Rural virtual power plant demand response optimization scheduling method and system considering bounded rationality
Through an optimized scheduling method that takes into account the limited rational characteristics of rural virtual power plants, the problem that traditional solutions cannot be directly applied to rural energy systems is solved, and efficient, scientific and reliable management of rural energy systems is achieved.
Patent Information
- Application Number
- CN202411733334.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-29
- Publication Date
- 2025-04-18
AI Technical Summary
Traditional virtual power plant solutions cannot be directly applied to rural energy systems, and ignore the limited rational characteristics of the participants, resulting in decision-making that is not in line with reality and it is difficult to achieve the expected regulatory goals.
It provides a rural virtual power plant demand response optimization scheduling method that calculates limited rationality. By obtaining flexible resources, determining scheduling priorities, building user response optimization objective functions, and quantitatively characterizing finite rational behaviors based on prospect theory and mass coefficients, optimized solutions to achieve rural virtual power plant demand response optimization scheduling.
A scheduling priority evaluation system specially suitable for rural scenarios has been built, which reduces the deviation from actual transaction behavior, improves the reliability of optimized scheduling strategies, and realizes efficient management and green and low-carbon operation of rural energy systems.
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Figure CN119849790A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of electric power systems, and in particular relates to a rural virtual power plant demand response optimization scheduling method and system taking into account bounded rationality. Background Art
[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.
[0003] At present, most of the research related to virtual power plants is aimed at urban industrial users, large commercial buildings, integrated parks and other resources, and there are relatively few application studies on rural energy systems. Rural energy systems are rich in renewable resources and have high controllable load flexibility, but they also have disadvantages such as low utilization rate of new energy and extensive management. Through virtual power plant technology, various distributed energy, energy storage, flexible load and other resources can be reasonably allocated to promote the consumption of new energy in rural areas, reduce dependence on traditional power grid power supply modes, improve the interactive ability of regional power grid supply and demand, and promote the transformation of rural energy systems to refined and intelligent management, so as to achieve green and efficient utilization of rural energy systems and low-carbon operation of rural production and life.
[0004] However, the inventors found that the traditional virtual power plant solution cannot be directly applied to the rural energy system. The reason is that compared with the industrial and commercial users, integrated parks and other resources with large electricity consumption and concentrated energy consumption in conventional virtual power plants, the various resources in the rural energy system have the characteristics of small energy consumption, obvious seasonal energy consumption, and energy consumption is easily affected by electricity price fluctuations. The two have obvious differences in energy supply and consumption; traditional scheduling priority evaluation indicators often do not fully consider the uniqueness of rural energy supply systems.
[0005] Secondly, in the process of participating in electricity trading, various types of resource entities in rural areas generally have few channels for obtaining market information, low sensitivity to information, and relatively poor judgment ability. Most of them show obvious limited rationality characteristics such as herd mentality and loss aversion. Limited rationality characteristics greatly affect users' response willingness and response behavior, and traditional virtual power plant solutions often ignore the limited rationality characteristics of participating entities, resulting in unrealistic decision-making and difficulty in achieving the expected regulation goals. Summary of the invention
[0006] The present invention provides a rural virtual power plant demand response optimization scheduling method and system taking into account limited rationality, so as to solve the problem that traditional virtual power plant demand response optimization scheduling schemes cannot be directly applied to rural energy systems, and traditional schemes often ignore the limited rational characteristics of participating entities, resulting in unrealistic decisions and difficulty in achieving the expected regulation goals.
[0007] According to a first aspect of an embodiment of the present invention, a rural virtual power plant demand response optimization scheduling method taking into account bounded rationality is provided, comprising: Access to flexibility resources within pre-defined rural areas; Determine the scheduling priority of different flexibility resources based on the indicator characteristics corresponding to the flexibility resources; wherein the indicator characteristics include but are not limited to the value of electricity output per kilowatt-hour, seasonal sensitivity, and the average age of the main members of the user; The user response optimization objective function is constructed to minimize the gap between the response benefit of the flexible resources participating in the optimal scheduling and the expected benefit; wherein the user response optimization objective function quantitatively characterizes the limited rational behavior of the flexible resources by introducing prospect theory and conformity coefficient; By optimizing and solving the user response optimization objective function, feasible solutions for different flexibility resources to participate in demand response are determined; Based on the feasible solutions for different flexibility resources to participate in demand response, the flexibility resources that participate in the optimal dispatch of demand response are determined by maximizing the operating income of the virtual power plant operator; Based on the dispatch priorities of different flexibility resources, the order and scope of the flexibility resources participating in the optimized dispatch are determined to achieve demand response optimized dispatch of rural virtual power plants.
