A source-grid-load-storage interaction scheduling method and device based on demand response

Abstract

The application 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, obtaining daily output load of each unit, historical target power load data and daily power price dynamic change factors of a target area; step 2, based on the daily output load of each unit, the historical target power load data and the daily power price dynamic change factors of 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 the power generation side, and obtaining an optimal solution of the target function; and step 3, based on the optimal solution of the target function, configuring daily output load of each unit and daily power load of production equipment of industrial enterprises. Through the application, the optimal unit daily output can be obtained, and a more accurate and efficient daily production power configuration scheme is provided for enterprises.

Description

Technical Field

[0001] This invention relates to the field of energy optimization scheduling technology, and more specifically to a method and apparatus for demand response-based source-grid-load-storage interactive scheduling. Background Technology

[0002] In recent years, countries around the world have reduced greenhouse gas emissions through global agreements. With the increasing depletion of fossil fuels and the growing tension in the supply of conventional energy, the desire to address climate change and protect the environment has become stronger. The development and utilization of carbon-negative technologies and new energy sources have become a research hotspot. Currently, carbon-negative technologies are still in the research and demonstration stage, and their application costs are relatively high. Distributed energy systems, mainly based on new energy power generation such as photovoltaics and wind power, have developed rapidly. However, with the rapid increase in the installed capacity of new energy power generation, the problem of orderly absorption has led to a high curtailment rate of solar and wind power during the power generation process.

[0003] Traditional methods for optimizing the power output of wind, solar, hydro, and thermal power units still face several technical challenges, including how to accurately characterize the seasonal operating characteristics of renewable energy generator units and industrial production equipment while reducing data dimensionality during the demand response process; how to improve computational efficiency; how to simulate the demand response behavior of generator units to enterprise power supply and other loads and incorporate it into the wind and solar power output optimization model; and how to efficiently and accurately solve the generator unit output and enterprise production power consumption optimization model.

[0004] Therefore, how to provide a demand-response-based source-grid-load-storage interactive scheduling method and device that can solve the above problems is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

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

[0006] To achieve the above objectives, the present invention provides the following technical solution:

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

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

[0009] Step 2: Based on the daily power load of each unit in the target area, historical target electricity load data, and dynamic changes in daily electricity prices, construct an objective function with the goal of minimizing the total annual electricity cost for industrial users and the generation cost on the power generation side in the target area, and 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 interaction scheduling scheme.

[0011] Optionally, after obtaining the daily power load of each generator unit in the target area, historical target enterprise electricity load data, daily electricity price data, and daily power optimization influencing factors of each unit in step 1, the following steps are also included:

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

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

[0014] Optional factors affecting daily unit power output optimization include:

[0015] The daily power load of each generating unit, the electricity load of each generating unit and the dynamic changes in the daily electricity price of each generating unit.

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

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

[0018] Constraints on the upper and lower limits of each generator unit's output, power balance constraints, electricity price fluctuation constraints, and constraints on industrial enterprises' participation in demand response.

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

[0020] Before finding the optimal solution to the source-load interaction optimization scheduling model, a genetic algorithm is used iteratively to obtain the optimal position of each individual, specifically including:

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

[0022] The population is sorted into non-dominated categories based on the objective function value, and individuals are assigned to different non-dominated levels.

[0023] For each individual in a non-dominant hierarchy, crowding is calculated to measure the spatial density around the individual.

[0024] Individuals with higher density are selected for priority retention in order to maintain population diversity;

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

[0026] The fitness function value of each particle is calculated based on the objective function value and the constraint penalty term function value of each particle.

[0027] Based on the crossover probability, crossover is performed on the selected parent population to generate new offspring individuals;

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

[0029] The parent population and the offspring population are merged to form a joint population of size 2N;

[0030] The combined population was subjected to non-dominated ordination again to determine the next generation of population;

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

[0032] Repeatedly delete the individual with the lowest crowding, and recalculate the crowding after each deletion, until the population size meets the requirements;

[0033] Based on the non-dominated ranking results and the improved crowding, individuals with higher non-dominated levels and greater crowding are selected to form a new parent population.

