A multi-objective demand response optimization method considering low carbon and user satisfaction

Abstract

The present invention discloses a multi-objective demand response optimization method that considers low carbon and user satisfaction, belonging to the field of demand-side management and optimized scheduling technology. The method comprises: randomly generating an initial scheduling solution set containing population 1 and population 2, establishing a multi-objective scheduling model that simultaneously includes power balance constraints, user comfort constraints, and resource capacity constraints; processing infeasible solutions in population 1 through an adaptive penalty function, optimizing population 2 with a feasibility priority strategy, and evolving and updating the population by combining crossover, mutation, tournament selection, and environmental selection mechanisms; evaluating the multi-objective fitness value of the scheduling solution in each iteration, screening the Pareto frontier solution set, and continuously updating until the termination condition is met, and selecting a compromise optimized scheduling solution from the final Pareto solution set. The present invention integrates low carbon, cost, and user satisfaction, adopts a dual population optimization algorithm, realizes resource coordinated scheduling, and significantly improves system efficiency and environmental benefits.

Description

Technical Field

[0001] The present invention relates to the technical field of demand-side management and optimized scheduling, and in particular to a multi-objective demand response optimization method considering low carbon and user satisfaction. Background Art

[0002] Existing multi-objective demand-side management optimization methods that consider supplier carbon emissions involve user psychology, supplier costs, and green and low-carbon factors. To achieve these goals, the commonly used technical approach in related fields aggregates various controllable load devices to respond to relevant dispatch instructions, thereby meeting the system's peak and valley demand while balancing user cost-benefit. Typically, such methods comprehensively consider user comfort and supplier costs, formulating them as an optimization problem and optimizing the dispatch of demand-side resources within each dispatch cycle.

[0003] However, existing technologies have certain shortcomings. On the one hand, the integration of large-scale renewable energy sources such as wind and photovoltaics has slowed the consumption of traditional energy and reduced the system's carbon emissions. On the other hand, the output of wind and photovoltaic resources has a strong random volatility, which puts greater pressure on the system's peak regulation and increases the demand for flexible resources. Current demand-side management technologies have not fully considered the combined effects of low-carbon peak regulation on the environment and scheduling costs, and have not effectively coordinated and optimized user psychology, peak regulation costs, and carbon emissions, making it difficult to achieve an effective balance between demand-side resource regulation and green and low-carbon goals.

[0004] With the increasing complexity of new energy system structures and the widespread adoption of renewable energy, demand-side management faces new problems and challenges. Low-carbon optimized scheduling helps users, suppliers, and system operators identify effective carbon reduction paths and measures, providing a theoretical basis for carbon reduction behavior and operations. Therefore, there is an urgent need to explore methods that integrate low-carbon optimization and demand-side management. This paper proposes a constrained multi-objective evolutionary optimization method based on a dual population. This method coordinates the evolutionary processes of two populations to obtain a high-quality Pareto frontier solution set. It can effectively coordinate the resource scheduling of various demand-side resources (such as air conditioners, electric water heaters, etc.) and renewable energy (wind power, photovoltaics), while comprehensively considering user psychological factors, peak-shaving costs, and carbon emissions. This method addresses the problem that existing technologies fail to fully account for the comprehensive environmental and cost impacts of low-carbon peak-shaving. It can effectively balance resource regulation with green and low-carbon goals, significantly reducing system carbon emissions and alleviating peak-shaving pressure. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is: the problem that the existing technology does not adequately consider the impact of low-carbon peak regulation on the system environment and scheduling costs, and achieves a balance between demand-side resource regulation and green and low-carbon goals, thereby reducing the system's carbon emission level and alleviating peak regulation pressure.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: a multi-objective demand response optimization method considering low carbon and user satisfaction, which includes the following steps: randomly generating an initial scheduling solution set including population 1 and population 2 for a multi-objective evolutionary optimization process, and establishing a multi-objective scheduling model that simultaneously includes power balance constraints, user comfort constraints and resource capacity constraints; using the c-DPEA algorithm, processing infeasible solutions in population 1 through an adaptive penalty function, and optimizing population 2 with a feasibility priority strategy, and combining crossover, mutation, tournament selection and environmental selection mechanisms to evolve and update the population; evaluating the multi-objective fitness value of the scheduling solution in each generation iteration, screening the Pareto frontier solution set, and continuously updating until the termination condition is met, and selecting a compromise optimization scheduling scheme from the final Pareto solution set to achieve a comprehensive optimal balance between user satisfaction, carbon emissions and peak-shaving costs.

