Step 1: Based on the physical characteristics and electricity consumption patterns of residential loads, establish load aggregation models for air conditioning loads, electric water heaters, and electric vehicles respectively;

[0007] Step 2: Considering the power grid peak shaving scenario, establish a load regulation optimization model with the goal of minimizing the user's electricity purchase cost;

[0008] Step 3: Integrate the simulated annealing algorithm into the Archimedes optimization algorithm to generate an improved Archimedes optimization algorithm;

[0009] Step 4: Combining time-of-use electricity price information, the improved Archimedes optimization algorithm is used to optimize the load control optimization model and obtain the optimal control scheme for the load within the calculation period.

[0010] Furthermore, in step 1, assuming that each air conditioner load operates independently and their operating states do not interfere with each other, all air conditioner load parameters are independent of each other, and the actual power consumption of the air conditioner load is related to its operating state, then based on the physical model and load characteristics of the individual air conditioner load, the load aggregation model of the air conditioner load is established as follows:

[0011]

[0012] In the formula, α represents the total power after the air conditioning load cluster is aggregated; α is the air conditioning load aggregation coefficient, which ranges from [0,1]. This represents the maximum power output after the air conditioning load is aggregated. This represents the minimum power output after the air conditioning load is aggregated.

[0013] The formulas for calculating the maximum and minimum power values ​​after the air conditioning load aggregation are as follows:

[0014]

[0015]

[0016] In the formula, N1 represents the number of air conditioners in the air conditioning load cluster; T o Indicates the temperature of the air outside the building; T ACset,i δ represents the temperature setpoint of the i-th air conditioner; ACi η represents the temperature dead zone of the i-th air conditioner load; i R represents the energy efficiency ratio of the i-th air conditioner load when cooling or heating at rated power; ai Let represent the equivalent resistance of the i-th air conditioner in the first-order ETP model.

[0017] Operating status of air conditioning load S AC It can be expressed by the following formula:

[0018]

[0019] In the formula, S AC (t), S AC (t+1) represents the operating status of the air conditioning load at time t and time t+1, respectively, where 0 indicates that the air conditioning is in a power-off state and 1 indicates that the air conditioning is in a working state; T ACi (t+1) represents the indoor temperature of the building where the air conditioner is located at time t+1; T ACimax T ACimin These represent the upper and lower limits of the air conditioning load temperature setting value, respectively.

[0020] Furthermore, in step 1, taking full account of user comfort, a minimum acceptable water temperature is set, and the load aggregation model of the electric water heater is established as follows:

[0021]

[0022] In the formula, N2 represents the total number of electric water heaters on the user side; P WHj S represents the heating power of the j-th electric water heater load; WHj (t) represents the on / off state of the j-th electric water heater when it participates in the aggregation;

[0023] The state of the electric water heater participating in the polymerization is represented as follows:

[0024]

[0025] Among them, T WH (t+1),T WH (t) represent the temperatures of the water stored in the electric water heater at times t+1 and t, respectively; T commin This indicates the lowest value of the user-specified comfortable water temperature range for the electric water heater's storage.

[0026] Furthermore, in step 1, considering that the operating state of an electric vehicle is related to its initial charging time and battery state of charge (SOC), an aggregated model of the electric vehicle load is established as follows:

[0027]

[0028] In the formula, N3 represents the number of electric vehicles on the user side; P EVk S represents the charging power of the k-th electric vehicle; EVk (t) represents the working state of the kth electric vehicle participating in the aggregation;

[0029] The working state of the electric vehicle participating in the aggregation is represented as follows:

[0030]

[0031] In the formula, SOC(t+1) represents the state of charge of the electric vehicle at time t+1; P EV Q represents the charging power of an electric vehicle; EV t represents the total battery capacity of an electric vehicle; C Indicates the charging time of an electric vehicle; η EV Indicates the charging efficiency of an electric vehicle; SOC min This represents the minimum acceptable state of charge for an electric vehicle after the charging process has ended; S EV(t) represents the working state of the electric vehicle at time t, where 1 indicates that the electric vehicle is charging and 0 indicates that it is not charging.