[0034] Check if the termination condition has been met. If the termination condition is met, output the Pareto front solution set and analyze the optimization effect. Otherwise, repeat the crossover and mutation operations and continue iterating until the termination condition is met.

[0035] Optionally, it also includes: determining the 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 demand-response-based source-grid-load-storage interactive scheduling device, comprising:

[0038] The acquisition module is used to acquire the daily power load of each unit in the target area, historical target electricity load data, and dynamic factors affecting the electricity price.

[0039] The construction module is used to construct an objective function based on the daily power load of each unit in the target area, historical target electricity load data, and dynamic changes in daily electricity prices, with the goal of minimizing the total annual electricity cost for industrial users in the target area and minimizing the power generation cost on the generation side, and to obtain the optimal solution of the objective function.

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

[0041] As can be seen from the above technical solution, 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 depicting the daily power load of historical generating units and industrial production electricity consumption, and considering the demand response behavior of continuous, discontinuous, and daily power consumption equipment of enterprises, an objective function and constraints are constructed to achieve the optimal configuration of the daily power output of each generating unit. By solving the objective function with the goal of minimizing the total annual electricity cost of industrial users in the target area and minimizing the power generation cost of the power generation department, the optimal daily power output of generating units can be obtained. This provides enterprises with a more accurate and efficient daily production electricity configuration scheme, thereby improving the self-sufficiency and self-consumption rate of wind and solar power and helping to reduce the electricity cost of enterprises. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

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

[0044] Figure 2 This is a flowchart illustrating the improved NSGA-II multi-objective optimization algorithm provided by this invention. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] The purpose of this invention is to provide a demand-response-based source-grid-load-storage interactive scheduling method and apparatus. The method includes: Step 1: Obtaining the daily power load, historical target electricity load data, and dynamic changes in daily electricity prices for each generating unit in the target area; Step 2: Based on the daily power load, historical target electricity load data, and dynamic changes in daily electricity prices for each generating unit in the target area, constructing an objective function that minimizes the total annual electricity cost for industrial users and the generation cost on the generation side within the target area, and obtaining the optimal solution of the objective function; Step 3: Based on the optimal solution of the objective function, configuring the daily power load of each generating unit and the daily electricity load of industrial enterprises' production equipment. This provides an effective solution to the shortcomings of traditional wind, solar, hydro, and thermal power unit output optimization configuration methods.

[0047] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] Figure 1 This is a flowchart illustrating a demand-response-based source-grid-load-storage interactive scheduling method provided by the present invention, as shown below. Figure 1 As shown, the method includes the following steps:

[0049] Step 1: Obtain historical daily power load, historical target electricity load data, and dynamic factors of daily electricity price changes for thermal power units, hydropower units, wind power units, and photovoltaic units in the target area. The historical target demand data includes historical electricity load data for continuous power consumption equipment, non-continuous power consumption equipment, and daily power consumption equipment in the target area.

[0050] Step 2: Based on the historical daily power load, historical target electricity load data and dynamic changes in daily electricity prices of thermal power units, hydropower units, wind power units and photovoltaic units in the target area, construct an objective function with the goal of minimizing the total annual electricity cost of enterprise users in the target area and minimizing the power generation cost of the power generation sector, and 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 interaction scheduling scheme, and configure the daily power load of each unit and the daily electricity load of the production equipment of industrial enterprises.

