Iterative point-nearest neighbor algorithm-based demand-side resource automatic coordination method and system

Abstract

The application is based on the demand side resource automatic coordination method and system of the iterative nearest point algorithm, which comprises: establishing a controllable load model; determining a dynamic electricity price strategy, and publishing the demand response electricity price to each controllable load after initializing the dynamic electricity price strategy according to the fixed load; each controllable load updates the optimal power consumption with the lowest electricity cost after receiving the demand response electricity price information; updating the demand response electricity price according to the optimal power consumption with the lowest electricity cost of each controllable load participating in the demand response; judging whether the demand response process meets the convergence criterion according to the updated optimal power consumption until the iteration reaches the upper limit or the difference between the sum of the optimal power consumptions of all controllable loads before and after the update is smaller than the given error, and then determining the current result. The system comprises a modeling module, a data processing module, a data updating module and a result judging module. The application can guarantee the safe operation and economic efficiency of the power system.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of power systems, and particularly relates to a demand-side resource automatic coordination method and system based on an iterative neighbor point algorithm. BACKGROUND

[0002] Renewable energy power and clean energy replacement are key means to solve energy and climate problems, and also pose challenges to power system operation. With the increase in randomness of power generation side, the traditional "source following load" dispatching mode is difficult to adapt, forcing the reform of energy consumption mode to release the potential of the load side and realize the development and utilization of large-scale renewable energy power. Using load flexibility to improve power system operation is one of the important means of demand response, which realizes peak load reduction, improves power grid construction and operation efficiency, and helps to accommodate renewable energy power.

[0003] With the development of advanced measurement technology, price signal is considered as the main means for residents to participate in demand response, which can play the role of user autonomous response and solve the dilemma of large number of residential users, small size, multiple types and scattered distribution. Price signal is divided into two categories, static price and dynamic price, which are distinguished by whether the price level changes with the current system load level. Static price only changes with time, such as time-of-use price, while dynamic price is related to both time and system load level, which can distinguish different time and different load level of power load. When a large number of power users adjust their power consumption scheme with the goal of minimizing power consumption cost, they will choose to use electricity at low price, forming a load peak and causing a price inversion phenomenon. The root cause of this phenomenon is that static price only changes with time and cannot reflect and guide the supply and demand changes of power system. As the recipients of the price, the system load curve formed by power users is difficult to reflect to power market operators, resulting in giving low price when high price is given and giving high price when low price is given, which is not conducive to the safe operation and economic efficiency of power system. At the same time, in the process of demand response implementation, the determination of optimal strategy of diversified residential user decision-making subject in power market is extremely challenging. The traditional optimal theory system of single subject decision-making cannot meet the strategy optimization between multiple decision-making subjects. Therefore, game theory for solving multi-decision-making optimization problem is expected to become a powerful tool for solving demand side of power, to form a set of demand-side resource automatic coordination method, and to provide a theoretical basis for calculating power consumption scheme under controllable load equilibrium state and realizing autonomous response behavior of power users.

[0004] In summary, determining the power consumption decision-making behavior of controllable load participating in demand response is a key problem in future power system demand response analysis and design, and plays a fundamental role in numerous researches and applications. However, traditional price demand response often uses static price, which cannot reflect and guide the supply and demand level of power system. SUMMARY

[0005] The present application aims at the deficiency of the existing traditional static electricity price demand response, starting from the lowest load power consumption cost, considering the autonomous response ability of the user, using the method of game theory, providing a demand side resource automatic coordination method and system based on iterative proximal point algorithm, describing the power consumption decision under the controllable load equilibrium state, and laying a foundation for solving the demand response analysis and electricity price design problems considering the autonomous response ability of the user.

