Cluster adjustable load regulation and control method and system

Abstract

The invention discloses a cluster adjustable load regulation and control method, which comprises the steps of obtaining adjustable load dynamic response data, and constructing a cluster adjustable load dynamic response model according to the adjustable load dynamic response data, the load dynamic response process of the cluster adjustable load dynamic response model comprises an adjustment process, a stable maintenance process, a recovery process and a stable state process after recovery; based on the cluster adjustable load dynamic response model, constructing a cluster adjustable load regulation and control model according to a target function and constraint conditions; and solving the cluster adjustable load regulation and control model by using an optimization algorithm to obtain an optimization regulation and control result of the cluster adjustable load, thereby improving the economy and reliability of the power system.

Description

Technical Field

[0001] The present invention relates to the technical field of adjustable load control, and in particular to a cluster adjustable load control method and system. Background Art

[0002] With the rapid development of my country's social economy, the demand for electricity consumption continues to grow, and new adjustable loads with flexible adjustment capabilities on the user side are growing rapidly. The participation of large-scale aggregated adjustable load resources in grid interaction helps to alleviate the contradiction between electricity supply and demand and support the safe, reliable and stable operation of new power systems. Adjustable load regulation is an important technical means to ensure the efficient participation of large-scale adjustable resources in grid regulation. Existing research on adjustable load regulation mainly focuses on load management, market incentives, smart home control, and load elastic scheduling. Foreign research has improved the supply and demand balancing ability and system stability of the power grid through intelligent gateways, market incentives and optimal scheduling of multi-type loads, combined with mixed integer linear programming and other methods, but most studies pay less attention to the recovery process after the end of load regulation, and usually use a simple "switch model" for recovery, which may lead to load spike problems. Domestic research focuses on the orderly charging of electric vehicles, clustered regulation of distributed loads, and the design of a multi-layer regulation framework to improve load response effects and grid flexibility. However, existing adjustable load regulation mainly focuses on the start time, duration and regulation capacity of regulation, and often ignores the dynamic characteristics of the load recovery process, which can easily lead to load spikes after regulation.

[0003] In modern power systems, with the popularization of renewable energy and the diversification of user electricity demand, grid load management faces unprecedented challenges. Traditional load control methods usually ignore the dynamic characteristics of the load recovery process, resulting in poor control effects and difficulty in fully realizing the potential of adjustable loads. Therefore, developing a cluster adjustable load control method that can take into account the load recovery process is of great significance for improving the control capability and operation efficiency of the power grid. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: the present invention solves the problem that the traditional load control method usually ignores the dynamic characteristics of the load recovery process, resulting in poor control effect and difficulty in fully realizing the potential of the adjustable load.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a cluster adjustable load control method, comprising: obtaining adjustable load dynamic response data, building a cluster adjustable load dynamic response model according to the adjustable load dynamic response data, wherein the load dynamic response process of the cluster adjustable load dynamic response model includes an adjustment process, a stable maintenance process, a recovery process, and a stable state process after recovery;

[0008] Based on the cluster adjustable load dynamic response model, a cluster adjustable load control model is constructed according to the objective function and constraint conditions;

[0009] The cluster adjustable load control model is solved using an optimization algorithm to obtain an optimized control result of the cluster adjustable load.

[0010] As a preferred solution of the cluster adjustable load control method of the present invention, the cluster adjustable load control model is constructed according to the objective function and the constraint conditions, including:

[0011] The objective function of the cluster adjustable load control model is that the total cost after response is equal to the energy cost after the user load response minus the compensation cost of the user load response.

[0012] As a preferred solution of the cluster adjustable load control method of the present invention, it also includes:

[0013] The constraint condition of the cluster adjustable load control model is that the start time of the i+1th control event is greater than the start time of the i-th control time plus the adjustment time of the i-th control event plus the adjustment duration of the i-th control event plus the recovery time of the i-th control event.

[0014] As a preferred solution of the cluster adjustable load control method of the present invention, the method of solving the cluster adjustable load control model by using an optimization algorithm includes:

[0015] The initial population is randomly generated in the parameter space, and the mutant individuals are generated through differential operation;

[0016] Cross the mutated individual with the current individual to generate a new candidate solution;

[0017] Select the next generation population based on the objective function value;

[0018] When the predetermined number of iterations or error requirements are reached, the optimization control result of the cluster's adjustable load is terminated and output.