[0052] The specific optimal source-grid-load-storage interaction scheduling schemes include:

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

[0054] Specifically, once the output of each generating unit is obtained at the lowest annual total electricity cost for industrial users, i.e., the optimal output of each generating unit, the optimal configuration scheme for the output of each generating unit can be determined. In this scheme, the output of wind power generation and photovoltaic generator 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, this invention, through precise characterization of historical daily power load of generating units and industrial production electricity consumption, and considering the demand response behavior of continuous, non-continuous, and daily power consumption equipment of enterprises, constructs an objective function and constraints for optimizing the daily power output of each generating unit. By solving the objective function with the goal of minimizing the total annual electricity cost of industrial users in the target area and minimizing the power generation cost of the power generation department, the optimal daily power output of generating units can be obtained. This provides enterprises with a more accurate and efficient daily production electricity configuration scheme, improves the self-sufficiency and self-consumption rate of wind and solar power, and reduces the enterprise's electricity costs.

[0056] In one specific embodiment, step 1, which involves acquiring historical daily power load of each generating unit in the target area, historical target electricity load data, and dynamic factors affecting daily electricity prices, further includes:

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

[0058] Optionally, the daily power load, enterprise power load, and electricity price data of the target area for each historical year can be divided into electricity demand data matrices according to the four seasons: spring, summer, autumn, and winter. The rows of the matrix represent each day of each season, and the columns of the matrix represent each 15 minutes of each day.

[0059] Optionally, after dividing the historical annual daily power load, enterprise power load data, and electricity price data of the target area into power demand data matrices for each season (spring, summer, autumn, and winter), the 90-day × 24-hour × 15-minute matrices for each season can be imported into the model. The model result includes power demand data, unit output data, or electricity price data for each category, which are distributed every 15 minutes of each 24-hour period.

[0060] Optional data includes measured output data of thermal power, hydropower, wind power, and photovoltaic power every 15 minutes throughout the day, installed capacity of each power generation technology, electricity consumption of each production and non-production equipment of the enterprise every 15 minutes throughout the day, equipment start-up and shutdown status, initial electricity price, and dynamic upper and lower limit constraints based on electricity prices in other provinces and cities.

[0061] In one specific embodiment, the present invention provides a demand-response-based source-grid-load-storage interactive scheduling method. The improved NSGA-II algorithm accurately characterizes the daily power load of generating units and the typical characteristics of enterprise electricity consumption in different seasons, while reducing data dimensions and improving computational efficiency. At the same time, it obtains the daily power load of generating units, the electricity load of enterprises, and the electricity price data every 15 minutes on typical days of each season in the target area, which facilitates the subsequent construction of constraints for solving the objective function.

[0062] In one specific embodiment, the factors influencing the optimization of wind and solar generator output include:

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

[0064] In one specific embodiment, the unit's economic parameters can be parameters related to the unit's costs, such as the unit's unit capacity investment cost or operation and maintenance cost.

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

[0066] In one specific embodiment, the electricity price in the target area can be the enterprise electricity purchase price in the target area or the grid connection price in the target area.

[0067] In one specific embodiment, after obtaining the factors affecting the optimization of unit output, namely the technical parameters of the generator set, the economic parameters of the generator set, the operating parameters of the flexible load of enterprises in the target area, and the electricity price of the target area, an objective can be constructed based on the above parameters. The objective is to minimize the total annual electricity cost of enterprise users in the target area and the annual power generation cost of the generator set, thereby obtaining the optimal solution for the unit output.

[0068] In one specific embodiment, the present invention provides a demand-response-based source-grid-load-storage interactive scheduling method. By acquiring the factors influencing unit output optimization—namely, unit technical parameters, unit economic parameters, operating parameters of enterprise flexible loads in the target area, and the electricity price of the target area—a target function can be constructed based on these parameters. This provides enterprise users with a more accurate and efficient power configuration scheme for production, improves wind and solar self-sufficiency and local consumption capacity, avoids wind and solar power reduction and large-scale grid connection, and reduces electricity costs for enterprise users. Historical target load data represents the operating parameters of the flexible loads.