[0006] In order to achieve the above-mentioned purpose, the present application adopts the following technical scheme:

[0007] The demand side resource automatic coordination method based on iterative proximal point algorithm comprises:

[0008] 1) The original data parameters of controllable load and fixed load in a certain area are processed, and a controllable load model is established;

[0009] 2) The dynamic electricity price strategy is determined, and after initializing the dynamic electricity price strategy according to the fixed load, the demand response electricity price is published to each controllable load;

[0010] 3) According to the controllable load model, each controllable load updates the optimal power consumption power with the lowest power consumption cost after receiving the demand response electricity price information;

[0011] 4) According to the optimal power consumption power of each controllable load participating in the demand response with the lowest power consumption cost, the demand response electricity price is updated, and the iteration number is updated by 1;

[0012] 5) According to the updated optimal power consumption power, it is judged whether the demand response process meets the convergence criterion, if the modulus of the difference between the sum of the optimal power consumption powers of all controllable loads and the sum of the optimal power consumption powers before updating is less than a given error, the final power consumption power of the controllable load, the system load curve and the demand response electricity price are determined, if not, return to step 3) to update the demand response electricity price information published to each controllable load again, until the iteration number reaches the iteration upper limit or the modulus of the difference between the sum of the optimal power consumption powers of all controllable loads and the sum of the optimal power consumption powers before updating is less than a given error, then the current result is determined.

[0013] The further improvement of the present application is that in step 1), the original data parameters of the controllable load include start time, stop time, minimum power, maximum power and power consumption, and according to the different operating characteristics and structural parameters of the controllable load, the controllable load model is established,

[0014] p a,min u a,t ≤p a,t ≤p a,max u a,t (1)

[0015]

[0016] wherein p a,t represents the power consumption of the controllable load a at time t; u a,t is a 0-1 state parameter related to the user-set allowable use time, and takes 1 to indicate that the controllable load is started, and takes 0 to indicate that the controllable load is turned off; p a,min represents the minimum power of the controllable load a; p a,max represents the maximum power of the controllable load a; E a represents the total power consumption of the controllable load a; γ a,t represents the loss of power consumption.

[0017] The further improvement of the present application is that in step 2), the dynamic electricity price strategy is determined, the dynamic electricity price strategy is initialized according to the fixed load, then the demand response electricity price is published to each controllable load, and the update parameter, the maximum iteration number and the convergence criterion are set according to the past operation experience;

[0018] The dynamic electricity price strategy is determined as follows:

[0019] λ t = a t L t + b t (3)

[0020] wherein a t , b t are dynamic electricity price parameters, which are set according to the past operation experience, and a t is set to a non-negative value; L t is the system load level at t of the controllable load participating in demand response; λ t is the electricity price at t;

[0021] The system load curve is composed of the fixed load and the controllable load, and is expressed as follows:

[0022]

[0023] wherein D t is the fixed load of the user participating in demand response at t, which is obtained from historical data; A is the number of controllable loads participating in demand response;

[0024] In the case of determining the dynamic electricity price strategy, the dynamic electricity price is initialized using the fixed load data, as follows:

[0025] λ t (0) = a t D t + b t (5).

[0026] The further improvement of the present application is that in step 3), according to the controllable load model, each controllable load updates the optimal power consumption with the lowest power consumption cost after receiving the demand response price information, comprising:

[0027] After receiving the published signal, the controllable load adjusts the power consumption scheme to minimize the power consumption cost, and the corresponding power consumption cost function c a is,

[0028]

[0029] When the iteration number is k, the adjustment strategy of the controllable load a is,

[0030]

[0031] Wherein, η and γ are update parameters selected according to operation experience, p a is the power consumption sequence of the controllable load a over time.

[0032] The further improvement of the present application is that in step 4), according to the optimal power consumption of each controllable load determined by the current demand response price, the current demand response price strategy is updated, and the iteration number is increased by one.

[0033] After receiving the power consumption reported by each controllable load, the current demand response price strategy is adjusted, and when the iteration number is k, the updated dynamic price is,

[0034]

[0035] The further improvement of the present application is that in step 5), each time the sum L (k+1) of the optimal power consumption reported by each controllable load is received, (k) According to the following formula, it is judged whether the iteration is less than a given error δ,

[0036] ||L (k+1) -L (k) ||≤δ (9)

[0037] If formula (9) is established or the maximum iteration number is reached, the iteration process is ended, the final power price is published, and the power consumption of each controllable load is determined; if formula (9) is not established, the iteration process has not converged, and the iteration process is returned to step 3) to continue.

