Method, device and equipment for generating a quantity and price offer scheme for an industrial load production flow

Abstract

The present application relates to the technical field of electric power, and particularly relates to a method, device and equipment for generating a load reporting and pricing scheme of an industrial load production process, comprising: constructing a demand response strategy combined with price and incentive based on historical load data of the industrial load production process; constructing a production process model of the industrial load production process according to coupling relationships of multiple production procedures of the industrial load production process; constructing a linear programming load reporting and pricing model based on the demand response strategy combined with price and incentive and the production process model; inputting a time period of a demand response event and user power consumption data into the linear programming load reporting and pricing model in response to the demand response event issued by a power grid, and outputting a load reporting and pricing scheme of the industrial load production process by the linear programming load reporting and pricing model. Thus, the problems that the related art is difficult to fully tap the adjustable potential of the industrial load when the industrial load participates in the demand response of the power grid due to the complexity of the production process of the industrial load object, and the regulation and control effect is poor, etc. are solved.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a method, device and equipment for generating a quantity and quotation plan for an industrial load production process. Background Art

[0002] Given the high installation costs of current energy storage technologies and the fact that the safety of certain forms of energy storage still requires comprehensive assessment, load-side resources have become a new avenue for seeking flexibility resources. Currently, grid dispatch primarily considers residential and commercial loads, while overlooking the adjustable potential of industrial loads. Industrial loads offer advantages such as high electricity consumption, numerous transferable loads, centralized and easily reformed resources, and high participation rates, thus meeting the requirements for load participation in grid dispatch. For energy-intensive enterprises such as cement and steel plants, electricity costs constitute a significant portion of production costs. Therefore, their role in exploring flexibility resources in the power system cannot be ignored.

[0003] With the deepening of electricity market reform, demand response (DR) has emerged as a key market mechanism. It uses electricity price signals or economic incentives to guide consumers to change their electricity usage during periods of tight supply and demand, thereby improving the operational flexibility of the power system. Compared to traditional methods such as building new thermal power units or investing in energy storage facilities, DR offers the advantages of speed, efficiency, cost-effectiveness, and environmental protection. However, industrial loads such as steel and cement have complex production processes, and regulating only a single piece of equipment fails to fully tap the company's adjustable potential, resulting in poor regulatory effectiveness. Summary of the Invention

[0004] The present invention provides a method, device and equipment for generating a quotation and quotation scheme for an industrial load production process, so as to solve the problems in related technologies such as the complexity of the production process of industrial load objects, which makes it difficult to fully tap the adjustable potential of industrial loads when participating in the demand response of the power grid and the poor regulation effect.

[0005] The first aspect of the present invention provides a method for generating a quantity quotation scheme for an industrial load production process, comprising the following steps: obtaining historical load data and multiple production processes of the industrial load production process; constructing a demand response strategy that combines price and incentives based on the historical load data; determining production constraints and mathematical expressions according to the coupling relationship of multiple production processes, and constructing a production process model of the industrial load production process based on the production constraints and mathematical expressions; constructing a linear programming quantity quotation model based on the industrial load production process based on the demand response strategy that combines price and incentives, and the production process model; responding to a demand response event issued by the power grid, inputting the time period of the demand response event and the user's power consumption data into the linear programming quantity quotation model, and the linear programming quantity quotation model outputting a quantity quotation scheme for the industrial load production process.

[0006] Optionally, a demand response strategy combining price and incentive is constructed based on historical load data, including: calculating the benchmark load for each response period based on historical load data; constructing a price-based demand response strategy based on the benchmark load and electricity price data for each response period; and constructing an incentive-based demand response strategy based on the benchmark load and response incentive for each response period.

[0007] Alternatively, the formula for the price-based demand response strategy is:

[0008] ;

[0009] in, Cost of purchasing electricity for industry; is the fitting coefficient of industrial electricity purchase cost and industrial load power consumption; is the time period number, , The total number of time periods in a 24-hour day; For the The electricity price for each period; is the time period length; For the The power consumption of industrial load in each period;

[0010] The formula for the incentive-based demand response strategy is:

[0011] ;

[0012] ;

[0013] ;

[0014] ;

[0015] ;

[0016] in, To respond to costs; In response to subsidies; For assessment expenses; The coefficient of the relationship between the response subsidy and the clearing price based on the actual production of each process in the industrial production process; is the number of hours in a day; is the hour number, ; For the 1-hour demand response power clearing price; For industrial load users hours of effective response capacity; For industrial load users hours of actual response capacity; For users hours of demand response bid capacity; For industrial load users Average value of baseline load over one hour; For industrial load users Average value of actual load in 2 hours; is the coefficient of the relationship between the assessment price and the clearing price.

[0017] Optionally, the production constraint condition includes at least one of a material balance constraint, a power balance constraint, an equipment utilization constraint, an inventory level constraint, a variable frequency motor power characteristic constraint, and an unschedulable constraint. The mathematical expression of the production constraint condition is as follows:

[0018] The mathematical expression of the material balance constraint is:

[0019] ;

[0020] ;

[0021] in, is the state object serial number, , is a collection of state objects; It is the fitting coefficient of the available quantity of state materials and the total number of main production equipment in the link of industrial load production process; is the fitting coefficient of the total number of main production equipment and the equipment in the startup state; For state objects In the period Available quantity; For state objects In the period Available quantity; is the task number, , For the task collection; For the task A major production equipment in the period Generate (+) or consume (-) state items the number of For the period Tasks in the Startup state The total number of major production equipment; is the device serial number, , For the task A collection of major production equipment; For the period Task No. The device is in the startup state;

[0022] The mathematical expression of the power balance constraint is:

[0023] ;

[0024] in, For industry during the period Power consumption; For the task A major production equipment in the period Power consumption; is the task number, , For the task collection; For the period Tasks in the Startup state The total number of major production equipment;

[0025] The mathematical expression of the equipment utilization constraint is:

[0026] ;

[0027] in, For equipment in time period Average utilization rate; For the task Devices in the time period The actual utilization rate;

[0028] The mathematical expression of the inventory level constraint is:

[0029] ;

[0030] in, For state objects In the period Available quantity; For state objects In the period Available quantity; For the period The number of new items in stock; For the period The quantity shipped out;

[0031] The mathematical expression of the power characteristic constraint of the variable frequency motor is:

[0032] ;

[0033] in, It is the time period No. The set power of the variable frequency motor in each production process, It is the time period No. The set frequency of the variable frequency motor in each production process, It is the proportional coefficient between the set power of the variable frequency motor and the set frequency of the variable frequency motor.

[0034] Optionally, the linear programming quantity quotation model includes a linear programming quantity quotation model based on demand response and a linear programming quotation model based on demand response, wherein the linear programming quantity quotation model is used to plan the quantity quotation plan of the industrial load production process, and the linear programming quotation model is used to plan the quotation plan of the industrial load production process.

[0035] Optionally, the linear programming quota model includes a first objective function and a first constraint condition, wherein:

[0036] The first objective function is:

[0037] ;

[0038] ;

[0039] in, is the total cost; is the tth hour in the response period; For the production process; is the total number of industrial load production processes; The total response time period for participating responses; Is the response period A certain hour No. Unit cost of each production process; Is the response period A certain hour No. The load reduction of each production process; P is the scheduling profit; Response period A certain hour Response subsidies;

[0040] The first constraints include:

[0041] Production constraints:

[0042] ;

[0043] ;

[0044] in, Is the response time hour No. The production process Contribution coefficient of production demand; For the production process; is the total number of production processes; Is the response time hour No. Minimum load of each production process; Is the response time hour No. The minimum value of the production demand; Is the response time hour No. Normal load of a production process when it does not participate in demand response; Is the response period A certain hour No. The amount of load reduction in each production process;

[0045] Load shedding constraints:

[0046] ;

[0047] in, Response time hour No. The amount of reduction in each production process; Is the response time hour No. The maximum load that can be reduced in a production process;

[0048] The first non-negativity constraint is:

[0049] ;

[0050] in, Response time hour No. The reduction in production process.

[0051] Optionally, the linear programming quotation model includes a second objective function and a second constraint condition, wherein:

[0052] The second objective function is:

[0053] ;

[0054] in, is the total cost; The total response time period for participating responses; Response period Middle Quotes for a certain time period; Response period Middle Sales volume for a time period; time period Divide into units of one hour;

[0055] The second constraint includes:

[0056] Market demand constraints:

[0057] ;

[0058] in, for Sales volume for a period of time, It is The maximum demand in a time period;

[0059] Price constraints:

[0060] ;

[0061] ;

[0062] in, Is the response period Middle Quotes for a certain time period; Is the response period Unit cost of product in a given time period; Is the response period A certain hour No. Unit cost of each production process; Is the response period Response costs for each time period;

[0063] The second non-negativity constraint:

[0064] ;

[0065] in, yes Sales volume for a period of time; Is the response period Middle Quotes for a certain time period.