[0069] In one specific embodiment, in step 2, based on the daily power load of each generator unit within the target area, historical target enterprise electricity load data, daily electricity price data, and factors influencing the output optimization of each unit, an objective function and constraints are constructed. Specifically, the daily power load of thermal power units, hydropower units, wind power units, and photovoltaic units are divided according to spring, summer, autumn, and winter; the electricity demand data of continuous, discontinuous, and daily power consumption equipment of enterprises are also divided according to spring, summer, autumn, and winter; and daily electricity prices also exhibit seasonal differences. Specifically, this includes:

[0070] Step 2.1: Divide the data obtained in Step 1 into power output data of each unit and power demand data matrix of continuous power consumption equipment, non-continuous power consumption equipment and daily power consumption equipment of the enterprise 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 in the data matrix of unit output, electricity price, and electricity demand of continuous, non-continuous and daily electrical equipment of enterprises for each season every 15 minutes, that is, the number of time periods included in each season;

[0072] Step 2.3: Based on the output of different types of generating units, electricity prices, and the number of rows in the data matrix of electricity demand data for different types of electrical equipment obtained in Step 2.2, optimize and match the output of different types of generating units every 15 minutes, electricity prices, and electricity demand data for continuous, non-continuous, and daily electrical equipment of enterprises in each season.

[0073] Specifically, the operating status matrix of the electrical equipment is shown below:

[0074] Suppose S is the set of states of the electrical equipment of industrial enterprises on time T, that is, the solution domain of the scheduling problem of electrical equipment for all enterprises. Let s∈S be the matrix of electrical equipment states, and h be the time period variable, h∈T, then we have:

[0075]

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

[0077] The enterprise electrical load data matrix is ​​shown below:

[0078] Define P as the set of all loads of all electrical equipment in T for a company. Transforming s yields the load matrix p:

[0079]

[0080] Suppose p∈P, p x =(P x，1 p x，2 , ..., p x，h , ..., p x，t ) represents the load vector of electrical equipment x on T, p x，h Let x represent the load value of electrical equipment x during time period h. Assume the set of electrical loads for a single production unit in an industrial enterprise is L, where L = {L...} x Let |x∈E}, 1≤t≤β x , l x，t Let l represent the electrical load of electrical equipment x during the t-th operating period, and l x，t >0.

[0081] Based on the generator set's technical parameters, economic parameters, operating parameters of the enterprise's flexible load in the target area, and the electricity price in the target area, the objective function is established.

[0082] Based on one or more of the following: the power output load of typical days in each season in the target area every 15 minutes, the dynamic electricity price of typical days in each season every 15 minutes, the historical target demand load data of typical days in each season every 15 minutes, the technical parameters of each generator set, the economic parameters of each generator set, the operating parameters of industrial enterprises in the target area, and the electricity price of the target area, constraints for solving the objective function are constructed. The constraints include one or more of the following: upper and lower limits of generator output, power balance constraints, electricity price fluctuation constraints, and constraints on industrial enterprises' participation in demand response.

[0083] In one specific embodiment, 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 are used to establish an objective function, which can be expressed by the following formula:

[0084]

[0085] In the formula:

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

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

[0088] C RES This indicates the operating cost of renewable energy generator sets;

[0089] C loss Indicates the cost of abandoning electricity;

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

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

[0092] a i b i With c i This represents the fuel cost coefficient for thermal power units;

[0093] ST i This indicates the start-up and shutdown costs of thermal power units;

[0094] This indicates the start-up and shutdown status of the thermal power unit, where δ = 1 indicates the start-up 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 This indicates the operating cost of the photovoltaic unit;

[0097] C WT This indicates the operating cost of the wind turbine unit;

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

[0099] π t This indicates the real-time electricity price for participating in demand response;

[0100] This indicates the total electricity consumption within 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 demand and electricity safety of enterprise users, one or more constraints can be constructed to solve the objective function. These constraints may include, but are not limited to, upper and lower limits of the output of each generator set, power balance constraints, electricity price fluctuation constraints, and constraints on industrial enterprises' participation in demand response.

