Optimization method, device and system for participation of high-energy-consumption industrial load in demand response

Abstract

The invention discloses an optimization method for participation of a high-energy-consumption industrial load in demand response, and the method comprises the steps: constructing a revenue function for participation of a high-energy-consumption industrial user in demand response according to the production revenue, excitation revenue and shutdown cost of the high-energy-consumption industrial load, and calculating the production revenue according to the yield of a product of the last process of the high-energy-consumption industrial load, according to the response quantity fed back by the high-energy-consumption industrial load, the excitation income is calculated, and according to the operation state of the continuous process in the high-energy-consumption industrial load, the shutdown cost is calculated; an operation cost function of the power grid operator is constructed according to the response compensation cost, the power purchase cost and the load shedding cost of the power grid operator, the response compensation cost is calculated according to the response amount fed back by the high-energy-consumption industrial load, the power purchase cost is calculated according to the power purchase amount of the power grid operator, and the load shedding cost is calculated according to the load shedding amount of the power grid operator; and according to the revenue function and the operation cost function, adopting an iterative algorithm based on a golden section method to calculate a subsidy incentive update value.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, in particular to an optimization method, device and system for high-energy-consumption industrial load participating in demand response. BACKGROUND

[0002] With the acceleration of the construction of new power systems, deep mining of load-side resources has become a core path to realize the optimization of energy efficient utilization and low-carbon transformation. DR (Demand Response) is a mechanism that guides power users to adjust their power consumption behavior to adapt to the supply and demand changes of the power system through price signals or incentive mechanisms. DR is one of the important means to tap the potential of the load side, and the regulation objects include residential load, commercial load and industrial load. High-energy-consumption industrial load is the largest power consumer and has significant adjustment potential due to its large power consumption. Through DR, the flexibility of the power system can be effectively improved, but the complex production process and material constraints limit the willingness of enterprises to participate.

[0003] Traditional modeling methods quantify power as an adjustable interval, which is difficult to represent response potential. Therefore, some studies have proposed to model industrial load as a whole model, for example, industrial load curves can be divided into three-peak, double-peak, flat and peak-shaving types, and a demand response and energy storage collaborative operation model for industrial load is constructed. Another is to construct a demand response whole regulation model to realize multi-time scale regulation of different types of industrial load demand response.

[0004] The production process of high-energy-consumption industrial load has significant complexity, involving deep coupling between multiple processes and interaction of multiple time scales. It is difficult to accurately depict the regulation characteristics of different devices within multiple processes by relying solely on a whole model. Although existing technologies can accurately depict energy flow between processes, they ignore the dynamic coupling relationship between material consumption and transmission among multiple production links when industrial users participate in long-term DR, making it impossible to accurately represent the constraints of production material flow among multiple processes on DR, resulting in difficulty in balancing the grid regulation demand and the actual feasibility of industrial production, and limiting the potential of high-energy-consumption industrial load as a DR adjustable resource. SUMMARY

[0005] Based on the above status, the main purpose of the present application is to provide a high-energy-consumption industrial load participating in demand response strategy optimization method considering the coupling of multi-process energy flow and material flow, which maximizes the income of high-energy-consumption enterprises while ensuring low cost for power grid suppliers, thereby improving the enthusiasm of high-energy-consumption industrial load participating in demand response.

[0006] To achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0007] An optimization method for high-energy-consumption industrial load participating in demand response, comprising the steps of:

[0008] S100, constructing a benefit function F of the high energy-consuming industrial user participating in demand response according to production benefit, incentive benefit and shutdown cost of the high energy-consuming industrial load I , wherein the production benefit is calculated according to output of last-process product of the high energy-consuming industrial load, the incentive benefit is calculated according to response amount fed back by the high energy-consuming industrial load, and the shutdown cost is calculated according to running state of continuous processes in the high energy-consuming industrial load;

[0009] S200, constructing an operation cost function F of the power grid operator according to response compensation cost, power purchase cost and load shedding cost of the power grid operator G , wherein the response compensation cost is calculated according to response amount fed back by the high energy-consuming industrial load, the power purchase cost is calculated according to power purchase amount of the power grid operator, and the load shedding cost is calculated according to load shedding amount of the power grid operator;

[0010] S300, calculating the updated value of the subsidy incentive of the power grid operator by using an iterative algorithm based on the golden section method according to the benefit function F I and the operation cost function F G .

[0011] Preferably, the benefit function F of the high energy-consuming industrial user participating in demand response is constructed according to production benefit, incentive benefit and shutdown cost of the high energy-consuming industrial load in the step S100 I :

[0012]

[0013] , wherein B P is the production benefit, B DR is the incentive benefit, C CON is the shutdown cost, and t is a time period after equal division of a demand response period.

[0014] Preferably, the production benefit B P is calculated by using the following formula:

[0015]

[0016] , wherein T is a demand response period, is output of last-process product of the high energy-consuming industrial load, F k is a benefit coefficient of last-process of the high energy-consuming industrial load, PR min is minimum output requirement, and PR k,t is output of product of process k at time period t.

[0017] Preferably, the incentive benefit B is calculated by the following formula DR :

[0018] B DR = λ t R i,t

[0019] wherein λ t is the subsidy incentive provided by the grid operator at time period t, and R i,t is the response amount fed back by the high energy consumption industrial load i at time period t.

[0020] Preferably, the shutdown cost C is calculated by the following formula CON :

[0021]

[0022] wherein C con is the shutdown cost coefficient of the continuous process in the high energy consumption industrial load, is the running state of the continuous process k in the high energy consumption industrial load at time period t.

[0023] Preferably, the operation cost function F of the grid operator in step S200 is constructed according to the response compensation cost, the electricity purchase cost and the load shedding cost of the grid operator, and is as follows: G :

[0024]

[0025] wherein C DR is the response compensation cost, C B is the electricity purchase cost, C R is the load shedding cost, and t is the time period after the demand response period is evenly divided.

[0026] Preferably, the response compensation cost C of the grid operator is calculated by the following formula DR :

[0027] C DR = λ t ·R i,t

[0028] λ min ≤ λ t ≤ λ max

[0029] wherein λ t is the subsidy incentive provided by the grid operator at time period t, and R i,t is the response amount fed back by the high energy consumption industrial load i at time period t, λ min is the lower limit value of the subsidy incentive, and λ maxThis is the upper limit for subsidies and incentives.