[0066] The second aspect of the present invention provides a device for generating a quantity quotation scheme for an industrial load production process, including: an acquisition module for acquiring historical load data and multiple production processes of the industrial load production process; a first construction module for constructing a demand response strategy that combines price and incentives based on historical load data; a second construction module for determining production constraints and mathematical expressions according to the coupling relationship of multiple production processes, and constructing a production process model of the industrial load production process based on the production constraints and mathematical expressions; a third construction module for constructing a linear programming quantity quotation model based on the industrial load production process based on the demand response strategy that combines price and incentives, and the production process model; a response module for responding to demand response events issued by the power grid, inputting the time period of the demand response event and the user's power consumption data into the linear programming quantity quotation model, and the linear programming quantity quotation model outputs the quantity quotation scheme for the industrial load production process.

[0067] Optionally, the first construction module is further used to: calculate the benchmark load for each response period based on historical load data; construct a price-based demand response strategy based on the benchmark load and electricity price data for each response period; and construct an incentive-based demand response strategy based on the benchmark load and response incentive for each response period.

[0068] Alternatively, the formula for the price-based demand response strategy is:

[0069] ;

[0070] in, Cost of purchasing electricity for industry; is the fitting coefficient of industrial electricity purchase cost and industrial load power consumption; is the time period number, , The total number of time periods in a 24-hour day; For the The electricity price for each period; is the time period length; For the The power consumption of industrial load in each period;

[0071] The formula for the incentive-based demand response strategy is:

[0072] ;

[0073] ;

[0074] ;

[0075] ;

[0076] ;

[0077] in, To respond to costs; In response to subsidies; For assessment expenses; The coefficient of the relationship between the response subsidy and the clearing price based on the actual production of each process in the industrial production process; is the number of hours in a day; is the hour number, ; For the 1-hour demand response power clearing price; For industrial load users hours of effective response capacity; For industrial load users hours of actual response capacity; For users hours of demand response bid capacity; For industrial load users Average value of baseline load over one hour; For industrial load users Average value of actual load in 2 hours; is the coefficient of the relationship between the assessment price and the clearing price.

[0078] Optionally, the production constraint condition includes at least one of a material balance constraint, a power balance constraint, an equipment utilization constraint, an inventory level constraint, a variable frequency motor power characteristic constraint, and an unschedulable constraint. The mathematical expression of the production constraint condition is as follows:

[0079] The mathematical expression of the material balance constraint is:

[0080] ;

[0081] ;

[0082] in, is the state object serial number, , is a collection of state objects; It is the fitting coefficient of the available quantity of state materials and the total number of main production equipment in the link of industrial load production process; is the fitting coefficient of the total number of main production equipment and the equipment in the startup state; For state objects In the period Available quantity; For state objects In the period Available quantity; is the task number, , is the task set; is the task a main production equipment in time period generates (+) or consumes (-) the quantity of state substance; ; is the total number of main production equipment of the task in the start state in time period ; is the equipment number, , is the task main production equipment set; is the time period the task the first equipment in the start state;

[0083] The mathematical expression of the power balance constraint is:

[0084] ;

[0085] wherein, is the power consumption of the industry in time period ; is the power consumption of a main production equipment of the task in time period ; is the task number, , is the task set; is the total number of main production equipment of the task in the start state in time period ;

[0086] The mathematical expression of the equipment utilization constraint is:

[0087] ;

[0088] wherein, is the average utilization of the equipment in time period ; is the actual utilization of the equipment of the task in time period ;

[0089] The mathematical expression of the inventory level constraint is:

[0090] ;

[0091] wherein, is the state substance in time period the available number of; the state of the time period the available number of; the time period the number of newly entered; the time period the number of taken out;

[0092] The mathematical expression of the power characteristic constraint of the variable frequency motor is:

[0093] ;

[0094] wherein, the time period the set power of the variable frequency motor of the th production procedure, the time period the set frequency of the variable frequency motor of the th production procedure, is the proportional coefficient of the set power of the variable frequency motor and the set frequency of the variable frequency motor.

[0095] Optionally, the linear programming quantity reporting model comprises a linear programming quantity reporting model based on demand response and a linear programming price reporting model based on demand response, wherein the linear programming quantity reporting model is used to plan a quantity reporting scheme of the industrial load production process, and the linear programming price reporting model is used to plan a price reporting scheme of the industrial load production process.

[0096] Optionally, the linear programming quantity reporting model comprises a first objective function and a first constraint condition, wherein,

[0097] The first objective function is:

[0098] ;

[0099] ;

[0100] wherein, the total cost; the tth hour in the response time period; the th production procedure; the total number of industrial load production procedures; the total response time period participating in the response; the unit cost of the th production procedure in a certain hour in the response time period ; the unit cost of the th production procedure in a certain hour in the response time period ;The load reduction of each production process; P is the scheduling profit; Response period A certain hour Response subsidies;

[0101] The first constraint conditions include:

[0102] Production constraints:

[0103] ;

[0104] ;

[0105] in, Is the response time hour No. The production process Contribution coefficient of production demand; For the production process; is the total number of production processes; Is the response time hour No. Minimum load of each production process; Is the response time hour No. The minimum value of the production demand; Is the response time hour No. Normal load of a production process when it does not participate in demand response; Is the response period A certain hour No. The amount of load reduction in each production process;

[0106] Load shedding constraints:

[0107] ;

[0108] in, Response time hour No. The amount of reduction in each production process; Is the response time hour No. The maximum load that can be reduced in a production process;

[0109] The first non-negativity constraint is:

[0110] ;

[0111] in, Response time hour The first The reduction amount of the first

[0112] Optionally, the linear programming pricing model comprises a second objective function and a second constraint condition, wherein,

[0113] The second objective function is:

[0114] ;

[0115] wherein, is the total cost; is the total response time period of participation; is the price of the th time period in the response time period ; is the sales volume of the th time period in the response time period ; the time period is divided into one hour;

[0116] The second constraint condition comprises:

[0117] Market demand constraint:

[0118] ;

[0119] wherein, is the sales volume of the th time period, is the maximum demand of the th time period;

[0120] Price constraint:

[0121] ;

[0122] ;

[0123] wherein, is the price of the th time period in the response time period ; is the unit cost of the product in the th time period of the response time period; is the unit cost of the th production process in a certain hour in the response time period ; is the response cost of the th time period of the response time period;

[0124] Second non-negative constraint:

[0125] ;

[0126] in, yes Sales volume for a period of time; Is the response period Middle Quotes for a certain time period.

[0127] A third aspect of the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement a method for generating a quantity quotation scheme for an industrial load production process as described in the above embodiment.

[0128] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program or instruction stored thereon, which is executed by a processor to implement a method for generating a quantity and quotation scheme for an industrial load production process as described in the above embodiment.

[0129] Thus, the present invention includes the following beneficial effects:

[0130] The embodiment of the present invention can analyze the historical load data of the industrial load production process to construct a demand response strategy that combines price and incentives, and analyze the coupling relationship between multiple production processes in the industrial load production process, determine the production constraint conditions and mathematical expressions based on the coupling relationship, and then construct a production process model of the industrial load production process based on the production constraint expression and mathematical expression, and construct a linear programming quotation model for the industrial load production process based on the demand response strategy combined with price and incentives and the production process model. The linear programming quotation model is input according to the time period of the demand response event released by the power grid and the user's power consumption data, and the linear programming quotation model outputs a quotation scheme for the industrial load production process. By comprehensively considering factors such as the production process, production constraints and power consumption data of the industrial load, a scientific and reasonable quotation strategy is formulated to improve the power grid's control effect on industrial loads, increase the enthusiasm and effectiveness of industrial loads in participating in power grid demand response, and fully tap the adjustable potential of industrial loads participating in power grid demand response. Therefore, the technical problems in the related art that the complexity of the production process of industrial load objects leads to the difficulty in fully tapping their adjustable potential when participating in power grid demand response and the poor control effect are solved.

[0131] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0132] The above and / or additional aspects and advantages of the present application will become apparent and more readily appreciated from the following description, taken in conjunction with the following drawings of which:

[0133] Figure 1 A flow chart of a method for generating a quantity report and quotation scheme of an industrial load production process according to an embodiment of the present application;

[0134] Figure 2 A flow chart of a method for generating a quantity report and quotation scheme of a cement plant according to an embodiment of the present application;

[0135] Figure 3 A flow chart of a method for generating a quantity report and quotation scheme of a cement plant according to an embodiment of the present application;

[0136] Figure 4 A flow chart of a method for generating a quantity report and quotation scheme of a cement plant according to an embodiment of the present application;

[0137] Figure 5 A flow chart of a method for generating a quantity report and quotation scheme of a cement plant according to an embodiment of the present application;

[0138] Figure 6 A flow chart of a method for generating a quantity report and quotation scheme of a cement plant according to an embodiment of the present application;

[0139] Figure 7 A flow chart of a method for generating a quantity report and quotation scheme of an industrial load production process according to an embodiment of the present application;

[0140] Figure 8 A flow chart of a method for generating a quantity report and quotation scheme of an industrial load production process according to an embodiment of the present application; DETAILED DESCRIPTION

[0141] Embodiments of the present application are described in detail below with reference to the attached drawing figures, wherein the same or like reference numerals and characters are used throughout the figures to denote the same or like components. The embodiments described below are illustrative only, and are not intended to be limiting on the present application.