[0038] The demand side resource automatic coordination system based on the iteration proximal point algorithm comprises:

[0039] The modeling module is used for processing the original data parameters of the controllable load and the fixed load in a certain area, and establishing a controllable load model.

[0040] a data processing module configured to determine a dynamic electricity price strategy and publish a demand response price to each controllable load after initializing the dynamic electricity price strategy according to a fixed load;

[0041] a first data updating module configured to update an optimal power consumption of each controllable load to be the lowest power consumption cost after the controllable load receives the demand response price information according to a controllable load model;

[0042] a second data updating module configured to update the demand response price and update an iteration number to be one more according to the optimal power consumption of each controllable load participating in the demand response to be the lowest power consumption cost;

[0043] a result judging module configured to judge whether the demand response process meets a convergence criterion according to the updated optimal power consumption, and determine a final power consumption of the controllable load, a system load curve and the demand response price if a modulus of a difference between a sum of the optimal power consumptions of all controllable loads and a sum of optimal power consumptions before updating is less than a given error, or return the first data updating module to update the demand response price information published to each controllable load again until the iteration number reaches an iteration upper limit or the modulus of the difference between the sum of the optimal power consumptions of all controllable loads and the sum of the optimal power consumptions before updating is less than the given error, and then determine a current result.

[0044] The application provides a computer readable storage medium storing a computer program, which, when executed by a processor, implements steps of the demand side resource automatic coordination method based on an iterative proximal point algorithm.

[0045] Compared with the prior art, the application has at least the following beneficial technical effects:

[0046] The demand side resource automatic coordination method and system based on the iterative proximal point algorithm provided by the application start from operation characteristics and structural parameters of controllable loads, consider characteristics of dynamic electricity prices, take into account interactions between users and between the users and a system, and calculate a system load level in an equilibrium state. Firstly, the operation characteristics and the structural parameters of the loads are represented by establishing a controllable load model, and the deficiency that a macroscopic characteristic method is difficult to guarantee that a load use constraint is met is overcome. Secondly, a determined dynamic electricity price demand response rule can reflect a system load level in real time and effectively reflect and guide a supply-demand relationship of a power system. Further, an iterative proximal point algorithm is used to update a response of the controllable load to the electricity price, and a user's autonomous behavior can be reflected, and information such as load characteristics and power consumption habits of power users is prevented from being leaked. Compared with an existing traditional static electricity price demand response method, the application can automatically avoid emergence of a new peak of a system load curve, guarantee safe operation and economic efficiency of the power system, and has significant advantages. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 Flow chart of the automatic coordination method of demand side resources based on the iterative proximal point algorithm of the present application;

[0048] Figure 2 For the fixed load data curve;

[0049] Figure 3 For the load curve in the non-interaction and equilibrium state;

[0050] Figure 4 Structure block diagram of the automatic coordination system of demand side resources based on the iterative proximal point algorithm of the present application. DETAILED DESCRIPTION

[0051] In order to make the purpose and technical scheme of the present application clearer and more convenient to understand. The present application is further described in detail below in combination with the drawings and examples, and the specific examples described herein are only used to explain the present application, and not to limit the present application.

[0052] In the description of the present application, it should be understood that the terms "center", "longitudinal", "transverse", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second" are only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, unless otherwise specified, the meaning of "multiple" is two or more. In the description of the present application, it should be noted that, unless otherwise specified and limited, the terms "mounting", "connection", "connection" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium, or it can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0053] As Figure 1 shown, the automatic coordination method of demand side resources based on the iterative proximal point algorithm provided by the present application comprises:

[0054] 1) Processing the original data parameters of controllable load and fixed load in a certain area, and establishing a controllable load model;

[0055] 2) Determine the dynamic price strategy, according to the fixed load initialization dynamic price strategy, the demand response price is issued to each controllable load;

