CN-116050906-B - Industrial load multi-energy flexibility assessment method considering uncertainty

CN116050906BCN 116050906 BCN116050906 BCN 116050906BCN-116050906-B

Abstract

The invention discloses an industrial load multi-energy flexibility assessment method considering uncertainty. The method comprises the steps of constructing an energy consumption and yield relation model of a production flow in an industrial load considering yield adjustment constraint, constructing storage and yield requirement constraint of the production flow and a product storage warehouse in a related mode by using a graph theory method, constructing a discrete state set of the industrial load, searching a feasible section to construct a multi-functional feasible section, searching the feasible section to obtain multi-functional flexibility of the industrial load, and evaluating the multi-functional flexibility. The method evaluates the multi-energy flexible adjustment capability of the industrial load considering production uncertainty, can be used for adjusting the requirements of the industrial load on various energy forms at the running level, can be used in multiple fields such as electric power, multi-energy demand response and the like, further assists an energy system in utilizing the adjustment capability of the industrial load, enhances the flexibility of the system and supports the flexible running optimization scheduling of the energy system.

Inventors

DING YI
Hui Hengyu
BAO MINGLEI
SONG YONGHUA

Assignees

浙江大学
浙江大学绍兴研究院

Dates

Publication Date: 20260505
Application Date: 20230110

Claims (8)

