Source network load storage interactive scheduling method and device based on demand response

Abstract

The invention discloses a source network load storage interactive scheduling method and device based on demand response, and belongs to the technical field of energy optimization scheduling. The method comprises the following steps: step 1, acquiring daily output load, historical target electrical load data and daily electricity price dynamic change factors of each unit in a target area; 2, based on the daily output load, historical target power load data and daily electricity price dynamic change factors of each unit in the target area, constructing a target function with the lowest annual total power consumption cost of industrial users in the target area and the lowest power generation cost of a power generation side, and obtaining an optimal solution of the target function; and 3, based on the optimal solution of the objective function, configuring the daily output load of each unit and the daily electrical load of production equipment of the industrial enterprise. According to the invention, the optimal unit daily output can be obtained, and a more accurate and efficient daily production and power utilization configuration scheme is provided for enterprises.

Description

Technical Field

[0001] The present invention relates to the technical field of energy optimization scheduling, and more specifically to a source-grid-load-storage interactive scheduling method and device based on demand response. Background Art

[0002] In recent years, countries around the world have reduced greenhouse gas emissions through global agreements. With the increasing depletion of fossil energy, the supply of conventional energy is becoming increasingly tight, and the willingness to respond to climate change and environmental protection is becoming increasingly strong. The development and utilization of negative carbon technology and new energy have become a research hotspot that has attracted much attention. At present, negative carbon technology is still in the research and development and demonstration stage, and the application cost is relatively high. Distributed energy systems based on new energy power generation such as photovoltaics and wind power have developed rapidly. However, with the rapid increase in installed capacity of new energy power generation, the problem of difficulty in orderly absorption has led to a high rate of abandoned light / wind in the power generation process.

[0003] Traditional methods for optimizing the output of wind, solar, water and thermal units still face multiple technical challenges, including how to accurately characterize the seasonal characteristics of renewable energy generators and industrial enterprise production equipment while reducing data dimensions in the process of incorporating demand response, how to improve computing efficiency, how to simulate the response behavior of unit power generation to enterprise power supply and other load demands and incorporate them into the wind and solar output optimization configuration model; how to efficiently and accurately solve the optimization configuration model of generator unit output and enterprise production electricity consumption, etc.

[0004] Therefore, how to provide a demand-response-based source-grid-load-storage interactive scheduling method and device that can solve the above-mentioned problems is an issue that technical personnel in this field urgently need to solve. Summary of the invention

[0005] In view of this, the present invention provides a source-grid-load-storage interactive scheduling method and device based on demand response, which at least solves one of the technical problems existing in the prior art.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] The present invention provides a source-grid-load-storage interactive scheduling method based on demand response, comprising the following steps:

[0008] Step 1: Obtain the daily power load, historical target power load data and dynamic change factors of the electricity price of each unit in the target area;

[0009] Step 2: Based on the daily power load of each unit in the target area, the historical target power load data and the dynamic change factors of the daily electricity price, the objective function is constructed with the lowest annual total electricity cost of industrial users in the target area and the lowest power generation cost on the power generation side to obtain the optimal solution of the objective function;

[0010] Step 3: Based on the optimal solution of the objective function, output the optimal source-grid-load-storage interactive scheduling plan.

[0011] Optionally, after obtaining the daily power load of each generating unit in the target area, the historical target enterprise power load data and the daily power price data and the daily power optimization influencing factors of each generating unit in step 1, it also includes:

[0012] The historical daily output load, historical target daily demand load and historical electricity price data of each unit in the target area are divided according to the season.

[0013] Optionally, the historical target power load data includes historical power load data of continuous power-consuming equipment, non-continuous power-consuming equipment and daily power-consuming equipment in the target area.

[0014] Optionally, the factors affecting the optimization of unit daily output include:

[0015] The daily power load of each unit, the enterprise power load of each unit and the daily electricity price of each unit change dynamically.

[0016] Optionally, the units include: thermal power units, hydropower units, wind power units and photovoltaic units.

[0017] Optionally, the constraints of the objective function include:

[0018] Upper and lower limit constraints on the output of each generating unit, power balance constraints, electricity price fluctuation constraints, and constraints on industrial enterprises’ participation in demand response.