[0030] Preferably, the electricity purchase cost C of the power grid operator is calculated using the following formula. B :

[0031] C B =S t ·P B,t

[0032] P B,t =ΔP B,t +P B,N

[0033] P B,N =PD N +PR N

[0034]

[0035] Among them, S t P represents the electricity purchase price that the power grid operator pays to its superior authority. B,t The electricity purchased by the grid operator is ΔP B,t To purchase additional electricity, P B,N For power purchases by the distribution network when there is no load deficit, PD N For high-energy-consuming industrial loads, PR N For non-high energy-consuming industrial loads, ΔP R,t This is the load shedding amount.

[0036] Preferably, the load-cutting cost C of the power grid operator is calculated using the following formula. R :

[0037] C R =C r ·ΔP R,t

[0038] Among them, C r ΔP is the load shedding cost factor. R,t This is the load shedding amount.

[0039] Preferably, in step S300, the method based on the benefit function F... I and the operating cost function F G The steps for calculating the updated subsidy incentive value for the power grid operator using an iterative algorithm based on the golden section method include:

[0040] S301, the first and second division point values ​​are calculated using the golden section method based on the maximum and minimum values ​​of the subsidy incentive;

[0041] S302, a first operation cost value is calculated according to the first split point value, and a second operation cost value is calculated according to the second split point value;

[0042] S303, within a preset iteration number range, when a difference between the maximum value and the minimum value of the subsidy incentive is greater than a preset convergence precision, the maximum value and the minimum value of the subsidy incentive are adjusted until the difference between the maximum value and the minimum value of the subsidy incentive is less than or equal to the preset convergence precision, and an updated value of the subsidy incentive of the power grid operator is output.

[0043] Preferably, the adjusting the maximum value and the minimum value of the subsidy incentive in the step S303 comprises:

[0044] determining whether the second operation cost value is greater than or equal to the first operation cost value, if yes, performing a step S304, otherwise, performing a step S305;

[0045] S304, the maximum value of the subsidy incentive is updated to the second split point value, the second split point value is updated to the first split point value, and the second operation cost value is updated to the first operation cost value; an updated first split point value is recalculated according to the updated maximum value and the minimum value of the subsidy incentive; a maximum value of the revenue function is calculated according to updated data, and a split coefficient of the first split point is updated to the maximum value of the revenue function; an updated first operation cost value is calculated according to the recalculated first split point value and the updated split coefficient of the first split point;

[0046] S305, the minimum value of the subsidy incentive is updated to the first split point value, the first split point value is updated to the second split point value, and the first operation cost value is updated to the second operation cost value; an updated second split point value is recalculated according to the updated maximum value and the minimum value of the subsidy incentive; a maximum value of the revenue function is calculated according to updated data, and a split coefficient of the second split point is updated to the maximum value of the revenue function; an updated second operation cost value is calculated according to the recalculated second split point value and the updated split coefficient of the second split point.

[0047] Preferably, the updated value of the subsidy incentive of the power grid operator is an average value of the first split point value and the second split point value when the difference between the maximum value and the minimum value of the subsidy incentive is less than or equal to the preset convergence precision.

[0048] The application further discloses an optimization device for high-energy-consumption industrial load participating in demand response, the device comprising a processor and a memory having a computer program capable of running on the processor stored thereon; wherein the computer program, when executed by the processor, implements the steps of the optimization method for high-energy-consumption industrial load participating in demand response according to any one of the application.

[0049] The application further discloses a power grid operator demand response automatic adjustment device, which adopts the method according to any one of the application to optimize the high-energy-consumption industrial load participating in demand response.

[0050] The application further discloses a computer readable storage medium having a data processing program stored thereon, the data processing program, when executed by a processor, implements the steps of the optimization method for high-energy-consumption industrial load participating in demand response according to any one of the application.

[0051] The application further discloses an optimization system for high-energy-consumption industrial load participating in demand response, the system comprising a power grid operator and a high-energy-consumption industrial user, the power grid operator adopts the optimization method for high-energy-consumption industrial load participating in demand response according to any one of the application to update the subsidy incentive; the high-energy-consumption industrial user adjusts the process operation state according to the updated subsidy incentive and feeds back the response amount after adjusting the process operation state to the power grid operator; the power grid operator updates the subsidy incentive according to the response amount after adjusting the process operation state until the response amount fed back by the high-energy-consumption industrial user is less than a preset error value.

[0052] Preferably, the adjusting of the process operation state according to the updated subsidy incentive comprises controlling the start-stop state of a discrete process and / or the power output of a continuous process.

[0053] In the technical scheme of the application, the profit function F I of the high-energy-consumption industrial load is constructed by the production profit, the incentive profit and the shutdown cost, and the load regulation boundary of the high-energy-consumption industrial load is accurately described by comprehensively considering the yield, the response amount and the process state of the high-energy-consumption industrial load. G The operation cost function F of the power grid operator is constructed by the response compensation cost, the power purchase cost and the load shedding cost of the power grid operator (DSO), and the demand response strategy of the subsidy incentive is formulated by comprehensively considering the power purchase amount, the load shedding amount and the received response amount of the power grid operator, thereby constructing the master-slave game model between the power grid operator and the high-energy-consumption industrial user and solving by using the iterative algorithm based on the golden section method.

[0054] Other benefits of the present application will be described in the specific embodiments by introducing specific technical features and technical solutions, and those skilled in the art should be able to understand the beneficial technical effects brought by the technical features and technical solutions through the introduction of the technical features and technical solutions. BRIEF DESCRIPTION OF DRAWINGS

[0055] The preferred embodiment of the optimization method of high-energy-consuming industrial load participating in demand response according to the present application will be described below with reference to the accompanying drawings. In the drawings:

[0056] Figure 1 The flow chart of the optimization method of high-energy-consuming industrial load participating in demand response according to a preferred embodiment of the present application;

[0057] Figure 2 The process schematic diagram of high-energy-consuming industrial load according to a preferred embodiment of the present application;

[0058] Figure 3 The classification method schematic diagram of "discrete / continuous" process according to a preferred embodiment of the present application;

[0059] Figure 4 The flow chart of the method for calculating the updated value of the subsidy incentive of the grid operator using the iterative algorithm based on the golden section method according to a preferred embodiment of the present application;

[0060] Figure 5 The flow chart of the method for adjusting the maximum and minimum values of the subsidy incentive according to a preferred embodiment of the present application;

[0061] Figure 6 The market clearing incentive change diagram of the grid operator executed according to the technical solution of the present application;

[0062] Figure 7 The load response amount change diagram of high-energy-consuming industrial load executed according to the technical solution of the present application. DETAILED DESCRIPTION

[0063] In order to describe the technical solution of the present application in more detail and facilitate further understanding of the present application, the specific embodiments of the present application will be described below in conjunction with the accompanying drawings. It should be understood that all the illustrative embodiments and their descriptions are used to explain the present application and do not constitute the only limitation of the present application.

