A distributed control method and system based on power system load side demand response

Abstract

The application discloses a kind of distributed control method and system based on power system load side demand response, based on the heterogeneous resource of load side involved in demand response, according to the operating characteristics of heterogeneous resource of load side, construct general load side flexibility model, and determine model parameter, according to the power consumption power average value of the heterogeneous resource of load side participating in demand response in general load side flexibility model, demand response price, iteration number, maximum iteration calculation number and convergence criterion are initialized;For the scene that a large number of heterogeneous resources of load side cooperatively participate in demand response under the power market environment, the privacy protection requirements between resources are considered, the contradiction and cooperativity of privacy protection and multi-agent collaborative control are considered, the distributed control framework proposed is helpful to balance between the two, and the control and response ability of mass heterogeneous resources of load side is enhanced.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system regulation and management, and in particular relates to a distributed control method and system based on power system load-side demand response. Background Art

[0002] With the integration of a high proportion of renewable energy into the power system, the demand for operational flexibility is increasing, and flexibility is the guarantee of power system reliability. As part of the power system, load-side resources (such as electric vehicles, industrial flexible loads, and temperature-controlled loads) are often used as flexible resources due to their easy control, large number, and wide distribution. Currently, the flexibility of load-side resources is widely utilized in the form of demand response to enhance the power system's ability to accommodate renewable energy and improve its reliability. Therefore, the coordinated distributed control of multiple heterogeneous load-side resources in the context of power demand response is of great significance and value to the safe and economic operation of the power system.

[0003] Existing research on the regulation and management of load-side resources falls into two main categories: one using load management systems for unified dispatch and control, and the other using electricity prices to indirectly guide load-side resources to participate in demand response. Research on unified dispatch and control methods based on load management systems primarily focuses on demand forecasting, uncertainty characterization, and robust control, with only a small portion considering privacy protection issues among loads with different ownership. Regarding control methods that indirectly guide load-side resources to participate in demand-side response using electricity prices, existing research generally employs a joint solution of multiple Stackelberg game problems for optimal control. However, this method is computationally intensive and exhibits poor convergence, making it difficult to adapt to real-time computing requirements.

[0004] Existing research has been limited in its focus on the regulation and management of large-scale, diverse load-side resources. A commonly used control method is the mean field game. However, existing mean field game-based regulation and management methods can only control a single resource type and are difficult to scale to multiple heterogeneous load-side resources. Given the lack of unified modeling methods and control strategies for these diverse load-side resources, coordinated control of these multiple types of load-side resources presents challenges. Furthermore, existing control methods are computationally heavy, making them difficult to meet the deployment requirements of real-world equipment. Summary of the Invention

[0005] The purpose of the present invention is to provide a distributed control method and system based on load-side demand response of a power system, so as to overcome the problems of difficulty and heavy computational burden in the existing control methods for coordinated control of multiple types of load-side resources.

[0006] A distributed control method based on load-side demand response of a power system comprises the following steps:

[0007] S1, based on the load-side heterogeneous resources involved in demand response and the operating characteristics of the load-side heterogeneous resources, a general load-side flexibility model is constructed and the model parameters are determined;

[0008] S2, based on operating experience, initializes the average power consumption, demand response price, number of iterations, maximum number of iteration calculations, and convergence criteria of the load-side heterogeneous resources participating in demand response in the general load-side flexibility model;

[0009] S3: Based on the initialized universal load-side flexibility model, each load-side heterogeneous resource solves its own optimal response strategy to the demand response price signal in parallel with the goal of maximizing its own benefit, and obtains the optimal power consumption;

[0010] S4, based on the optimal power consumption of each load-side heterogeneous resource participating in the demand response price signal, set the number of iterations to be increased by 1, and update the average power consumption of the load-side heterogeneous resources and the updated demand response price;

[0011] S5. If the absolute value of the difference between the average power consumption of the load-side heterogeneous resources after the update and the average power consumption before the update is less than or equal to the set threshold, the demand response price corresponding to the optimal power consumption of each load-side heterogeneous resource participating in the demand response price signal and the power consumption plan of each load-side resource are made public; otherwise, return to step S3 and continue to iterate until the number of iterations meets the iteration upper limit or the absolute value of the difference between the average power consumption after the update and the average power consumption before the update is less than or equal to the set threshold, and the current result is output.