[0019] Optionally, before obtaining the optimal solution of the objective function, an improved NSGA-II multi-objective optimization algorithm is used to solve and obtain the optimal position of the individual, including:

[0020] Before solving the optimal solution of the source-load interaction optimization scheduling model, a genetic algorithm is used to iterate and obtain the optimal position of the individual, including:

[0021] Randomly generate an initial population of size N and calculate the objective function value of each individual;

[0022] According to the objective function value, the population is non-dominated and the individuals are assigned to different non-dominated levels;

[0023] For each individual in the nondominated hierarchy, crowding is calculated, which measures the density of space around the individual;

[0024] Individuals with greater crowding are selected and retained first to maintain the diversity of the population;

[0025] Select better individuals from the current population to form the parent population;

[0026] Based on the objective function value of each particle and the constraint penalty function value of each particle, the fitness function value of each particle is calculated;

[0027] According to the crossover probability, the selected parent population is crossovered to generate new offspring individuals;

[0028] According to the mutation probability, the offspring individuals are mutated to increase the diversity of the population;

[0029] Merge the parent population with the child population to form a joint population of size 2N;

[0030] Perform non-dominated sorting on the joint population again to determine the new generation population;

[0031] In the frontier solution, after deleting the individual with the smallest crowding degree, the crowding degree of the remaining individuals is recalculated;

[0032] Repeatedly delete the individuals with the smallest crowding degree and recalculate the crowding degree after each deletion until the population size meets the requirements;

[0033] According to the non-dominated sorting results and the improved crowding degree, individuals with higher non-dominated ranks and greater crowding degrees are preferentially selected to form a new parent population;

[0034] Check whether the termination condition is met. If so, output the Pareto front solution set and analyze the optimization effect. Otherwise, repeat the crossover and mutation operations and continue to iterate until the termination condition is met.

[0035] Optionally, the method further includes: determining optimal values ​​of multiple variables in the objective function based on the optimal positions of all individuals;

[0036] Based on the optimal values ​​of multiple variables in the objective function, the output load of each generator set corresponding to the lowest annual total electricity cost for industrial users in the target area and the lowest power generation cost on the power generation side is determined as the optimal solution for the unit output.

[0037] The present invention also provides a source-grid-load-storage interactive scheduling device based on demand response, comprising:

[0038] An acquisition module is used to obtain the daily power load of each unit in the target area, historical target power load data and dynamic change factors of the electricity price;

[0039] A construction module is used to construct an objective function based on the daily power load of each unit in the target area, historical target power load data and dynamic change factors of the daily power price, with the annual total power cost of industrial users in the target area being the lowest and the power generation cost on the power generation side being the lowest, to obtain the optimal solution of the objective function;

[0040] The configuration module is used to output an optimal source-grid-load-storage interactive scheduling plan based on the optimal solution of the objective function.

[0041] It can be seen from the above technical solution that compared with the prior art, the present invention discloses a source-grid-load-storage interactive scheduling method and device based on demand response. By accurately characterizing the daily power load of historical units and industrial production electricity consumption, and considering the demand response behavior of the company's continuous power equipment, non-continuous power equipment and daily power equipment, an objective function and constraint conditions for achieving the optimal configuration of daily power output of each unit are constructed. By solving the objective function with the lowest annual total electricity cost of industrial users in the target area and the lowest power generation cost of the power generation department as the goal, the optimal unit daily power output can be obtained, providing enterprises with a more accurate and efficient daily production electricity configuration plan, thereby improving the self-sufficiency and self-consumption rate of wind and solar power, and helping to reduce the electricity cost of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0043] Figure 1 A flow chart of a source-grid-load-storage interactive scheduling method based on demand response provided by the present invention;

[0044] Figure 2 It is a flow chart of the improved NSGA-II multi-objective optimization algorithm provided by the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] The purpose of the present invention is to provide a source-grid-load-storage interactive scheduling method and device based on demand response, the method comprising: step 1: obtaining the daily power load, historical target power load data and dynamic change factors of the daily electricity price of each unit in the target area; step 2: based on the daily power load, historical target power load data and dynamic change factors of the daily electricity price of each unit in the target area, construct an objective function with the lowest annual total electricity cost of industrial users in the target area and the lowest power generation cost on the power generation side, and obtain the optimal solution of the objective function; step 3: based on the optimal solution of the objective function, configure the daily power load of each unit and the daily power load of the production equipment of the industrial enterprise. Provide an effective solution to the defects of the traditional wind, solar, water and thermal unit output optimization configuration method.

[0047] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] Figure 1 is a flow chart of a source-grid-load-storage interactive scheduling method based on demand response provided by the present invention, such as Figure 1 As shown, the method comprises the following steps:

[0049] Step 1, obtaining the daily power load, historical target power load data and dynamic change factors of the daily electricity price of historical thermal power units, hydropower units, wind power units and photovoltaic units in the target area, wherein the historical target demand data includes the historical power load data of continuous power equipment, non-continuous power equipment and daily power equipment in the target area;

[0050] Step 2: Based on the daily power loads of historical thermal power units, hydropower units, wind power units and photovoltaic units in the target area, historical target power load data and dynamic change factors of daily electricity prices, with the goal of minimizing the annual total electricity cost of enterprise users in the target area and the goal of minimizing the power generation cost of the power generation department, an objective function is constructed to obtain the optimal solution of the objective function;

[0051] Step 3: Based on the optimal solution of the objective function, output the optimal source-grid-load-storage interactive scheduling plan to configure the daily power load of each unit and the daily power load of the production equipment of the industrial enterprise.