[0064] In the present application, "first", "second", etc. are only used for description purposes and cannot be understood as indicating or implying relative importance.

[0065] Figure 1The optimization method flowchart for the high-energy-consuming industrial load participating in demand response according to the preferred embodiment of the present application comprises the following steps: S100, constructing a benefit function F of the high-energy-consuming industrial user participating in demand response according to production benefit, incentive benefit and shutdown cost of the high-energy-consuming industrial load I , wherein the production benefit is calculated according to the output of the last process product of the high-energy-consuming industrial load, the incentive benefit is calculated according to the response amount fed back by the high-energy-consuming industrial load, and the shutdown cost is calculated according to the running state of the continuous process of the high-energy-consuming industrial load; S200, constructing an operation cost function F of the grid operator according to response compensation cost, power purchase cost and load shedding cost of the grid operator G , wherein the response compensation cost is calculated according to the response amount fed back by the high-energy-consuming industrial load, the power purchase cost is calculated according to the power purchase amount of the grid operator, and the load shedding cost is calculated according to the load shedding amount of the grid operator; S300, calculating the subsidy incentive update value of the grid operator according to the benefit function F I and the operation cost function F G The iteration algorithm based on the golden section method is used to calculate the subsidy incentive update value of the grid operator. In the present application, the subsidy incentive update value is the subsidy incentive value calculated by the iteration algorithm based on the golden section, which makes the operation cost function F G minimum and the benefit function F I maximum, and the subsidy incentive update value is usually the clearing incentive of the grid operator. The demand response period is preset by the grid operator.

[0066] In the technical solution of the present application, the benefit function F of the high-energy-consuming industrial load I is constructed by the production benefit, the incentive benefit and the shutdown cost, which comprehensively considers the output, the response amount and the process state of the high-energy-consuming industrial load, thereby accurately depicting the load adjustment boundary of the high-energy-consuming industrial load. The operation cost function F of the grid operator is constructed by the response compensation cost, the power purchase cost and the load shedding cost of the grid operator G , which comprehensively considers the power purchase amount, the load shedding amount and the received response amount of the grid operator, thereby formulating the demand response strategy of the subsidy incentive, constructing the master-slave game model between the grid operator and the high-energy-consuming industrial user, and using the iteration algorithm based on the golden section method to solve. The method can effectively encourage enterprises to participate in demand response, reduce the regulation and control cost, and improve the availability and economy of the high-energy-consuming industrial load as a demand response resource.

[0067] In a preferred embodiment, the benefit function F of the high-energy-consuming industrial user participating in demand response is constructed according to the production benefit, the incentive benefit and the shutdown cost of the high-energy-consuming industrial load in step S100 I , which can be as follows:

[0068]

[0069] where B P is the production benefit, B DR is the incentive benefit, C CON is the shutdown cost, t is the time period after the demand response cycle is evenly divided, for example, 6 hours is a demand response cycle T, and 15 minutes is a time period t, so 24 time periods t can be obtained.

[0070] The above technical scheme couples the material flow of the high-energy-consuming industrial load into the optimization model through the calculation of product yield, couples the energy flow into the optimization model through the response quantity fed back by the high-energy-consuming industrial load, and constructs a high-energy-consuming industrial load energy optimization model considering the coupling of energy flow and material flow. In actual application, the maximum benefit parameter of the high-energy-consuming industrial load is obtained through calculation, so that the enthusiasm of the high-energy-consuming industrial load in participating in the DR can be improved.

[0071] The high-energy-consuming industrial load mentioned in the application can cover high-energy-consuming industries such as steel plants, electrolytic copper, and cement plants. Taking a cement plant as an example, the energy flow refers to the input, transformation, transmission, and loss process of various types of energy (such as fuel, electricity, waste heat, etc.) in the cement production process. The material flow refers to the crushing, batching, grinding, calcining, and transformation process of various types of materials (such as limestone, clay, clinker, etc.) in the cement production process.

[0072] The process flow of the high-energy-consuming industrial load has complex process coupling characteristics. In order to accurately depict the load regulation potential, it is necessary to start from the nature of the production process. According to the energy continuity, the internal process is divided into two types: discrete process and continuous process. For example, the main production equipment in the electrolytic refining link is the electrolytic cell, and the electrolyte temperature in the electrolytic cell needs to be maintained at a certain level. The switching interruption will have a great impact on the temperature, which belongs to the continuous process. The rolling link needs to periodically adjust the rolling parameters and can only output after a certain time, which belongs to the discrete process. Different high-energy-consuming industrial loads may have different processes, but they can be classified as discrete processes or continuous processes. Figure 2 As shown in the figure: the production process of a steel plant covers the following key links: sintering ore, ironmaking, steelmaking, and rolling, which can be divided into discrete process, continuous process, continuous process, and discrete process in turn. The process of electrolytic copper load mainly includes sintering ore, copper smelting, and electrolytic refining, which can be summarized as discrete process, continuous process, and continuous process in turn. The load process of a cement plant can be summarized as raw material treatment, clinker calcination, and cement grinding, which can be defined as discrete process, continuous process, and discrete process in turn.

[0073] The classification method of "discrete / continuous" process is based on the essential difference of two dimensions as shown in the figure: Figure 3

[0074] ​(1) Energy interaction dimension: continuous processes present stable power consumption curves, while discrete processes show intermittent step changes;

[0075] (2) Material interaction dimension: continuous processes require continuous material flow, while discrete processes adopt batch production mode.

[0076] The production process of high energy-consuming industrial load is accompanied by the generation and consumption of energy and material. At the energy level, continuous processes and discrete processes have different characteristics in terms of demand and utilization of electric energy; at the material flow level, each process needs to input raw materials and produce products, which are transported to the next process for subsequent production. In the complex production system of high energy-consuming industrial load, energy flow and material flow are closely coupled. Electric energy drives the operation of each production equipment, promotes the transfer and transformation of materials between different processes, and drives the entire high energy-consuming industrial production to proceed in an orderly manner.