[0142] The following describes, with reference to the accompanying drawings, a method, device, and apparatus for generating a quantity and quotation plan for an industrial load production process according to an embodiment of the present invention. In response to the problems mentioned in the above background technology that the industrial load production process is complicated, and it is difficult to fully tap the enterprise's adjustable potential by only regulating a single device, and the regulation effect is poor, the present invention provides a method for generating a quantity quotation scheme for an industrial load production process. In this method, historical load data of the industrial load production process can be analyzed to construct a demand response strategy that combines price and incentives, and the coupling relationship between multiple production processes in the industrial load production process can be analyzed. Production constraints and mathematical expressions are determined based on the coupling relationship, and then a production process model of the industrial load production process is constructed based on the production constraint expression and mathematical expression. A linear programming quantity quotation model for the industrial load production process is constructed based on the demand response strategy that combines price and incentives and the production process model. The linear programming quantity quotation model is input according to the time period of the demand response event published by the power grid and the user's power consumption data. The linear programming quantity quotation model outputs a quantity quotation scheme for the industrial load production process. By comprehensively considering factors such as the industrial load's production process, production constraints, and power consumption data, a scientific and reasonable quantity quotation strategy is formulated, thereby improving the power grid's regulation effect on industrial loads, increasing the enthusiasm and effectiveness of industrial loads in participating in power grid demand response, and fully tapping the adjustable potential of industrial loads in participating in power grid demand response. This solves the problem in related technologies that due to the complexity of the production process of industrial load objects, it is difficult to fully tap the adjustable potential of industrial loads when participating in the demand response of the power grid, and the regulation effect is poor.

[0143] Specifically, Figure 1 The present invention provides a flowchart of a method for generating a quantity and quotation scheme for an industrial load production process.

[0144] like Figure 1 As shown, the method for generating a quotation scheme for the industrial load production process includes the following steps:

[0145] In step S101 , historical load data and multiple production processes of an industrial load production process are obtained.

[0146] Among them, historical load data is the electricity data generated by the industrial load production process when it did not participate in the demand response mechanism in the period before pre-dispatch; the production process (also known as the production link) is the production process in the industrial production process. Taking cement plant production as an example, it includes raw material crushing, raw material grinding, raw material homogenization, clinker burning, cement grinding, etc.

[0147] In step S102, a demand response strategy combining price and incentive is constructed based on historical load data.

[0148] It is understandable that embodiments of the present invention can construct a demand response strategy that combines price and incentives based on historical compliance data.

[0149] Among them, DR is an important market mechanism that guides electricity consumers to change their electricity usage behavior during periods of tight supply and demand through electricity price signals or economic incentives, thereby improving the operational flexibility of the power system. Compared with traditional methods such as building new thermal power units or investing in energy storage facilities, DR has the advantages of being fast, efficient, economical and environmentally friendly.

[0150] In an embodiment of the present invention, a demand response strategy combining price and incentive is constructed based on historical load data, including: calculating the benchmark load for each response period based on historical load data; constructing a price-based demand response strategy based on the benchmark load and electricity price data for each response period; and constructing an incentive-based demand response strategy based on the benchmark load and response incentive for each response period.

[0151] It can be understood that the embodiments of the present invention can calculate the benchmark compliance of each response period based on historical load data, and construct a price-type demand response strategy based on the benchmark load and electricity price data of each response period, and construct an incentive-type demand response strategy based on the benchmark load and response incentive of each response period, so that a linear programming quotation model for industrial production processes can be constructed based on the demand response strategy.

[0152] In this embodiment of the present invention, the power load includes both weekday and non-weekday loads. For the weekday power compliance analysis process, the parameter period can be the N1 (e.g., five or six) pre-dispatched weekdays that do not participate in the demand response mechanism. For each response period, the hourly benchmark load is established by calculating the mean of the 15-minute energy meter readings for these N reference days. The arithmetic mean of these means is then used to define the benchmark average load for that hour. Simultaneously, the peak value of the four 15-minute energy meter readings per hour is selected as the benchmark maximum load. For non-weekdays, the first N2 non-weekdays that do not participate in the demand response mechanism are selected as references. When screening the hourly benchmark load samples, abnormally low values ​​below 25% of the sample average load and abnormally high values ​​above 200% of the sample average load are eliminated, and corresponding recursive calibration is performed.

[0153] In the embodiment of the present invention, the formula of the price-based demand response strategy is:

[0154] ;

[0155] in, Cost of purchasing electricity for industry; is the fitting coefficient of industrial electricity purchase cost and industrial load power consumption; is the time period number, , The total number of time periods in a 24-hour day; For the The electricity price for each period; is the time period length; For the The power consumption of industrial load in each period;

[0156] The formula for the incentive-based demand response strategy is:

[0157] ;

[0158] ;

[0159] ;

[0160] ;

[0161] ;

[0162] in, To respond to costs; In response to subsidies; For assessment expenses; The coefficient of the relationship between the response subsidy and the clearing price based on the actual production of each process in the industrial production process; is the number of hours in a day; is the hour number, ; For the 1-hour demand response power clearing price; For industrial load users hours of effective response capacity; For industrial load users hours of actual response capacity; For users hours of demand response bid capacity; For industrial load users Average value of baseline load over one hour; For industrial load users Average value of actual load in 2 hours; is the coefficient of the relationship between the assessment price and the clearing price.

[0163] Specifically, price-based demand includes time-of-use electricity prices and peak electricity prices. The price ratio of peak, high, flat and low periods can be 1.92:1.6:1:0.4. Based on the price ratio of these four parts, the industrial electricity purchase cost can be expressed as follows:

[0164] ;

[0165] in, Cost of purchasing electricity for industry; is the fitting coefficient of industrial electricity purchase cost and industrial load power consumption; The total number of time periods in a 24-hour day; is the time period number, ; For the The electricity price for each period; is the time period length; For the The power consumption of industrial load in a certain period of time.

[0166] Industrial load users participating in demand-side bidding can receive a certain response subsidy if their response is effective, and must pay additional assessment fees if their response is insufficient:

[0167] ;

[0168] ;

[0169] ;

[0170] ;

[0171] ;

[0172] in, To respond to costs; In response to subsidies; For assessment expenses; The coefficient of the relationship between the response subsidy and the clearing price based on the actual production of each process in the industrial production process; is the number of hours in a day; is the hour number, ; For the 1-hour demand response power clearing price; For industrial load users hours of effective response capacity; For industrial load users hours of actual response capacity; For users hours of demand response bid capacity; For industrial load users Average value of baseline load over one hour; For industrial load users Average value of actual load in 2 hours; is the coefficient of the relationship between the assessment price and the clearing price.

[0173] In step S103, production constraints and mathematical expressions are determined according to the coupling relationship between multiple production processes, and a production process model of the industrial load production process is constructed based on the production constraints and mathematical expressions.

[0174] Among them, the production process model can be constructed using the STN method.

[0175] It can be understood that the embodiment of the present invention can determine the production constraints and mathematical expressions based on the coupling relationship of multiple production processes, and based on the production constraints and mathematical expressions, construct a production process model of the industrial load production process, so as to subsequently construct a linear programming quantity and quotation model of the industrial load production process.

[0176] In an embodiment of the present invention, the production constraint condition includes at least one of a material balance constraint, a power balance constraint, an equipment utilization constraint, an inventory level constraint, a variable frequency motor power characteristic constraint, and an unschedulable constraint. The mathematical expression of the production constraint condition is as follows:

[0177] The mathematical expression of the material balance constraint is:

[0178] ;

[0179] ;

[0180] in, is the state object serial number, , is a collection of state objects; It is the fitting coefficient of the available quantity of state materials and the total number of main production equipment in the link of industrial load production process; is the fitting coefficient of the total number of main production equipment and the equipment in the startup state; For state objects In the period Available quantity; For state objects In the period Available quantity; is the task number, , For the task collection; For the task A major production equipment in the period Generate (+) or consume (-) state items the number of For the period Tasks in the Startup state The total number of major production equipment; is the device serial number, , For the task A collection of major production equipment; For the period Task No. The device is in the startup state.