[0052] The specific optimal source-grid-load-storage interactive scheduling scheme includes:

[0053] Configure the output of each unit, configure the daily output load of each unit and the daily power load of the industrial enterprise's production equipment, configure the capacity of each generating unit, etc.

[0054] Specifically, after obtaining the output of each unit corresponding to the lowest annual total electricity cost of industrial users, that is, the optimal output of each unit, the optimal output configuration plan of each unit can be determined. In this plan, the output of wind power generation and photovoltaic power generation units is prioritized, which can improve the self-sufficiency and self-consumption rate of wind and solar power and reduce the electricity cost of enterprises.

[0055] More specifically, the present invention constructs an objective function and constraints for achieving optimal configuration of daily power output of each unit based on the accurate characterization of historical unit daily power loads and industrial production electricity consumption, while taking into account the demand response behavior of the company's continuous power equipment, discontinuous power equipment and daily power equipment. By solving the objective function with the lowest annual total electricity cost for industrial users in the target area and the lowest power generation cost for the power generation department as the goals, the optimal unit daily power output can be obtained, providing the company with a more accurate and efficient daily production electricity configuration plan, improving the self-sufficiency and self-consumption rate of wind and solar power, and reducing the company's electricity cost.

[0056] In a specific embodiment, in step 1, the historical daily power load of each unit in the target area, the historical target power load data and the dynamic change factors of the electricity price are obtained, and the following is also included:

[0057] Optionally, the historical data of the target area can be divided into spring, summer, autumn and winter, and the historical output load of each unit in the target area every 15 minutes on a typical day in each season, the historical power load data of each target typical day in each season and the historical electricity price data of each typical day in each season can be divided into seasons.

[0058] Optionally, the target area's historical annual daily power load, enterprise power load data and electricity price data for every 15 minutes are divided into power demand data matrices of daily power load, enterprise power load data and electricity price data according to spring, summer, autumn and winter, with the matrix rows representing each day of each season and the matrix columns representing every 15 minutes of each day.

[0059] Optionally, after dividing the target area's historical annual daily power load every 15 minutes, enterprise power load data and electricity price data into power demand data matrices of unit daily power load, enterprise power load data and electricity price data according to spring, summer, autumn and winter, the 90 days × 24 hours × 15 minutes matrix of each season can be imported into the model, and the model result is that each category includes power demand data or unit output data or electricity price data every 15 minutes for 24 hours a day.

[0060] Optionally, the measured data of thermal power, hydropower, wind power and photovoltaic power output every 15 minutes throughout the day, the installed capacity of each power generation technology, the power consumption of each production and non-production equipment of the enterprise every 15 minutes throughout the day, the start and stop status of the equipment, the initial electricity price, and the upper and lower limits of the dynamics are set according to the electricity prices in other provinces and cities.

[0061] In a specific embodiment, the present invention provides a source-grid-load-storage interactive scheduling method based on demand response, which adopts an improved NSGA-II algorithm method to accurately characterize the daily power load of units in different seasons and the typical characteristics of enterprise electricity consumption, while reducing the data dimension and improving the calculation efficiency. At the same time, the daily power load of units, enterprise electricity load data and electricity price data of typical days in each season in the target area are obtained every 15 minutes, which is convenient for the subsequent construction of constraints for solving the objective function.

[0062] In a specific embodiment, the factors affecting the optimization of the wind-solar generator set output include:

[0063] Technical parameters of wind and solar power generating sets, economic parameters of wind and solar power generating sets, flexible load operation parameters of enterprises in the target area and electricity prices in the target area.

[0064] In a specific embodiment, the unit economic parameter may be a parameter related to the unit cost, such as the unit unit capacity investment cost or operation and maintenance cost.

[0065] In a specific embodiment, the flexibility loads in an enterprise include production power loads and non-production power loads.

[0066] In a specific embodiment, the electricity price in the target area may be an enterprise electricity purchase price in the target area or an on-grid electricity price in the target area.

[0067] In a specific embodiment, after obtaining the factors affecting the optimization of the unit output, namely, the technical parameters of the generator set, the economic parameters of the generator set, the operating parameters of the enterprise flexibility load in the target area and the electricity price in the target area, a target can be constructed based on the above parameters, with the goal of minimizing the annual total electricity cost of enterprise users in the target area and minimizing the annual power generation cost of the unit, to obtain the optimal solution for the unit output.