[0032] Furthermore, in step 2, the load control optimization model aimed at minimizing the user's electricity purchase cost is established as follows:

[0033] minF1=min{p AC (t)+p WH (t)+p EV (t)}

[0034] In the formula, p AC (t) represents the electricity cost for air conditioning load; p WH (t) represents the electricity cost for the electric water heater load; p EV (t) represents the electricity purchase cost for electric vehicles, expressed by the following formulas:

[0035]

[0036]

[0037]

[0038] In the formula, P ACaggi P WHaggj and P EVaggk These are the aggregated power controlled by air conditioning load, water heater load, and electric vehicle load, respectively; S ACi S WHj and S EVk These represent the working states of air conditioning load, water heater load, and electric vehicle participation in the aggregation process, respectively; Δt i Δt j and Δt k These represent the duration of regulation for air conditioning load, water heater load, and electric vehicle, respectively; p(t) is the price at which the user purchases electricity from the grid at time t.

[0039] Furthermore, the specific steps for improving the Archimedes optimization algorithm in step 3 are as follows:

[0040] Step 3.1: Randomly generate N particles and initialize the particle position, volume, density, and acceleration parameters;

[0041] Step 3.2: Iteratively update the particle density and volume;

[0042] Step 3.3: Calculate the transfer factor TF to determine the collision state of the object, and use different acceleration update methods depending on whether the object is in the exploration state or the development state;

[0043] Step 3.4: Based on the updated object position, calculate the objective function difference between the updated position and the current optimal position, and determine whether the objective function difference is greater than zero. If the objective function difference is less than zero, accept this position as the current optimal position; if the objective function difference is greater than zero, determine whether the new solution can be accepted according to the Metropolis acceptance criterion.

[0044] Step 3.5: Determine whether the iteration termination condition is met. If it is met, output the current result as the optimal solution; otherwise, repeat steps 3.1 to 3.4 until the iteration termination condition is met and the result is output.

[0045] Compared with the prior art, the significant advantages of this invention are:

[0046] (1) Based on the physical characteristics and electricity consumption patterns of residential loads, this invention establishes load aggregation models for air conditioning loads, electric water heaters and electric vehicles respectively, solving the problem that individual residential loads have small capacity, large number and cannot directly participate in load regulation; (2) Considering the power grid peak shaving scenario, a load regulation optimization model with the goal of minimizing user electricity purchase cost is established. On the basis of reducing the load electricity purchase cost, it guides users to participate in power grid peak shaving and smooths the power grid load curve; (3) In view of the weakness of the local search capability of the Archimedes optimization algorithm, the simulated annealing algorithm is integrated into the Archimedes optimization algorithm, and an improved Archimedes algorithm is proposed, which improves the solution efficiency of the model.

[0047] The present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0048] Figure 1 This is a flowchart of the load aggregation control method based on the improved Archimedes algorithm of this invention.

[0049] Figure 2 It is a graph showing the air conditioning load curves before and after load aggregation control.

[0050] Figure 3 This is a load curve diagram of the electric water heater before and after load aggregation regulation.

[0051] Figure 4 It is a graph showing the load curves of electric vehicles before and after load aggregation regulation.

[0052] Figure 5 It is a daily load curve before and after the load aggregation load participates in peak shaving. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0054] It should be noted that if the embodiments of the present invention involve descriptions such as "first" and "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" and "second" may explicitly or implicitly include at least one of those features. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0055] In one embodiment, combined Figure 1 This paper provides a load aggregation control method based on an improved Archimedes algorithm, the method comprising the following steps:

[0056] Step 1: Based on the physical characteristics and electricity consumption patterns of residential loads, establish load aggregation models for air conditioning loads, electric water heaters, and electric vehicles respectively;

[0057] Residential loads, such as air conditioners, electric water heaters, and electric vehicles, are considered high-quality load resources for grid regulation due to their high prevalence and large individual power consumption. However, due to the dispersed distribution and high randomness of their operation, actual grid regulation faces challenges such as difficulties in issuing control commands and the generation of massive and complex control data. Therefore, this invention aggregates and manages a certain number of controllable loads in a unified manner, thereby enabling residential load resources to participate in grid regulation.

[0058] This invention assumes that each air conditioner load operates independently, with no interference between their operating states, and that all air conditioner load parameters are independent of each other. The actual power consumption of an air conditioner load is related to its operating state. Based on the physical model and load characteristics of individual air conditioner loads, a load aggregation model for air conditioner loads is established as follows:

[0059]

[0060] In the formula, α represents the total power after the air conditioning load cluster is aggregated; α is the air conditioning load aggregation coefficient, which ranges from [0,1]. This represents the maximum power output after the air conditioning load is aggregated. This represents the minimum power output after the air conditioning load is aggregated.