1. An uncertainty-considered industrial load multi-energy flexibility assessment method is characterized by comprising the following steps: 1) Building an energy consumption and yield relation model of a production process in an industrial load considering yield adjustment constraint; 2) Correlating the production flow in the industrial load in the step 1) with a product storage warehouse by using a graph theory method, and constructing storage constraint of the product storage warehouse and yield requirement constraint of the production flow; 3) Taking uncertainty in a production process in an industrial load as a random variable, discretizing the random variable to construct discrete state sets of the industrial load, wherein each discrete state set comprises a plurality of discrete states of the industrial load, and carrying out feasible interval searching on yield adjustment constraint, storage constraint of a product storage warehouse and yield requirement constraint of the production process under each discrete state according to an energy consumption and yield relation model to construct a multi-energy feasible interval under each discrete state; 4) Searching a feasible section meeting the preset probability value requirement in the multi-functional feasible sections in each discrete state according to the preset probability value requirement of the multi-functional flexibility of the industrial load, and obtaining the multi-functional flexibility of the industrial load, thereby realizing the evaluation of the multi-functional flexibility of the industrial load; in the step 1), the production flow in the industrial load comprises a discrete production flow, a continuous production flow and a flexible production flow, and the relation model of the energy consumption and the yield of the production flow in the industrial load is specifically as follows: The energy consumption and yield relation model of the discrete production process is specifically as follows: Wherein, the Representing the yield of the discrete production process; representing the number of started production lines during the discrete production flow; representing the throughput of a single production line within a discrete production flow; Representing the total number of process lines present in the discrete process flow; various energy consumption column vectors representing discrete production flows, wherein the various energy consumption column vectors comprise consumption of electric energy, natural gas energy and heat energy; representing various energy consumption column vectors of a single production line in a discrete production flow, wherein the various energy consumption column vectors comprise consumption amounts of electric energy, natural gas energy and heat energy; the energy consumption and yield relation model of the continuous production flow is specifically as follows: Wherein, the Various energy consumption column vectors representing the continuous production flow, wherein the various energy consumption column vectors comprise consumption of electric energy, natural gas energy and heat energy; representing the yield of a continuous production process; representing the relation between the energy consumption and the yield of various energy sources in the continuous production process; The energy consumption and yield relation model of the flexible production flow is specifically as follows: Wherein, the Various energy consumption column vectors representing flexible production flow, wherein the various energy consumption column vectors comprise consumption of electric energy, natural gas energy and heat energy; representing the yield of a flexible production process; Linear terms in various energy consumption and yield relationships representing flexible production flows; constant terms in various energy consumption and yield relationships representing flexible production flows; The yield adjustment constraint is specifically as follows: Wherein, the And Representing the minimum yield and the maximum yield of the discrete production process in one scheduling step length respectively; And Respectively representing the minimum yield and the maximum yield of the flexible production process in one scheduling step; in the step 3), according to the energy consumption and yield relation model, a feasible section search is performed on the yield adjustment constraint and the storage constraint of the product storage warehouse and the yield requirement constraint of the production process in each discrete state so as to construct a multi-functional feasible section in each discrete state, specifically, according to the yield adjustment constraint in the step 1) and the storage constraint of the product storage warehouse and the yield requirement constraint of the production process in the step 2), a vertex enumeration method is adopted to search feasible sets of all random variables, and after the feasible sets of all random variables in each discrete state are obtained, the yield and energy consumption relation model of the production process in the step 1) outputs the multi-functional feasible section of the industrial load in each discrete state.
2. The method for multi-functional flexibility assessment of industrial load with uncertainty as claimed in claim 1, wherein in the step 2), the production process and the product storage warehouse in the industrial load in the step 1) are related by using graph theory, specifically, the production process and the product storage warehouse are modeled as nodes, and the material flow between the production process and the product storage warehouse is the connection between the nodes.
3. An uncertainty-considered industrial load multi-energy flexibility assessment method according to claim 1, characterized in that: in the step 2), the storage constraint of the product storage warehouse is specifically as follows: Wherein, the And Respectively representing the maximum material output and the maximum material input of the production process in a scheduling step length; And Respectively representing the minimum and maximum material storage amounts of the product storage warehouse; Representing the stock change of a product storage warehouse in a dispatching step in industrial load; Representing the existing inventory of product storage warehouses within the industrial load at time t-1 prior to the schedule period at time t.
4. The method for evaluating industrial load multipotency flexibility considering uncertainty as in claim 2, wherein in said step 2), yield requirement constraints of the production process are as follows: Wherein, the A column vector representing the amount of change in inventory of all product storage warehouses within a scheduling step within an industrial load; a column vector representing an existing inventory of all product storage warehouses within the industrial load at time t-1 prior to the time t scheduling period; time representing the entire scheduling period of the production flow; a column vector representing the maximum yield of all production flows; an incidence matrix representing the production flow and the product repository; A target throughput column vector representing each production process over a scheduling period.
5. The method for evaluating industrial load multipotency flexibility in consideration of uncertainty of claim 4, wherein said correlation matrix of said production flow and said product storage warehouse Comprises a plurality of association elements, and is specifically as follows: Wherein, the Correlation matrix representing production flow and product repository Associated elements of the ith row and the jth column; representing the product conversion coefficient.
6. The method of claim 1, wherein in step 3), the random variables are discretized under the constraint of the product storage warehouse and the constraint of the production process yield requirement to construct discrete state sets of the industrial load, and each discrete state set is defined by The method is characterized by comprising the following steps: Wherein, the And Respectively representing the minimum yield and the maximum yield of the discrete production process in one scheduling step length when the random variable is in the 1 st discrete state; And Respectively representing the minimum yield and the maximum yield of the flexible production flow in one scheduling step length when the random variable is in the 1 st discrete state; And Respectively representing the minimum yield and the maximum yield of the discrete production process in one scheduling step length when the random variable is in the 2 nd discrete state; And Respectively representing the minimum yield and the maximum yield of the flexible production flow in one scheduling step length when the random variable is in the 2 nd discrete state; And Representing each random variable in the industrial load at the first position In discrete states, the minimum yield and the maximum yield of the discrete production process in a scheduling step length are calculated; And Representing each random variable in the industrial load at the first position In discrete states, the minimum yield and the maximum yield of the flexible production process in a scheduling step length are realized; And Representing each random variable in the industrial load at the first position In discrete states, the minimum yield and the maximum yield of the discrete production process in a scheduling step length are calculated; And Representing each random variable in the industrial load at the first position In discrete states, the flexible production flow has minimum and maximum yields within one scheduling step.
7. The method for evaluating the multi-functional flexibility of industrial load with uncertainty as claimed in claim 1, wherein the vertex enumeration method is characterized by firstly counting the number of discrete production flows in the industrial load as The number of continuous production processes is recorded as The number of flexible production flows is recorded as The number of product storage warehouses is recorded as Independent variables in the yield and energy consumption relationship model are Then, traversing from the yield adjustment constraint in the industrial load, and searching a feasible section, wherein the method is concretely as follows: First fix The values of the discrete process variables are randomly selected from the group consisting of the production adjustment constraints according to step 1) and the storage constraints of the product storage warehouse in step 2) and the production requirement constraints of the process Strip constraint, judge by Judging whether the matrix formed by the strips is full of rank, if so, obtaining a feasible solution of a group of independent variables, thereby obtaining the number of started production lines in the discrete production flow process in each production flow And inventory variation of the product repository within a scheduling step If not, go on traversing until by The judgment matrix formed by the strips is full of rank, and a set of feasible solutions of independent variables are obtained; after traversing, obtaining feasible solutions of a plurality of groups of independent variables, namely obtaining the number of started production lines in the discrete production flow process in each production flow Yield of continuous production process Yield of flexible production process And inventory variation of the product repository within a scheduling step The feasible solutions of the independent variables in each group form a multi-energy demand feasible solution set, and the feasible solutions of the independent variables in each group are recorded as a matrix ; The feasible solution of each group of independent variables in the feasible solution set of the multi-energy requirement is converted into the feasible solution of multiple energy sources through a yield and energy consumption relation model of the production process, and the method is concretely as follows: Wherein, the Indicating that the random variable is at the first The multi-energy requirements in discrete states can be solved; A conversion matrix representing variables between control variables and energy consumption of the production process yield; Solving the maximum and minimum values of single energy source for all solutions in the feasible solution set of the multi-energy requirement, thereby obtaining the following point A multi-energy feasible section of industrial load in discrete state.
8. The method for evaluating the multi-functional flexibility of the industrial load with consideration of uncertainty as set forth in claim 1, wherein in the step 4), according to the requirement of the multi-functional flexibility of the industrial load on the preset probability value, a possible interval meeting the requirement of the preset probability value is searched in the multi-functional possible intervals in each discrete state, and the multi-functional flexibility of the industrial load is obtained, specifically as follows: in the whole industrial load A random variable, each random variable including The number of discrete states is a function of the number of discrete states, discrete state sharing of industrial loads Obtaining an industrial load according to step 3) Searching flexible adjustment intervals of each energy source according to the multi-energy feasible intervals in each discrete state, firstly obtaining the maximum value and the minimum value of the feasible intervals of the energy source in all discrete states according to each energy source, starting searching calculation with preset equal step length from the minimum value, calculating the probability of each step length point, namely adding the probability values of the step length points in all discrete states, and finally screening out the flexible adjustment intervals of the energy source meeting the requirement of the preset probability value after obtaining the probability values of the step length points of the flexible adjustment intervals of the energy source, thereby finally obtaining the multi-energy flexibility of industrial load.