[0019] As a preferred solution of the cluster adjustable load control method of the present invention, the adjustable load response data includes:

[0020] The adjustable load response data may be an adjustment time from the start of response to reaching the target adjustment power and a recovery time from the end of response to reaching a stable state.

[0021] As a preferred solution of the cluster adjustable load control method of the present invention, the adjustment process and the stable maintenance process include:

[0022] The regulation process is that the cluster adjustable load starts to respond within the response start delay time, and the load reaches the target regulation power within the regulation time, which is expressed as:

[0023]

[0024] Among them, T delay is the delay time, ΔP1 is the target adjustment power, P L is the original load power, ω n is the natural frequency of the system, ω d is the oscillation frequency after damping, Damping ratio, t is the time variable;

[0025] The stable maintenance process is that after the cluster adjustable load reaches the target adjustment power, the load remains constant within the stable maintenance time, and the power change is the original load power minus the target adjustment power.

[0026] As a preferred solution of the cluster adjustable load control method of the present invention, the recovery process and the stable state process after recovery include:

[0027] The recovery process is that the cluster adjustable load ends the stable maintenance time and the load begins to recover. Within the recovery time, the load recovers to the original load power, which is expressed as:

[0028]

[0029] Among them, T rise T is the adjustment time from the start of response to reaching the target adjustment power. steady To maintain the stable time, T delay is the delay time, ΔP1 is the target adjustment power, P L is the original load power, ω n is the natural frequency of the system, ω d is the oscillation frequency after damping, Damping ratio, t is the time variable;

[0030] The stable state process after recovery is when the recovery time ends, the cluster can adjust the load to restore to the original load power, and the power change is that the load power is equal to the original load power.

[0031] In a second aspect, the present invention provides a cluster adjustable load control system, comprising:

[0032] A data acquisition module, used for acquiring adjustable load dynamic response data, and constructing a cluster adjustable load dynamic response model according to the adjustable load dynamic response data, wherein the load dynamic response process of the cluster adjustable load dynamic response model includes an adjustment process, a stable maintenance process, a recovery process, and a stable state process after recovery;

[0033] A model building module, used to build a cluster adjustable load control model based on the cluster adjustable load dynamic response model according to the objective function and constraint conditions;

[0034] The model solving module is used to solve the cluster adjustable load control model by using an optimization algorithm to obtain an optimized control result of the cluster adjustable load.

[0035] In a third aspect, the present invention provides a computing device, comprising:

[0036] Memory and processor;

[0037] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the cluster adjustable load control method are implemented.

[0038] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the cluster adjustable load control method.

[0039] Beneficial effects of the present invention: Compared with the prior art, the cluster adjustable load control method proposed in the present invention has the significant advantage that for the optimization control problem of adjustable loads, a second-order system model is used to accurately model the dynamic behavior of the load, and the dynamic behaviors such as oscillation and overshoot in the adjustable load response process can be flexibly simulated to support the refined control of the power grid; by considering the load recovery process to optimize the control of cluster adjustable loads, the stability and flexibility of the power grid's load control are improved, the user's electricity consumption behavior is optimized, the operating pressure of the power grid is reduced, and ultimately the economy and reliability of the power system are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0041] Figure 1 An overall flow chart of a cluster adjustable load control method provided by the present invention;

[0042] Figure 2 A cluster adjustable load dynamic response process of a cluster adjustable load control method provided by the present invention;

[0043] Figure 3 A load curve after optimized regulation considering the recovery process and demand elasticity of a cluster adjustable load regulation method provided by the present invention;

[0044] Figure 4 An electricity price curve for a cluster adjustable load control method provided by the present invention;

[0045] Figure 5 A basic load curve of a cluster adjustable load control method provided by the present invention. DETAILED DESCRIPTION

[0046] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0047] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0048] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0049] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0050] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0051] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0052] Example 1

[0053] Reference Figure 1-Figure 3 , is an embodiment of the present invention, and provides a cluster adjustable load control method, comprising:

[0054] S100: Obtaining adjustable load dynamic response data, and constructing a cluster adjustable load dynamic response model according to the adjustable load dynamic response data. The load dynamic response process of the cluster adjustable load dynamic response model includes an adjustment process, a stable maintenance process, a recovery process, and a stable state process after recovery.