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

[0103]

[0104] In the formula: This represents the projected total electricity demand for production by the company during time period t. The output load of RE unit j during time period t; and These represent the total amount of solar power curtailed and wind power curtailed during time period t, respectively.

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

[0106]

[0107] In the formula:

[0108] This indicates the lower limit of the output of thermal power units;

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

[0110] This indicates the upper limit of the output of renewable energy generator sets;

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

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

[0113] π min ≤π t ≤π max ;

[0114] In the formula:

[0115] π min Indicates the lower limit of electricity price changes, π t To participate in real-time electricity pricing for demand response, π max This indicates the upper limit of electricity price changes.

[0116] In one specific embodiment, the constraints for different types of electrical equipment in an enterprise to participate in demand response can be expressed by the following formula:

[0117]

[0118] In the formula:

[0119] P x,t This represents the company's total response load;

[0120] P' x,t This represents the load forecast value after the company adopts a demand response strategy;

[0121] r represents the rth hour of continuous operation of the continuously powered equipment.

[0122] Suppose set E is the set of all electrical devices within the time range T, then E = {resource1, resource2, ..., resourceN}, |E| = N, where N represents the total number of electrical devices, and resource represents the electrical device. Let x be the variable representing the electrical devices used by the enterprise, x ∈ E. For There exists a set of all working time periods for electrical equipment x. have Where λ x ∈T, θ x ∈T, and λ x ≤θ x , Let β x Let β be the maximum operating time of electrical equipment x, and 1 ≤ β x ≤θ x -λ x +1; Let Let x be the set of permitted start-up periods for electrical equipment. Then we have in and set up Let x be the set of actual working periods of electrical equipment, where Let α x This refers to the period during which electrical equipment x begins to start operating when demand response is not employed. Let σ x When demand response is adopted, the period during which electrical equipment x begins to start operation.

[0123] The electrical equipment group E includes E c E u and E d Three categories of electrical equipment, including E c and E u Both are sets of schedulable devices. Let E be... sc If E is the set of schedulable resources, then E c ∪E u =E sc Therefore, for the above set, there exists E sc ∪E d =E.

[0124] Let E u It is a collection of non-continuous electrical equipment, among which Then there is exist All can make Established, that is At this time, it exists

[0125] Let E c It is a collection of continuously powered electrical equipment, among which Then there is exist All can make Established, that is At this time, it exists

[0126] Let E d It is a collection of everyday electrical equipment, among which Then there is for like and Right now For everyday electrical equipment, the collection of its actual working hours It is a fixed value and does not participate in the daily scheduling and optimization process.

[0127] Specifically, this invention constructs a more accurate and efficient objective function and constraints by more accurately characterizing the output of renewable energy units and the seasonal characteristics of electricity consumption in enterprise production and operation, as well as simulating the demand response behavior of enterprise production and non-production equipment, thereby improving the efficiency and accuracy of solving the objective function.

[0128] In one specific embodiment, see Figure 2 As shown, before obtaining the optimal solutions for the unit output and enterprise electricity consumption, the improved NSGA-II multi-objective optimization algorithm is used to solve the problem, specifically including:

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

[0130] The population is sorted into non-dominated categories based on the objective function value, and individuals are assigned to different non-dominated levels.

[0131] For each individual in a non-dominant hierarchy, crowding is calculated to measure the spatial density around the individual.

[0132] Individuals with higher density are selected for priority retention in order to maintain population diversity;

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

[0134] The fitness function value of each particle is calculated based on the objective function value and the constraint penalty term function value of each particle.

[0135] Based on the crossover probability, crossover is performed on the selected parent population to generate new offspring individuals;

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

[0137] The parent population and the offspring population are merged to form a joint population of size 2N;

[0138] The combined population was subjected to non-dominated ordination again to determine the next generation of population;

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

[0140] Repeatedly delete the individual with the lowest crowding, and recalculate the crowding after each deletion, until the population size meets the requirements;

[0141] Based on the non-dominated ranking results and the improved crowding, individuals with higher non-dominated levels and greater crowding are selected to form a new parent population.