[0008] As a preferred solution of the multi-objective demand response optimization method considering low carbon and user satisfaction described in the present invention, the power balance constraint is used to ensure that the dynamic adjustment relationship between the grid load and the demand response resources is consistent, and through comprehensive coordination of unit output, user-side resource response and actual load demand, the supply and demand of the entire system are in a balanced state at all times.

[0009] As a preferred solution of the multi-objective demand response optimization method considering low carbon and user satisfaction described in the present invention, the user comfort constraint considers the individual user's sensitivity to temperature and charging time in the scheduling optimization process, and quantitatively describes the user experience by introducing user behavior models and preference functions.

[0010] As a preferred solution of the multi-objective demand response optimization method considering low carbon and user satisfaction described in the present invention, wherein: the population 1 in the c-DPEA algorithm adopts an adaptive penalty function mechanism to process the target vector of infeasible solutions in the multi-objective evolution process, and controls the dynamic balance of convergence and population diversity through the normalized expression of constraint violation values.

[0011] As a preferred solution of the multi-objective demand response optimization method considering low carbon and user satisfaction described in the present invention, wherein: the population 2 is used as the source of the final output solution set, the optimization process adopts a feasibility-oriented strategy to deal with infeasible solutions, and the infeasible individuals are mapped to the area near the maximum value of the objective function through the target vector translation strategy.

[0012] As a preferred solution of the multi-objective demand response optimization method considering low carbon and user satisfaction described in the present invention, the carbon emissions are estimated based on the carbon emission factor of each generator set to estimate the carbon emissions generated by the output power, and the carbon cost conversion coefficient is converted into an economic cost indicator, which is introduced into the multi-objective scheduling model for unified optimization, so as to meet the power supply demand while reducing the total carbon emissions during the operation of the power system.

[0013] As a preferred solution of the multi-objective demand response optimization method considering low carbon and user satisfaction described in the present invention, the comprehensive optimal balance adopts a compromise optimization strategy, and in the output Pareto front solution set, by comprehensively considering user satisfaction, carbon emission costs and system peak-shaving cost factors, the optimal feasible scheduling result is screened, taking into account environmental protection, economy and user experience.

[0014] Another object of the present invention is to provide a multi-objective demand response optimization system that takes low carbon and user satisfaction into consideration.

[0015] In order to solve the above technical problems, the present invention provides the following technical solutions: a multi-objective demand response optimization system considering low carbon and user satisfaction, including: a multi-objective optimization decision model construction module, a multi-objective evolutionary optimization module, and an optimal solution screening and output module. The multi-objective optimization decision model construction module is used to construct a multi-objective decision model that comprehensively considers carbon emissions, user satisfaction and peak-shaving costs. The multi-objective evolutionary optimization module is used for the optimal decision solution set. The optimal solution screening and output module is used to screen and output the actual demand-side scheduling decision plan based on the Pareto optimal solution set obtained by the optimization algorithm.

[0016] The present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, it implements the steps of a multi-objective demand response optimization method considering low carbon and user satisfaction.

[0017] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the multi-objective demand response optimization method considering low carbon and user satisfaction are implemented.