[0061] The formulas for calculating the maximum and minimum power values ​​after the air conditioning load aggregation are as follows:

[0062]

[0063]

[0064] In the formula, N1 represents the number of air conditioners in the air conditioning load cluster; T o Indicates the temperature of the air outside the building; T ACset,i δ represents the temperature setpoint of the i-th air conditioner; ACi η represents the temperature dead zone of the i-th air conditioner load; i R represents the energy efficiency ratio (EER) of the i-th air conditioner under rated power for cooling (heating); ai Let represent the first-order ETP model equivalent resistance of the i-th air conditioner.

[0065] Operating status of air conditioning load S AC It can be expressed by the following formula:

[0066]

[0067] In the formula, S AC (t), S AC (t+1) represents the operating status of the air conditioning load at time t and time t+1, respectively, where 0 indicates that the air conditioning is in a power-off state and 1 indicates that the air conditioning is in a working state; T ACi (t+1) represents the indoor temperature of the building where the air conditioner is located at time t+1; T ACimax T ACimin These represent the upper and lower limits of the air conditioning load temperature setting value, respectively.

[0068] This invention fully considers user comfort, sets a minimum acceptable water temperature for users, and establishes a load aggregation model for electric water heaters as follows:

[0069]

[0070] In the formula, N2 represents the total number of electric water heaters on the user side; P WHj S represents the heating power of the j-th electric water heater load; WHj (t) represents the on / off state of the j-th electric water heater when it participates in the aggregation.

[0071] The on / off state of the electric water heater during aggregation is represented as follows:

[0072]

[0073] Among them, T WH (t+1),T WH (t) represent the temperatures of the water stored in the electric water heater at times t+1 and t, respectively; T commin This indicates the lowest value of the user-specified comfortable water temperature range for the electric water heater's storage.

[0074] Considering that the operating state of an electric vehicle is related to its initial charging time and battery state of charge (SOC), the following aggregated model of electric vehicle load is established:

[0075]

[0076] In the formula, N3 represents the number of electric vehicles on the user side; P EVk S represents the charging power of the k-th electric vehicle; EVk (t) represents the working state of the k-th electric vehicle participating in the aggregation.

[0077] The working state of electric vehicles participating in aggregation is represented as follows:

[0078]

[0079] In the formula, SOC(t+1) represents the state of charge of the electric vehicle at time t+1; P EV Q represents the charging power of an electric vehicle; EV The total capacity of an electric vehicle battery is represented by t. C Indicates the charging time of an electric vehicle; η EV Indicates the charging efficiency of an electric vehicle; SOC min This represents the minimum acceptable state of charge for an electric vehicle after the charging process has ended; S EV (t) represents the working state of the electric vehicle at time t, where 1 indicates that the electric vehicle is charging and 0 indicates that it is not charging.

[0080] Step 2: Considering the power grid peak shaving scenario, establish a load regulation optimization model with the goal of minimizing the user's electricity purchase cost;

[0081] Based on the load participation in grid peak shaving regulation strategy, in order to meet user comfort requirements and minimize user charging costs, a load regulation model is established with the objective function of minimizing user electricity purchase costs. The objective function is expressed as:

[0082] minF1=min{p AC (t)+p WH (t)+p EV (t)}

[0083] In the formula, p AC (t) represents the electricity cost for air conditioning load; p WH (t) represents the electricity cost for the electric water heater load; p EV (t) represents the electricity purchase cost for electric vehicles, expressed by the following formulas:

[0084]

[0085]

[0086]

[0087] In the formula, P ACaggi P WHaggj and P EVaggk These are the aggregated power controlled by air conditioning load, water heater load, and electric vehicle load, respectively; S ACi S WHj and S EVk These represent the working states of air conditioning load, water heater load, and electric vehicle participation in the aggregation process, respectively; Δt i Δt j and Δt k These represent the duration of regulation for air conditioning load, water heater load, and electric vehicle, respectively; p(t) is the price at which the user purchases electricity from the grid at time t.

[0088] Based on user experience, corresponding constraints are set for air conditioning loads, electric water heater loads, and electric vehicle loads participating in power grid regulation. The constraints for each type of load are as follows:

[0089] T ACseta -T ACsetb ≤ΔT ACsetmax

[0090] t WHj ≤t WHmax

[0091] SOC(t)≤SOC comin

[0092] Among them, T ACsetb T ACseta These are the temperature setpoints before and after the air conditioning load is involved in the regulation; ΔT ACsetmax The maximum permissible temperature adjustment amount for air conditioning load to participate in regulation; t WHj The duration for which the j-th electric water heater participates in load regulation; t WHmax The maximum allowable response time for a single load of the electric water heater; SOC comin The minimum state of charge (SOC(t)) that allows electric vehicles to participate in grid regulation.

