Multi-target demand response optimization method considering low carbon and user satisfaction

Abstract

The invention discloses a multi-target demand response optimization method considering low carbon and user satisfaction, which belongs to the technical field of demand side management and optimization scheduling, and comprises the following steps: randomly generating an initial scheduling solution set containing a population 1 and a population 2, establishing a multi-target scheduling model simultaneously including power balance constraint, user comfort constraint and resource capacity constraint; an infeasible solution in the population 1 is processed through an adaptive penalty function, the population 2 is optimized through a feasibility priority strategy, and the population is evolved and updated in combination with crossover, variation, tournament selection and environment selection mechanisms; and evaluating a multi-target fitness value of a scheduling solution in each generation of iteration, screening a Pareto frontier solution set, continuously updating the Pareto frontier solution set until a termination condition is met, and selecting a compromise optimization scheduling scheme from the final Pareto solution set. According to the method, low carbon, cost and user satisfaction are integrated, a dual population optimization algorithm is adopted, resource coordinated scheduling is achieved, and system efficiency and environmental benefits are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of demand - side management and optimal scheduling, and particularly relates 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 considering suppliers' carbon emissions involve user psychology, suppliers' costs, and green - low - carbon factors. To achieve these goals, the commonly used technical method in related fields is to aggregate various controllable load devices to respond to relevant scheduling instructions, so as to meet the peak - valley demand of the system and take into account the cost - benefit of users. Usually, such methods comprehensively consider the comfort of users and the costs of suppliers, describe it as an optimization problem, and optimize the scheduling of demand - side resources in each scheduling period.

[0003] However, there are certain deficiencies in the existing technologies. On the one hand, the access of large - scale renewable energy such as wind energy and photovoltaic energy slows down the consumption of traditional energy and reduces the carbon emissions of the system. On the other hand, due to the strong random volatility of the output of wind energy and photovoltaic resources, this poses greater pressure on the peak regulation of the system and increases the demand for flexible resources. The current demand - side management technologies have not fully considered the comprehensive effect of low - carbon peak regulation on environmental impact and scheduling costs, and have not well coordinated user psychology, peak - regulation costs, and carbon emissions for optimization. Thus, it is difficult to effectively balance the regulation of demand - side resources and green - low - carbon goals.

[0004] With the increasing complexity of the new energy system structure and the large - scale popularization of renewable energy, demand - side management faces new problems and challenges. Low - carbon optimal scheduling is beneficial for users, suppliers, and system operators to find effective carbon - emission reduction paths and measures, and provides a reasonable basis for carbon - emission reduction behaviors and operations at the theoretical level. Therefore, it is urgent to deeply explore the integration method of low - carbon optimization and demand - side management. The present invention proposes a constraint multi - objective evolutionary optimization method based on dual populations, which can coordinate the evolutionary processes of the two populations, obtain a high - quality Pareto - front solution set, and can effectively coordinate the resource scheduling of various demand - side resources (such as air conditioners, electric water heaters, etc.) and renewable energy (wind energy, photovoltaic). At the same time, it comprehensively considers user psychological factors, peak - regulation costs, and carbon - emission levels. This method aims at the problem that the existing technologies fail to fully consider the comprehensive impact of low - carbon peak regulation on the environment and costs, can effectively balance the regulation of resources and green - low - carbon goals, significantly reduce the system's carbon emissions, and relieve the peak - regulation pressure. Summary of the Invention

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

[0006] Therefore, the technical problem to be solved by the present invention is the problem that the impact of low-carbon peak shaving on the system environment and scheduling cost is insufficiently considered in the prior art, to achieve the balance between demand-side resource regulation and green low-carbon goals, thereby reducing the system carbon emission level and alleviating the peak shaving pressure.

[0007] To solve the above technical problem, the present invention provides the following technical solution: 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 the 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; adopting the c-DPEA algorithm, processing the infeasible solutions in population 1 through an adaptive penalty function, and optimizing population 2 with a feasibility-first strategy, and evolving and updating the population by combining crossover, mutation, tournament selection, and environmental selection mechanisms; evaluating the multi-objective fitness values of the scheduling solutions in each generation of iteration, screening the Pareto front solution set, and continuously updating until the termination condition is met, and selecting a compromise optimization scheduling plan from the final Pareto solution set to achieve the comprehensive optimal balance among user satisfaction, carbon emission, and peak shaving cost.