[0008] Furthermore, the user response optimization objective function quantitatively characterizes the limited rational behavior of flexibility resources by introducing prospect theory and the herd coefficient. Specifically, the response power of flexibility resources is corrected by introducing the herd coefficient; the prospect theory utility function is characterized by adopting the value function and the weight function; and the user response optimization objective function is constructed based on the corrected result of the response power and the prospect theory utility function.
[0009] Furthermore, the user response optimization objective function is specifically expressed as follows:
[0010]
[0011]
[0012]
[0013] =
[0014]
[0015]
[0016] in, , Respectively represent resource subjects actual and expected returns; , are the risk preference coefficient and the risk aversion coefficient respectively; is the loss aversion coefficient, which indicates the degree of loss aversion of the resource subject; Positively correlated with electricity demand, i.e. ,in is the expected profit per unit of electricity; , are the risk attitude coefficients corresponding to gains and losses respectively; is the actual probability of the event occurring, is the distribution probability of daily random electricity demand; , Respectively represent resource subjects Demand response costs and demand response subsidy benefits.
[0017] Furthermore, the scheduling priorities of different flexibility resources are determined based on the indicator characteristics corresponding to the flexibility resources, specifically: based on the indicator characteristics corresponding to the flexibility resources, normalization processing is performed, and AHP-entropy weight method is used for weighting processing; based on the normalization processing results of each indicator characteristic and the weights corresponding to different indicators obtained, the TOPSIS method is used to calculate the comprehensive scores of different flexibility resources; based on the obtained comprehensive scores, sorting is performed to determine the scheduling priorities of different flexibility resources.
[0018] Furthermore, the electricity output value is specifically expressed as:
[0019] Among them, VOE represents the value of electricity output of flexible resources; represents the total value of production of flexible resources; represents the total electricity consumption of the flexibility resources; The seasonal sensitivity is specifically expressed as:
[0020] Among them, SE represents the seasonal sensitivity parameter of the flexibility resource; Indicates the maximum power load of the flexible resources in each month; Represents the annual maximum electricity load of the flexibility resource.
[0021] Furthermore, the indicator characteristics also include response capacity, duration and effective response rate.
[0022] According to a second aspect of an embodiment of the present invention, a rural virtual power plant demand response optimization scheduling system taking into account bounded rationality is provided, comprising: A data acquisition unit, which is used to acquire flexibility resources in a preset rural area; A scheduling priority determination unit, which is used to determine the scheduling priorities of different flexibility resources based on the indicator characteristics corresponding to the flexibility resources; wherein the indicator characteristics include but are not limited to the kilowatt-hour output value, seasonal sensitivity, and the average age of the main members of the user; A response optimization objective function construction unit, which is used to construct a user response optimization objective function by minimizing the gap between the response benefit of the flexible resources participating in the optimized scheduling and the expected benefit; wherein the user response optimization objective function quantitatively characterizes the limited rational behavior of the flexible resources by introducing prospect theory and conformity coefficient; The optimization scheduling unit is used to determine feasible solutions for different flexibility resources to participate in demand response by optimizing and solving the user response optimization objective function; based on the feasible solutions for different flexibility resources to participate in demand response, the flexibility resources participating in the demand response optimization scheduling are determined by maximizing the operating income of the virtual power plant operator; based on the scheduling priorities of different flexibility resources, the order and scope of the flexibility resources participating in the optimization scheduling are determined to realize the demand response optimization scheduling of rural virtual power plants.
[0023] According to a third aspect of an embodiment of the present invention, there is provided an electronic device comprising a memory, a processor and a computer program stored and running on the memory, wherein when the processor executes the program, the method for optimizing the dispatching of demand response of a rural virtual power plant taking into account bounded rationality is implemented.
[0024] According to a fourth aspect of an embodiment of the present invention, a non-transitory computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method for optimizing the dispatching of rural virtual power plant demand response taking into account bounded rationality is implemented.
[0025] According to a fifth aspect of an embodiment of the present invention, there is provided a computer program product, comprising a computer program, which, when executed by a processor, implements the above-mentioned method for optimizing the dispatching of rural virtual power plant demand response taking into account bounded rationality.
[0026] One or more of the above technical solutions have the following beneficial effects: The present invention provides a method and system for optimizing the demand response of rural virtual power plants taking into account bounded rationality. The scheme aims to address the characteristics of a large number of resource entities and poor behavioral differences in agricultural scenarios. In addition to conventional evaluation indicators such as response capacity, duration, and effective response rate, the scheme introduces indicators such as kilowatt-hour output value and seasonal sensitivity, thereby constructing an evaluation system for scheduling priorities specifically suitable for rural scenarios, providing more scientific and efficient decision-making support for rural energy management.
[0027] In view of the non-negligible limited rational behavior of users in agricultural scenarios, especially the problems of loss aversion and herd effect, the solution described in the present invention combines prospect theory with the herd effect correction method to construct a user response benefit model, which reduces the deviation from actual trading behavior and thus improves the reliability of the optimization scheduling strategy.