[0012] Preferably, a unified model is performed for various types of load-side flexibility resources, and the model is uniformly written as a combination of constraints on load power consumption.

[0013] Preferably, the demand response price rule is first determined, as shown in formula (5):

[0014] p(t)=qD(t)+b (9)

[0015] Where q and b are price parameters and are constant; D(t) is the total electricity load at time t;

[0016] The total electricity load can be expressed as the sum of fixed load and flexible load:

[0017]

[0018] Where D0(t) represents the total fixed load at time t, u i (t) represents the electricity consumption of the i-th flexible load at time t; N is the total number of flexible loads; z(t) is the average electricity consumption of the flexible loads at time t.

[0019] Preferably, after the demand response price rule is determined, the initial demand response price p (0) (t) Broadcast to each load-side flexible resource, p (0) (t) can be calculated based on the average power consumption of the initialized flexible load z (0) (t) calculation, the calculation method can be obtained by combining (5)-(6):

[0020] p (0) (t) = q(D0(t) + Nz (0) (t))+b (11)

[0021] Initialize the number of iterations k = 1 and determine the maximum allowed number of iterations k max , and the convergence criterion δ.

[0022] Preferably, the objective function of the load-side heterogeneous resources participating in demand response is to minimize the combination of two costs. Based on the unified universal load-side flexibility resource model, the objective function for participating in demand response control is constructed as follows:

[0023]

[0024] In the formula, the first term of the integrand is the electricity cost, and the second term is the opportunity cost of changing the original electricity plan; i is the penalty coefficient of opportunity cost, y i (t) is the original electricity usage plan.

[0025] Preferably, the optimal response strategy of each load-side heterogeneous resource to the demand response price signal is solved in parallel with the goal of maximizing its own benefit, that is, the power consumption plan u reported by the load-side heterogeneous flexibility resource i (t), update the number of iterations k = k + 1, and calculate the updated average power consumption z according to the following formula (k) (t):

[0026]

[0027] Preferably, according to formula (7) and the updated average power consumption z (k) (t) Calculate the electricity price after demand response:

[0028] p (k) (t) = q(D0(t) + N z (k) (t))+b (14).

[0029] A distributed control system based on load-side demand response of a power system, comprising a load-side resource control center and a load-side resource management center;

[0030] The load-side resource management center builds a universal load-side flexibility model based on the load-side heterogeneous resources involved in demand response and the operating characteristics of the load-side heterogeneous resources, and determines the model parameters;

[0031] The load-side resource control center initializes the average power consumption, demand response price, number of iterations, maximum number of iteration calculations, and convergence criteria of the load-side heterogeneous resources participating in the demand response in the general load-side flexibility model based on operating experience. The load-side resource control center transmits the demand response price signal to the load-side resource management center under its control.

[0032] Based on the initialized universal load-side flexibility model, the load-side resource management center calculates the optimal response strategy for the demand response price signal in parallel for each load-side heterogeneous resource with the goal of maximizing its own benefits, obtains the optimal power consumption, and returns the result to the load-side resource control center.

[0033] The load-side resource control center sets the number of iterations plus 1 based on the optimal power consumption of each load-side heterogeneous resource participating in the demand response price signal, and updates the average power consumption of the load-side heterogeneous resources and the updated demand response price; if the absolute value of the difference between the updated average power consumption of the load-side heterogeneous resources and the average power consumption before the update is less than or equal to the set threshold, the demand response price corresponding to the optimal power consumption of each load-side heterogeneous resource participating in the demand response price signal and the power consumption plan of each load-side resource are made public; otherwise, the iteration is continued until the number of iterations meets the iteration upper limit or the absolute value of the difference between the updated average power consumption and the average power consumption before the update is less than or equal to the set threshold, and the current result is output.

[0034] Preferably, a unified model is performed for various types of load-side flexibility resources, and the model is uniformly written as a combination of constraints on load power consumption.