[0056] 3) Each controllable load receives the demand response price information, and updates the optimal power consumption according to the controllable load model with the lowest power consumption cost;

[0057] 4) According to the optimal power consumption of each controllable load participating in demand response with the lowest power consumption cost, update the demand response price, and update the iteration number plus 1;

[0058] 5) According to the updated optimal power consumption, judge whether the demand response process meets the convergence criterion, if the difference between the sum of the optimal power consumption of all controllable loads and the sum of the optimal power consumption before updating is less than a given error, determine the final power consumption of the controllable load, the system load curve and the demand response price; If not, return to step 3 to update the demand response price information issued to each controllable load again until the iteration number reaches the iteration upper limit or the difference between the sum of the optimal power consumption of all controllable loads and the sum of the optimal power consumption before updating is less than a given error, then determine the current result.

[0059] In step 1), the original data parameters of the controllable load include start time, stop time, minimum power, maximum power and power consumption, and the controllable load model is established, so as to characterize the operating characteristics of the controllable load.

[0060] There are many controllable loads in user's home, such as electric vehicles, heat pumps, electric water heaters with heat storage devices and washing machines with flexible use time, etc. Their operating characteristics and structural parameters are affected by many factors, such as user-set start time, stop time, power characteristics, total power consumption and energy storage dynamic characteristics, etc.

[0061] p a,min u a,t ≤p a,t ≤p a,max u a,t (1)

[0062]

[0063] Where, p a,t represents the power consumption of controllable load a at time t; u a,t is a 0-1 state parameter related to the user-set allowable use time, which takes 1 when the controllable load starts and takes 0 when the controllable load shuts down; p a,min represents the minimum power of controllable load a; p a,max represents the maximum power of controllable load a; E arepresents the total power consumption of the controllable load a; γ a,t represents the loss of power consumption, which is related to charging efficiency, self-discharge and other factors.

[0064] Each controllable load has different operating characteristics, and its operating constraints are denoted as θ a .

[0065] In step 2), the dynamic price strategy is determined, and after initializing the dynamic price strategy according to the fixed load, the demand response price is published to each controllable load, and the update parameters, the maximum number of iterations and the convergence criterion are set according to the past operation experience.

[0066] The dynamic price strategy is determined as follows:

[0067] λ t = a t L t + b t (3)

[0068] wherein a t , b t are dynamic price parameters, which are set according to the past operation experience; L t is the system load level at t of the demand response participant. Considering the actual operation cost of the power system, the higher the system load level, the higher the operation cost, in order to make the price reflect the operation cost of the power system and embody the supply side relationship, a t is set to a non-negative value.

[0069] The system load curve is composed of the fixed load and the controllable load, and is expressed as follows:

[0070]

[0071] wherein D t is the fixed load of the demand response participant at t, which is obtained from historical data. A is the number of controllable loads participating in demand response;

[0072] In the case of determining the dynamic price strategy, the dynamic price is initialized using the fixed load data, as follows

[0073] λ t (0) = a t D t + b t (5)

[0074] In step 3), according to the controllable load model, each controllable load determines its optimal power consumption in a parallel manner based on the current demand response price, with the objective of minimizing its power consumption cost;

[0075] After receiving the published signal, the controllable load adjusts its power consumption to minimize the cost, which is an optimization problem, and the cost function c a For,

[0076]

[0077] The adjustment strategy of controllable load a is k when the iteration number is k,

[0078]

[0079] where η and γ are update parameters selected according to operating experience, and p a is the power consumption sequence of controllable load a over time.

[0080] In step 4), the current demand response price strategy is updated according to the optimal power consumption of each controllable load determined by the current demand response price, and the iteration number is incremented by one.

[0081] After receiving the optimal power consumption reported by each controllable load, the current demand response price strategy is adjusted, and the updated dynamic price is k when the iteration number is k,

[0082]

[0083] In step 5), it is determined whether the demand response process meets the convergence criterion according to the updated optimal power consumption. If the maximum iteration number is reached or the convergence criterion is met, the optimal power consumption scheme of each controllable load, the system load curve and the demand response price are determined.