[0181] The mathematical expression of the power balance constraint is:

[0182] ;

[0183] in, For industry during the period Power consumption; For the task A major production equipment in the period Power consumption; is the task number, , For the task collection; For the period Tasks in the Startup state The total number of major production equipment.

[0184] The mathematical expression of equipment utilization constraint is:

[0185] ;

[0186] in, For equipment in time period Average utilization rate; For the task Devices in the time period actual utilization rate.

[0187] The mathematical expression of the inventory level constraint is:

[0188] ;

[0189] in, For state objects In the period Available quantity; For state objects In the period Available quantity; For the period The number of new arrivals; For the period The quantity shipped out.

[0190] The mathematical expression of the power characteristic constraint of the variable frequency motor is:

[0191] ;

[0192] in, It is the time period No. The set power of the variable frequency motor in each production process, It is the time period No. The set frequency of the variable frequency motor in each production process, It is the proportional coefficient between the set power of the variable frequency motor and the set frequency of the variable frequency motor.

[0193] In step S104, based on the demand response strategy combining price and incentives and the production process model, a linear programming quantity and quotation model based on the industrial load production process is constructed.

[0194] It can be understood that the embodiments of the present invention can construct a linear programming quantity quotation model based on the industrial load production process based on the demand response strategy and production process model that combines price and incentives, fully considering the characteristics of industrial load production and the demand response strategy, and can guide factories and enterprises to participate in demand response.

[0195] In an embodiment of the present invention, the linear programming quantity quotation model includes a linear programming quantity quotation model based on demand response and a linear programming quotation model based on demand response, wherein the linear programming quantity quotation model is used to plan the quantity quotation plan of the industrial load production process, and the linear programming quotation model is used to plan the quotation plan of the industrial load production process.

[0196] It can be understood that the linear programming quantity quotation model in the embodiment of the present invention includes a linear programming quantity quotation model based on demand response and a linear programming quotation model based on demand response. The linear programming quantity quotation model is used to plan the quantity quotation plan of the industrial load production process, and the linear programming quotation model is used to plan the quotation plan of the industrial load production process, so as to achieve the normal production order and product quality of the industrial load production process while responding to demand.

[0197] In an embodiment of the present invention, the linear programming quota model includes a first objective function and a first constraint condition, wherein:

[0198] The first objective function (establishing the objective function with scheduling benefit as the maximization) is:

[0199] ;

[0200] ;

[0201] in, is the total cost; is the tth hour in the response period; For the production process; is the total number of industrial load production processes; The total response time period for participating responses; Is the response period A certain hour No. Unit cost of each production process; Is the response period A certain hour No. The load reduction of each production process; P is the scheduling profit; Response period A certain hour Response subsidies.

[0202] The first constraints include:

[0203] Production constraints:

[0204] ;

[0205] ;

[0206] in, Is the response time hour No. The production process Contribution coefficient of production demand; For the production process; is the total number of production processes; Is the response time hour No. Minimum load of each production process; Is the response time hour No. The minimum value of the production demand; Is the response time hour No. The normal load of a production process when it does not participate in demand response.

[0207] Load shedding constraints:

[0208] ;

[0209] in, Response time hour No. The amount of reduction in each production process; Is the response time hour No. The maximum load that can be reduced in a production process.

[0210] The first non-negativity constraint is:

[0211] ;

[0212] in, Response time hour No. The reduction in production process.

[0213] In an embodiment of the present invention, the linear programming quotation model includes a second objective function and a second constraint condition, wherein:

[0214] The second objective function (aiming at minimizing costs and maximizing competitiveness) is:

[0215] ;

[0216] in, is the total cost; The total response time period for participating responses; Response period Middle Quotes for a certain time period; Response period Middle Sales volume for a time period; time period Divide into one-hour units.

[0217] The second constraint includes:

[0218] Market demand constraints:

[0219] ;

[0220] in, for Sales volume for a period of time, It is The maximum demand in a time period.

[0221] Price constraints:

[0222] ;

[0223] ;

[0224] in, Is the response period Middle Quotes for a certain time period; Is the response period Unit cost of product in a given time period; Is the response period A certain hour No. Unit cost of each production process; Is the response period Response costs for each time period.

[0225] The second non-negativity constraint:

[0226] ;

[0227] in, yes Sales volume for a period of time; Is the response period Middle Quotes for a certain time period.

[0228] In step S105, in response to the demand response event issued by the power grid, the time period of the demand response event and the user's power consumption data are input into the linear programming quotation model, and the linear programming quotation model outputs a quotation plan for the industrial load production process.

[0229] Among them, users mainly refer to industrial load users, including factories or enterprises in high-energy-consuming industries.

[0230] It can be understood that the embodiments of the present invention can respond to demand response events issued by the power grid, input the time period of the demand response event and the user's power consumption data into the linear programming quotation model, and the linear programming quotation model outputs the quotation scheme for the industrial load production process, fully considering the characteristics of industrial load production and the demand response events issued by the power grid, optimizing the response range of power consumption, and effectively avoiding over-regulation or under-regulation of power load in the production process, ensuring that when industrial loads participate in demand response, they do not affect normal production order and product quality, effectively reducing electricity costs, improving market competitiveness, realizing refined management and decentralized control of industrial loads, actively guiding industrial users to participate in demand response, and improving the power grid regulation capability.

[0231] Specifically, the main process of the method for generating a quantity quotation and price quotation scheme for an industrial load production process according to an embodiment of the present invention includes:

[0232] Step 1: Based on the electricity price policy for industrial users, propose a price and incentive combined demand response strategy;

[0233] Step 2: Build an industrial load production process model and determine the production constraints and mathematical expressions;

[0234] Step 3: Construct an industrial load linear programming reporting model based on demand response;

[0235] Step 4: Construct an industrial load linear programming quotation model based on demand response;

[0236] Step 5: Use the industrial load linear programming quantity reporting model and the industrial load linear programming quotation model to output the quantity reporting and quotation plan.

[0237] The method for generating a quotation scheme for an industrial load production process according to an embodiment of the present invention can optimize the setting of the response interval of power consumption by accurately analyzing the production characteristics of industrial load production, thereby effectively avoiding over- or under-regulation of power loads during the production process. This strategy fully considers the real-time production status of industrial loads and the demand of the electricity market, and formulates a flexible quotation scheme to ensure that corporate users do not affect normal production order and product quality while participating in demand response. In addition, the quotation method designed by the present invention realizes the refined management and decentralized control of industrial loads. Through this method, high-energy-consuming enterprises can effectively reduce electricity costs, improve market competitiveness, and contribute to the safe and stable operation of the power system while meeting the demand response requirements of the power grid.

[0238] The following describes the method for generating a quotation scheme for industrial load production process of the present invention through a specific embodiment. Combining the main process of the method for generating a quotation scheme for industrial load production process, taking the participation of a cement plant in grid demand response as an example, the overall process is as follows: Figure 2 As shown, in addition, the following embodiment is set to participate in the evening peak demand response in a cement plant with 6 production links. The response period is from 15:00 to 18:00, a total of 3 hours. The demand response is implemented for the cement plant, taking into account the quotation and quotation strategy of the high-energy-consuming industrial load production process. The specific implementation flow chart of the cement plant load reduction and quotation scheme is shown in the figure below. Figure 3 As shown, the following steps are included:

[0239] Step 1: Based on the electricity price policy for industrial users, propose a price and incentive combined demand response strategy.

[0240] 1.1 Baseline load estimation and real-time electricity consumption data collection.

[0241] During the weekday power load analysis, the five weekdays prior to the scheduled start date, which were not participating in the demand response mechanism, were used as the reference period. For each response period, the hourly baseline load was established by calculating the average of the 15-minute energy meter readings during these five reference days. The arithmetic mean of these averages was then used to define the baseline average load for that hour. The peak value of the average of the four 15-minute energy meter readings within each hour was selected as the baseline maximum load. For non-weekdays, the two previous non-weekdays not participating in the demand response mechanism were used as the reference. When selecting the hourly baseline load sample, abnormally low values ​​below 25% of the sample average load and abnormally high values ​​above 200% of the sample average load were eliminated, and appropriate recursive calibration was performed. Real-time electricity consumption data was collected at fixed 15-minute intervals. The real-time average load for a given hour was calculated by taking the arithmetic mean of the four 15-minute energy meter readings within that hour, while the real-time maximum load was determined by the maximum value of these four 15-minute readings.

[0242] 1.2 Build a price-based demand response strategy.

[0243] Price-based demand response includes time-of-use electricity prices and peak electricity prices. The price ratio of peak, high, flat, and low electricity prices is 1.92:1.6:1:0.4. Based on the price ratio of these four parts, the cement plant's electricity purchase cost can be expressed as follows:

[0244] ;

[0245] in, Cost of purchasing electricity for the cement plant; is the fitting coefficient of the enterprise's electricity purchase cost and the cement plant's load power consumption; The total number of time periods in a 24-hour day; is the time period number, ; For the The electricity price for each period; is the time period length; For the The electricity consumption of the cement plant load during each period.