[0035] Preferably, the demand response price rule is determined as shown in formula (5):

[0036] p(t)=qD(t)+b (15)

[0037] Where q and b are price parameters and are constant; D(t) is the total electricity load at time t;

[0038] The total electricity load can be expressed as the sum of fixed load and flexible load:

[0039]

[0040] Where D0(t) represents the total fixed load at time t, u i(t) represents the electricity consumption of the i-th flexible load at time t; N is the total number of flexible loads; z(t) is the average electricity consumption of the flexible loads at time t.

[0041] Compared with the prior art, the present invention has the following beneficial technical effects:

[0042] The present invention provides a distributed control method based on load-side demand response of an electric power system. Based on the load-side heterogeneous resources involved in the demand response and in accordance with the operating characteristics of the load-side heterogeneous resources, a universal load-side flexibility model is constructed, and model parameters are determined. The average power consumption, demand response price, number of iterations, maximum number of iteration calculations, and convergence criterion of the load-side heterogeneous resources participating in the demand response in the universal load-side flexibility model are initialized according to operating experience. For the scenario in which a large number of load-side heterogeneous resources collaboratively participate in the demand response in an electric power market environment, the method takes into account the privacy protection requirements between the resources, and the contradiction and synergy between privacy protection and multi-agent collaborative control. The proposed distributed control framework helps to achieve a balance between the two and enhance the control and response capabilities of the massive heterogeneous resources on the load side.

[0043] The distributed control method proposed in the present invention has theoretical advantages such as the existence and uniqueness of the equilibrium point and guaranteed convergence, which ensures the advantages of high controllability of the calculation process, high calculation efficiency, and low usage of computing resources. It can further reduce the equipment cost of collaborative control and is easy to deploy to terminal controlled devices.

[0044] The present invention first constructs a unified universal model for heterogeneous load-side resources and converts the operating parameters of these heterogeneous resources into universal unified model parameters, thereby achieving unified modeling of heterogeneous resources. This unified universal model can effectively improve the convergence and computational efficiency of the proposed distributed control method, reduce control time, and further enhance the response efficiency of coordinated control. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 Schematic diagram of the flow of a distributed control method for load side of a power system in an embodiment of the present invention.

[0046] Figure 2 This is the application effect of coordinated regulation of load-side resources in an embodiment of the present invention.

[0047] Figure 3 This is the overall demand response effect of the method proposed in the embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0049] like Figure 1 As shown, a distributed control method based on power system load side demand response includes the following steps:

[0050] S1, based on the load-side heterogeneous resources involved in demand response and according to the operating characteristics of the load-side heterogeneous resources, a universal load-side flexibility model is constructed and the model parameters are determined to achieve homogeneous modeling of the heterogeneous resources;

[0051] S2, the load-side resource control center (load aggregator or market operation center) initializes the average power consumption z of the load-side heterogeneous resources participating in demand response based on operating experience (0) and the demand response price p (0) , the number of iterations k = 1, the maximum number of iterations k max , and the convergence criterion δ; at the same time, the load-side resource control center transmits the demand response price signal to the load-side resource management center controlled by the load-side resource control center;

[0052] S3, each load side heterogeneous resource parallel solves its own optimal response strategy to the demand response price signal (with the goal of maximizing its own benefits) to obtain the optimal power consumption And return the result to the load side resource control center;

[0053] S4, the load side resource control center collects the optimal power consumption of each load side heterogeneous resource participating in the demand response price signal Set the number of iterations k = k + 1 and update the average power consumption z of the heterogeneous resources on the load side (k) , and the updated demand response price p (k) ;

[0054] S5, the load side resource control center determines whether the iteration meets the condition || z (k) -z (k-1) ||≤δ. If satisfied, the demand response price corresponding to the optimal power consumption of each load-side heterogeneous resource participating in the demand response price signal and the power consumption plan of each load-side resource are made public to complete market clearing. If not satisfied, return to step 3 and continue iteration. If the number of iterations exceeds the upper limit, the iteration is also terminated, the current result is output, and market clearing is completed.