[0084] Each time the power consumption reported by each controllable load is received, the total power consumption L (k+1) is calculated. (k) The sum of the power consumption L (k+1) obtained in the last iteration is compared with the sum of the power consumption L (k) to determine whether the iteration is less than a given error δ according to the following formula:

[0085] ||L (k+1) -L (k) ||≤δ (9)

[0086] If formula (9) is true, the iteration process is ended, the final power price is published, and the power consumption of each controllable load is determined; if formula (9) is not true, the iteration process has not converged, and step 3) is entered to continue the iteration process.

[0087] Embodiment

[0088] An example is used to illustrate the implementation process of the present application. The fixed load data is derived from IEEE-RTS, and its curve is shown in Figure 2 Table 1 gives a type of controllable load parameters, a total of 2000. The published system price λt The electrical load L in the same system t satisfy:

[0089] λ t (L t )=aL t +b $ / MWh (10)

[0090] Table 1 Controllable Load Parameters

[0091]

[0092] This example is based on 2000 controllable loads, with the electricity price coefficients uniformly set as a = 0.02 and b = 20. The dynamic electricity price then satisfies the following relationship:

[0093] λ t (L t ) = 0.02L t +20 $ / MWh (11)

[0094] A comparative analysis of static and dynamic electricity pricing reveals that dynamic pricing takes into account interactive behavior, corresponding to an equilibrium state. Under the static pricing framework, electricity users, as price takers, cannot account for interactive behavior. Compared to the load cost of $1.70417 without interactive behavior, the equilibrium cost is $1.69909, indicating a reduction in the cost of controllable loads. Without interactive behavior, similar loads choosing the same electricity consumption scheme not only leads to new peak loads in the system, but also... Figure 3 As shown, actual electricity costs have increased.

[0095] In contrast, under the framework of dynamic electricity pricing, equilibrium considers the feedback effects of interactive behavior and load behavior on system electricity prices. At equilibrium, the cost of electricity consumption by the load is significantly reduced, and no new load peaks appear on the system load curve. Therefore, to further reduce costs, the load will adjust its electricity consumption plan until an equilibrium state is reached.

[0096] like Figure 4 As shown, the automatic demand-side resource coordination system based on the iterative nearest neighbor algorithm provided by this invention includes:

[0097] The modeling module is used to process the raw data parameters of controllable load and fixed load in a certain area and establish a controllable load model.

[0098] The data processing module is used to determine the dynamic electricity pricing strategy, and after initializing the dynamic electricity pricing strategy based on the fixed load, it publishes the demand response price to each controllable load.

[0099] The first data update module is used to update the optimal power consumption for each controllable load based on the lowest electricity cost after receiving the demand response electricity price information, according to the controllable load model.

[0100] a second data updating module, configured to update the demand response price according to the optimal power consumption of each controllable load participating in the demand response with the lowest power consumption cost, and update the iteration number by 1;

[0101] a result judging module, configured to judge whether the demand response process meets a convergence criterion according to the updated optimal power consumption, and if the modulus of the difference between the sum of the optimal power consumptions of all controllable loads and the sum of the optimal power consumptions of all controllable loads before the update is less than a given error, determine the final power consumption of the controllable loads, the system load curve and the demand response price; if not, return to the first data updating module to update the demand response price information and publish it to each controllable load again until the iteration number reaches an iteration upper limit or the modulus of the difference between the sum of the optimal power consumptions of all controllable loads and the sum of the optimal power consumptions of all controllable loads before the update is less than the given error, and then determine the current result.

[0102] The application provides a computer readable storage medium, which stores a computer program.

[0103] Those skilled in the art should understand that embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0104] The present application is described with reference to flowcharts and / or block diagrams of the methods, the systems and the computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device implemented in the flowcharts and / or block diagrams. Figure 1 a system that facilitates the function specified in a flow or multiple flows and / or blocks Figure 1 a system that facilitates the function specified in a flow or multiple flows and / or blocks

[0105] These computer program instructions can also be stored in a computer readable storage medium that can direct the computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable storage medium produce a manufactured product including instruction devices that implement the flowcharts and / or block diagrams.Figure 1 one or more processes and / or functions described in one or more blocks. Figure 1 one or more blocks or multiple blocks.