[0142] Check if the termination condition has been met. If the termination condition is met, output the Pareto front solution set and analyze the optimization effect. Otherwise, repeat the crossover and mutation operations and continue iterating until the termination condition is met.

[0143] In one 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 enterprises for production and operation, an algorithm can be used to solve for the optimal solution of each parameter in the objective function, such as a genetic algorithm.

[0144] In one specific embodiment, in order to find the optimal solution for each parameter in the objective function, the present invention uses an improved NSGA-II multi-objective optimization algorithm. Compared with other algorithms, the improved NSGA-II multi-objective optimization algorithm improves the search performance and convergence speed, resulting in higher efficiency and accuracy in solving the objective function.

[0145] Specifically, after constructing the objective function and constraints, this invention can use the improved NSGA-II multi-objective optimization algorithm to obtain the optimal position of each individual as the optimal result of each variable in the objective function. This facilitates obtaining the optimal solution for unit output and enterprise electricity consumption with the goal of minimizing the power generation cost and enterprise electricity consumption within the target area, thereby improving search performance and convergence speed, and making the objective function solution more efficient and accurate.

[0146] In one specific embodiment, obtaining the optimal solution for the unit output with the objective of minimizing the power generation cost within the target area includes:

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

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

[0149] In one specific embodiment, obtaining the optimal solution for enterprise electricity consumption with the goal of minimizing enterprise electricity consumption within the target area includes:

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

[0151] Based on the optimal values ​​of multiple variables in the objective function, the electricity 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's electricity load.

[0152] The following describes the enterprise electrical equipment provided by the present invention. The enterprise electrical equipment described below can be referred to in correspondence with the enterprise power optimization configuration method described above.

[0153] Specifically, the enterprise electrical equipment provided by this invention is mainly divided into three types: continuous power resource type, discontinuous power resource type, and daily power resource type. The first two types will affect the results during the scheduling optimization process, i.e., they are scheduling power resources, while daily power resources are resource types that cannot participate in scheduling. Specifically, this includes:

[0154] Specifically, continuous power consumption equipment: This type of equipment can participate in scheduling during its working period, but once started, it cannot be interrupted or stopped during the production process until the production task is completed. Let E... c It is a collection of continuously powered electrical equipment, among which Then there is exist All can make Established, that is At this time, it exists

[0155] Discontinuous power consumption equipment: This type of equipment can participate in scheduling during its working period, and its operation may be interrupted during production, but the total power consumption time must be guaranteed to complete the predetermined production tasks. Let E... u It is a collection of non-continuous electrical equipment, among which Then there is exist All can make Established, that is At this time, it exists

[0156] Specifically, for everyday electrical equipment: the working time of this type of equipment is a fixed value, and once started, it cannot be interrupted or stopped. This means that this resource cannot participate in scheduling optimization during its working hours; that is, the operating time of this equipment is all its working hours. Let E... d It is a collection of everyday electrical equipment, among which Then there is for like and Right now For everyday electrical equipment, the collection of its actual working hours It is a fixed value and does not participate in the daily scheduling and optimization process.

[0157] In summary, the electrical equipment set E in this invention includes E... c E u and E d Three categories of electrical equipment, including E c and E u Both are sets of schedulable devices. Let E be... sc If E is the set of schedulable resources, then E c ∪E u =E sc Therefore, for the above set, there exists E sc ∪E d =E.

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

[0159] In one specific embodiment, based on the aforementioned electrical equipment, the set of states of the industrial enterprise's electrical equipment throughout the day is obtained, which represents the solution domain of the overall enterprise electrical equipment scheduling problem. The states of the industrial enterprise's electrical equipment throughout the day can be represented by the following formula:

[0160]

[0161] In the formula:

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

[0163] S represents the set of states of electrical equipment used by industrial enterprises.