[0008] As a preferred scheme of the multi-objective demand response optimization method considering low-carbon and user satisfaction according to the present invention, wherein: 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 the comprehensive coordination of the unit output, the user-side resource response, and the actual load demand, the supply and demand of the entire system are in a balanced state at each moment.

[0009] As a preferred scheme of the multi-objective demand response optimization method considering low-carbon and user satisfaction according to the present invention, wherein: the user comfort constraint considers the sensitivity of individual users to temperature and charging time during the scheduling optimization process, and quantitatively describes the user experience by introducing a user behavior model and a preference function.

[0010] As a preferred scheme of the multi-objective demand response optimization method considering low-carbon and user satisfaction according to the present invention, wherein: in the c-DPEA algorithm, population 1 adopts an adaptive penalty function mechanism, which is used to process the objective vectors of infeasible solutions during the multi-objective evolutionary process, and controls the dynamic balance of convergence and population diversity through the normalized expression of the constraint violation value.

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

[0012] As a preferred embodiment of the multi-objective demand response optimization method considering low carbon and user satisfaction according to the present invention, wherein: the carbon emissions are estimated based on the carbon emission factors of each generator set for the carbon emissions generated by the output power, and are converted into an economic cost index through a carbon cost conversion coefficient, and introduced into the multi-objective scheduling model for unified optimization, so as to reduce the total carbon emissions during the operation of the power system while meeting the power supply demand.

[0013] As a preferred embodiment of the multi-objective demand response optimization method considering low carbon and user satisfaction according to the present invention, wherein: the comprehensive optimal balance adopts a compromise optimization strategy, and in the output Pareto front solution set, by comprehensively considering factors such as user satisfaction, carbon emission cost, and system peak shaving cost, the optimal feasible scheduling result is screened to take 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 considering low carbon and user satisfaction.

[0015] 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 cost. The multi-objective evolutionary optimization module is used for the optimal decision solution set, and the optimal solution screening and output module is used to screen and output the actual demand-side scheduling decision plan according to the Pareto optimal solution set obtained by the optimization algorithm.

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

[0017] The present invention provides a computer-readable storage medium, on which a computer program is stored, and is 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 realized.

[0018] Advantages of the present invention: The present invention not only focuses on minimizing the peak shaving cost of demand-side management, but also on the low-carbon operation of the system (minimizing carbon emissions) and user satisfaction. This means that while effectively balancing the peak shaving demand, the system reduces carbon emissions and improves user acceptance, ensuring the efficient operation and green low-carbon of the system. In addition, by using the constraint multi-objective evolutionary optimization method based on dual populations, the solution set can be effectively evaluated, balancing the three objectives of peak shaving cost, carbon emissions, and user satisfaction, and obtaining a compromise scheduling plan for the operator. The present invention considers demand-side resources such as air conditioners, electric water heaters, electric vehicles, etc., as well as renewable energy sources such as wind power and photovoltaic power for scheduling. The proposed method can effectively coordinate and allocate various resources. And it considers power balance constraints, user comfort constraints, and resource capacity constraints. The combination of various constraints makes the scheme more in line with the actual demand-side management application scenario and can more effectively solve the actual power grid demand. While minimizing the peak shaving cost to the greatest extent, it ensures the low-carbon operation of the system and user satisfaction, improving the reliability and carbon emission level of the power system. By using an efficient constraint multi-objective optimization method, an efficient scheduling strategy can be obtained for the dispatching operator. These advantages increase the efficiency and effectiveness of this technical solution when dealing with complex demand-side management scheduling problems and have obvious advantages over the prior art. Brief Description of the Drawings

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

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

[0021] Figure 2 It is the flowchart of the algorithm implementation plan of a multi-objective demand response optimization method considering low carbon and user satisfaction provided by an embodiment of the present invention. Detailed Embodiments

[0022] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will give a detailed description of the specific embodiments of the present invention in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0023] Embodiment 1, referring to Figure 1 andFigure 2 , which is the first embodiment of the present invention. This embodiment provides a multi-objective demand response optimization method considering low carbon and user satisfaction, including: The present invention relates to a multi-objective demand side management method considering low carbon optimization, aiming to solve the problem that the impact of low carbon peak shaving on the system environment and scheduling cost is insufficiently considered in the prior art. By comprehensively considering the influence of user psychology, peak shaving cost, and carbon emissions, this method realizes the balance between demand side resource regulation and green low carbon goals. In the demand side management model considering multi-objective carbon emissions, multiple constraints are considered, specifically as follows.