[0055] In the embodiment of the present application, the adjustable load response data may be the adjustment time from the start of the response to the target adjustment power and the recovery time from the end of the response to the stable state.

[0056] Specifically, the adjustable load response data may also include the time to start the response, the target regulated power to be stably achieved, and the stable maintenance time for stably achieving the regulated power.

[0057] It should be noted that by collecting these detailed response process data, a dynamic response model of the cluster adjustable load can be established more accurately, and the dynamic behavior of the load during the adjustment process, such as oscillation and overshoot, can be simulated and analyzed.

[0058] In the embodiment of the present application, the regulation process is that the cluster adjustable load starts to respond within the response start delay time, and the load reaches the target regulation power within the regulation time, which is expressed as:

[0059]

[0060] Among them, T delayis the delay time, ΔP1 is the target adjustment power, P L is the original load power, ω n is the natural frequency of the system, ω d is the oscillation frequency after damping, Damping ratio, t is the time variable;

[0061] The stable maintenance process is that after the cluster adjustable load reaches the target adjustment power, the load remains constant within the stable maintenance time, and the power change is the original load power minus the target adjustment power.

[0062] The recovery process and post-recovery steady-state process include:

[0063] The recovery process is that the cluster adjustable load ends the stable maintenance time and the load begins to recover. Within the recovery time, the load recovers to the original load power, which is expressed as:

[0064]

[0065] Among them, T rise T is the adjustment time from the start of response to reaching the target adjustment power. steady To maintain the stable time, T delay is the delay time, ΔP1 is the target adjustment power, P L is the original load power, ω n is the natural frequency of the system, ω d is the oscillation frequency after damping, Damping ratio, t is the time variable;

[0066] The stable state process after recovery is when the recovery time ends, the cluster can adjust the load to restore to the original load power, and the power change is that the load power is equal to the original load power.

[0067] Specifically, the change in load power during the regulation process can be described by the response formula of the second-order system;

[0068] The power change of the stable maintenance process is expressed as: P t =P L -ΔP1, where ΔP1 is the target adjustment power, P L is the original load power;

[0069] During the recovery process, the load gradually returns to the original load power during the recovery time, and overshoot may occur;

[0070] After the recovery time is over, the cluster can adjust the load to restore to the original load power. The power change is expressed as: P t =P L .

[0071] For example, the load of the load cluster is set to 100 kW, the load adjustment amount is set to 20 kW, the start time is set to the 3rd minute, and the duration is set to 10 minutes. Figure 2 As shown, the dynamic response process of the cluster's adjustable load is demonstrated, including the adjustment process, the stable maintenance process, the recovery process, and the stable state process after recovery;

[0072] like Figure 2 As shown in the figure, the regulation process starts from t1 to t2. The power grid or load aggregator sends a response signal, and the cluster load drops rapidly to reach the maximum regulation power. The regulation process shows the cluster's ability to respond quickly after the response signal. During the stable maintenance process from t2 to t3, the cluster load remains at a stable regulation level, providing a continuous load reduction effect. This stage shows that the cluster can maintain a stable response output during the regulation period, ensuring the reliability and predictability of power grid regulation. The recovery process starts from t3, and the cluster load gradually recovers to the initial level and reaches a stable state at t4. The small overshoot and oscillation in the recovery process reflect the complexity of the load in dynamic adjustment. By adjusting the model parameters (such as damping ratio and natural frequency), the overshoot and oscillation phenomena can be effectively controlled to ensure smooth load recovery. After t4, it is a stable state process after recovery.

[0073] It should be noted that cluster adjustable loads have obvious stage characteristics in the response process, and the dynamic behavior in the adjustment and recovery process can be effectively described and optimized by the second-order system model. In practical applications, through reasonable load control strategies, the regulation capacity of the power grid can be effectively improved, the peak-to-valley difference can be reduced, and the stability and economy of the system operation can be improved.

[0074] S200: Based on the cluster adjustable load dynamic response model, a cluster adjustable load control model is constructed according to the objective function and constraint conditions;

[0075] In an embodiment of the present application, the objective function of the cluster adjustable load control model is that the total cost after the response is equal to the energy cost after the user load response minus the compensation cost of the user load response.

[0076] The constraint condition of the cluster adjustable load control model is that the start time of the i+1th control event is greater than the start time of the i-th control time plus the adjustment time of the i-th control event plus the adjustment duration of the i-th control event plus the recovery time of the i-th control event.