[0164] s represents the matrix representing the state of electrical equipment;

[0165] H represents the time period variable.

[0166] Based on the daily status set of the electrical equipment of the aforementioned industrial enterprises, a collective load matrix of all electrical equipment of the enterprises can be obtained, which can be represented by the following formula:

[0167]

[0168] In the formula:

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

[0170] p x,h This indicates the load value of electrical equipment at different times.

[0171] Based on the combined load matrix of all electrical equipment of the above-mentioned enterprises, the set of electrical loads for a single production equipment of an industrial enterprise is established, which can be represented by the following formula:

[0172]

[0173] In the formula,

[0174] L represents the set of electrical loads of a single production equipment in an industrial enterprise;

[0175] l x,t This represents the electrical load of electrical equipment x during the t-th operating period.

[0176] Based on the different optimization scheduling methods adopted for the different types of electrical equipment, there are three power scheduling scenarios according to the three types of electrical equipment:

[0177] The first scenario is the power consumption scheduling optimization task mode for the company's daily (basic) electrical equipment, which can be represented by the following formula:

[0178] when hour,

[0179] The second scenario is the power dispatching optimization mode for continuous production equipment in enterprises, which can be expressed by the following formula:

[0180] when hour,

[0181] The third scenario is the power scheduling optimization mode for interruptible production equipment in enterprises, which can be expressed by the following formula:

[0182] when hour,

[0183] In the formula,

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

[0185] Based on the above conditions and constraints, an objective function and constraints are constructed to realize the "source-load interaction" optimized scheduling method. By solving the objective function with the goal of minimizing the total annual electricity cost of industrial users in the target area and minimizing the power generation cost of the power generation sector, the optimal daily power output of the generating units can be obtained. This provides enterprises with a more accurate and efficient daily production electricity configuration scheme, improves the self-sufficiency and self-consumption rate of wind and solar power, and reduces the enterprise's electricity cost and power generation cost.

[0186] On the other hand, embodiments of the present invention also disclose a demand-response-based source-grid-load-storage interactive scheduling device, comprising:

[0187] The acquisition module is used to acquire the daily power load, historical target electricity load data and dynamic changes in electricity prices of each unit in the target area. The historical electricity load data includes the historical electricity load data of continuous power consumption equipment, non-continuous power consumption equipment and daily power consumption equipment in the target area.

[0188] The construction module is used to construct an objective function based on the daily power load of each unit in the target area, historical target electricity load data, and dynamic changes in daily electricity prices, with the goal of minimizing the total annual electricity cost for industrial users in the target area and minimizing the power generation cost on the generation side, and 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 electricity load of the production equipment of industrial enterprises based on the optimal solution of the objective function.

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

[0191] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0192] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily 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 invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded 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, Includes the following steps: Step 1: Obtain daily power load, historical target electricity load data, and dynamic factors affecting daily electricity prices for each unit in the target area; Step 2: Based on the daily power load of each unit in the target area, historical target electricity load data, and dynamic changes in daily electricity prices, construct an objective function with the goal of minimizing the total annual electricity cost for industrial users and the generation cost on the power generation side in the target area, and 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 interaction scheduling scheme; The expression for the objective function is: In the formula: and Let these represent the objective functions of the power supply system and the power consumption system, respectively. This indicates the operating cost of thermal power units; This indicates the operating cost of renewable energy generator sets; Indicates the cost of abandoning electricity; Indicates the output load of the thermal power unit; t represents all time periods throughout the day; , and This represents the fuel cost coefficient for thermal power units; This indicates the start-up and shutdown costs of thermal power units; Indicates the start-up and shutdown status of thermal power units, where This indicates that the device is powered on. Indicates that the device is powered off; i represents a thermal power unit, i=(1,2,3,…,I-1,I); This indicates the operating cost of the photovoltaic unit; This indicates the operating cost of the wind turbine unit; Indicates the participation cost of demand response; This indicates the real-time electricity price for participating in demand response; This indicates the total electricity consumption within the industrial park; The constraints on the objective function include: The upper and lower limit constraints on the output of each generator unit are expressed as follows: ； In the formula: This represents the projected total electricity demand for production by the company during time period t. The output load of RE unit j during time period t; and These represent the total amount of solar power curtailed and wind power curtailed during time period t, respectively. The power balance constraint is expressed as follows: In the formula: This indicates the lower limit of the output of thermal power units; This indicates the upper limit of the output of the thermal power unit; This indicates the upper limit of the output of renewable energy generator sets; The electricity price fluctuation constraint is expressed as follows: ； In the formula: Indicates the lower limit of electricity price changes. To participate in real-time electricity pricing for demand response, Indicates the upper limit of electricity price changes; The constraints for industrial enterprises participating in demand response are expressed as follows: In the formula: This represents the company's total response load; This represents the load forecast value after the company adopts a demand response strategy; r represents the rth hour of continuous operation of the continuously powered equipment.