[0028] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS
[0029] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.
[0030] Figure 1 A schematic diagram of a scheduling priority evaluation index library described in an embodiment of the present invention; Figure 2 This is a flow chart of a rural virtual power plant demand response optimization scheduling method taking into account bounded rationality described in an embodiment of the present invention. DETAILED DESCRIPTION
[0031] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.
[0032] It should be noted that the terms used herein are for describing specific embodiments only and are not intended to be limiting of exemplary embodiments according to the present invention.
[0033] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.
[0034] Terminology explanation: Flexibility resources: refers to various resources that can provide or absorb power by changing electricity consumption or production patterns to cope with changes in supply and demand in the power system, such as energy storage, adjustable industrial users, temperature-controlled loads, etc.
[0035] In one or more embodiments, Figure 2 As shown, this embodiment provides a rural virtual power plant demand response optimization scheduling method taking into account bounded rationality, which specifically includes the following processing procedures: Step 1: obtaining flexibility resources in a preset rural area; Step 2: Determine the scheduling priority of different flexibility resources based on the indicator characteristics corresponding to the flexibility resources; wherein the indicator characteristics include but are not limited to the kilowatt-hour output value, seasonal sensitivity, and the average age of the main members of the user; In a specific implementation, the indicator characteristics also include response capacity, duration and effective response rate.
[0036] In the specific implementation, the indicator characteristics corresponding to the flexibility resources are used to determine the scheduling priorities of different flexibility resources. Specifically, based on the indicator characteristics corresponding to the flexibility resources, normalization processing is performed and weighting processing is performed using the AHP-entropy weight method; based on the normalization processing results of each indicator characteristic and the weights corresponding to different indicators obtained, the TOPSIS method is used to calculate the comprehensive scores of different flexibility resources; based on the obtained comprehensive scores, sorting is performed to determine the scheduling priorities of different flexibility resources.
[0037] Specifically, considering the characteristics of rural energy consumption, a dispatch priority evaluation index library was established from two aspects: characteristic index and response characteristic index, such as Figure 1 As shown, the specific indicator characteristics include the following: 1) Characteristic evaluation indicators ① VOE: refers to the revenue value that can be generated by consuming each kilowatt-hour of electricity. The VOE in rural areas is relatively low, so it is easier to participate in demand response through incentives. The lower the VOE, the greater the benefits of participating in demand response, and the more suitable it is to participate in demand response. Among them, the gross domestic product of the resource subject can be obtained through questionnaires or information registration. The data does not require complete accuracy, but is only used to distinguish the response potential of different types of resource subjects. The specific formula is as follows:
[0038] Among them, VOE represents the value of electricity output of the resource subject; It represents the total production value of the resource subject; Represents the total electricity usage of the resource subject.
[0039] ②Seasonal sensitivity SE: refers to the ratio of the average value of the maximum power load in each month of the year to the maximum power load of the year, which can be obtained through the user's power load data. Rural areas have obvious seasonal characteristics of energy consumption. This indicator mainly reflects the imbalance of the maximum monthly load changes of farmers in a year. The smaller the value, the more obvious the seasonal change, which means the greater the potential for seasonal adjustment. The specific formula is as follows:
[0040] Among them, SE represents the seasonal sensitivity parameter of the resource entity; Indicates the maximum power load of the resource entity in each month; Indicates the annual maximum power load of the resource entity.
[0041] ③ Average age AA: refers to the average age of the main family members (between 15 and 55 years old). In rural areas, the appropriate average age of the main family members means the higher the acceptance of emerging things and the ability to use intelligent products, the greater the probability of participating in demand response. This evaluation indicator can be scored through questionnaires or information registration to obtain quantitative evaluation. The scoring basis is as follows:
[0042] 2) Other conventional response characteristic evaluation indicators Other conventional response characteristic evaluation indicators mainly include response capacity, duration and effective response rate.
[0043] ①Response capacity
[0044] Refers to the load capacity that the resource subject can reduce or transfer. For the first time participating in demand response, this value can temporarily adopt the response capacity value agreed in the compensation contract. The specific calculation formula is as follows:
[0045] in, Indicates the resource subject response capacity; Indicates that the resource subject is in Real-time load at all times; Indicates that the resource subject is in The minimum load allowed at all times; , are the start and end times of the grid demand response instruction respectively.
[0046] ② Duration
[0047] Refers to the time from the start of the response behavior to the end of the behavior. For the first time participating in demand response, this value can temporarily use the response capacity value agreed in the compensation contract. The calculation formula is as follows:
[0048] in, represents the average duration of demand response; represents the total number of responses on a typical day; , represents a typical day The start and end times of the response.