[0024] (1) Power balance constraint: Ensure that the total regulation capacity of demand side resources matches the grid peak shaving demand. That is, the regulation amount of demand response is the difference between the power of the system units and the actual load, specifically: , where, , and respectively represent the power output of the i-th unit, the power change of demand response resources, and the actual load demand. n is the total number of dispatchable generating units in the system, and m is the category of demand side resources participating in demand response in the system.

[0025] (2) User comfort constraint: Consider the user's satisfaction with demands such as temperature and charging time to avoid overly affecting the user experience.

[0026] ,

[0027] (3) Resource capacity constraint: Consider the regulation capacity limitations of various demand side resources (such as air conditioners, electric water heaters, electric vehicles, etc.).

[0028] , ,

[0029] where, and are respectively the maximum regulation amount of demand response resources and the maximum capacity of the unit.

[0030] (4) Objective function: Minimize the peak shaving cost to reduce the scheduling cost of power suppliers; minimize carbon emissions to reduce the overall carbon emission level of the system; maximize user satisfaction to improve the user's acceptance of demand side regulation. Since the demand side management optimization model of the present invention aims to optimize user satisfaction, supplier cost, and system carbon emissions, the objective function can be written as: , where, represents the power supply cost of the i-th unit, Represents the conversion of carbon emission costs for the i-th unit, Represents the scheduling cost of demand response resources. Is the carbon emission cost conversion coefficient. Is the weight coefficient of the user, Is the demand function after the demand response resources respond to the grid dispatch (such as the temperature in the air-conditioned room or the charging amount of the electric vehicle), Is the demand function before the response. T is the total number of time steps in the optimization dispatch period.

[0031] ,

[0032] Among them, Is the carbon emission factor coefficient of the i-th unit. Is the regulation power output of the i-th unit.

[0033] In addition, the c-DPEA algorithm is a constraint multi-objective evolutionary optimization method based on dual populations. Compared with the unconstrained multi-objective optimization problem, the constrained multi-objective optimization problem is usually more challenging. Under the constraint conditions, some search spaces may be directly excluded, resulting in the algorithm can only search and optimize within a limited feasible region. In extreme cases, there may even be no feasible solutions in the search space, so an effective optimization strategy cannot be obtained. c-DPEA simultaneously evolves two populations, namely population 1 and population 2, in a collaborative manner. The main difference between these two populations lies in the handling method of infeasible solutions, in which a new adaptive fitness function bCAD is introduced to balance convergence and diversity. Next, the algorithm realizes the interaction and cooperation of the two populations through the offspring reproduction step, and further optimizes the population performance by combining the environmental selection mechanism.

[0034] For population 1, in order to utilize the objective function and the constraint violation information of infeasible solutions, c-DPEA designs a parameter-free adaptive penalty function to handle the infeasible solutions in population 1. Among them, the objective space can be divided into N non-overlapping "sub-regions" by N weight vectors. Each weight vector Specifies a unique sub-region .

[0035] ,

[0036] Among them, , Is the vector And The angle between. The penalty function for any infeasible solution , its objective vector is modified to the following formula: , Among them, can be expressed as: , , where, and are the original and modified target vectors respectively; is composed of the maximum value of each target in the target vectors within the region; is the infeasible solution obtained during the optimization process of population 1; is the constraint violation value normalization; is the maximum constraint violation value in the overall population; β is the exponential value of the penalty function in the following fitness function, which depends on the current generation t, the maximum number of generations T, and the relationship coefficient describing populations 1 and 2 .

[0037] For population 2, as the final output solution of c-DPEA, a feasibility-oriented method is adopted to ensure the feasibility of the output solution. For any infeasible solution , its target vector is modified to the following formula , where, is composed of the maximum value of each target of all solutions in the current population; is the weight vector related to .

[0038] The specific process of the multi-objective demand response optimization considering carbon emissions can be expressed as follows: (1) Initialize individuals: The present invention first randomly generates an initial demand response scheduling population, including population 1 and population 2.

[0039] (2) Solution set evaluation: In each iteration, a two-objective calculation is performed on the population, respectively regarding the peak shaving cost, system carbon emissions, and user satisfaction.

[0040] (3) Pareto front update: After obtaining the target values of each solution, a two-objective front is screened, and then the Pareto solution set is updated.

[0041] (4) Solution set update: Screen the optimal solution set, perform tournament selection operations, and perform crossover and mutation operations to update the solutions during iteration, and continuously perform steps (2)-(4).