[0018] The present invention not only focuses on minimizing peak-shaving costs through demand-side management (DSM), but also on ensuring low-carbon operation (minimizing carbon emissions) and user satisfaction. This means that while effectively balancing peak-shaving demand, the system also reduces carbon emissions and improves user acceptance, ensuring efficient and green system operation. Furthermore, a constrained multi-objective evolutionary optimization method based on a dual population can effectively evaluate a set of solutions, balancing the three objectives of peak-shaving costs, carbon emissions, and user satisfaction, and generating a compromise scheduling plan for operators. This method considers demand-side resources such as air conditioners, electric water heaters, and electric vehicles, as well as renewable energy sources such as wind power and photovoltaics, effectively coordinating the allocation of these resources. Furthermore, it considers power balance constraints, user comfort constraints, and resource capacity constraints. The combination of these multiple constraints makes the solution more suitable for practical DSM application scenarios and more effectively addresses the needs of actual power grids. While minimizing peak-shaving costs, it ensures low-carbon operation and user satisfaction, improving power system reliability and carbon emissions. Using this efficient constrained multi-objective optimization method, dispatching operators can obtain efficient scheduling strategies. These advantages enable this technical solution to increase efficiency and benefits when dealing with complex demand-side management scheduling problems, and have obvious advantages over existing technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 This is an overall flow chart of a multi-objective demand response optimization method considering low carbon and user satisfaction, provided by one embodiment of the present invention.

[0021] Figure 2 This is a flowchart of an algorithm implementation plan for a multi-objective demand response optimization method that considers low carbon and user satisfaction, provided as an embodiment of the present invention. DETAILED DESCRIPTION

[0022] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0023] Example 1, with reference to Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a multi-objective demand response optimization method considering low carbon and user satisfaction, including:

[0024] This invention involves a multi-objective demand-side management method that considers low-carbon optimization. It aims to address the existing problem of insufficient consideration of the impact of low-carbon peak-shaving on the system environment and scheduling costs. This method balances demand-side resource regulation with green and low-carbon goals by comprehensively considering the impact of user psychology, peak-shaving costs, and carbon emissions. The multi-objective demand-side management model for carbon emissions incorporates multiple constraints, as detailed below.

[0025] (1) Power balance constraint: Ensure that the total regulation capacity of demand-side resources matches the peak regulation demand of the power grid. That is, the regulation amount of demand response is the difference between the power of the system unit and the actual load, specifically:

[0026] ,

[0027] in, 、 and They represent the power output of the i-th unit, the power change of the demand response resources and the actual load demand respectively, n is the total number of dispatchable generators in the system, and m is the category of demand-side resources participating in demand response in the system.

[0028] (2) User comfort constraints: Consider the user's satisfaction with temperature, charging time, and other requirements to avoid excessively affecting the user experience.

[0029] ,

[0030] (3) Resource capacity constraints: Consider the regulation capacity limitations of various demand-side resources (such as air conditioners, electric water heaters, electric vehicles, etc.).

[0031] ,

[0032] ,

[0033] in, and They are the maximum regulation amount of demand response resources and the maximum capacity of the unit respectively.

[0034] (4) Objective function: Minimize peak-shaving costs to reduce the dispatching costs of power suppliers; minimize carbon emissions to reduce the overall carbon emission level of the system; maximize user satisfaction to improve user acceptance of demand-side regulation. Since the demand-side management optimization model of the present invention aims to optimize user satisfaction, supplier costs and system carbon emissions, the objective function can be written as:

[0035] ,

[0036] in, represents the power supply cost of the i-th unit, represents the carbon emission cost conversion of the i-th unit, Represents the dispatch cost of demand response resources. is the carbon emission cost conversion factor. is the user's weight coefficient, is the demand function after the corresponding resource responds to the grid dispatch (such as the temperature in the air-conditioned room or the charge of the electric vehicle), is the demand function before response. T is the total number of time steps in the optimization scheduling period.

[0037] ,

[0038] in, is the carbon emission factor coefficient of the i-th unit. is the regulated power output of the i-th unit.

[0039] In addition, the c-DPEA algorithm is a constrained multi-objective evolutionary optimization method based on dual populations. Compared with unconstrained multi-objective optimization problems, constrained multi-objective optimization problems are usually more challenging. Under the constraints, part of the search space may be directly excluded, resulting in the algorithm only being able to search and optimize within a limited feasible area. In extreme cases, there may not even be a feasible solution in the search space, making it impossible to obtain an effective optimization strategy. c-DPEA simultaneously evolves two populations, population 1 and population 2, in a collaborative manner. The main difference between the two populations lies in the way they deal with infeasible solutions. A new adaptive fitness function bCAD is introduced to balance convergence and diversity. Next, the algorithm realizes the interaction and collaboration of the two populations through the offspring reproduction step, and further optimizes the population performance in combination with the environmental selection mechanism.