Description

Industrial load multi-energy flexibility assessment method considering uncertainty Technical Field The invention relates to a multi-energy flexibility assessment method, in particular to an industrial load multi-energy flexibility assessment method considering uncertainty. Background Industrial loads are important loads of energy systems, and are also loads with the highest energy consumption ratio in energy systems. With the development of energy systems, the increase of the new energy ratio requires the energy systems to have greater flexibility for digestion and balance. Therefore, the adjustable capacity of the industrial load in the energy system can be reasonably utilized to provide flexibility for the energy system at the system operation level. But the industrial load itself is subject to uncertainty such as fluctuations in production, changes in orders, etc. While the flexible resources required by the energy system must be deterministic and controllable, otherwise the burden of flexible scheduling is also increased. In the current stable and reliable mode of building industrial loads to participate in flexible interaction of an energy system, high-reliability research for considering uncertainty on multi-energy flexibility of the industrial loads is lacking. Disclosure of Invention The invention provides an uncertainty-considered industrial load multi-energy flexibility assessment method, which can obtain a multi-energy adjustable interval with an industrial load having a probability value, wherein the adjustment capability can be applied to optimal scheduling by an energy system, and the operation flexibility of the energy system is enhanced. The technical scheme adopted by the invention is as follows: the industrial load multi-energy flexibility assessment method comprises the following steps: 1) An energy consumption and yield relationship model of the production process in the industrial load is constructed taking into account yield adjustment constraints. 2) And (3) correlating the production flow in the industrial load in the step 1) with a product storage warehouse by using a graph theory method, and constructing storage constraint of the product storage warehouse and yield requirement constraint of the production flow based on serial-parallel connection relation among the production flows. 3) Taking uncertainty in a production flow in an industrial load as a random variable, discretizing the random variable to construct discrete state sets of the industrial load, wherein each discrete state set comprises a plurality of discrete states of the industrial load, and according to an energy consumption and yield relation model, carrying out feasible interval search on yield adjustment constraint and storage constraint of a product storage warehouse and yield requirement constraint of the production flow in each discrete state to construct a multi-possibility feasible interval and respective probability in each discrete state. 4) According to the requirement of the multi-functional flexibility of the industrial load on the preset probability value, searching the feasible intervals meeting the requirement of the preset probability value in the multi-functional feasible intervals in each discrete state to obtain the multi-functional flexibility of the industrial load, thereby realizing the evaluation of the multi-functional flexibility of the industrial load. The uncertainty refers to uncertainty in the industrial production process. Uncertainty in the industrial production process refers to uncertainty in the industrial production process, such as equipment faults, temporary arrival or cancellation of orders, changes in yield stock caused by unit yield fluctuation, changes in yield caused by equipment parameter fluctuation, and the like, and the uncertainty affects energy flow and material flow balance of a system. The production flow in the industrial load refers to a link in industrial production which is distinguished by workshops or working procedures, and the dividing standard is that no obvious time constraint exists between different production flows, namely, materials or products processed by the previous production flow can be stored without entering the next production flow immediately. The industrial load is divided into production flows, so that the industrial production management is facilitated, stable intermediate products or materials are produced by different production flows, and the production management is convenient for scheduling and manpower arrangement of different production flows. From the equipment level, a production process may be only one equipment, or may be a processing system composed of a plurality of equipment. When the production process is a processing system consisting of a plurality of devices, it is stated that there are strict time and sequence constraints on the materials during the production process, i.e. after the processing of a certain device is compl