[0093] Step 3: Integrate the simulated annealing algorithm into the Archimedes optimization algorithm to generate an improved Archimedes optimization algorithm;

[0094] While the Archimedes algorithm can effectively balance local and global search capabilities, it relies on random individuals to lead the group in finding the optimal solution during the optimization process. Furthermore, due to the issue of TF values, the object exploration process is relatively short while the development process is relatively long. Therefore, the algorithm suffers from low optimization accuracy and weak global search capabilities.

[0095] This invention combines the strong local search capability of the simulated annealing algorithm with the Archimedes optimization algorithm to form the Archimedes-Simulated Annealing algorithm (AOA-SA). Specifically, in the Archimedes solution-finding process, a judgment step is added to the newly found solution, thereby avoiding premature entrapment in local optima and improving the algorithm's optimization capability. The specific implementation steps are as follows:

[0096] Step 3.1: Randomly generate N particles and initialize parameters such as particle position, volume, density, and acceleration;

[0097] Step 3.2: Iteratively update the particle density and volume;

[0098] Step 3.3: Calculate the transfer factor TF to determine the collision state of the object, and use different acceleration update methods depending on whether the object is in the exploration state or the development state;

[0099] Step 3.4: Based on the updated object position, calculate the objective function difference between the updated position and the current optimal position, and determine whether the objective function difference is greater than zero. If the objective function difference is less than zero, accept this position as the current optimal position; if the objective function difference is greater than zero, determine whether the new solution can be accepted according to the Metropolis acceptance criterion.

[0100] Step 3.5: Determine if the iteration termination condition has been met. If it is, output the current result as the optimal solution; otherwise, repeat steps 3.1 to 3.4 until the iteration termination condition is met and the result is output.

[0101] Step 4: Combining time-of-use electricity price information, the improved Archimedes optimization algorithm is used to optimize the load control optimization model and obtain the optimal control scheme for the load within the calculation period.

[0102] In one embodiment, a load aggregation control system based on an improved Archimedes algorithm is provided, the system comprising:

[0103] The first model building module is used to establish load aggregation models for air conditioning load, electric water heater and electric vehicle respectively based on the physical characteristics and electricity consumption patterns of residential load;

[0104] The second model building module is used to establish a load control optimization model with the goal of minimizing the user's electricity purchase cost;

[0105] The algorithm improvement module is used to integrate the simulated annealing algorithm into the Archimedes optimization algorithm to generate an improved Archimedes optimization algorithm.

[0106] The optimization solution module is used to combine time-of-use electricity price information and use an improved Archimedes optimization algorithm to optimize the load control optimization model and obtain the optimal control scheme for the load within the calculation period.

[0107] Specific limitations regarding the load aggregation control system based on the improved Archimedes algorithm can be found in the limitations of the load aggregation control method based on the improved Archimedes algorithm mentioned above, and will not be repeated here. Each module in the aforementioned load aggregation control system based on the improved Archimedes algorithm can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0108] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0109] Step 1: Based on the physical characteristics and electricity consumption patterns of residential loads, establish load aggregation models for air conditioning loads, electric water heaters, and electric vehicles respectively;

[0110] Step 2: Considering the power grid peak shaving scenario, establish a load regulation optimization model with the goal of minimizing the user's electricity purchase cost;

[0111] Step 3: Integrate the simulated annealing algorithm into the Archimedes optimization algorithm to generate an improved Archimedes optimization algorithm;

[0112] Step 4: Combining time-of-use electricity price information, the improved Archimedes optimization algorithm is used to optimize the load control optimization model and obtain the optimal control scheme for the load within the calculation period.

[0113] For specific limitations on each step, please refer to the limitations on the load aggregation control method based on the improved Archimedes algorithm mentioned above, which will not be repeated here.

[0114] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0115] Step 1: Based on the physical characteristics and electricity consumption patterns of residential loads, establish load aggregation models for air conditioning loads, electric water heaters, and electric vehicles respectively;

[0116] Step 2: Considering the power grid peak shaving scenario, establish a load regulation optimization model with the goal of minimizing the user's electricity purchase cost;

[0117] Step 3: Integrate the simulated annealing algorithm into the Archimedes optimization algorithm to generate an improved Archimedes optimization algorithm;

[0118] Step 4: Combining time-of-use electricity price information, the improved Archimedes optimization algorithm is used to optimize the load control optimization model and obtain the optimal control scheme for the load within the calculation period.