[0077] Specifically, the cluster adjustable load control model considers demand elasticity and recovery process, introduces demand elasticity to more accurately predict and analyze the changes of cluster adjustable load under different electricity price levels, and thus formulates a more reasonable load control strategy. According to the demand elasticity coefficient, the basic load curve is adjusted to obtain the actual load curve, which is expressed as:

[0078]

[0079] Among them, L a is the load curve after demand elasticity, L b is the basic load curve, E d is the demand elasticity coefficient, P a is the electricity price curve, P m is the average electricity price.

[0080] Specifically, an adjustable load control model considering demand elasticity and recovery process is constructed. Demand elasticity analysis and load response dynamic process are combined, electricity cost and demand response compensation are comprehensively considered, and the optimization goal of minimizing energy cost is adopted. Constraints are set taking into account that no overlap can occur in each adjustment event, and a load optimization model with multiple adjustments is constructed.

[0081] The objective function comprehensively considers electricity cost and demand response compensation, which is expressed as:

[0082] C total =C use -C compensation

[0083] Among them, C total is the total cost after the response, C use is the energy cost after user load response, C compensation Compensation for user load response.

[0084] Constraints To avoid overlapping of adjustment events, nonlinear constraints are defined to ensure that the end time of each adjustment event is less than the start time of the next adjustment event, expressed as:

[0085]

[0086] in, is the starting time of the i+1th regulation event, is the starting time of the ith regulation event, is the adjustment time of the ith adjustment event, is the duration of regulation of the ith regulation event, is the recovery time of the ith regulation event.

[0087] The response amount constraint is that the response amount of each adjustment is within the user's adjustment capability and is expressed as:

[0088] 0≤ΔPl i ≤ΔPl max

[0089] Among them, ΔPl i is the adjustment amount issued to the user, ΔPl max The maximum value of the user adjustment amount;

[0090] The adjustment start time constraint is that the start time of each adjustment must be within the time range of one day as follows:

[0091]

[0092] in, To adjust the start time, T day is the number of hours in a day;

[0093] The adjustment duration limit is expressed as:

[0094]

[0095] in, To adjust the duration, and are the maximum and minimum regulation durations, respectively.

[0096] It should be noted that by comprehensively considering electricity costs and demand response compensation, the model helps to achieve the optimal allocation of power resources and reduce overall energy costs. By minimizing the total cost objective function, the model helps to reduce the economic burden of grid operators and users and improve the economic operation efficiency of the power system.

[0097] S300: solving the cluster adjustable load control model by using an optimization algorithm to obtain an optimization control result of the cluster adjustable load;

[0098] In the embodiment of the present application, an initial population is randomly generated in the parameter space, and mutant individuals are generated through a differential operation;

[0099] Cross the mutated individual with the current individual to generate a new candidate solution;

[0100] Select the next generation population based on the objective function value;

[0101] When the predetermined number of iterations or error requirements are reached, the optimization control result of the cluster's adjustable load is terminated and output.

[0102] Specifically, the optimization algorithm may be a genetic algorithm, a particle swarm algorithm, or a differential evolution algorithm. Since the nonlinearity of the constructed model constraints is too strong, commercial solvers cannot be used directly. In this method, the differential evolution algorithm is preferred.

[0103] Specifically, the number of iterations of the differential evolution algorithm depends on the problem size (i.e., the number of variables) and the complexity of the calculation. Generally speaking, the number of iterations set initially can be between 50 and 500. If the number of variables in the problem is large or the objective function is complex, the number of iterations can be appropriately increased.

[0104] The error requirement is usually set as the range of the objective function value. When the change of the objective function value in multiple consecutive iterations is less than a certain set value (such as 10 -6 ), the algorithm is considered to have converged and can be terminated.

[0105] It should be noted that after solving the problem, the load curve after optimizing the recovery process and demand elasticity is as follows: Figure 3 As shown in the figure, the load curve after optimized regulation is effectively controlled during peak hours, avoiding excessive power load and reducing the pressure on the power grid. During the off-peak period, the load is reasonably increased, making full use of power resources and improving power efficiency. The load curve after optimized regulation is smoother and the volatility is reduced, which shows the effectiveness of the optimized regulation strategy. The smooth load curve contributes to the stable operation of the power system, reduces the loss of equipment caused by power fluctuations, and improves the reliability of the system. It can be seen that demand elasticity and optimized regulation strategies play a positive role in load regulation. Through reasonable electricity price mechanisms and regulation strategies, users can be guided to optimize their electricity consumption behaviors and achieve the goal of energy conservation and emission reduction. In practical applications, power companies can use demand response and optimized regulation strategies to further improve the economy and stability of the power system through price signals and load regulation measures.