[0106] These computer program instructions can also be loaded into computer or other programmable data processing devices, so that a series of operational steps are performed on the computer or other programmable data processing devices to generate a computer-implemented process, so that the instructions executed on the computer or other programmable data processing devices provide steps for implementing the function described in one or more processes and / or functions described in one or more blocks. Figure 1 one or more processes and / or functions described in one or more blocks. Figure 1 one or more blocks or multiple blocks.

[0107] The above is only to illustrate the technical idea of the present application, and cannot limit the protection scope of the present application. Any modification made according to the technical idea of the present application on the basis of the technical scheme falls within the protection scope of the claims of the present application.

Claims

1. A method for automatic coordination of demand side resources based on iterative proximal point algorithm, characterized in that, Comprise: 1) the controllable load and fixed load original data parameters of a certain area are processed, and a controllable load model is established; 2) the dynamic electricity price strategy is determined, the demand response electricity price is published to each controllable load after the dynamic electricity price strategy is initialized according to the fixed load, and the updating parameter, the maximum iteration number and the convergence criterion are set according to the past operation experience; The dynamic electricity price strategy is determined as follows: (3) wherein a t , b t is a dynamic price parameter, set according to past operation experience, and a t is set to a non-negative value; L t is the system load level at t when demand response is participated in; The system load curve is composed of fixed load and controllable load, and is expressed as follows: t is the electricity price at t ; In the case of determining the dynamic electricity price strategy, the dynamic electricity price is initialized using the fixed load data, as follows: (4) wherein D t is the fixed load of the demand response participant at t time, obtained from historical data; A is the number of controllable loads of the demand response participant; p a,t represents the power consumption of the controllable load a at t time; 3) according to the controllable load model, each controllable load updates the optimal power consumption with the lowest electricity cost after receiving the demand response electricity price information, including: (5) wherein 4) according to the optimal power consumption of each controllable load participating in demand response with the lowest electricity cost, the demand response electricity price is updated, and the iteration number is updated by 1; t (0) is t the initial price of the initialization; 5) whether the demand response process meets the convergence criterion is judged according to the updated optimal power consumption, if the modulus of the difference between the sum of the optimal power consumptions of all controllable loads and the sum of the optimal power consumptions before updating is less than a given error, the final power consumption of the controllable load, the system load curve and the demand response electricity price are determined; if not, return to step 3) to update the demand response electricity price information published to each controllable load again, until the iteration number reaches the iteration upper limit or the modulus of the difference between the sum of the optimal power consumptions of all controllable loads and the sum of the optimal power consumptions before updating is less than the given error, then the current result is determined. After receiving the issued signal, the controllable load adjusts its power consumption scheme to minimize the power consumption cost, which corresponds to the power consumption cost function c a For, (6) The iteration number is recorded as k Controllable load a The adjustment strategy is that (7) wherein, In step 1), the original data parameters of the controllable load include start time, stop time, minimum power, maximum power and power consumption, according to the different operation characteristics and structural parameters of the controllable load, the controllable load model is established, and In step 4), according to the optimal power consumption of each controllable load determined by the current demand response electricity price, the current demand response electricity price strategy is updated, and the iteration number is updated by 1; are updating parameters selected according to operating experience, p a is a controllable load a a sequence of electricity consumption over time; If formula (9) is established or the maximum iteration number is reached, the iteration process is ended, the final electricity price is published, and the power consumption of each controllable load is determined; if formula (9) is not established, the iteration process has not converged, and step 3) is returned to continue the iteration process. Comprise:

2. The demand side resource auto-coordination method based on the iterative nearest neighbor algorithm according to claim 1, characterized in that, The modeling module is used for processing the controllable load and fixed load original data parameters of a certain area, and establishing a controllable load model; (1) (2) wherein, u a,t is a 0-1 state parameter related to the user-set allowable use time, and takes 1 to indicate that the controllable load is started, and takes 0 to indicate that the controllable load is turned off; p a,min represents the minimum power of the controllable load a ; p a,max represents the maximum power of the controllable load a ; E a represents the total power consumption of the controllable load a ; The data processing module is used for determining the dynamic electricity price strategy, publishing the demand response electricity price to each controllable load after initializing the dynamic electricity price strategy according to the fixed load, and setting the updating parameter, the maximum iteration number and the convergence criterion according to the past operation experience; a,t represents the loss of power consumption.