[0077] The coupling characteristics of material flow and energy flow define the boundary conditions of dynamic adjustment of high energy-consuming industrial load. On the one hand, it ensures the stability of the material production process and avoids excessive interference with the production plan caused by power regulation; on the other hand, in the presence of load shortage, the coupling relationship between material flow and energy flow is coordinated to alleviate the pressure of DSO. This coupling mechanism enables it to meet production demand while optimizing energy consumption and maximizing profit through intermittent shutdown of discrete processes and load regulation of continuous processes.

[0078] In a preferred embodiment, the production benefit B can be calculated using the following formula P :

[0079]

[0080] where T is the demand response period, F is the output of the last process of the high energy-consuming industrial load, and F k is the benefit coefficient of the last process of the high energy-consuming industrial load.

[0081] The high energy-consuming industrial load also needs to meet the output constraint within the total time step to ensure that the agreed final product output is completed within the specified production period, and to ensure that the material inventory of each process k in each time step is not negative.

[0082]

[0083] PR min is the minimum output requirement, PR k,t is the output of the product of process k at time t.

[0084] The production of the high energy-consuming industrial load is also affected by the material, and the production characteristics of the discrete process are also discrete. The output is usually in fixed batches, that is, the discrete process can only output products after the production time RT. A binary variable is used to represent the output state of the discrete process k:

[0085]

[0086] wherein, is an intermediate variable of , RT k represents the production time of the discrete process k, is a binary variable used to represent the start-stop state of the discrete process k. In the production process of the high energy-consuming industrial load, the generation and consumption of the material run through all processes. The material of the discrete process k at time period t can be represented as:

[0087]

[0088] The material production of the continuous process has the characteristic of continuity, which means that in the production process, as long as the continuous process k does not stop, the generation and consumption of the material will not be interrupted. The material of the continuous process k at time period t can be represented as:

[0089]

[0090] wherein, B k,t and B k,t-1 represent the material of the discrete / continuous process k at time period t and time period t-1, and represent the production / consumption coefficient of the material in the discrete / continuous process k and process k+1, represents the power consumption of the continuous process k at time period t, PD k+1,t is the power consumption of the discrete / continuous process k+1 after adjustment at time period t, which can be calculated by the following formula:

[0091]

[0092] K con represents the set of continuous processes, represents the power consumption of the continuous process k at time period t, represents the power of the continuous process k after adjustment at time period t, is a binary variable used to represent the start-stop state of the continuous process k.

[0093] In calculating the production benefit, the production process of the high energy-consuming industrial load and the production of the product are considered, and the production benefit of the high energy-consuming industrial load is accurately described.

[0094] The high energy-consuming industrial load participates in the DR according to the subsidy incentive information provided by the DSO and in combination with its own production situation, by regulating the start and stop of discrete processes and the power compression of continuous processes, to obtain incentive benefits. In a preferred embodiment, the incentive benefits B can be calculated by the following formula DR :

[0095] B DR =λ t R i,t

[0096] Wherein, λ t is the subsidy incentive provided by the grid operator at time period t, and R i,t is the response amount fed back by the high energy-consuming industrial load i at time period t.

[0097] Specifically, the response amount R i,t fed back by the high energy-consuming industrial load at time period t can be the difference between the power consumption of the high energy-consuming industrial load before time period t and the power consumption of the high energy-consuming industrial load after participating in the demand response. As analyzed above, the processes of the high energy-consuming industrial load include discrete processes and continuous processes, and the power consumption PD t of the high energy-consuming industrial load can be the sum of the power consumption of the discrete processes and the power consumption of the continuous processes:

[0098]

[0099]

[0100] The power adjustment of the continuous processes should satisfy the following constraints:

[0101]

[0102] Wherein, K dis represents the set of discrete processes, represents the power consumed by the discrete process k at time period t, represents the rated power of the discrete process k, is a binary variable used to represent the start and stop state of the discrete process k. K con represents the set of continuous processes, represents the power consumption of the continuous process k at time period t, represents the adjusted power of the continuous process k at time period t, is a binary variable used to represent the start and stop state of the continuous process k.

[0103] The shutdown cost of the high energy-consuming industrial load is mainly for continuous processes. The shutdown of the continuous process will cause irreversible damage to the equipment, thereby affecting the service life of the equipment, and will also adversely affect the product quality. In a preferred embodiment, the shutdown cost C can be calculated by the following formula CON :

[0104]

[0105] wherein C con is the shutdown cost coefficient of the continuous process in the high energy-consuming industrial load, which can be set according to experience; is the running state of the continuous process k in the high energy-consuming industrial load at time t. Generally, v is 0 indicating shutdown, and v is 1 indicating work.

[0106] According to the calculation method in the above embodiments, the benefit function F of the high energy-consuming industrial user participating in demand response can be obtained as follows: I :

[0107]

[0108] In the above embodiments, according to the energy consumption and production characteristics of the high energy-consuming industrial load, the production processes are divided into discrete processes and continuous processes, an energy consumption optimization model considering the coupling of multi-process energy flow and material flow is constructed, and the load adjustment boundary of different processes in the DR process is accurately described. Among them, the discrete process is represented by a binary variable indicating the start-stop state, the power is fixed, and the material is transferred in batches; the power of the continuous process can be adjusted and has upper and lower limit constraints, and the constraint relationship between energy consumption and material transfer and conversion is clear. Accurately describe the adjustment boundary of the high energy-consuming industrial load, and improve the feasibility of the DR strategy. After considering the coupling constraints of energy flow and material flow, the DR behavior of the high energy-consuming industrial load and the load shortage and incentive signal form a dynamic match. When the load shortage is small, maintain full-power production and accumulate materials; when the shortage increases, the discrete process adjusts the energy consumption by starting and stopping, and the continuous process responds by compressing the power, avoiding the problem of over-regulation leading to production interruption or insufficient regulation in the traditional model. The DR strategy not only meets the demand of grid regulation, but also conforms to the actual industrial production process, significantly improving the enthusiasm of the high energy-consuming industrial load participating in DR.