2. The 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 generator unit in the target area, historical target enterprise electricity load data, daily electricity price data, and daily power optimization influencing factors for each unit in step 1, the following steps are also included: The historical daily power load, historical target daily demand load, and historical electricity price data for each unit within the target area are divided according to the season.

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

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

5. The 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. The source-grid-load-storage interactive scheduling method based on demand response according to claim 1, characterized in that, Before obtaining the optimal solution to the objective function, an improved NSGA-II multi-objective optimization algorithm is used to solve for the optimal position of the individual, including: Before finding the optimal solution to the source-load interaction optimization scheduling model, a genetic algorithm is used iteratively to obtain the optimal position of each individual, specifically including: Randomly generate an initial population of size N, and calculate the objective function value for each individual; The population is sorted into non-dominated categories based on the objective function value, and individuals are assigned to different non-dominated levels. For each individual in a non-dominant hierarchy, crowding is calculated to measure the spatial density around the individual. Individuals with higher density are selected for priority retention in order to maintain population diversity; Select superior individuals from the current population to form the parent population; The fitness function value of each particle is calculated based on the objective function value and the constraint penalty term function value of each particle. Based on the crossover probability, crossover is performed on the selected parent population to generate new offspring individuals; Based on the mutation probability, offspring individuals are mutated to increase population diversity; The parent population and the offspring population are merged to form a joint population of size 2N; The combined population was subjected to non-dominated ordination again to determine the next generation of population; In the frontier solution, after removing the individual with the lowest crowding degree, the crowding degree of the remaining individuals is recalculated; Repeatedly delete the individual with the lowest crowding, and recalculate the crowding after each deletion, until the population size meets the requirements; Based on the non-dominated ranking results and the improved crowding, individuals with higher non-dominated levels and greater crowding are selected to form a new parent population. Check if the termination condition has been met. If the termination condition is met, output the Pareto front solution set and analyze the optimization effect. Otherwise, repeat the crossover and mutation operations and continue iterating until the termination condition is met.

7. A source-grid-load-storage interactive scheduling method based on demand response according to claim 6, characterized in that, Also includes: Based on the optimal positions of all individuals, determine the 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.

8. A demand-response-based source-grid-load-storage interactive scheduling device utilizing the demand-response-based source-grid-load-storage interactive scheduling method according to any one of claims 1-7, characterized in that, include: The acquisition module is used to acquire the daily power load of each unit in the target area, historical target electricity load data, and dynamic factors affecting the electricity price. The construction module is used to construct an objective function based on the daily power load of each unit in the target area, historical target electricity load data, and dynamic changes in daily electricity prices, with the goal of minimizing the total annual electricity cost for industrial users in the target area and minimizing the power generation cost on the generation side, and to obtain the optimal solution of the objective function. The configuration module is used to output the optimal source-grid-load-storage interaction scheduling scheme based on the optimal solution of the objective function.

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