3. The method of claim 1, wherein, The dynamic electricity price strategy is determined as follows: After receiving the power consumption of each controllable load, the current demand response price strategy is adjusted, and the iteration number is recorded as k The updated dynamic price is (8) wherein The system load curve is composed of fixed load and controllable load, and is expressed as follows: t (k+1) is t the electricity price at iteration number k +1.

4. The method of claim 1, wherein, In step 5), each time receiving the sum of the optimal power consumptions reported by the controllable loads L (k+1) , and the sum of the optimal power consumptions obtained in the last iteration L (k) , whether the iteration is less than a given error according to the following formula In the case of determining the dynamic electricity price strategy, the dynamic electricity price is initialized using the fixed load data, as follows: , (9) The first data updating module is used for updating the optimal power consumption with the lowest electricity cost according to the controllable load model, each controllable load updates the optimal power consumption with the lowest electricity cost after receiving the demand response electricity price information, including:

5. A demand side resource auto-coordination system based on iterative nearest neighbor algorithm, characterized in that, ​ ​ ​ ​ (3) wherein a t , b t is a dynamic price parameter, set according to past operation experience, and a t is set to a non-negative value; L t is a system load level at t when demand response is participated in; ​ t is a price at t . ​ (4) wherein D t is the fixed load of the demand response participant at t time, obtained from historical data; A is the number of controllable loads of the demand response participant; p a,t denotes the power consumption of the controllable load a at t time; ​ (5) wherein ​ t (0) is t the initial price at the time of initialization; ​ After receiving the issued signal, the controllable load adjusts its power consumption scheme to minimize the power consumption cost, corresponding to the power consumption cost function c a For, (6) The iteration number is recorded as k Controllable load a The adjustment strategy is that (7) wherein, ​ and ​ are updating parameters selected according to operating experience, p a is a controllable load a a sequence of electricity consumption over time; a second data updating module, configured to update the demand response price according to the optimal power consumption of each controllable load participating in the demand response to minimize electricity cost, and update the iteration number by 1; a result judging module, configured to judge whether the demand response process meets a convergence criterion according to the updated optimal power consumption, and determine the final power consumption of the controllable load, the system load curve and the demand response price if the modulus of the difference between the sum of the optimal power consumptions of all controllable loads and the sum of the optimal power consumptions of all controllable loads before the update is less than a given error; otherwise, return to the first data updating module to update the demand response price information and publish it to each controllable load again until the iteration number reaches an iteration upper limit or the modulus of the difference between the sum of the optimal power consumptions of all controllable loads and the sum of the optimal power consumptions of all controllable loads before the update is less than the given error, and then determine the current result.

6. The demand side resource auto-coordination system based on the iterative point-neighbor algorithm according to claim 5, characterized in that, In the modeling module, the original data parameters of the controllable load include start time, stop time, minimum power, maximum power and power consumption, and the controllable load model is established according to different operating characteristics and structural parameters of the controllable load, (1) (2) wherein, u a,t is a 0-1 state parameter related to the user-set allowable use time, and takes 1 to indicate that the controllable load is started, and takes 0 to indicate that the controllable load is shut down; p a,min represents the minimum power of the controllable load a ; p a,max represents the maximum power of the controllable load a ; E a represents the total power consumption of the controllable load a ; γ a,t represents the loss of power consumption.

7. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program, when executed by the processor, implements the steps of the demand side resource automatic coordination method based on the iterative proximal point algorithm in any one of claims 1-4.

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