[0068] In a specific embodiment, the present invention provides a source-grid-load-storage interactive scheduling method based on demand response. After obtaining the influencing factors of unit output optimization, namely, unit technical parameters, unit economic parameters, operating parameters of the enterprise flexibility load in the target area and the electricity price in the target area, an objective function can be constructed based on the above parameters, thereby providing enterprise users with a more accurate and efficient production power configuration plan, improving wind and solar self-sufficiency and local consumption capabilities, avoiding wind and solar curtailment and large-scale access to the grid, and reducing the electricity costs of enterprise users. Among them, the historical target power load data is the operating parameters of the flexibility load.

[0069] In a specific embodiment, in step 2, based on the daily power load of each generator set in the target area, the historical target enterprise power load data and the daily electricity price data and the factors affecting the output optimization of each unit, the objective function and constraint conditions are constructed, that is, the daily power load of thermal power units, hydropower units, wind power units and photovoltaic units are divided into spring, summer, autumn and winter; the power demand data of the enterprise's continuous power equipment, non-continuous power equipment and daily power equipment are also divided into spring, summer, autumn and winter; the daily electricity price also has seasonal differences. Specifically including:

[0070] Step 2.1: Divide the data obtained in step 1 into the output data of each unit and the power demand data matrix of the enterprise's continuous power-consuming equipment, non-continuous power-consuming equipment and daily power-consuming equipment according to spring, summer, autumn and winter. The rows of the matrix represent every 15 minutes of each season, and the columns of the matrix represent every 15 minutes of each day.

[0071] Step 2.2: Count the number of rows of the data matrix of the unit output, electricity price, and electricity demand of the enterprise's continuous power-consuming equipment, non-continuous power-consuming equipment, and daily power-consuming equipment every 15 minutes in each season, that is, the number of time periods included in each season;

[0072] Step 2.3: Based on the output of different types of units, electricity prices, and the number of rows of the power demand data matrix of different types of power-consuming equipment in the enterprise obtained in step 2.2, optimize the matching of the unit output, electricity prices, and power demand data of continuous power-consuming equipment, non-continuous power-consuming equipment, and daily power-consuming equipment of different categories in each season every 15 minutes.

[0073] Specifically, the working status matrix of the electrical equipment is as follows:

[0074] Assume that S is the state set of the power equipment of industrial enterprises on T, that is, the solution domain of the power equipment scheduling problem of all enterprises. In which, let s∈S be the matrix of the power equipment state, h be the time period variable, h∈T, then:

[0075]

[0076] When the electrical equipment x is in working state in time period h, then s h，x =1, when the electrical equipment x is in a stopped state in time period h, then s h，x =0 use.

[0077] The enterprise electricity load data matrix is ​​as follows:

[0078] Define P as the set of all loads of all electrical equipment in the enterprise in T. After transforming s, we can get the load matrix p:

[0079]

[0080] Assume p∈P, p x =(P x，1 , p x，2 , ..., p x，h , ..., p x，t ) represents the load vector of the electrical equipment x on T, p x，h represents the load value of the electrical equipment x in time period h. Assume that the electrical load set of a single production equipment of an industrial enterprise is L, L = {L x |x∈E}, let 1≤t≤β x , l x，t represents the power load of the electrical equipment x in the tth operating period, and l x，t >0.

[0081] Establishing the objective function based on the technical parameters of the generator set, the economic parameters of the generator set, the operating parameters of the enterprise flexibility load in the target area and the electricity price in the target area;

[0082] Based on one or more of the output load every 15 minutes on a typical day in each season of the target area, the dynamic electricity price every 15 minutes on a typical day in each season, the historical target demand load data every 15 minutes on a typical day in each season, the technical parameters of each generator set, the economic parameters of each generator set, the operating parameters of the industrial enterprises in the target area and the electricity price of the target area, a constraint condition for solving the objective function is constructed, and the constraint condition includes one or more of the following: upper and lower limit constraints on the output of each generator set, power balance constraints, electricity price fluctuation constraints, and constraints on industrial enterprises participating in demand response.