[0055] Unified general modeling of heterogeneous resources on the load side:

[0056] To enable coordinated participation of load-side resources in demand response regulation, it is first necessary to develop a unified model for various types of load-side flexibility resources. This model is expressed as a combination of constraints on load power consumption. The following describes a detailed explanation of this unified general modeling approach using common heterogeneous load-side resources: electric vehicles, industrial loads, and temperature-controlled loads.

[0057] 1) Electric vehicle model:

[0058] Use E EV (t) and u EV (t) represents the electric vehicle's power and charging power respectively. When charging an electric vehicle, it is necessary to consider the dynamic characteristics of the power change process and the upper and lower limits of the power. Charging power upper and lower limit constraints Initial power Constraint, full charge Constraints and available charging time Constraints; therefore, the electric vehicle electricity consumption model can be expressed as:

[0059]

[0060] represents the time derivative of the electric vehicle's charge.

[0061] 2) Industrial load model:

[0062] Most industrial loads are flexible loads, such as metallurgy, textile and other industries, which only need to complete the specified workload within the specified time. IFD (t) and u IFD (t) represents the workload and power consumption of industrial loads. Therefore, considering the workload variation characteristics of industrial loads and the upper and lower limit constraints of power consumption, Initial power Constraints and final workload Constraints and available working hours Constraints; therefore, the industrial load electricity consumption model can be expressed as:

[0063]

[0064] It represents the time derivative of industrial load electricity consumption.

[0065] 3) Temperature control load model:

[0066] Since inverter-based air conditioners occupy a large market share, the power consumption of the temperature control load can beTCL (t) is considered as a continuously adjustable real number and adopts θ TCL (t) represents the temperature of the temperature-controlled load. The power consumption of the temperature-controlled load must take into account the thermal dynamic characteristics and the acceptable temperature range constraints. Power Consumption Constraints Initial temperature Constraint, final temperature Constraints and temperature control periods Therefore, the electricity consumption model of the temperature control load can be expressed as:

[0067]

[0068] It represents the derivative of temperature control load temperature with respect to time.

[0069] Where θ r is the external ambient temperature, R and C are the thermal resistance and heat capacity of the temperature control load respectively, and η is the cooling / heating efficiency.

[0070] 4) Unified general model parameter table

[0071] Based on the power consumption characteristics of electric vehicles, industrial loads, and temperature control loads in (1)-(3), this application proposes a unified general model as shown below:

[0072]

[0073] The parameters in the formula can be set according to different load types. Taking electric vehicles, industrial loads, and temperature control loads as examples, the set parameters are shown in the following table.

[0074] Table 1 Comparison of heterogeneous resource model parameters in the unified general modeling link.

[0075]

[0076]

[0077] Distributed iterative computing link:

[0078] Based on the above-mentioned unified general model of heterogeneous resources on the load side, the details of the proposed coordinated control method are described in detail; the load-side resource control center initializes the demand response rules:

[0079] In this step, the load-side resource control center needs to first determine the demand response price rules, as shown in formula (5):

[0080] p(t)=qD(t)+b(21)

[0081] Where q and b are price parameters, which are constant values; D(t) is the total electricity load at time t. Generally, the electricity price to incentivize demand response should be higher when the electricity load is high and lower when the electricity load is low. Therefore, q is non-negative.

[0082] The total electricity load can be expressed as the sum of fixed load and flexible load, that is:

[0083]

[0084] Where D0(t) represents the total fixed load at time t, u i (t) represents the electricity consumption of the i-th flexible load at time t; N is the total number of flexible loads; z(t) is the average electricity consumption of the flexible loads at time t.

[0085] After the demand response price rules are determined, the load side resource control center needs to initialize the demand response price p (0) (t) Broadcast to each load-side flexible resource, p (0) (t) can be based on the average power consumption of the initialized flexible load a (0) (t) calculation, the calculation method can be obtained by combining (5)-(6), which is

[0086] p (0) (t) = q(D0(t) + Nz (0) (t))+b(23)

[0087] In addition, the load side resource control center initializes the number of iterations k = 1 and determines the maximum allowed number of iterations k max , and the convergence criterion δ.