[0109] When the power generation capacity of the upper grid suddenly decreases, the DSO will face a double crisis: on the one hand, the power purchase amount of the distribution network decreases and the grid access price rises; on the other hand, the power supply gap continues to expand, resulting in serious load shedding. In the face of such a crisis, the DSO will start the DR mechanism to guide the high-energy industrial load to adjust the energy consumption, alleviate the power supply pressure through market means, and reduce the power purchase cost and load shedding cost of the DSO. The cost of the DSO mainly includes the following aspects: first, the incentive cost provided for the high-energy industrial load, that is, the response compensation cost; second, the power purchase cost; and third, the load shedding cost, which together constitute the main expenditure of the DSO in the DR process.

[0110] In a preferred embodiment, the operation cost function F of the grid operator in step S200 is constructed according to the response compensation cost, the power purchase cost and the load shedding cost of the grid operator G may be:

[0111]

[0112] wherein C DR is the response compensation cost, C B is the power purchase cost, and C R is the load shedding cost, and t is the time period after the demand response period is evenly divided.

[0113] The load shortage adjustment measure of the power system generally adopts the differential control logic of "DR strategy is preferentially implemented, marketized power purchase means is supplemented, and load forced reduction measure is finally guaranteed". The load shortage is jointly filled by the additional power purchase amount, the response amount and the load shedding amount, as follows:

[0114]

[0115] ΔP t is the load shortage, and the load shedding amount ΔP R,t can be calculated according to the formula.

[0116] The technical solution establishes a strategy model for the DSO to formulate an incentive demand response. In actual use, the parameters for minimizing the operation cost of the grid operator are calculated to obtain an optimized DR strategy.

[0117] The response compensation cost of the grid operator refers to the economic compensation paid by the DSO to the high-energy industrial user for encouraging the user to actively adjust the power consumption load when the power supply and demand is tight or the system needs to be adjusted. In a preferred embodiment, the response compensation cost C DR of the grid operator can be calculated by the following formula:

[0118] C DR = λ t · R i,t

[0119] λ min ≤λ t ≤λ max

[0120] wherein λ t is the subsidy incentive provided by the grid operator at time period t, R i,t is the response amount fed back by the high energy-consuming industrial load i at time period t. To ensure the fairness of the DR market, the incentive provided by the DSO needs to be within a reasonable range, λ min is the lower limit value of the subsidy incentive, and λ max is the upper limit value of the subsidy incentive.

[0121] The electricity purchase cost refers to the total cost paid by the DSO to purchase electricity from the upper power grid or power generation enterprise, and is a core component of the distribution system operation cost. In a preferred embodiment, the electricity purchase cost C B of the grid operator to the upper level can be calculated using the following formula:

[0122] C B = S t · P B,t

[0123] P B,t = ΔP B,t + P B,N

[0124] P B,N = PD N + PR N

[0125]

[0126] wherein S t is the electricity purchase price of the grid operator to the upper level, P B,t is the electricity purchase amount of the grid operator, ΔP B,t is the additional electricity purchase amount, P B,N is the distribution grid electricity purchase amount without load shortage, i.e., the distribution grid electricity purchase amount under normal conditions, PD N is the electricity consumption amount of the high energy-consuming industrial load, PR N is the load amount of the non-high energy-consuming industrial load, and ΔP R,t is the load shedding amount.

[0127] The load shedding cost refers to the direct economic loss and indirect social cost caused by the forced power cut of the user by the DSO when the demand response amount provided by the high energy-consuming industrial user is insufficient to make up for the load shortage. In a preferred embodiment, the load shedding cost C R can be calculated using the following formula:

[0128] C R =C r ·ΔP R,t

[0129] Among them, C r ΔP is the load shedding cost coefficient. R,t This is the load shedding amount.

[0130] Based on the calculation methods described in the above embodiments, the operating cost function F of the power grid operator can be derived. G for:

[0131]

[0132] In a preferred embodiment, such as Figure 4 As shown, in step S300, the benefit function F is used... I and the operating cost function F G The calculation of the updated subsidy incentive value for the power grid operator using an iterative algorithm based on the golden section method may include: S301, based on the maximum value λ of the subsidy incentive. max and minimum value λ min The value of the first division point λ was calculated using the golden section method. change_1 The second dividing point value λ change_2 S302, based on the first segmentation point value λ change_1 The first operating cost value F is calculated. G,change_1 According to the second segmentation point value λ change_2 The second operating cost value F was calculated. G,change_2 S303, within the preset number of iterations N, when the maximum value λ of the subsidy incentive... max and minimum value λ min When the difference is greater than the preset convergence accuracy ε, the maximum value λ of the subsidy incentive is adjusted. max and minimum value λ min up to the maximum value λ of the subsidy incentive. max and minimum value λ min The difference is less than or equal to the preset convergence accuracy ε, and the updated subsidy incentive value λ of the power grid operator is output.

[0133] This invention employs the golden section method, which divides the search interval by the golden ratio and iteratively narrows the range to achieve efficient solution. It cleverly avoids the limitations of the two methods in the prior art and ensures the accuracy and real-time performance of the optimization results in complex scenarios.

[0134] In specific implementations, the preset number of iterations N and the convergence accuracy ε are usually set based on empirical values. If the preset number of iterations N is too small, the convergence condition may not be met; if it is too large, the computational load will increase. Typically, 60 is used. The convergence accuracy ε can usually be set to e. -6 .

[0135] In a preferred embodiment, such as Figure 5 As shown, in step S303, the maximum value λ of the subsidy incentive is adjusted. max and minimum value λ min This may include: determining the second operating cost value F G,change_2 Is it greater than or equal to the first operating cost value F? G,change_1 If yes, proceed to step S304; otherwise, proceed to step S305.

[0136] S304, the maximum value of the subsidy incentive λ max Updated to the second split point value λ change_2 The second dividing point value λ change_2 Updated to the first split point value λ change_1 Second operating cost value F G,change_2 Updated to the first operating cost value F G,change_1 Based on the updated maximum value λ of the subsidy incentive. max and minimum value λ min The updated first split point value λ was obtained by recalculation. change_1 ; Calculate the payoff function F based on the updated data G The maximum value, and the division coefficient R of the first division point. change_1 Updated to the maximum value of the profit function; based on the recalculated first split point value λ. change_1 The updated segmentation coefficient R of the first segmentation point change_1 The updated first operating cost value F is calculated. G,change_1 ;

[0137] S305, Minimum value of subsidy incentive λ min Updated to the first segmentation point value λ change_1 The first dividing point value λ change_1 Updated to the second split point value λ change_2 First operating cost value F G,change_1 Updated to the second operating cost value F G,change_2 Based on the updated maximum value λ of the subsidy incentive. max and minimum value λ min The updated second split point value λ was obtained by recalculation. change_2 ; Calculate the payoff function F based on the updated data G The maximum value, and the division coefficient R of the second division point. change_2the maximum value of the benefit function F G ; according to the re-calculated second split point value λ change_2 and the split coefficient R change_2 of the updated second split point, the updated second operation cost value F G,change_2 is calculated.