[0246] 1.3 Build an incentive-based demand response strategy.

[0247] Users participating in demand-side bidding can receive a certain response subsidy when their response is valid, and must pay additional assessment fees when their response is insufficient:

[0248] ;

[0249] ;

[0250] ;

[0251] ;

[0252] ;

[0253] in, To respond to costs; In response to subsidies; For assessment expenses; The coefficient of the relationship between the subsidy and the clearing price based on the actual production of each link in the cement plant; is the number of hours in a day; is the hour number, ; For the 1-hour demand response power clearing price; For users hours of effective response capacity; For users hours of actual response capacity; For users hours of demand response bid capacity; For users Average value of baseline load over one hour; For users Average value of actual load in 2 hours; is the coefficient of the relationship between the assessment price and the clearing price.

[0254] Step 2: Construct a cement plant production process model and determine the cement production constraints and mathematical expressions.

[0255] 2.1 Construct a cement plant production process model.

[0256] Based on the coupling relationship between each link in the production process of typical industrial load cement, the state-task network (STN) method is used to construct a cement plant production process model. The STN model block diagram of the cement plant production process is shown in the figure below. Figure 4 shown.

[0257] The cement production process is complex and involves multiple closely connected processes. From raw material crushing, raw material grinding, raw material homogenization, to clinker burning and cement grinding, each step is indispensable. At the same time, the fuel grinding process requires a continuous supply of coal powder to support clinker burning. The start or stop of any equipment on the production line will directly affect the generation and consumption of materials, and thus affect the stability and safety of cement production. Therefore, constructing a coupling model that considers the interaction between materials and processes is crucial for studying cement load equipment scheduling. Under the premise that similar equipment is operated at the same rated power, an STN model of cement production can be established. Under the premise that similar equipment is operated at the same rated power, an STN model of cement production can be established. The flow chart of each production link in the cement plant is as follows Figure 5 shown.

[0258] 2.2 Determine the cement production constraints and mathematical expressions.

[0259] 2.2.1 Material balance constraints.

[0260] Materials in time period The available quantity of the material is determined by the time period Available quantity, material in time period The generated quantity at the starting point, the material in the time period The consumption quantity at the starting point consists of three parts, and the mathematical expression is:

[0261] ;

[0262] ;

[0263] in, is a collection of state objects; is the state object serial number, ; It is the fitting coefficient of the available quantity of state materials and the total number of main production equipment in line with the industrial load production link; is the fitting coefficient of the total number of main production equipment and the equipment in the startup state; For state objects In the period Available quantity; For the task collection; is the task number, ; For the task A major production equipment in the period Generate (+) or consume (-) state items the number of For the period Tasks in the Startup state The total number of major production equipment; For the task A collection of major production equipment; is the device serial number, ; For the period Task No. The device is in the startup state.

[0264] 2.2.2 Power balance constraints.

[0265] User in time period The total power consumption is expressed as time period The total power consumption of all devices:

[0266] ;

[0267] in, For cement plants during the period Power consumption; For the task A major production equipment in the period of electrical power.

[0268] 2.2.3 Equipment utilization constraints.

[0269] Equipment in time period The utilization rate is expressed as the sum of the ratios of the actual output of all equipment in that period to the theoretical maximum output. The mathematical expression is:

[0270] ;

[0271] in, For equipment in time period Average utilization rate; For the task Devices in the time period actual utilization rate.

[0272] 2.2.4 Inventory level constraints.

[0273] Inventory in period The available quantity of the inventory in period (t-1), the available quantity of the period The new inventory quantity minus the outbound quantity is composed of three parts. The mathematical expression is:

[0274] ;

[0275] in, For the period The number of new items in stock; For the period The quantity shipped out; For state objects In the period Available quantity; For state objects In the period Available quantity.

[0276] Since each task has a corresponding warehouse / yard, it provides greater flexibility for load transfer in the cement plant. However, its storage capacity is limited, so an upper limit is set on the number of objects in each state. At the same time, to ensure safety and reliability, a minimum storage capacity must also be set to cope with unexpected situations:

[0277] ;

[0278] in, and State objects The storage lower and upper limits.

[0279] 2.2.5 Constraints on power characteristics of variable frequency motors.

[0280] In cement plant production processes, variable frequency motors are widely used in key processes such as raw material crushing, raw meal grinding, raw meal homogenization, fuel grinding, clinker burning, and cement grinding. The power characteristics of variable frequency motors enable precise control of production equipment, thereby improving production efficiency and product quality.

[0281] From the power characteristics of the variable frequency motor, we can know that the power of the variable frequency motor is proportional to the frequency. Its power characteristics can be expressed by the formula:

[0282] ;

[0283] in, It is the time period No. The set power of the variable frequency motor in each production link, It is the time period No. The set frequency of the variable frequency motor in each production link, It is the proportional coefficient between the set power of the variable frequency motor and the set frequency of the variable frequency motor.

[0284] Secondly, since the subsequent stages of the cement production process rely on raw materials provided by the previous stages, if the motor power of the drive belt in this stage is too low, it will have a significant impact on the subsequent production stages. To ensure production safety, the frequency adjustment amount of the variable frequency motor is subject to certain constraints, as shown below:

[0285] ;

[0286] in, and They are the minimum and maximum values ​​of the variable frequency motor frequency respectively.

[0287] 2.2.6 Constraints on the continuity of rotary kiln operation and the unschedulability of the clinker burning process.

[0288] In the cement production process, clinker burning plays a vital role, involving complex chemical reactions that transform raw materials into clinker. This process involves preheating and decomposing the materials before they enter a rotary kiln for burning. The burned clinker is then cooled and stored in a clinker silo. As the core equipment for clinker burning, the normal operation of the rotary kiln requires a constant temperature. Any power adjustment or interruption will significantly affect the temperature inside the kiln, causing the melt pool temperature to drop, and restoring the original temperature requires a large amount of energy. Therefore, the rotary kiln must operate continuously under normal production conditions and is only shut down for maintenance or production restrictions. It is considered non-dispatchable equipment and should always be maintained at the rated operating state during the dispatch period to ensure the stability and quality of clinker burning.

[0289] The continuity of clinker burning is crucial for subsequent processes, such as cement grinding. Any adjustment to the clinker burning load could disrupt the overall production process, affecting clinker quality, storage, and the stability of the cement grinding process. Therefore, the clinker burning process must remain non-adjustable to ensure continuity and stability, thus guaranteeing the efficient and normal operation of the entire cement production line.

[0290] Step 3: Construct a linear programming reporting model for cement plants based on demand response.

[0291] 3.1 Objective function

[0292] Under the demand response mechanism, cost control is of vital importance to improving the market competitiveness of enterprises, ensuring the effective use of resources, and achieving sustainable development. When establishing a linear programming model for the reporting strategy, the focus is usually on maximizing the scheduling revenue. The objective function is established as follows:

[0293] ;

[0294] ;

[0295] in, is the total cost; is the tth hour in the response period; For the production process; is the total number of industrial load production processes; The total response time period for participating responses; Is the response period A certain hour the unit cost of the first production process; is the load reduction of the first production process in the response hour; P is the dispatching benefit; is the response subsidy in the response hour.

[0296] Solving the above model can obtain the optimal load reduction scheme and the corresponding maximum dispatching benefit P.

[0297] 3.2 Constraint conditions.

[0298] 3.2.1 Production constraint.

[0299] When implementing the load reduction measures, the basic process and capacity of cement production must be maintained, and the load reduction amount cannot affect the basic demand of production, so as to ensure the stability of market supply. The mathematical expression is:

[0300]

[0301]

[0302] wherein, is the contribution coefficient of the first production process to the th production demand in the response hour; is the minimum load of the th production process in the response hour; is the minimum value of the th production demand in the response hour; is the normal load of the th production process when it does not participate in demand response in the response hour. wherein, the names of different equipment used by each production process of the cement plant, corresponding processes, and rated power of the equipment are shown in Table 1, which is the table of the number and rated power of core production equipment of each process of the cement plant. Table 1

[0303]

[0304]

[0305]

[0306] wherein, the rated power of the process is the rated power of the process in the response hour​​​​​​​​​ No. According to Table 1, draw a scatter plot of the relationship between the processes and rated power of different equipment in the cement plant. Figure 6 shown.

[0307] 3.2.2 Load reduction constraints.

[0308] ;

[0309] in, Response time hour No. The amount of reduction in each production link; Is the response time hour No. The maximum load that can be reduced in each production link.

[0310] 3.3.3 Non-negativity constraints.

[0311] ;

[0312] in, Response time hour No. The reduction in production links.

[0313] 3.3 Solution period No. Unit cost reduction in each production link .