[0106] The above is a schematic scheme of a cluster adjustable load control method of this embodiment. It should be noted that the technical scheme of the device for cluster adjustable load control and the technical scheme of the above cluster adjustable load control method belong to the same concept, and the details of the technical scheme of the device for cluster adjustable load control in this embodiment that are not described in detail can all be referred to the description of the technical scheme of the above cluster adjustable load control method.

[0107] The device for cluster adjustable load control in this embodiment includes:

[0108] A data acquisition module, used for acquiring adjustable load dynamic response data, and constructing a cluster adjustable load dynamic response model according to the adjustable load dynamic response data, wherein the load dynamic response process of the cluster adjustable load dynamic response model includes an adjustment process, a stable maintenance process, a recovery process, and a stable state process after recovery;

[0109] A model building module, used to build a cluster adjustable load control model based on the cluster adjustable load dynamic response model according to the objective function and constraint conditions;

[0110] The model solving module is used to solve the cluster adjustable load control model by using an optimization algorithm to obtain an optimized control result of the cluster adjustable load.

[0111] This embodiment further provides a computing device, which is applicable to a cluster adjustable load control situation, including:

[0112] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the cluster adjustable load control method proposed in the above embodiment.

[0113] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for realizing cluster adjustable load control proposed in the above embodiment is implemented.

[0114] The storage medium proposed in this embodiment and the method for implementing cluster adjustable load control proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0115] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer's floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform the methods of various embodiments of the present invention.

[0116] Example 2

[0117] Reference Figure 4-Figure 5 , which is different from the first embodiment, provides a verification test of a cluster adjustable load control method to verify and illustrate the technical effects adopted in this method.

[0118] Take the simulation of the basic load data of the adjustable load cluster for one day as an example, the time interval is 5 minutes, and there are 288 data points in total. The simulated electricity price data has a time interval of 15 minutes and is repeated to 5 minutes, with a total of 288 data points. The demand elasticity coefficient is assumed to be -0.1, reflecting the user's sensitivity to changes in electricity prices. It runs on a computer with an Inteli7 processor and 16GB of memory. It uses Python 3.8, and the main dependent libraries include NumPy, Matplotlib, and SciPy.

[0119] The electricity price is the time-of-use electricity price during peak, valley and normal periods. Figure 4 As shown in the chart, the early morning 00:00-06:00 and night 22:00-24:00 are valley hours, with an electricity price of 0.322 yuan / kWh. The morning 08:00-11:00 and evening 18:00-21:00 are peak hours, with a significant increase in electricity prices to 1.112 yuan / kWh. The 06:00-08:00, 11:00-18:00 and 21:00-22:00 periods are normal hours, with electricity prices slightly lowered to 0.667 yuan / kWh.

[0120] like Figure 5 As shown in the figure, the basic load curve of the load cluster is low at night, with a load value of about 40kW and a small fluctuation. The load rises rapidly from 7 to 9 in the morning, and the load value rises from 30kW to 70kW. The peak period is from 9 to 12 in the morning, and the load value is stable at about 70kW. The load drops slightly from 12 to 13 noon due to the lunch break. The load is basically maintained at 60kW from 13 to 17 in the afternoon, with a small fluctuation. The load rises again from 17 to 22 in the evening, and the fluctuation is large, reaching about 70kW. The load drops to a lower level again from 22 to 24 at night.

[0121] In order to verify the effectiveness of the adjustable load optimization control method considering the load recovery process, the following two scenarios are set for comparative analysis:

[0122] Scenario 1: After considering demand elasticity, the dynamic response process of load regulation and recovery is not considered in the objectives and constraints of the optimization control. The control scheme is optimized and the load dynamic process simulation is performed based on the control scheme.