[0088] Optimal response of heterogeneous resources on the load side:

[0089] After receiving the demand response price information broadcast by the load-side resource control center in step 1), the load-side heterogeneous resources make the best response to the price, adjust the power consumption plan, and report the power consumption plan at the current price to the load-side resource control center. This step is detailed below.

[0090] The optimal response can be considered an optimization problem. Generally, the objective function for heterogeneous load-side resources participating in demand response is to minimize the combination of two costs: electricity cost and the opportunity cost of changing the original electricity plan. Based on the unified universal load-side flexibility resource model, the objective function for its participation in demand response control can be constructed as:

[0091]

[0092] In the formula, the first term of the integrand is the electricity cost, and the second term is the opportunity cost of changing the original electricity plan.i is the penalty coefficient of opportunity cost, y i (t) is the original electricity usage plan.

[0093] The constraint condition of the optimal response optimization problem is (4). This problem can be transformed into a traditional linear optimization problem by using methods such as differential analysis, and can be solved quickly. After the problem is solved, the load-side heterogeneous resources will report their power consumption plans after participating in the demand response to the load-side resource control center.

[0094] The load-side resource control center updates the demand response price. In this step, the load-side resource control center receives the electricity consumption plan reported by each load-side heterogeneous resource, updates the number of iterations, and calculates the latest demand response price. This step is detailed below:

[0095] The load-side resource control center receives the power consumption plan u reported by N load-side heterogeneous flexibility resources. i (t), update the number of iterations k = k + 1, and then calculate the updated average power consumption z according to the following formula (k) (t):

[0096]

[0097] Then, the load side resource control center calculates the average power consumption z according to formula (7) and the updated average power consumption z (k) (t) Calculate the electricity price after demand response:

[0098] p (k) (t) = q(D0(t) + N z (t) )+b(26)

[0099] Then the price p (k) (t) Broadcast to the load-side flexibility resources.

[0100] The load-side resource control center determines whether the demand response iteration phase has converged:

[0101] In this step, the load side resource control center calculates the power consumption plan of each load side resource based on the current iteration and the previous iteration. and Determine whether the iteration has converged according to the following formula:

[0102]

[0103] If formula (11) holds true, it is determined that the iterative calculation link of the demand response plan has converged, and the process proceeds to step 5). If formula (11) does not hold true, it is determined that the iterative calculation link of the demand response plan has not converged, and the process proceeds to step 2) to continue the iterative link.

[0104] 5) Load side resource regulation center discloses demand response price, and completes market clearing:

[0105] Load side resource regulation center discloses demand response price, and declares that clearing link ends, and each load side resource fixed electricity plan.

[0106] Test and application of load side heterogeneous resource participating in demand response cooperative control:

[0107] The present application is based on actual load data, and the performance of the cooperative control method is verified by using an example containing 3000 load side flexibility resources (1000 electric vehicles, 1000 industrial loads, 1000 temperature control loads). In order to further reflect the heterogeneous characteristics of load side flexibility resources, the control time of electric vehicles is set to 21 o'clock in the evening to 8 o'clock in the morning; the control time of industrial load is set to 7 o'clock in the morning to 18 o'clock in the evening; the control time of temperature control load is set to 24 hours a day. In addition, the initial electric quantity of electric vehicle, the demand of industrial load and the initial temperature of temperature control load are divided into 10 groups, and the number in each group is set according to normal distribution. At the same time, in order to reflect the universality of the method, three demand response pricing methods are set for comparative analysis.

[0108] The method proposed in the present application meets the convergence criterion δ≤0.001 after 14 iterations under the regulation scenario of the initial flexibility load power average value z(0)(t)=0, and the time consumption of single iteration is 0.82 seconds, so the convergence is strong, the calculation efficiency is high, and the actual regulation demand can be met.