[0040] For population 1, in order to utilize the constraint violation information of the objective function and infeasible solutions, c-DPEA designs a parameter-free adaptive penalty function to deal with infeasible solutions in population 1. The objective space can be divided into N non-overlapping “sub-regions” by N weight vectors. Each weight vector Specify a unique sub-region .

[0041] ,

[0042] in, , is a vector and The penalty function is used for any infeasible solution. , its target vector is modified as follows:

[0043] ,

[0044] in, and β can be expressed as:

[0045] ,

[0046] ,

[0047] in, and are the original and modified target vectors, respectively; It consists of the maximum value of each target in the target vector within the region; is the infeasible solution obtained during the optimization process of population 1; Yes Normalize constraint violation values; is the maximum constraint violation value in the population; β is the exponential value of the penalty function in the fitness function below, which depends on the current generation t, the maximum generation number T, and the coefficient describing the relationship between population 1 and population 2 .

[0048] For population 2, which is the final output solution of c-DPEA, in order to ensure the feasibility of the output solution, a feasibility-oriented method is adopted. , its target vector is modified as follows

[0049] ,

[0050] in, It consists of the maximum value of each objective of all solutions in the current population; is with The associated weight vector.

[0051] The specific process of multi-objective demand response optimization considering carbon emissions can be expressed as follows:

[0052] (1) Initialization of individuals: The present invention first randomly generates an initial demand response scheduling population, including population 1 and population 2.

[0053] (2) Solution set evaluation: In each iteration, the population is calculated with two objectives, namely, peak-shaving cost, system carbon emissions, and user satisfaction.

[0054] (3) Pareto front update: After obtaining the target values ​​of each solution, the dual-objective frontier is screened and then the Pareto solution set is updated.

[0055] (4) Solution set update: Filter the optimal solution set, perform tournament selection operations, and perform crossover and mutation operations to iteratively update the solution, continuing steps (2) to (4).

[0056] (5) Scheduling plan output: Filter out the compromise solution from the decision solution set and output the final demand response optimization plan.

[0057] This framework uses a dual population-based constrained multi-objective evolutionary optimization method to achieve a balance between system carbon emissions, peak-shaving costs, and user satisfaction. During implementation, a multi-objective genetic algorithm explores the fitness functions of different solutions, obtains the Pareto frontier, and ultimately selects the optimal demand response scheduling solution. This minimizes system carbon emissions while optimizing peak-shaving costs and improving user satisfaction, thereby enhancing the system's ability to cope with peak-shaving demand and ensuring its green, low-carbon, and efficient operation.

[0058] Example 2 is the second embodiment of the present invention, which is different from the previous embodiment in that:

[0059] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or the portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0060] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0061] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, and then editing, interpreting, or processing in another suitable manner as necessary, and then storing it in a computer memory.

[0062] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, it may be implemented using a combination of any of the following technologies known in the art: discrete logic circuits having logic gate circuits for implementing logic functions on data signals, application-specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0063] Example 3, the third embodiment of the present invention, provides a multi-objective demand response optimization system that considers low carbon and user satisfaction, including a multi-objective optimization decision model construction module, a multi-objective evolutionary optimization module, and an optimal solution screening and output module; the multi-objective optimization decision model construction module is used to construct a multi-objective decision model that comprehensively considers carbon emissions, user satisfaction and peak-shaving costs; the multi-objective evolutionary optimization module is used for the optimal decision solution set; the optimal solution screening and output module is used to screen and output the actual demand-side scheduling decision plan based on the Pareto optimal solution set obtained by the optimization algorithm.