[0314] For cement plants, unit cost reduction It can include the following four aspects:

[0315] Direct energy costs: Reduce the extra electricity costs that may be required for production, start and stop of machines, etc.

[0316] Variable production costs: costs directly related to production volume, including raw and auxiliary materials consumed after variable production.

[0317] Fixed cost allocation: Fixed costs that need to be allocated even if production decreases, such as rent, depreciation, etc.

[0318] Potential losses: Loss of orders or market share that may result from reduced production.

[0319] The mathematical expression of decomposition is:

[0320] ;

[0321] in, Response time hour No. Reduce electricity costs in each production link unit cost; Response time hour No. Variable production costs in the unit cost reduction of each production link; Response time hour No. Allocation of fixed costs in unit cost reduction of each production link; Response time hour potential losses in unit cost reduction.

[0322] therefore, The calculation formula is:

[0323] ;

[0324] ;

[0325] ;

[0326] in, is the unit variable cost; is the total fixed cost; Is the response time hour Normal production volume; Is the response time hour reduced production; is an estimate of potential losses.

[0327] Taking into account the equipment quantity and rated power of each cement production link, the equipment characteristics and dispatchability of each production link, and introducing multiple model constraints, the minimum load of each production link can be obtained. (kw) and the maximum load that can be reduced (kw) As shown in Table 2, Table 2 shows the minimum load and the maximum load that can be reduced in each production link of the cement plant.

[0328] Table 2

[0329]

[0330] Based on the preset simulation scenarios and hypothetical implementation conditions, a detailed analysis of the demand response mechanism in the implementation example is conducted. The ratio of (yuan / kw) is 3:5:4, which represents the intensity of grid subsidies in the three response periods. At the same time, considering the cost structure of demand response power, the corresponding cost price is formulated. The ratio of (yuan / kw) is 2:5:3.5 to reflect the differentiated configuration and composition of power cost prices in different demand response time periods.

[0331] By applying the Python programming language and the corresponding pulp optimization library, a linear programming algorithm is executed to solve the optimal solution of the objective function under given constraints, and the optimal load reduction scheme under the embodiment is obtained, as shown in Table 3. Table 3 shows the optimal load reduction scheme for each production link of the cement plant.

[0332] Table 3

[0333]

[0334] Step 4: Construct a linear programming quotation model for cement plants based on demand response.

[0335] 4.1 Objective function

[0336] When establishing a linear programming bidding model for cement plants based on a demand response mechanism, the goal is to minimize costs and maximize competitiveness. To achieve this goal, the model design will focus on optimizing bidding strategies. While ensuring that market demand is met, the model will also focus on reducing bids to enhance the competitiveness of bids, thereby achieving the goal of winning the demand response bid and, on this basis, striving to achieve substantial economic benefits. Therefore, cost minimization is set as the model's objective function:

[0337] ;

[0338] in, is the total cost; The total response time period for participating responses; Is the response period Middle Quotes for a certain time period; Is the response period Middle Sales volume for a time period; time period Divide into one-hour units.

[0339] 4.2 Constraints.

[0340] 4.2.1 Market demand constraints.

[0341] Sales volume cannot exceed market demand. Sales volume exceeding market demand will lead to product backlog, which will not only waste storage costs, but may also reduce the market value of the product, thereby affecting the company's economic benefits.

[0342] Market demand constraints:

[0343] ;

[0344] in, for Sales volume for a period of time, It is The maximum demand in a time period.

[0345] 4.2.2 Price constraints.

[0346] The quotation cannot be lower than the cost.

[0347] ;

[0348] ;

[0349] in, Is the response period Middle Quotes for a certain time period; Is the response period Unit cost of product in a given time period; Is the response period Response costs for each time period.

[0350] 4.2.3 Non-negativity constraints.

[0351] Sales volumes and quotes cannot be negative.

[0352] ;

[0353] in, yes Sales volume for a period of time; Is the response period Middle Quotes for a certain time period.

[0354] 4.3 Model solution.

[0355] Solve the above model to get the optimal quotation solution and the corresponding maximum benefit .

[0356] Step 5: Use the linear programming quantity quotation model and the load linear programming quotation model to output the cement plant quantity quotation and quotation plan.

[0357] Example Response Period Middle Sales volume for each time period Can be done by one Matrix Indicates that the rows represent links , columns represent time periods .matrix The specific contents are as follows:

[0358] ;

[0359] An analysis of market demand for cement plants reveals certain regularities within different time periods. For this example, let's assume that the market demand for cement is 150 tons between 3:00 PM and 4:00 PM. Between 4:00 PM and 5:00 PM, demand rises to 180 tons. Between 5:00 PM and 6:00 PM, demand decreases slightly to 170 tons.

[0360] When evaluating the production cost structure of a cement plant, multiple cost factors are considered, including electricity consumption costs, raw material and auxiliary material costs, variable manufacturing costs, fixed cost amortization, and potential losses. By analyzing these key cost factors, the unit cost of each link in the cement production process is calculated within the three specified demand response time periods. (Yuan / ton), and use a Matrix Indicates that the rows represent links , columns represent time periods .matrix The specific contents are as follows:

[0361] ;

[0362] Meanwhile, in this embodiment, the ratio of the response costs (unit: yuan) of the cement plant for demand response in the three time periods can be roughly estimated to be 0.9:1.12:1.5.

[0363] Based on the specific data of the above embodiment, the linear programming quotation model and model constraints are comprehensively considered. By applying the Python programming language and the corresponding pulp optimization library, a linear programming algorithm is executed to solve the optimal solution of the objective function under the given constraints, and the optimal quotation solution under the embodiment is obtained:

[0364] The price for period 1 (15:00-16:00): 2.90 yuan / kW;

[0365] The price for period 2 (16:00-17:00): 3.04 yuan / kW;

[0366] The price for period 3 (17:00-18:00): 3.08 yuan / kW;

[0367] The minimum cost quoted is 5412.00 yuan.

[0368] In summary, the above-mentioned embodiment uses the cement plant as the core implementation entity, meticulously arranging and regulating each key link in its production process to actively participate in the grid's demand response. The implementation of this strategy not only guides enterprises to participate in demand response and improves the grid's regulatory capabilities, effectively mitigating the impact of renewable energy fluctuations on grid stability, especially when faced with a high proportion of renewable energy access, but also significantly improves the economic benefits of enterprises through precise resource optimization and allocation. Furthermore, the implementation of this strategy ensures the continuity and safety of the production process, improves the reliability of the entire system by reducing reliance on complex communication facilities, simplifies operational processes, and reduces implementation costs.

[0369] According to the method for generating a quotation scheme for an industrial load production process proposed in an embodiment of the present invention, historical load data of the industrial load production process can be analyzed to construct a demand response strategy that combines price and incentives, and the coupling relationship between multiple production processes in the industrial load production process can be analyzed. The production constraint conditions and mathematical expressions are determined according to the coupling relationship, and then a production process model of the industrial load production process is constructed based on the production constraint expression and the mathematical expression. A linear programming quotation model for the industrial load production process is constructed according to the demand response strategy and the production process model that combine price and incentives. The linear programming quotation model is input according to the time period of the demand response event released by the power grid and the user's power consumption data. The linear programming quotation model outputs a quotation scheme for the industrial load production process. By comprehensively considering factors such as the production process, production constraints and power consumption data of the industrial load, a scientific and reasonable quotation strategy is formulated to improve the power grid's regulation effect on industrial loads, increase the enthusiasm and effectiveness of industrial loads in participating in the power grid demand response, and fully tap the adjustable potential of industrial loads in participating in the power grid demand response.

[0370] Next, a device for generating a quantity and quotation scheme for an industrial load production process according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0371] Figure 7 It is a block diagram of a device for generating a quantity and quotation scheme for an industrial load production process according to an embodiment of the present invention.

[0372] like Figure 7 As shown, the device 10 for generating a quantity quotation and price quotation scheme for an industrial load production process includes: an acquisition module 100 , a first construction module 200 , a second construction module 300 , a third construction module 400 and a response module 500 .

[0373] The acquisition module 100 is configured to acquire historical load data and a plurality of production processes of an industrial load production process; the first construction module 200 is configured to construct a price and incentive combined demand response strategy based on the historical load data; the second construction module 300 is configured to determine production constraint conditions and mathematical expressions according to coupling relationships of the plurality of production processes, and construct a production process model of the industrial load production process based on the production constraint conditions and the mathematical expressions; the third construction module 400 is configured to construct a linear programming quantity reporting pricing model based on the industrial load production process based on the price and incentive combined demand response strategy and the production process model; and the response module 500 is configured to input a time period of a demand response event and user power consumption data into the linear programming quantity reporting pricing model in response to the demand response event issued by a power grid, and output a quantity reporting pricing scheme of the industrial load production process by the linear programming quantity reporting pricing model.