[0119] For specific limitations on each step, please refer to the limitations on the load aggregation control method based on the improved Archimedes algorithm mentioned above, which will not be repeated here.

[0120] As a specific example, the invention will be further verified and illustrated in one embodiment.

[0121] This example selects a province as the research object. Based on the typical daily load curve fluctuations in summer in this province, the curve is divided into peak electricity consumption period, flat electricity consumption period, and low electricity consumption period. The peak electricity consumption period is from 9:00 to 11:00 and from 18:00 to 21:00; the low electricity consumption period is from 0:00 to 5:00 and from 21:00 to 24:00; and the remaining time is the flat electricity consumption period. The electricity price is set at 0.8 yuan / kWh during the peak electricity consumption period; 0.6 yuan / kWh during the flat electricity consumption period; and 0.3 yuan / kWh during the low electricity consumption period.

[0122] The simulation setup includes 10 prefecture-level control centers participating in the provincial dispatch, with each center's load resources comprising three categories: air conditioners, electric water heaters, and electric vehicles. One particular prefecture-level control center contains 100,000 units each of air conditioner load, electric water heater load, and electric vehicle load.

[0123] The load curves of air conditioners, electric water heaters, and electric vehicles before and after their participation in grid regulation are as follows: Figures 2-5 As shown, the simulation results lead to the following conclusions: After receiving control commands, the air conditioning load reduces its load during both peak and off-peak electricity periods; while the electric water heater load, after receiving control commands, reduces its power consumption during peak electricity periods, but experiences a slight increase in aggregated power during off-peak periods. This is because residents have minimum requirements for the water temperature stored in electric water heaters, and interrupting heating during peak periods necessitates heating the stored water during other times to meet users' hot water temperature requirements; the electric vehicle load responds to grid control by shifting all charging time to off-peak periods, thus playing a significant role in grid peak shaving.

[0124] After calculating the electricity consumption before and after the regulation, the electricity purchase cost for users decreased before and after the residential load participated in the grid peak shaving. The electricity purchase cost for users before and after the regulation is shown in Table 1 below:

[0125] Table 1 Comparison of electricity purchase costs before and after optimization.

[0126]

[0127] Comparing the load curves and user electricity purchase costs before and after regulation, it can be seen that the load participation in grid peak shaving strategy proposed in this invention is quite effective. During peak electricity price periods, the power of residential loads decreases significantly, which can play a good role in peak shaving. During off-peak electricity price periods, the power of residential loads increases, which is the result of some loads shifting their electricity consumption time after responding to regulation.

[0128] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention without departing from its spirit and scope should be included within the protection scope of the present invention.