[0042] (5) Scheduling plan output: Screen the compromise solution from the decision solution set and output the final demand response optimization plan.

[0043] In this framework, a constraint multi-objective evolutionary optimization method based on a dual population is used to achieve a balance among system carbon emissions, peak shaving costs, and user satisfaction. During the implementation of the solution, the multi-objective genetic algorithm explores the fitness functions of different solutions, obtains the Pareto front, and finally screens out the optimal demand response scheduling solution. On the one hand, it maximally reduces the system's carbon emissions, and on the other hand, it optimizes the peak shaving costs and improves user satisfaction, thereby enhancing the system's ability to respond to peak shaving demands and ensuring the green, low-carbon, and efficient operation of the system.

[0044] Example 2 is the second example of the present invention, which is different from the previous example in that: If the described function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this 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, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0045] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or in combination with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device.

[0046] More specific examples (nonexhaustive list) of computer-readable media include the following: electrical connections (electronic devices) having one or more wirings, portable computer diskettes (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber devices, and portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing it as appropriate, and then storing it in a computer memory.

[0047] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), and the like.

[0048] Embodiment 3, the third embodiment of the present invention, provides 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 comprehensively considering carbon emissions, user satisfaction, and peak shaving cost; 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 solution according to the Pareto optimal solution set obtained by the optimization algorithm.

[0049] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within 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 in that: including Randomly generate an initial scheduling solution set including population 1 and population 2 for the multi-objective evolutionary optimization process, and establish a multi-objective scheduling model that simultaneously includes power balance constraints, user comfort constraints, and resource capacity constraints; Adopt the c-DPEA algorithm to handle the infeasible solutions in population 1 through an adaptive penalty function and optimize population 2 with a feasibility-first strategy. Combine the mechanisms of crossover, mutation, tournament selection, and environmental selection to evolve and update the population; In each generation of iteration, evaluate the multi-objective fitness values of the scheduling solutions, screen the Pareto front solution set, and continuously update until the termination condition is met. Select a compromise optimization scheduling plan from the final Pareto solution set to achieve an overall optimal balance among user satisfaction, carbon emissions, and peak shaving cost.

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

3. The multi-objective demand response optimization method considering low carbon and user satisfaction as claimed in claim 2, characterized in that: The user comfort constraint considers the sensitivity of individual users to temperature and charging time during the scheduling optimization process. By introducing a user behavior model and a preference function, the user experience is quantitatively described.

4. The multi-objective demand response optimization method considering low carbon and user satisfaction as claimed in claim 3, characterized in that: In the c-DPEA algorithm, population 1 adopts an adaptive penalty function mechanism to handle the objective vectors of infeasible solutions during the multi-objective evolutionary process. Through the normalized expression of the constraint violation value, the dynamic balance of convergence and population diversity is controlled.

5. The multi-objective demand response optimization method considering low carbon and user satisfaction as claimed in claim 4, wherein: Population 2 serves as the source of the final output solution set. The optimization process adopts a feasibility-oriented strategy to handle infeasible solutions, and maps infeasible individuals to the region near the maximum value of the objective function through the objective vector translation strategy.

6. The multi-objective demand response optimization method considering low carbon and user satisfaction as claimed in claim 4, characterized in that: The carbon emissions are estimated based on the carbon emission factors of each generator set for the carbon emissions generated by the output power, and are converted into an economic cost index through a carbon cost conversion coefficient, which is introduced into the multi-objective scheduling model for unified optimization. While meeting the power supply demand, the total carbon emissions during the operation of the power system are reduced.

7. The multi-objective demand response optimization method considering low carbon and user satisfaction according to claim 4, characterized in that: The overall optimal balance adopts a compromise optimization strategy. In the output Pareto front solution set, by comprehensively considering factors such as user satisfaction, carbon emission cost, and system peak shaving cost, the optimal feasible scheduling result is screened to balance environmental protection, economy, and user experience.

8. A multi-objective demand response optimization system considering low carbon and user satisfaction, which applies a multi-objective demand response optimization method as described in any one of claims 1 to 7, characterized in that 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 cost; 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 according to the Pareto optimal solution set obtained by the optimization algorithm.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of a multi-objective demand response optimization method according to any one of claims 1 to 7 that considers low carbon and user satisfaction.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of a multi-objective demand response optimization method according to any one of claims 1 to 7 that considers low carbon and user satisfaction.

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