[0109] The regulation effect of the method proposed in the present application on electric vehicles, industrial loads and temperature loads in the test case is shown in Figure 2 Figure 2 Figures (a), (b) and (c) in Figure 2 respectively represent the electricity characteristics of electric vehicles, industrial loads and temperature control loads without being regulated by the method proposed in the present application, Figure 2 Figures (d), (e) and (f) respectively represent the electricity characteristics of electric vehicles, industrial loads and temperature control loads after being cooperatively regulated by the method proposed in the present application. From the comparison of

[0110] , under the cooperative regulation method proposed in the present application, each type of heterogeneous load side flexibility resource appears a strategic participation in demand response behavior to minimize its own cost: such as electric vehicles appearing a discharging phenomenon during the high electricity price period of the evening peak to obtain income and reduce cost; industrial loads appearing load shifting phenomenon during the day to increase electricity power in low electricity price period, and reduce electricity power in high electricity price period; temperature control loads appearing pre-cooling phenomenon in low electricity price period to reduce electricity power in high electricity price period.

[0110] The effect of the method proposed in the present application on the cooperative regulation of load side overall participation in demand response is shown in Figure 3 .

[0111] The application is directed to the scenario of a large number of load-side heterogeneous resources participating in demand response in an electricity market environment, considers the privacy protection requirements between resources, and avoids the leakage of electricity consumption characteristics, operating conditions and user privacy. The application considers the contradiction and synergy of privacy protection and multi-agent collaborative control, and the proposed distributed control framework helps to balance between the two, enhances the control and response capability of the massive heterogeneous resources on the load side.

[0112] The application is based on the related theory of mean field game, and the proposed distributed regulation method has the theoretical advantages of existence and uniqueness of equilibrium point, guaranteed convergence, etc., ensures high controllability, high calculation efficiency, and low calculation resource usage, etc., can further reduce the equipment cost of collaborative control, and is easy to deploy to terminal controlled devices.

[0113] The application first constructs a unified general model of heterogeneous load-side resources, converts the operating parameters of heterogeneous resources into general unified model parameters, thereby realizing the unified modeling of heterogeneous resources. The unified general model can effectively improve the convergence and calculation efficiency of the proposed distributed regulation method, reduce the required control time, and further enhance the response efficiency of collaborative control.

Claims

1. A distributed control method based on load-side demand response of a power system, characterized in that: The following steps are involved: S1, based on the load-side heterogeneous resources involved in demand response and the operating characteristics of the load-side heterogeneous resources, a general load-side flexibility model is constructed and the model parameters are determined; S2, based on operating experience, initializes the average power consumption, demand response price, number of iterations, maximum number of iteration calculations, and convergence criteria of the load-side heterogeneous resources participating in demand response in the general load-side flexibility model; S3: Based on the initialized universal load-side flexibility model, each load-side heterogeneous resource solves its own optimal response strategy to the demand response price signal in parallel with the goal of maximizing its own benefit, and obtains the optimal power consumption; S4, based on the optimal power consumption of each load-side heterogeneous resource participating in the demand response price signal, set the number of iterations to be increased by 1, and update the average power consumption of the load-side heterogeneous resources and the updated demand response price; S5: If the absolute value of the difference between the average power consumption of the load-side heterogeneous resources after the update and the average power consumption before the update is less than or equal to the set threshold, the demand response price corresponding to the optimal power consumption of each load-side heterogeneous resource participating in the demand response price signal and the power consumption plan of each load-side resource are disclosed; otherwise, return to step S3 and continue iterating until the number of iterations meets the iteration upper limit or the absolute value of the difference between the average power consumption after the update and the average power consumption before the update is less than or equal to the set threshold, and the current result is output; Determine the demand response price rule, as shown in formula (5): p(i)=qD(t)+b (1) Where α and b are price parameters and are fixed values; D(t) is the total electricity load at time t; The total electricity load can be expressed as the sum of fixed load and flexible load: Where D0(t) represents the total fixed load at time t, u i (t) represents the electricity consumption of the i-th flexible load at time t; N is the total number of flexible loads; z(t) is the average electricity consumption of flexible loads at time t; After the demand response price rule is determined, the initial demand response price p (0) (t) Broadcast to each load-side flexible resource, p (0) (t) can be calculated based on the average power consumption of the initialized flexible load z (0) (t) calculation, the calculation method can be obtained by combining (5)-(6): p (0) (t)=q(D0(t)+Nz (0) (t))+b (3) Initialize the number of iterations k = 1 and determine the maximum allowed number of iterations k max , and the convergence criterion δ.