[0138] In the specific embodiment, when the difference between the maximum value and the minimum value of the subsidy incentive of the grid operator is less than or equal to the preset convergence precision, the first split point value λ change_1 and the second split point value λ change_2 are the average value.

[0139] The subsidy incentive updated value calculated by the above method can not only ensure that the cost of the grid operator is the lowest, that is, the operation cost function F G reaches the minimum value, but also can ensure that the benefit of the high-energy-consuming industrial load is the maximum, that is, the benefit function F I reaches the maximum value.

[0140] Specifically, the golden section algorithm is adopted, and the input can include: iteration number N, convergence precision ε, incentive interval [λ min , λ max ], golden section coefficient r, DSO operation cost function F G , and high-energy-consuming industrial load benefit function F I . The output is the optimal subsidy incentive λ. The specific function code is as follows:

[0141] Calculate split points λ change_1 , λ change_2

[0142] λ change_1 ← λ max -r(λ max -λ min )

[0143] λ change_2 ← λ min +r(λ max -λ min )

[0144] Calculate DSO operation cost F G,change_1 , F G,change_2 .

[0145] for i=1 to N do

[0146] if |λ max -λ min |≤ε do

[0147] break

[0148] if F G,change_2 ≥F G,change_1 do

[0149] update incentive interval and operating cost

[0150] λ max ←λ change_2 ,λ change_2 ←λ change_1

[0151] F G,change_2 ←F G,change_1 ,λ change_1 ←λ max -r(λ max -λ min )

[0152] R change_1 ←max(F I ),F G,change_1 ←F G (λ change_1 ,R change_1 )

[0153] else

[0154] update incentive interval and operating cost

[0155] λ min ←λ change_1 ,λ change_1 ←λ change_2

[0156] F G,change_1 ←F G,change_2 ,λ change_2 ←λ min +r(λ max -λ min )

[0157] R change_2 ←max(F I ),F G,change_2 ←F G (λ change_2 ,R change_2 )

[0158] End if

[0159] End for

[0160] returnλ←(λ change_1 +λ change_2 ) / 2

[0161] DSO and high energy-consuming industrial users exist as independent decision-making subjects, and the objective functions of both sides conflict, making it difficult to achieve global optimization. Therefore, the application constructs a master-slave game framework between DSO and high energy-consuming industrial users, and realizes efficient solution through hierarchical decision-making mechanism. DSO first generates compensation incentive prices according to the contract signed with high energy-consuming industrial users, with the objective of minimizing its own cost, and publishes the prices to each high energy-consuming industrial user. Each high energy-consuming industrial user responds with the objective of maximizing its revenue, and feeds back the response result to DSO. This process constitutes a typical master-slave game model, with DSO as the decision maker and each high energy-consuming industrial user as the follower. The solution of the master-slave game model usually adopts two ways: mathematical optimization method based on KKT condition or heuristic algorithm. However, due to the consideration of the adjustment ability of high energy-consuming industrial load, the lower layer high energy-consuming industrial load energy optimization model has a large number of complex constraints and 0-1 variables, and the lower layer variables cannot be transformed into constraints through KKT condition; the heuristic algorithm is difficult to search for the optimal solution in the solving process, and the time consumption is relatively long. Therefore, the application solves the master-slave game model by using the golden section method, selects the division points λ change_1 and λ change_2 , divides the search interval, and efficiently reduces the range by using the self-similarity characteristic of the golden ratio until the optimal solution is obtained.

[0162] The application provides a master-slave game optimization method for high-energy-consumption industrial load participating in demand response based on energy flow-material flow coupling, to solve the problem that the dynamic coupling relationship between material consumption and transmission among multiple production links is ignored when industrial users participate in long-time DR in the prior art, so that the constraints of production material flow among multiple processes on DR cannot be accurately characterized. A dynamic coupling energy flow-material flow high-energy-consumption industrial load energy consumption optimization model is constructed: according to energy consumption and production characteristics, the production processes of the high-energy-consumption industrial load are classified, discrete processes and continuous processes are distinguished, the high-energy-consumption industrial load energy consumption optimization model considering the energy flow-material flow coupling of multiple processes is constructed, the load adjustment boundary of different processes in the DR process is accurately described, and the constraint relationship between energy consumption and material transmission and conversion is clarified. A master-slave game optimization model considering the adjustment capacity of the high-energy-consumption industrial load is constructed: the hierarchical relationship that the DSO is the decision maker and the high-energy-consumption industrial load is the follower is clarified, the master-slave game optimization model considering the adjustment capacity of the high-energy-consumption industrial load is constructed, and the market competition situation is truly simulated. The DSO formulates a price incentive strategy with the objective of minimizing its own cost, and realizes the collaborative balance of load shortage, on-grid power and DR response amount; the high-energy-consumption industrial load adjusts the start-stop state of the discrete process and the power output of the continuous process according to the incentive signal, and participates in the DR demand response with the objective of maximizing its own benefit on the premise of meeting production requirements. The enthusiasm of the high-energy-consumption industrial load for demand response is improved, the effective incentive of the high-energy-consumption industrial load participating in demand response is realized, the system regulation and control cost is reduced, and the availability and economy are improved.

[0163] The application also discloses an optimization device for high-energy-consumption industrial load participating in demand response, the device comprising: a processor; a memory having a computer program capable of running on the processor stored thereon; wherein the computer program, when executed by the processor, realizes the steps of the optimization method for high-energy-consumption industrial load participating in demand response according to any one of the application.

[0164] The application also discloses a power grid operator demand response automatic adjustment equipment, and the automatic adjustment adopts the method according to any one of the application to optimize the high-energy-consumption industrial load participating in demand response.

[0165] The application also discloses a computer readable storage medium, and the computer readable storage medium has a data processing program stored thereon, and the data processing program, when executed by a processor, realizes the steps of the optimization method for high-energy-consumption industrial load participating in demand response according to any one of the application.