[0083] In a specific embodiment, the generator set technical parameters, the generator set economic parameters, the operating parameters of the enterprise flexibility load in the target area and the electricity price in the target area are used to establish an objective function, which can be expressed by the following formula:

[0084]

[0085] Where:

[0086] f1(s) and f2(s) represent the objective functions of the power supply system and the power consumption system, respectively;

[0087] C regular represents the operating cost of thermal power units;

[0088] C RES represents the operating cost of renewable energy generators;

[0089] C loss represents the cost of curtailed electricity;

[0090] Indicates the output load of thermal power units;

[0091] t represents the time periods throughout the day;

[0092] a i , b i With c i It represents the fuel cost coefficient of thermal power units;

[0093] ST i represents the start-up and shutdown cost of thermal power units;

[0094] Indicates the start and stop status of the thermal power unit, where δ=1 indicates the start state, and δ=0 indicates the shutdown state;

[0095] i represents a thermal power unit, i=(1,2,3,…,I-1,I);

[0096] C PV Represents the operating cost of the photovoltaic unit;

[0097] C WT represents the operating cost of the wind turbine;

[0098] C adjust represents the cost of participation in demand response;

[0099] π t Indicates the real-time electricity price for participation in demand response;

[0100] Represents the total electricity consumption in the industrial park.

[0101] Specifically, after constructing the objective function, in order to meet the continuous and stable power supply of the generator sets and the basic electricity needs and electricity safety of corporate users, one or more constraints for solving the objective function can be constructed, which may include but are not limited to upper and lower limit constraints on the output of each generator set, power balance constraints, electricity price fluctuation constraints, and constraints on industrial enterprises' participation in demand response.

[0102] In a specific embodiment, the power balance constraint condition can be expressed by the following formula:

[0103]

[0104] Where: represents the forecast value of the total production electricity demand of the enterprise in time period t, is the output load of RE unit j in period t; and are the total amount of abandoned solar power and the total amount of abandoned wind power in period t respectively.

[0105] In a specific embodiment, the upper and lower limit constraints of the unit output can be expressed by the following formula:

[0106]

[0107] Where:

[0108] Indicates the lower limit of thermal power unit output;

[0109] Indicates the upper limit of thermal power unit output;

[0110] Indicates the upper limit of the output of renewable energy generating units;

[0111] In a specific embodiment, for parameter data of non-optimization results, since it is difficult to obtain the values ​​of typical days, the data of typical days in each season can be obtained by surveying enterprise users.

[0112] In a specific embodiment, the demand response electricity price constraint condition can be expressed by the following formula:

[0113] π min ≤π t ≤π max ;

[0114] Where:

[0115] π min Indicates the lower limit of electricity price change, π t is the real-time electricity price for demand response, π max Indicates the upper limit of electricity price changes.

[0116] In a specific embodiment, the constraint conditions for different types of electric devices of an enterprise to participate in demand response can be expressed by the following formula:

[0117]

[0118] Where:

[0119] P x,t represents the total response load of the enterprise;

[0120] P' x,t It represents the load forecast value after the enterprise adopts the demand response strategy;

[0121] r represents the rth hour that the continuous power-consuming equipment has been running continuously.

[0122] Assume that the set E is the set of all electrical equipment within the time range set T, then E = {resource1, resource2, ..., resourceN}, |E| = N, N represents the total number of electrical equipment, resource represents electrical equipment. Let x be the variable of enterprise electrical equipment, x∈E. For There is a set of all available working time periods for electrical equipment x have where λ x ∈T,θ x ∈T, and λ x ≤θ x , Assume β x is the maximum operating time of the electrical equipment x, 1≤β x ≤θ x -λ x +1; set is the set of time periods during which the electrical equipment x is allowed to start working, then in and set up is the actual working period set of the electrical equipment x, where Let α x is the period when the power equipment x starts to operate when demand response is not adopted, Let σ x When demand response is adopted, the period during which the electric device x starts to operate is

[0123] The electrical equipment set E includes E c 、E u and E d Three types of electrical equipment, including E c and E u are all schedulable equipment sets. sc is a set of schedulable resources, then E c ∪E u =E sc . Then for the above set, there exists E sc ∪E d =E.

[0124] Assume E u A collection of non-continuous power equipment, including Then there is exist Can be ordered Established, that is Existing at this time

[0125] Assume E c A collection of continuous power-consuming equipment, including Then there is exist Can be ordered Established, that is Existing at this time

[0126] Assume E d It is a collection of daily electrical equipment, including There is like and Right now For daily electrical equipment, the actual working hours are It is a fixed value and does not participate in the daily scheduling optimization process.

[0127] Specifically, the present invention constructs more accurate and efficient objective functions and constraints through more accurate characterization of the output of renewable energy units and the seasonal characteristics of enterprise production and operation electricity consumption, as well as simulation of the demand response of enterprise production and non-production equipment, thereby improving the efficiency and accuracy of solving the objective function.