[0374] In the embodiment of the present application, the first construction module 200 is further configured to calculate a reference load of each response time period according to the historical load data, construct a price type demand response strategy according to the reference load of each response time period and power price data, and construct an incentive type demand response strategy according to the reference load of each response time period and response incentives.

[0375] In the embodiment of the present application, the formula of the price type demand response strategy is:

[0376] ;

[0377] wherein, is an industrial power purchase cost; is a fitting coefficient of the industrial power purchase cost and power of the industrial load; is a time period serial number, , is a total number of time periods in 24 hours of a day; is a power price of the i th time period; is a time period length; is power of the industrial load in the i th time period; The formula of the incentive type demand response strategy is:

[0378]

[0379] ;

[0380] ;

[0381] ;

[0382] ;

[0383] ; ​​

[0384] in, To respond to costs; In response to subsidies; For assessment expenses; The coefficient of the relationship between the response subsidy and the clearing price based on the actual production of each process in the industrial production process; is the number of hours in a day; is the hour number, ; For the 1-hour demand response power clearing price; For industrial load users hours of effective response capacity; For industrial load users hours of actual response capacity; For users hours of demand response bid capacity; For industrial load users Average value of baseline load over one hour; For industrial load users Average value of actual load in 2 hours; is the coefficient of the relationship between the assessment price and the clearing price.

[0385] In an embodiment of the present invention, the production constraint condition includes at least one of a material balance constraint, a power balance constraint, an equipment utilization constraint, an inventory level constraint, a variable frequency motor power characteristic constraint, and an unschedulable constraint. The mathematical expression of the production constraint condition is as follows:

[0386] The mathematical expression of the material balance constraint is:

[0387] ;

[0388] ;

[0389] in, is the state object serial number, , is a collection of state objects; It is the fitting coefficient of the available quantity of state materials and the total number of main production equipment in the link of industrial load production process; is the fitting coefficient of the total number of main production equipment and the equipment in the startup state; For state objects In the period Available quantity; For state objects In the period Available quantity; is the task number, , For the task collection; For the task A major production equipment in the period Generate (+) or consume (-) state items the number of For the period Tasks in the Startup state The total number of major production equipment; is the device serial number, , For the task A collection of major production equipment; For the period Task No. The device is in the startup state;

[0390] The mathematical expression of the power balance constraint is:

[0391] ;

[0392] in, For industry during the period Power consumption; For the task A major production equipment in the period Power consumption; is the task number, , For the task collection; For the period Tasks in the Startup state The total number of major production equipment;

[0393] The mathematical expression of the equipment utilization constraint is:

[0394] ;

[0395] in, For equipment in time period Average utilization rate; For the task Devices in the time period The actual utilization rate;

[0396] The mathematical expression of the inventory level constraint is:

[0397] ;

[0398] in, For state objects In the period Available quantity; For state objects In the period Available quantity; For the period The number of new arrivals; For the period The quantity shipped out;

[0399] The mathematical expression of the power characteristic constraint of the variable frequency motor is:

[0400] ;

[0401] in, It is the time period No. The set power of the variable frequency motor in each production process, It is the time period No. The set frequency of the variable frequency motor in each production process, It is the proportional coefficient between the set power of the variable frequency motor and the set frequency of the variable frequency motor.

[0402] In an embodiment of the present invention, the linear programming quantity quotation model includes a linear programming quantity quotation model based on demand response and a linear programming quotation model based on demand response, wherein the linear programming quantity quotation model is used to plan the quantity quotation plan of the industrial load production process, and the linear programming quotation model is used to plan the quotation plan of the industrial load production process.

[0403] In an embodiment of the present invention, the linear programming quota model includes a first objective function and a first constraint condition, wherein:

[0404] The first objective function is:

[0405] ;

[0406] ;

[0407] in, is the total cost; is the tth hour in the response period; For the production process; is the total number of industrial load production processes; The total response time period for participating responses; Is the response period A certain hour No. Unit cost of each production process; Is the response period A certain hour No. The load reduction of each production process; P is the scheduling profit; Response period A certain hour Response subsidies;

[0408] The first constraint conditions include:

[0409] Production constraints:

[0410] ;

[0411] ;

[0412] in, Is the response time hour No. The production process Contribution coefficient of production demand; For the production process; is the total number of production processes; Is the response time hour No. Minimum load of each production process; Is the response time hour No. The minimum value of the production demand; Is the response time hour No. Normal load of a production process when it does not participate in demand response; Is the response period A certain hour No. The amount of load reduction in each production process;

[0413] Load shedding constraints:

[0414] ;

[0415] in, Response time hour No. The amount of reduction in each production process; Is the response time hour No. The maximum load that can be reduced in a production process;

[0416] The first non-negativity constraint is:

[0417] ;

[0418] in, Response time hour No. The reduction in production process.

[0419] In an embodiment of the present invention, the linear programming quotation model includes a second objective function and a second constraint condition, wherein:

[0420] The second objective function is:

[0421] ;

[0422] in, is the total cost; The total response time period for participating responses; Response period Middle Quotes for a certain time period; Response period Middle Sales volume for a time period; time period Divide into units of one hour;

[0423] The second constraint includes:

[0424] Market demand constraints:

[0425] ;

[0426] in, for Sales volume for a period of time, It is The maximum demand in a time period;

[0427] Price constraints:

[0428] ;

[0429] ;

[0430] in, Is the response period Middle Quotes for a certain time period; Is the response period Unit cost of product in a given time period; Is the response period A certain hour No. Unit cost of each production process; Is the response period Response costs for each time period;

[0431] The second non-negativity constraint:

[0432] ;

[0433] in, yes Sales volume for a period of time; Is the response period Middle Quotes for a certain time period.

[0434] It should be noted that the above explanation of the embodiment of the method for generating a quantity quotation scheme for an industrial load production process is also applicable to the device for generating a quantity quotation scheme for an industrial load production process of this embodiment, and will not be repeated here.

[0435] According to the embodiment of the present invention, the device for generating a quantity quotation scheme for an industrial load production process proposed can analyze the historical load data of the industrial load production process to construct a demand response strategy that combines price and incentives, and analyze the coupling relationship between multiple production processes in the industrial load production process, determine the production constraint conditions and mathematical expressions based on the coupling relationship, and then construct a production process model of the industrial load production process based on the production constraint expression and the mathematical expression, and construct a linear programming quantity quotation model for the industrial load production process based on the demand response strategy and production process model that combine price and incentives. The linear programming quantity quotation model is input according to the time period of the demand response event released by the power grid and the user's power consumption data, and the linear programming quantity quotation model outputs the quantity quotation scheme for the industrial load production process. By comprehensively considering factors such as the production process, production constraints and power consumption data of the industrial load, a scientific and reasonable quantity quotation strategy is formulated, the power grid's regulation effect on the industrial load is improved, the enthusiasm and effectiveness of the industrial load in participating in the power grid demand response is improved, and the adjustable potential of the industrial load in participating in the power grid demand response is fully tapped.

[0436] Figure 8 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device may include:

[0437] A memory 801 , a processor 802 , and a computer program stored in the memory 801 and executable on the processor 802 .

[0438] When the processor 802 executes the program, the method for generating a quantity and quotation scheme for an industrial load production process provided in the above embodiment is implemented.

[0439] Furthermore, the electronic device further includes:

[0440] The communication interface 803 is used for communication between the memory 801 and the processor 802 .

[0441] The memory 801 is used to store computer programs that can be run on the processor 802.

[0442] The memory 801 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0443] If the memory 801, processor 802, and communication interface 803 are implemented independently, the communication interface 803, memory 801, and processor 802 can be connected to each other via a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 8 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0444] Optionally, in a specific implementation, if the memory 801, the processor 802 and the communication interface 803 are integrated on a chip, the memory 801, the processor 802 and the communication interface 803 can communicate with each other through an internal interface.

[0445] The processor 802 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0446] An embodiment of the present invention further provides a computer-readable storage medium having a computer program or instruction stored thereon. When the computer program or instruction is executed by a processor, the method for generating a quantity and quotation plan for an industrial load production process as described above is implemented.

[0447] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0448] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "N" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0449] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or N executable instructions for implementing a custom logical function or step of a process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0450] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, the steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array, a field programmable gate array, etc.

[0451] A person skilled in the art may understand that all or part of the steps carried out in the method for implementing the above-mentioned embodiment may be completed by instructing the relevant hardware through a program, and the above-mentioned program may be stored in a computer-readable storage medium, which, when executed, includes one of the steps of the method embodiment or a combination thereof.