[0166] In such an embodiment, the computer program can be downloaded and installed from a network through a communication interface, or installed from a memory. When the computer program is executed by the processor, the above-mentioned functions defined in the method of the above-mentioned embodiment are executed.

[0167] It should be noted that the computer readable medium described above can include, but is not limited to: can include volatile memory such as random access memory (RAM); the memory can also include non-volatile memory such as read-only memory (ROM), flash memory, hard disk drive (HDD) or solid-state drive (SSD); the memory can also include a combination of the above kinds of memories.

[0168] In this application, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or apparatus.

[0169] The computer readable medium described above can be contained in the electronic device described above; or it can exist separately and not be assembled into the electronic device.

[0170] The application also discloses an optimization system for high-energy-consuming industrial load participating in demand response, which comprises a power grid operator and a high-energy-consuming industrial user, the power grid operator adopts the optimization method for high-energy-consuming industrial load participating in demand response according to any one of the application to update the subsidy incentive, the high-energy-consuming industrial user adjusts the process operation state according to the updated subsidy incentive, and feeds back the response quantity after adjusting the process operation state to the power grid operator, the power grid operator updates the subsidy incentive according to the response quantity after adjusting the process operation state until the response quantity fed back by the high-energy-consuming industrial user is less than a preset error value, or the response quantity fed back by the high-energy-consuming industrial user no longer changes. Specifically, when the response quantity is less than the preset error, the operation cost function F G Generally, the minimum value is obtained, and the benefit function F I of the high-energy-consuming industrial user will obtain the maximum value.

[0171] In the system composed of the power grid operator DSO and the high-energy-consuming industrial load, the DSO first generates an initial incentive signal based on the system load shortage and the power purchase quantity constraint; the high-energy-consuming industrial user adjusts the energy use plan according to the signal and feeds back the response quantity; the DSO updates the incentive strategy according to the feedback result, and repeats iteration until the strategies of both sides reach the equilibrium state, so as to finally realize the win-win goal of reducing the power distribution network regulation cost and improving the response enthusiasm of the industrial user. The equilibrium state refers to that the operation cost of the power distribution system operator DSO is the lowest, and at the same time, the benefit of the high-energy-consuming industrial load is the maximum.

[0172] In the technical scheme of the present application, the hierarchical relationship of DSO as an upper decision maker and high-energy-consuming industrial load as a lower follower is defined, and a master-slave game optimization model is constructed. DSO formulates an incentive strategy with the goal of minimizing its own cost, and realizes the collaborative balance of load shortage, electricity purchase quantity and DR response quantity; the high-energy-consuming industrial load adjusts the start-stop state of discrete processes and the power output of continuous processes according to the incentive signal, and participates in DR with the goal of maximizing its own revenue. Through the master-slave game framework, the market decision interaction is truly simulated. When the load shortage is small, DSO can make up for the shortage by additional electricity purchase, at this time, the incentive signal is weak. As the load shortage increases, relying only on additional electricity purchase will lead to a large electricity purchase cost and load shedding cost, DSO guides the high-energy-consuming industrial load to participate in DR by increasing the incentive level, at this time, the load shortage is made up by the additional electricity purchase quantity and the response quantity. The operation cost of DSO is reduced, the comprehensive revenue of the industrial user is improved, and the two-way optimization of distribution network regulation and industrial production is realized.

[0173] In the specific embodiment, adjusting the process operation state according to the updated subsidy incentive can include controlling the start-stop state of the discrete process and / or the power output of the continuous process.

[0174] Figure 6 and 7 It is shown that the market clearing incentive and the response quantity of the high-energy-consuming industrial load in each response period simulated by the technical scheme of the present application, Figure 6 It is shown that the market clearing incentive of the grid operator in each period within a production cycle of 24 hours, Figure 7 It is shown that the load response quantity of the high-energy-consuming industrial load in each period within a production cycle of 24 hours. As can be seen from the figure, the subsidy incentive of the grid operator is changing in real time, and the response quantity of the high-energy-consuming industrial load is also changing with the subsidy incentive, so that the two parties can reach a balance, reduce the cost of the grid operator under the condition of ensuring the revenue of the high-energy-consuming industrial load.

[0175] It should be noted that the step numbers (letter or number numbers) are used in the present application to refer to certain specific method steps, which is only for the purpose of convenience and brevity, and is not intended to limit the order of the method steps by letters or numbers. Those skilled in the art can understand that the order of the related method steps should be determined by the technology itself, and should not be improperly limited by the step numbers.

[0176] Those skilled in the art can understand that the above preferred schemes can be freely combined and superimposed without conflict.

[0177] It should be understood that the above-described implementations are merely exemplary, and are not limiting, and that various obvious or equivalent modifications or substitutions for the above-described details can be made by those skilled in the art without departing from the spirit of the present application, and all such modifications or substitutions are intended to be included within the scope of the claims of the present application.

Claims

1. An optimization method for high energy-consuming industrial load participating in demand response, characterized in that, The method comprises the steps of: S100, constructing a benefit function F of a high energy consumption industrial user participating in demand response according to production benefit, incentive benefit and shutdown cost of the high energy consumption industrial load I wherein the production benefit is calculated according to the output of the last process product of the high energy consumption industrial load, the incentive benefit is calculated according to the response amount fed back by the high energy consumption industrial load, and the shutdown cost is calculated according to the running state of the continuous process in the high energy consumption industrial load; S200, construct an operation cost function F of the grid operator according to the response compensation cost, the power purchase cost and the load shedding cost of the grid operator G wherein the response compensation cost is calculated according to the response amount fed back by the high-energy-consumption industrial load, the power purchase cost is calculated according to the power purchase amount of the grid operator, and the load shedding cost is calculated according to the load shedding amount of the grid operator; S300, according to the revenue function F I and the operating cost function F G The iteration algorithm based on the golden section method is used to calculate the updated value of the subsidy incentive of the power grid operator.

2. The optimization method for demand response participation of high energy-consuming industrial loads according to claim 1, characterized in that, The step S100 constructs a benefit function F of the high energy-consuming industrial user participating in demand response according to the production benefit, the incentive benefit and the shutdown cost of the high energy-consuming industrial load I is: where B P is the production benefit, B DR is the incentive benefit, C CON is the shutdown cost, and t is the time period after the demand response cycle is evenly divided.