[0128] In a specific embodiment, see Figure 2 As shown, before obtaining the optimal solution of the unit output and the enterprise power consumption, it also includes using the improved NSGA-II multi-objective optimization algorithm to solve, specifically including:

[0129] Randomly generate an initial population of size N and calculate the objective function value of each individual;

[0130] According to the objective function value, the population is non-dominated and the individuals are assigned to different non-dominated levels;

[0131] For each individual in the nondominated hierarchy, crowding is calculated, which measures the density of space around the individual;

[0132] Individuals with greater crowding are selected and retained first to maintain the diversity of the population;

[0133] Select better individuals from the current population to form the parent population;

[0134] Based on the objective function value of each particle and the constraint penalty function value of each particle, the fitness function value of each particle is calculated;

[0135] According to the crossover probability, the selected parent population is crossovered to generate new offspring individuals;

[0136] According to the mutation probability, the offspring individuals are mutated to increase the diversity of the population;

[0137] Merge the parent population with the child population to form a joint population of size 2N;

[0138] Perform non-dominated sorting on the joint population again to determine the new generation population;

[0139] In the frontier solution, after deleting the individual with the smallest crowding degree, the crowding degree of the remaining individuals is recalculated;

[0140] Repeatedly delete the individuals with the smallest crowding degree and recalculate the crowding degree after each deletion until the population size meets the requirements;

[0141] According to the non-dominated sorting results and the improved crowding degree, individuals with higher non-dominated ranks and greater crowding degrees are preferentially selected to form a new parent population;

[0142] Check whether the termination condition is met. If so, output the Pareto front solution set and analyze the optimization effect. Otherwise, repeat the crossover and mutation operations and continue to iterate until the termination condition is met.

[0143] In a specific embodiment, after obtaining the objective function and constraints, in order to obtain the optimal solution for the output of renewable energy units and the electricity consumption of enterprise production and operation, an algorithm can be used to solve the optimal solution of each parameter in the objective function, such as a genetic algorithm.

[0144] In a specific embodiment, in order to solve the optimal solution of each parameter in the objective function, the present invention adopts an improved NSGA-II multi-objective optimization algorithm to solve it. Compared with other algorithms, the improved NSGA-II multi-objective optimization algorithm improves the search performance and convergence speed, making the objective function solution efficiency and result accuracy higher.

[0145] Specifically, after constructing the objective function and constraints, the present invention can adopt an improved NSGA-II multi-objective optimization algorithm based on the objective function and the constraints to obtain the optimal position of the individual as the optimal result of each variable in the objective function, so as to obtain the optimal solution of the unit output and the enterprise electricity consumption with the goal of minimizing the power generation cost and the enterprise electricity consumption cost in the target area, improve the search performance and convergence speed, and make the efficiency of solving the objective function and the result accuracy higher.

[0146] In a specific embodiment, taking the lowest power generation cost in the target area as the goal, obtaining the optimal solution for the unit output includes:

[0147] Based on the optimal positions of all individuals, determining optimal values ​​of multiple variables in the objective function;

[0148] Based on the optimal values ​​of multiple variables in the objective function, the unit installation capacity corresponding to the lowest annual total power generation cost of the generator set in the target area is determined as the optimal capacity of the photovoltaic installation capacity, and then the optimal solution for the unit output in the optimal unit capacity configuration scheme is determined.

[0149] In a specific embodiment, taking the lowest electricity consumption of enterprises in the target area as the goal, obtaining the optimal solution for the electricity consumption of the enterprises includes:

[0150] Based on the optimal positions of all individuals, determining optimal values ​​of multiple variables in the objective function;

[0151] Based on the optimal values ​​of multiple variables in the objective function, the power load corresponding to the lowest annual total electricity cost for residential users in the target area is determined as the optimal solution for the enterprise power load.

[0152] The enterprise power equipment provided by the present invention is described below. The enterprise power equipment described below and the enterprise power optimization configuration method described above can be referenced to each other.

[0153] Specifically, the enterprise power equipment provided by the present invention is mainly divided into three types: continuous power resource type, discontinuous power resource type and daily power resource type, among which the first two types will affect the results in the scheduling optimization process, that is, the power resources that can be scheduled, while the daily power resources are resource types that cannot participate in scheduling. Specifically including:

[0154] Specifically, the type of continuous power-consuming equipment: This type of equipment can participate in scheduling during the working time period, but once started, it is not allowed to be interrupted or stopped during the production process until the production task is completed. c A collection of continuous power-consuming equipment, including Then there is exist Can be ordered Established, that is Existing at this time

[0155] Non-continuous power consumption equipment type: This type of equipment can not only participate in scheduling during the working time period, but also interrupt operation during the production process, but the total power consumption time must be guaranteed to complete the established production work tasks. u A collection of non-continuous power equipment, including Then there is exist Can be ordered Established, that is Existing at this time

[0156] Specifically, the type of daily power equipment: the working time period of this type of equipment is fixed, and once started, it cannot be interrupted or stopped, which means that the resource cannot participate in scheduling optimization during the working period, that is, the operating time period of the equipment is all its working periods. d It is a collection of daily electrical equipment, including There is like and Right now For daily electrical equipment, the actual working hours are It is a fixed value and does not participate in the daily scheduling optimization process.