[0064] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A multi-objective demand response optimization method considering low carbon and user satisfaction, characterized by: include, Randomly generate an initial dispatch solution set containing population 1 and population 2 for the multi-objective evolutionary optimization process, and establish a multi-objective dispatch model that includes power balance constraints, user comfort constraints, and resource capacity constraints. The c-DPEA algorithm is used to deal with infeasible solutions in population 1 through an adaptive penalty function, and to optimize population 2 with a feasibility-first strategy. The population is then evolved and updated by combining crossover, mutation, tournament selection, and environmental selection mechanisms. In each iteration, the multi-objective fitness value of the scheduling solution is evaluated, the Pareto front solution set is screened, and it is continuously updated until the termination condition is met. A compromise optimization scheduling solution is selected from the final Pareto solution set to achieve a comprehensive optimal balance between user satisfaction, carbon emissions, and peak-shaving costs. The power balance constraint is expressed as: in, and They represent the power output of the i-th unit, the power change of the demand response resource, and the actual load demand, respectively; n is the total number of dispatchable generators in the system; and m is the category of demand-side resources participating in demand response in the system; The user comfort constraint is expressed as: The resource capacity constraint is expressed as: in, and are the maximum regulation amount of demand response resources and the maximum capacity of the unit respectively; The objective function is expressed as: Among them, C i represents the power supply cost of the i-th unit, E i represents the carbon emission cost conversion of the i-th unit, D i represents the dispatching cost of demand response resources; λ is the carbon emission cost conversion coefficient; ω(t) is the user's weight coefficient, is the demand function after the corresponding resource responds to the grid dispatch, is the demand function before response; T is the total number of time steps in the optimization scheduling cycle; Among them, γ i is the carbon emission factor coefficient of the i-th unit.

2. The multi-objective demand response optimization method considering low carbon and user satisfaction according to claim 1, characterized in that: The power balance constraint is used to ensure that the dynamic adjustment relationship between the grid load and the demand response resources is consistent. By comprehensively coordinating the unit output, user-side resource response and actual load demand, the supply and demand of the entire system are balanced at all times.

3. The multi-objective demand response optimization method considering low carbon and user satisfaction according to claim 2, characterized in that: In the c-DPEA algorithm, population 1 adopts an adaptive penalty function mechanism to process the target vector of infeasible solutions during the multi-objective evolution process, and controls the dynamic balance between convergence and population diversity through the normalized expression of constraint violation values.

4. The multi-objective demand response optimization method considering low carbon and user satisfaction according to claim 3, characterized in that: The population 2 is used as the source of the final output solution set. The optimization process adopts a feasibility-oriented strategy to deal with infeasible solutions, and maps infeasible individuals to the area near the maximum value of the objective function through the target vector translation strategy.

5. The multi-objective demand response optimization method considering low carbon and user satisfaction according to claim 4, characterized in that: The carbon emissions are estimated based on the carbon emission factors of each generator set, and the carbon emissions generated by the output power are converted into economic cost indicators through the carbon cost conversion coefficient, and introduced into the multi-objective scheduling model for unified optimization, so as to meet the power supply demand while reducing the total carbon emissions during the operation of the power system.

6. The multi-objective demand response optimization method considering low carbon and user satisfaction according to claim 4, characterized in that: The comprehensive optimal balance adopts a compromise optimization strategy. In the output Pareto front solution set, it comprehensively considers user satisfaction, carbon emission costs and system peak-shaving cost factors to screen the optimal feasible scheduling result, taking into account environmental protection, economy and user experience.

7. A multi-objective demand response optimization system considering low carbon and user satisfaction, applying a multi-objective demand response optimization method considering low carbon and user satisfaction as claimed in any one of claims 1 to 6, characterized in that: include: Multi-objective optimization decision model construction module, multi-objective evolutionary optimization module, optimal solution screening and output module; The multi-objective optimization decision model construction module is used to construct a multi-objective decision model that comprehensively considers carbon emissions, user satisfaction and peak-shaving costs; The multi-objective evolutionary optimization module is used for the optimal decision solution set; The optimal solution screening and output module is used to screen and output the actual demand-side scheduling decision plan based on the Pareto optimal solution set obtained by the optimization algorithm.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the processor implements the steps of a multi-objective demand response optimization method considering low carbon and user satisfaction according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a multi-objective demand response optimization method considering low carbon and user satisfaction are implemented as described in any one of claims 1 to 6.

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