3. The optimization method for demand response participation of high energy-consuming industrial loads according to claim 2, characterized in that, The production benefit B is calculated using the following formula P : where T is the demand response period, F is the output of the last process product of the high energy consuming industrial load, k PR is the profit coefficient of the last process of the high energy consuming industrial load, min PR is the minimum output requirement, k,t Fk(t) is the output of the product of process k at time period t.

4. The optimization method for demand response participation of high energy-consuming industrial loads according to claim 2, characterized in that, The incentive income B is calculated by using the following formula DR : B DR = λ t R i,t where λ t is the subsidy incentive provided by the grid operator at time period t, R i,t is the response quantity fed back by the high-energy-consuming industrial load i at time period t.

5. The optimization method for demand response participation of high energy-consuming industrial loads according to claim 2, characterized in that, The downtime cost C is calculated using the following formula CON : wherein C con is the shutdown cost coefficient of the continuous process in the high energy consumption industrial load, is the running state of the continuous process k in the t period.

6. The optimization method for demand response participation of high energy-consuming industrial loads according to any one of claims 1-5, characterized in that, The step S200 constructs the operation cost function F of the power grid operator according to the response of the power grid operator, the compensation cost, the power purchase cost and the load shedding cost G is: where C DR is the response compensation cost, C B is the electricity purchase cost, C R is the load shedding cost, and t is the time period after the demand response cycle is evenly divided.

7. The optimization method for demand response participation of high energy-consuming industrial loads according to claim 6, characterized in that, The response compensation cost C of the power grid operator is calculated by using the following formula DR : C DR = λ t · R i,t λ min ≤λ t ≤λ max wherein λ t is the subsidy incentive provided by the grid operator at time period t, R i,t is the response quantity fed back by the high energy-consuming industrial load i at time period t, λ min is the lower limit value of the subsidy incentive, λ max is the upper limit value of the subsidy incentive. 8.The method of claim 6, wherein, The electricity purchase cost C of the grid operator is calculated using the following formula B : C B = S t · P B,t P B,t = ΔP B,t + P B,N P B,N = PD N + PR N wherein S t is the electricity purchase price of the grid operator to the superior, P B,t is the electricity purchase quantity of the grid operator, ΔP B,t is the additional electricity purchase quantity, P B,N is the distribution network electricity purchase quantity without load shortage, PD N is the high energy consumption industrial load electricity consumption, PR N is the load quantity of non-high energy consumption industrial load, ΔP R,t is the load shedding quantity. 9.The method of claim 6, wherein, The load shedding cost C of the power grid operator is calculated by using the following formula R : C R = C r • ΔP R,t Wherein, C r is the cutting load cost coefficient, ΔP R,t is the cutting load amount.

10. The method of claim 1, wherein, The step S300 includes calculating the updated value of the subsidy incentive of the power grid operator according to the revenue function F I and the operation cost function F G The step of calculating the updated value of the subsidy incentive of the power grid operator by using the iterative algorithm based on the golden section method includes the steps of: S301, calculating a first split point value and a second split point value according to a maximum value and a minimum value of the subsidy incentive by using a golden section method; S302, calculating a first operation cost value according to the first split point value and a second operation cost value according to the second split point value; S303, within a preset iteration number range, when a difference between the maximum value and the minimum value of the subsidy incentive is greater than a preset convergence precision, adjusting the maximum value and the minimum value of the subsidy incentive until the difference between the maximum value and the minimum value of the subsidy incentive is less than or equal to the preset convergence precision, and outputting an updated value of the subsidy incentive of the power grid operator.

11. The optimization method for demand response participation of high energy-consuming industrial loads according to claim 10, characterized in that, The step S303 of adjusting the maximum value and the minimum value of the subsidy incentive comprises: judging whether the second operation cost value is greater than or equal to the first operation cost value, if yes, executing step S304, otherwise, executing step S305; S304, updating the maximum value of the subsidy incentive as the second split point value, updating the second split point value as the first split point value, updating the second operation cost value as the first operation cost value, recalculating an updated first split point value according to the updated maximum value and the minimum value of the subsidy incentive, calculating a maximum value of the revenue function according to the updated data, and updating a split coefficient of the first split point as the maximum value of the revenue function, and calculating an updated first operation cost value according to the recalculated first split point value and the updated split coefficient of the first split point; S305, updating the minimum value of the subsidy incentive as the first split point value, updating the first split point value as the second split point value, updating the first operation cost value as the second operation cost value, recalculating an updated second split point value according to the updated maximum value and the minimum value of the subsidy incentive, calculating a maximum value of the revenue function according to the updated data, and updating a split coefficient of the second split point as the maximum value of the revenue function, and calculating an updated second operation cost value according to the recalculated second split point value and the updated split coefficient of the second split point.

12. The optimization method for demand response participation of high energy-consuming industrial loads according to claim 11, characterized in that, The updated value of the subsidy incentive of the power grid operator is: an average value of the first split point value and the second split point value when the difference between the maximum value and the minimum value of the subsidy incentive is less than or equal to the preset convergence precision.

13. An optimization device for high energy-consuming industrial loads participating in demand response, characterized in that, The device comprises: a processor; a memory having a computer program stored thereon and executable on the processor; wherein the computer program is executed by the processor to implement the steps of the optimization method of high-energy-consuming industrial load participating in demand response according to any one of claims 1 to 12.

14. A grid operator demand response automated adjustment device, characterized by, The automatic adjustment adopts the method according to any one of claims 1 to 12 to optimize the high-energy-consuming industrial load participating in demand response.

15. A computer-readable storage medium, characterized in that, The computer readable storage medium has a data processing program stored thereon, and the data processing program is executed by the processor to implement the steps of the optimization method of high-energy-consuming industrial load participating in demand response according to any one of claims 1 to 12.

16. An optimization system for high energy-consuming industrial loads participating in demand response, characterized by, The system comprises a power grid operator and a high-energy-consuming industrial user, The power grid operator adopts the optimization method for high-energy-consuming industrial load participation in demand response according to any one of claims 1 to 12 to update the subsidy incentive; The high-energy-consuming industrial user adjusts the process operation state according to the updated subsidy incentive, and feeds back the response quantity after adjusting the process operation state to the power grid operator; The power grid operator updates the subsidy incentive according to the response quantity after adjusting the process operation state until the response quantity fed back by the high-energy-consuming industrial user is less than a preset error value.

17. The system, as recited in claim 16, wherein, The adjustment of the process operation state according to the updated subsidy incentive comprises controlling the start-stop state of a discrete process and / or the power output of a continuous process.

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