[0157] In summary, the electrical equipment set E in the present invention includes E c 、E u and E d Three types of electrical equipment, including E c and E u are all schedulable equipment sets. sc is a set of schedulable resources, then E c ∪E u =E sc . Then for the above set, there exists E sc ∪E d =E.

[0158] Specifically, in the above description, x represents an electrical device.

[0159] In a specific embodiment, based on the above power-consuming equipment, the state set of the power-consuming equipment of the industrial enterprise throughout the day is obtained, that is, the solution domain of the power-consuming equipment scheduling problem of all enterprises. The state of the power-consuming equipment of the industrial enterprise throughout the day can be expressed by the following formula:

[0160]

[0161] Where:

[0162] T represents the set of time periods within the dispatch time range of industrial enterprises’ electricity consumption;

[0163] S represents the state set of electrical equipment in industrial enterprises.

[0164] s represents the matrix of the electrical equipment status;

[0165] H represents the time period variable.

[0166] Based on the state set of the electrical equipment of the above industrial enterprises throughout the day, the collective load matrix of all electrical equipment of the enterprise can be obtained, which can be expressed by the following formula:

[0167]

[0168] Where:

[0169] P represents the set of all loads of all electrical equipment in the enterprise in T;

[0170] p x,h Indicates the load value of electrical equipment in each time period.

[0171] Based on the collective load matrix of all electrical equipment in the above enterprise, the electrical load set of a single production equipment of an industrial enterprise is established. This set can be expressed by the following formula:

[0172]

[0173] In the formula,

[0174] L represents the electricity load set of a single production equipment of an industrial enterprise;

[0175] l x,t Represents the power load of the electrical equipment x in the tth operating period

[0176] Based on the different optimization scheduling methods adopted by the above-mentioned different types of power-consuming equipment, there are three power scheduling situations according to the above-mentioned three types of power-consuming equipment:

[0177] The first case is the power dispatch optimization task mode of the daily (basic) power-consuming equipment of the enterprise, which can be expressed by the following formula:

[0178] when hour,

[0179] The second case is the optimization mode of power dispatching of continuous production power equipment of the enterprise, which can be expressed by the following formula:

[0180] when hour,

[0181] The third case is the enterprise's interruptible production equipment power dispatch optimization mode, which can be expressed by the following formula:

[0182] when hour,

[0183] In the formula,

[0184] p x,h Indicates the load of electrical equipment.

[0185] Based on the above conditions and constraints, the objective function and constraints for realizing the "source-load interaction" optimization scheduling method are constructed. By solving the objective function with the lowest annual total electricity cost for industrial users in the target area and the lowest power generation cost for the power generation department as the goal, the optimal unit daily output can be obtained, providing enterprises with a more accurate and efficient daily production electricity configuration plan, improving the self-sufficiency and self-consumption rate of wind and solar power, and reducing the enterprise's electricity cost and power generation cost.

[0186] On the other hand, the embodiment of the present invention further discloses a source-grid-load-storage interactive scheduling device based on demand response, including:

[0187] An acquisition module is used to acquire the daily power load of each unit in the target area, historical target power load data and dynamic change factors of the daily power price, wherein the historical power load data includes the historical power load data of continuous power consumption equipment, non-continuous power consumption equipment and daily power consumption equipment in the target area;

[0188] A construction module is used to construct an objective function based on the daily power load of each unit in the target area, historical target power load data and dynamic change factors of the daily power price, with the annual total power cost of industrial users in the target area being the lowest and the power generation cost on the power generation side being the lowest, to obtain the optimal solution of the objective function;

[0189] The configuration module is used to configure the daily power load of each unit and the daily power load of the production equipment of the industrial enterprise based on the optimal solution of the objective function.

[0190] It can be understood that the source-grid-load-storage interactive scheduling device based on demand response provided by the present invention corresponds to the source-grid-load-storage interactive scheduling method based on demand response provided by the above-mentioned embodiments. The relevant technical features of the source-grid-load-storage interactive scheduling device based on demand response of the present invention can refer to the relevant technical features of the source-grid-load-storage interactive scheduling method based on demand response provided by the above-mentioned embodiments, and will not be repeated here.