[0123] Scenario 2: After considering demand elasticity, the dynamic response process of load regulation and recovery is considered in the objectives and constraints of optimal regulation, the regulation scheme is optimized, and the load dynamic process simulation is performed based on the regulation scheme;

[0124] Table 1 Comparison of control results in different scenarios

[0125]

[0126] As shown in Table 1, both Scenario 1 and Scenario 2 regulated the load cluster twice, with the regulation amount and duration both reaching the maximum of 10kW and 120 minutes, but the starting time was different, resulting in different changes in the final cluster load curve over time. The total cost after regulation in Scenario 2 was lower than that in Scenario 1, and it was more economical, which verified the effectiveness of this method.

[0127] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A cluster adjustable load control method, characterized in that: include: Acquire adjustable load dynamic response data, and construct a cluster adjustable load dynamic response model according to the adjustable load dynamic response data, wherein the load dynamic response process of the cluster adjustable load dynamic response model includes an adjustment process, a stable maintenance process, a recovery process, and a stable state process after recovery; Based on the cluster adjustable load dynamic response model, a cluster adjustable load control model is constructed according to the objective function and constraint conditions; The cluster adjustable load control model is solved using an optimization algorithm to obtain an optimized control result of the cluster adjustable load.

2. The cluster adjustable load control method according to claim 1, characterized in that: The cluster adjustable load control model is constructed based on the objective function and constraints, including: The objective function of the cluster adjustable load control model is that the total cost after response is equal to the energy cost after the user load response minus the compensation cost of the user load response.

3. The cluster adjustable load control method according to claim 2, characterized in that: Also includes: The constraint condition of the cluster adjustable load control model is that the start time of the i+1th control event is greater than the start time of the i-th control time plus the adjustment time of the i-th control event plus the adjustment duration of the i-th control event plus the recovery time of the i-th control event.

4. The cluster adjustable load control method according to claim 3, characterized in that: Solving the cluster adjustable load control model using an optimization algorithm includes: The initial population is randomly generated in the parameter space, and the mutant individuals are generated through differential operation; Cross the mutated individual with the current individual to generate a new candidate solution; Select the next generation population based on the objective function value; When the predetermined number of iterations or error requirements are reached, the optimization control result of the cluster's adjustable load is terminated and output.

5. The cluster adjustable load control method according to claim 1 or 4, characterized in that: Adjustable load response data includes: The adjustable load response data may be an adjustment time from the start of response to reaching the target adjustment power and a recovery time from the end of response to reaching a stable state.

6. The cluster adjustable load control method according to claim 5, characterized in that: The adjustment process and the smooth maintenance process include: The regulation process is that the cluster adjustable load starts to respond within the response start delay time, and the load reaches the target regulation power within the regulation time, which is expressed as: Among them, T delay is the delay time, ΔP1 is the target adjustment power, P L is the original load power, ω n is the natural frequency of the system, ω d is the oscillation frequency after damping, Damping ratio, t is the time variable; The stable maintenance process is that after the cluster adjustable load reaches the target adjustment power, the load remains constant within the stable maintenance time, and the power change is the original load power minus the target adjustment power.

7. The cluster adjustable load control method according to claim 6, characterized in that: The recovery process and post-recovery steady-state process include: The recovery process is that the cluster adjustable load ends the stable maintenance time and the load begins to recover. Within the recovery time, the load recovers to the original load power, which is expressed as: Among them, T rise T is the adjustment time from the start of response to reaching the target adjustment power. steady To maintain the stable time, T delay is the delay time, ΔP1 is the target adjustment power, P L is the original load power, ω n is the natural frequency of the system, ω d is the oscillation frequency after damping, Damping ratio, t is the time variable; The stable state process after recovery is when the recovery time ends, the cluster can adjust the load to restore to the original load power, and the power change is that the load power is equal to the original load power.

8. A system for a cluster adjustable load control method, characterized in that: include: A data acquisition module, used for acquiring adjustable load dynamic response data, and constructing a cluster adjustable load dynamic response model according to the adjustable load dynamic response data, wherein the load dynamic response process of the cluster adjustable load dynamic response model includes an adjustment process, a stable maintenance process, a recovery process, and a stable state process after recovery; A model building module, used to build a cluster adjustable load control model based on the cluster adjustable load dynamic response model according to the objective function and constraint conditions; The model solving module is used to solve the cluster adjustable load control model by using an optimization algorithm to obtain an optimized control result of the cluster adjustable load.

9. An electronic device, characterized in that: The device comprises: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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