Claims

1. A load aggregation control method based on an improved Archimedes algorithm, characterized in that, The method includes the following steps: Step 1: Based on the physical characteristics and electricity consumption patterns of residential loads, establish load aggregation models for air conditioning loads, electric water heaters, and electric vehicles respectively; Step 2: Considering the power grid peak shaving scenario, establish a load regulation optimization model with the goal of minimizing the user's electricity purchase cost; Step 3: Integrate the simulated annealing algorithm into the Archimedes optimization algorithm to generate an improved Archimedes optimization algorithm; Step 4: Combining time-of-use electricity price information, the improved Archimedes optimization algorithm is used to optimize and solve the load control optimization model to obtain the optimal control scheme for the load within the calculation period; In step 1, it is assumed that each air conditioner load operates independently, their operating states do not interfere with each other, all air conditioner load parameters are independent of each other, and the actual power consumption of the air conditioner load is related to its operating state. Based on the physical model and load characteristics of individual air conditioner loads, the load aggregation model of the air conditioner loads is established as follows: In the formula, This represents the total power after the air conditioning load cluster is aggregated. This is the air conditioning load aggregation coefficient, and its value ranges from [0,1]. This represents the maximum power output after the air conditioning load is aggregated. This represents the minimum power output after the air conditioning load is aggregated. The formulas for calculating the maximum and minimum power values ​​after the air conditioning load aggregation are as follows: In the formula, This indicates the number of air conditioners in the air conditioning load cluster; Indicates the temperature of the air outside the building; Indicates the first The temperature setting of the air conditioner; Indicates the first Temperature dead zone of air conditioning load; Indicates the first The energy efficiency ratio of an air conditioner under rated power for cooling or heating; Indicates the first The equivalent resistance of a first-order ETP model of an air conditioner; Operating status of air conditioning load It can be expressed by the following formula: In the formula, , They are respectively time, The status of the air conditioner load at any time: 0 indicates that the air conditioner is in a power-off state, and 1 indicates that the air conditioner is in a working state. for The indoor temperature of the building where the air conditioner is located at all times; , These represent the upper and lower limits of the air conditioning load temperature setpoint, respectively. In step 1, taking full account of user comfort, a minimum acceptable water temperature is set, and the load aggregation model of the electric water heater is established as follows: In the formula, This indicates the total number of electric water heaters on the user side; Indicates the first The heating power of the Teclast water heater under load; Indicates the first The on / off status of the Teclast water heater when participating in aggregation; The state of the electric water heater participating in the polymerization is represented as follows: in, , They represent , The temperature of the water stored in the electric water heater at all times; This indicates the lowest value within the user-specified comfortable range of water temperature for the electric water heater's storage. In step 1, considering that the operating state of an electric vehicle is related to its initial charging time and battery state of charge (SOC), an aggregated model of the electric vehicle load is established as follows: In the formula, Indicates the number of electric vehicles on the user side; Indicates the first The charging power of electric vehicles in Taiwan; Indicates the first The working status of Taiwanese electric vehicles participating in the aggregation; The working state of the electric vehicle participating in the aggregation is represented as follows: In the formula, This represents the state of charge of the electric vehicle at time t+1; Indicates the charging power of electric vehicles; This indicates the total battery capacity of the electric vehicle; Indicates the charging time of an electric vehicle; Indicates the charging efficiency of electric vehicles; This indicates the minimum acceptable state of charge for an electric vehicle after charging has ended; Indicates in The status of the electric vehicle is displayed at any time; 1 indicates that the electric vehicle is charging, and 0 indicates that it is not charging. The specific steps for improving the Archimedes optimization algorithm in step 3 are as follows: Step 3.1: Randomly generate N particles and initialize the particle position, volume, density, and acceleration parameters; Step 3.2: Iteratively update the particle density and volume; Step 3.3: Calculate the transfer factor TF to determine the collision state of the object, and use different acceleration update methods depending on whether the object is in the exploration state or the development state; Step 3.4: Based on the updated object position, calculate the objective function difference between the updated position and the current optimal position, and determine whether the objective function difference is greater than zero. If the objective function difference is less than zero, accept this position as the current optimal position; if the objective function difference is greater than zero, determine whether the new solution can be accepted according to the Metropolis acceptance criterion. Step 3.5: Determine whether the iteration termination condition is met. If it is met, output the current result as the optimal solution; otherwise, repeat steps 3.1 to 3.4 until the iteration termination condition is met and the result is output.

2. The load aggregation control method based on the improved Archimedes algorithm according to claim 1, characterized in that, In step 2, the load control optimization model aimed at minimizing the user's electricity purchase cost is established as follows: In the formula, Electricity purchase cost for air conditioning load; Electricity purchase cost for electric water heater load; The electricity purchase cost for electric vehicle loads is expressed by the following formulas: In the formula, , and These are the combined power of air conditioning load, water heater load, and electric vehicle load, respectively; , and These represent the working status of air conditioning load, water heater load, and electric vehicle participation in the aggregation process, respectively. , and These are the air conditioning load, water heater load, and the duration of electric vehicle participation in regulation, respectively. for The price at which users purchase electricity from the grid at any given time.

3. A load aggregation control system based on an improved Archimedes algorithm, characterized in that, The system is used to implement the method as described in any one of claims 1 to 2, the system comprising: The first model building module is used to establish load aggregation models for air conditioning load, electric water heater and electric vehicle respectively based on the physical characteristics and electricity consumption patterns of residential load; The second model building module is used to establish a load control optimization model with the goal of minimizing the user's electricity purchase cost; The algorithm improvement module is used to integrate the simulated annealing algorithm into the Archimedes optimization algorithm to generate an improved Archimedes optimization algorithm. The optimization solution module is used to combine time-of-use electricity price information and use an improved Archimedes optimization algorithm to optimize the load control optimization model and obtain the optimal control scheme for the load within the calculation period.

4. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 2.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 2.

Images