[0191] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0192] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A source-grid-load-storage interactive scheduling method based on demand response, characterized in that: The following steps are involved: Step 1: Obtain the daily power load, historical target power load data and dynamic change factors of the electricity price of each unit in the target area; Step 2: Based on the daily power load of each unit in the target area, the historical target power load data and the dynamic change factors of the daily electricity price, the objective function is constructed with the lowest annual total electricity cost of industrial users in the target area and the lowest power generation cost on the power generation side to obtain the optimal solution of the objective function; Step 3: Based on the optimal solution of the objective function, output the optimal source-grid-load-storage interactive scheduling plan.

2. A source-grid-load-storage interactive scheduling method based on demand response according to claim 1, characterized in that: After obtaining the daily power load of each generating unit in the target area, the historical target enterprise power load data and the daily power price data and the daily power optimization influencing factors of each generating unit in step 1, it also includes: The historical daily output load, historical target daily demand load and historical electricity price data of each unit in the target area are divided according to the season.

3. A source-grid-load-storage interactive scheduling method based on demand response according to claim 2, characterized in that: The historical target power load data includes historical power load data of continuous power-consuming equipment, non-continuous power-consuming equipment and daily power-consuming equipment in the target area.

4. A source-grid-load-storage interactive scheduling method based on demand response according to claim 2, characterized in that: The factors affecting the optimization of unit daily output include: The daily power load of each unit, the enterprise power load of each unit and the daily electricity price of each unit change dynamically.

5. A source-grid-load-storage interactive scheduling method based on demand response according to claim 4, characterized in that: The units include: thermal power units, hydropower units, wind power units and photovoltaic units.

6. A source-grid-load-storage interactive scheduling method based on demand response according to claim 1, characterized in that: The constraints of the objective function include: Upper and lower limit constraints on the output of each generating unit, power balance constraints, electricity price fluctuation constraints, and constraints on industrial enterprises’ participation in demand response.

7. The source-grid-load-storage interactive scheduling method based on demand response according to claim 1 is characterized in that: Before obtaining the optimal solution of the objective function, the improved NSGA-II multi-objective optimization algorithm is used to solve and obtain the optimal position of the individual, including: Before solving the optimal solution of the source-load interaction optimization scheduling model, a genetic algorithm is used to iterate and obtain the optimal position of the individual, including: Randomly generate an initial population of size N and calculate the objective function value of each individual; According to the objective function value, the population is non-dominated and the individuals are assigned to different non-dominated levels; For each individual in the nondominated hierarchy, crowding is calculated, which measures the density of space around the individual; Individuals with greater crowding are selected and retained first to maintain the diversity of the population; Select better individuals from the current population to form the parent population; Based on the objective function value of each particle and the constraint penalty function value of each particle, the fitness function value of each particle is calculated; According to the crossover probability, the selected parent population is crossovered to generate new offspring individuals; According to the mutation probability, the offspring individuals are mutated to increase the diversity of the population; Merge the parent population with the offspring population to form a joint population of size 2N; Perform non-dominated sorting on the joint population again to determine the new generation population; In the frontier solution, after deleting the individual with the smallest crowding degree, the crowding degree of the remaining individuals is recalculated; Repeatedly delete the individuals with the smallest crowding degree and recalculate the crowding degree after each deletion until the population size meets the requirements; According to the non-dominated sorting results and the improved crowding degree, individuals with higher non-dominated ranks and greater crowding degrees are preferentially selected to form a new parent population; Check whether the termination condition is met. If so, output the Pareto front solution set and analyze the optimization effect. Otherwise, repeat the crossover and mutation operations and continue to iterate until the termination condition is met.

8. A source-grid-load-storage interactive scheduling method based on demand response according to claim 7, characterized in that: Also includes: Based on the optimal positions of all individuals, determining optimal values ​​of multiple variables in the objective function; Based on the optimal values ​​of multiple variables in the objective function, the output load of each generator set corresponding to the lowest annual total electricity cost for industrial users in the target area and the lowest power generation cost on the power generation side is determined as the optimal solution for the unit output.

9. A source-grid-load-storage interactive scheduling device based on demand response using the source-grid-load-storage interactive scheduling method based on demand response according to any one of claims 1 to 8, characterized in that: include: An acquisition module is used to obtain the daily power load of each unit in the target area, historical target power load data and dynamic change factors of the electricity price; A construction module is used to construct an objective function based on the daily power load of each unit in the target area, historical target power load data and dynamic change factors of the daily power price, with the annual total power cost of industrial users in the target area being the lowest and the power generation cost on the power generation side being the lowest, to obtain the optimal solution of the objective function; The configuration module is used to output an optimal source-grid-load-storage interactive scheduling plan based on the optimal solution of the objective function.

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