2. A distributed control method based on load-side demand response of a power system according to claim 1, characterized in that: A unified modeling is performed for various types of load-side flexibility resources, and the model is uniformly written as a combination of constraints on load power consumption.

3. A distributed control method based on load-side demand response of a power system according to claim 1, characterized in that: The objective function of load-side heterogeneous resources participating in demand response is to minimize the combination of two costs. Based on the unified universal load-side flexibility resource model, the objective function for participating in demand response control is constructed as follows: J i =∫0 T p(t)u i (t)+γ i (u i (t)-y i (t)) 2 dt (4) In the formula, the first term of the integrand is the electricity cost, and the second term is the opportunity cost of changing the original electricity plan; i is the penalty coefficient of opportunity cost, y i (t) is the original electricity usage plan.

4. A distributed control method based on power system load side demand response according to claim 3, characterized in that: Obtain the optimal response strategy of each load-side heterogeneous resource to the demand response price signal in parallel with the goal of maximizing its own benefits, that is, the electricity consumption plan u reported by the load-side heterogeneous flexibility resources i (t), update the number of iterations k = k + 1, and calculate the updated average power consumption z according to the following formula (k) (t):

5. A distributed control method based on load-side demand response of a power system according to claim 4, characterized in that: According to formula (7) and the updated average power consumption z (k) (t) Calculate the electricity price after demand response: p (k) (t)=q(D0(t)+Nz (k) (t))+b (6)。 6. A distributed control system for power system load side demand response based on the distributed control method for power system load side demand response according to claim 1, characterized in that: Including load side resource control center and load side resource management center; The load-side resource management center builds a universal load-side flexibility model based on the load-side heterogeneous resources involved in demand response and the operating characteristics of the load-side heterogeneous resources, and determines the model parameters; The load-side resource control center initializes the average power consumption, demand response price, number of iterations, maximum number of iteration calculations, and convergence criteria of the load-side heterogeneous resources participating in the demand response in the general load-side flexibility model based on operating experience. The load-side resource control center transmits the demand response price signal to the load-side resource management center under its control. Based on the initialized universal load-side flexibility model, the load-side resource management center calculates the optimal response strategy for the demand response price signal in parallel for each load-side heterogeneous resource with the goal of maximizing its own benefits, obtains the optimal power consumption, and returns the result to the load-side resource control center. The load-side resource control center sets the number of iterations plus 1 based on the optimal power consumption of each load-side heterogeneous resource participating in the demand response price signal, and updates the average power consumption of the load-side heterogeneous resources and the updated demand response price; if the absolute value of the difference between the updated average power consumption of the load-side heterogeneous resources and the average power consumption before the update is less than or equal to the set threshold, the demand response price corresponding to the optimal power consumption of each load-side heterogeneous resource participating in the demand response price signal and the power consumption plan of each load-side resource are made public; otherwise, the iteration is continued until the number of iterations meets the iteration upper limit or the absolute value of the difference between the updated average power consumption and the average power consumption before the update is less than or equal to the set threshold, and the current result is output.

7. A distributed control system based on load-side demand response of a power system according to claim 6, characterized in that: A unified modeling is performed for various types of load-side flexibility resources, and the model is uniformly written as a combination of constraints on load power consumption.

8. A distributed control system based on load-side demand response of a power system according to claim 6, characterized in that: Determine the demand response price rule, as shown in formula (5): p(t)=qD(t)+b (7) Where q and b are price parameters and are constant; D(t) is the total electricity load at time t; The total electricity load can be expressed as the sum of fixed load and flexible load: Where D0(t) represents the total fixed load at time t, u i (t) represents the electricity consumption of the i-th flexible load at time t; N is the total number of flexible loads; z(t) is the average electricity consumption of the flexible loads at time t.

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