[0452] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for generating a quotation scheme for an industrial load production process, characterized in that: The following steps are involved: Obtain historical load data and multiple production processes of industrial load production processes; Building a demand response strategy combining price and incentives based on the historical load data; Production constraints and mathematical expressions are determined based on the coupling relationship of the multiple production processes. Based on the production constraints and mathematical expressions, a production process model of the industrial load production process is constructed. The production constraints include at least one of a material balance constraint, a power balance constraint, an equipment utilization constraint, an inventory level constraint, a variable frequency motor power characteristic constraint, and an unschedulable constraint. The mathematical expression of the inventory level constraint is: ； in, For state objects In the period Available quantity; For state objects In the period Available quantity; For the period The number of new arrivals; For the period The quantity shipped out; The mathematical expression of the variable frequency motor power characteristic constraint is: ； in, It is the time period No. The set power of the variable frequency motor for each production process; It is the time period No. The set frequency of the variable frequency motor in each production process, , and are the minimum and maximum values ​​of the variable frequency motor frequency respectively; It is the proportional coefficient between the set power of the variable frequency motor and the set frequency of the variable frequency motor; Based on the demand response strategy combining price and incentives, and the production process model, a linear programming quantity quotation model based on the industrial load production process is constructed, wherein the linear programming quantity quotation model includes a linear programming quantity quotation model based on demand response and a linear programming quotation model based on demand response. The linear programming quantity quotation model includes a first objective function and a first constraint condition. The optimization goal of the first objective function is to maximize the scheduling benefit. The first constraint condition includes a production constraint, a load reduction constraint, and a first non-negative constraint. The linear programming quotation model includes a second objective function and a second constraint condition. The optimization goal of the second objective function is to minimize the total cost. The second constraint condition includes a market demand constraint and a second non-negative constraint. The first objective function is: ； ； in, is the total cost; is the tth hour in the response period; For the production process; is the total number of industrial load production processes; The total response time period for the participant response; Is the response period A certain hour No. Unit cost of each production process; Is the response period A certain hour No. The load reduction of each production process; P is the scheduling profit; Response period A certain hour Response subsidies; In response to a demand response event issued by the power grid, the time period of the demand response event and the user's power consumption data are input into the linear programming quotation model, and the linear programming quotation model outputs a quotation plan for the industrial load production process.

2. The method for generating a quotation scheme for an industrial load production process according to claim 1, characterized in that: The demand response strategy combining price and incentive is constructed based on the historical load data, including: Calculating a baseline load for each response period based on the historical load data; Constructing a price-based demand response strategy based on the benchmark load and electricity price data for each response period; An incentive-based demand response strategy is constructed according to the benchmark load and response incentive in each response period.

3. The method for generating a quotation scheme for an industrial load production process according to claim 2, characterized in that: The formula of the price-based demand response strategy is: ； in, Cost of purchasing electricity for industry; is the fitting coefficient of industrial electricity purchase cost and industrial load power consumption; is the time period number, , The total number of time periods in a 24-hour day; For the The electricity price for each period; is the time period length; For the The power consumption of industrial load in each period; The formula of the incentive-based demand response strategy is: ； ； ； ； ； in, To respond to costs; In response to subsidies; For assessment expenses; The coefficient of the relationship between the response subsidy and the clearing price based on the actual production of each process in the industrial production process; is the number of hours in a day; is the hour number, ; For the 1-hour demand response power clearing price; For industrial load users hours of effective response capacity; For industrial load users hours of actual response capacity; For users hours of demand response bid capacity; For industrial load users Average value of baseline load over one hour; For industrial load users Average value of actual load in 2 hours; is the coefficient of the relationship between the assessment price and the clearing price.

4. The method for generating a quotation scheme for an industrial load production process according to claim 1, characterized in that: The mathematical expression of the production constraint is as follows: The mathematical expression of the material balance constraint is: ； ； in, is the state object serial number, , is a collection of state objects; It is the fitting coefficient of the available quantity of state materials and the total number of main production equipment in the link of industrial load production process; is the fitting coefficient between the total number of main production equipment and the main production equipment in the startup state; For state objects In the period Available quantity; For state objects In the period Available quantity; is the task number, , For the task collection; For the task A major production equipment in the period Generate or consume status items the number of For the period Tasks in the Startup state The total number of major production equipment; is the serial number of the main production equipment, , For the task A collection of major production equipment; For the period Task No. The main production equipment is in the startup state; The mathematical expression of the power balance constraint is: ； in, For industry during the period Power consumption; For the task A major production equipment in the period Power consumption; is the task number, , For the task collection; For the period Tasks in the Startup state The total number of major production equipment; The mathematical expression of the equipment utilization constraint is: ； in, For the main production equipment in the period Average utilization rate; For the task The main production equipment in the period The actual utilization rate; For the period Tasks in the Startup state The total number of major production equipment.

5. The method for generating a quantity quotation scheme for an industrial load production process according to claim 1, characterized in that: The step of inputting the time period of the demand response event and the user power consumption data into the linear programming quotation model, and the linear programming quotation model outputting the quotation scheme for the industrial load production process, includes: The linear programming quantity quotation model is used to plan the quantity quotation plan of the industrial load production process, and the linear programming quotation model is used to plan the quotation plan of the industrial load production process.

6. The method for generating a quantity quotation scheme for an industrial load production process according to claim 5, characterized in that: The first constraint condition includes: Production constraints: ； ； in, Is the response time hour No. The production process Contribution coefficient of production demand; For the production process; is the total number of production processes; Is the response time hour No. Minimum load of each production process; Is the response time hour No. The minimum value of the production demand; Is the response time hour No. Normal load of a production process when it does not participate in demand response; Is the response period A certain hour No. The amount of load reduction in each production process; Load shedding constraints: ； in, Response time hour No. The amount of reduction in each production process; Is the response time hour No. The maximum load that can be reduced in a production process; The first non-negativity constraint is: ； in, Response time hour No. The reduction in production process.

7. The method for generating a quantity quotation scheme for an industrial load production process according to claim 5, characterized in that: The second objective function is: ； in, is the total cost; The total response time period for the participant response; Response period Middle Quotes for a certain time period; Response period Middle Sales volume for a time period; time period Divide into units of one hour; The second constraint condition includes: Market demand constraints: ； in, for Sales volume for a period of time, It is The maximum demand in a time period; Price constraints: ； ； in, Is the response period Middle Quotes for a certain time period; Is the response period Unit cost of product in a given time period; Is the response period A certain hour No. Unit cost of each production process; Is the response period Response costs for each time period; The second non-negativity constraint: ； in, yes Sales volume for a period of time; Is the response period Middle Quotes for a certain time period.

8. A device for generating a quotation plan for an industrial load production process, characterized in that: include: An acquisition module is used to obtain historical load data and multiple production processes of industrial load production processes; A first building module is configured to build a demand response strategy combining price and incentive based on the historical load data; The second construction module is configured to determine production constraints and mathematical expressions based on the coupling relationship between the multiple production processes, and to construct a production process model of the industrial load production process based on the production constraints and mathematical expressions, wherein the production constraints include at least one of a material balance constraint, a power balance constraint, an equipment utilization constraint, an inventory level constraint, a variable frequency motor power characteristic constraint, and an unschedulable constraint; and the mathematical expression of the inventory level constraint is: ； in, For state objects In the period Available quantity; For state objects In the period Available quantity; For the period The number of new arrivals; For the period The quantity shipped out; The mathematical expression of the variable frequency motor power characteristic constraint is: ； in, It is the time period No. The set power of the variable frequency motor for each production process; It is the time period No. The set frequency of the variable frequency motor in each production process, , and are the minimum and maximum values ​​of the variable frequency motor frequency respectively; It is the proportional coefficient between the set power of the variable frequency motor and the set frequency of the variable frequency motor; The third construction module is used to construct a linear programming quantity quotation model based on the industrial load production process based on the demand response strategy of the combination of price and incentive, and the production process model, wherein the linear programming quantity quotation model includes a linear programming quantity quotation model based on demand response and a linear programming quotation model based on demand response, and the linear programming quantity quotation model includes a first objective function and a first constraint condition, the optimization goal of the first objective function is to maximize the scheduling benefit, and the first constraint condition includes a production constraint, a load reduction constraint and a first non-negative constraint; the linear programming quotation model includes a second objective function and a second constraint condition, the optimization goal of the second objective function is to minimize the total cost, the second constraint condition includes a market demand constraint and a second non-negative constraint, and the first objective function is: ； ； in, is the total cost; is the tth hour in the response period; For the production process; is the total number of industrial load production processes; The total response time period for the participant response; Is the response period A certain hour No. Unit cost of each production process; Is the response period A certain hour No. The load reduction of each production process; P is the scheduling profit; Response period A certain hour Response subsidies; The response module is used to respond to the demand response event issued by the power grid, input the time period of the demand response event and the user's power consumption data into the linear programming quotation model, and the linear programming quotation model outputs the quotation plan for the industrial load production process.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for generating a quantity quotation scheme for an industrial load production process as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed, the method for generating a quantity quotation and price quotation scheme for an industrial load production process according to any one of claims 1 to 7 is implemented.

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