[0049] ③Effective response rate Refers to the ratio of the number of effective responses of the resource subject to the total number of application responses. Effective response means that during the response process, the action volume of the resource reaches the preset baseline load threshold, meets the effective response judgment conditions required by the regulations, and reaches the transaction settlement standard. When implementing peak shaving response, the resource must meet the following two conditions at the same time during the response period: first, the maximum load during the response period is lower than the maximum load of the baseline, and second, the average load during the response period is lower than the average load of the baseline, and the difference is not less than the required range of the declared response volume. The effective determination method for a single response is as follows:
[0050]
[0051]
[0052] in, Indicates the maximum load during the response period; represents the baseline maximum load during the response period; represents the average load during the response period; represents the baseline average load during the response period; is the load response rate, where is the actual response load, Indicates the amount of declared response; for The constant between represents the minimum load response rate required for determining effective demand response, and the requirements vary from province to province.
[0053] The effective response rate is calculated as follows:
[0054] in, represents the effective response rate; Indicates the number of effective peak load regulation times on a typical day; Represents the total number of responses on a typical day.
[0055] 3) Use AHP-entropy weight method for weighting The indicator data of each flexible resource subject are normalized and weighted using the AHP-entropy weight method. 4) TOPSIS algorithm is used for priority sorting The technique of ranking by close to ideal solutions (TOPSIS) is used to calculate the comprehensive score of each flexibility resource, and the dispatch priority for participating in demand response is obtained through sorting.
[0056] Step 3: Construct a user response optimization objective function to minimize the gap between the response benefit of the flexible resources participating in the optimal scheduling and the expected benefit; wherein the user response optimization objective function quantitatively characterizes the limited rational behavior of the flexible resources by introducing prospect theory and conformity coefficient; In the specific implementation, the user response optimization objective function quantitatively characterizes the limited rational behavior of flexibility resources by introducing prospect theory and the herd coefficient, specifically: the response power of flexibility resources is corrected by introducing the herd coefficient; the prospect theory utility function is characterized by adopting the value function and the weight function; the user response optimization objective function is constructed based on the correction result of the response power and the prospect theory utility function.
[0057] Specifically, in the face of uncertainties in the electricity trading process, agricultural resource entities are often not completely rational in their trading behaviors due to factors such as incomplete market information acquisition or limited individual subjective perspectives, and have limited rational characteristics such as loss aversion and herd mentality. In order to better describe the actual response behavior of resource entities, prospect theory and herd coefficient are introduced to characterize their limited rational behavior, and then a more general demand response model is established to carry out cost-benefit analysis of resource entities.
[0058] ① Response power correction considering the herd effect The similarity matrix is established through the historical electricity consumption data of resource entities to describe the electricity consumption similarity between different entities. , the specific expression is as follows:
[0059]
[0060]
[0061] in, Resource Subject Resource Subject The Euclidean distance between power loads is used to measure the similarity of power consumption between them; the matrix elements is the standardized Euclidean distance. The closer its value is to 0, the more likely the resource is Resource Subject The lower the similarity of the curves, the greater the difference in their electricity usage habits. Resource Subject exist The electricity load at the moment.
[0062] The more similar the electricity usage habits are, the greater the bandwagon effect will be between resource entities. The greater similarity The response power correction of each subject under the herd effect is:
[0063] in, Indicates the revised resource body exist The amount of participating response power in the time period; Represents the quantitative coefficient of herd mentality, which is used to measure the resource subject The degree of herd mentality, The larger the value, the more susceptible you are to the decisions of others; Representation and resource body The most similar electricity usage behavior A collection of resource entities.
[0064] ②Prospect theory utility function considering loss aversion The utility function of prospect theory is usually characterized by value function and weight function. The value function represents the difference between actual return and expected return, that is, the gain or loss relative to the reference point; the weight function represents the impact of the probability of different results on the decision.
[0065] The specific expression of the value function is as follows:
[0066] in, , Respectively represent resource subjects actual and expected returns; , are the risk preference coefficient and the risk aversion coefficient respectively; is the loss aversion coefficient, which indicates the degree of loss aversion of the resource subject; Positively correlated with electricity demand, i.e. ,in is the expected profit per unit of electricity.
[0067] The specific expression of the weight function is as follows:
[0068] in, , are the risk attitude coefficients corresponding to gains and losses respectively; is the actual probability of the event occurring, is the distribution probability of daily random electricity demand.
[0069] ③Build a user response optimization model This patent combines prospect theory and the herd effect to construct a response benefit model for resource entities based on limited rationality. From the theoretical knowledge of prospect effect, it can be known that the user's response decision depends on the gap between the result and the expectation, rather than the result itself. The specific expression of its optimization goal is as follows:
[0070]
[0071]
[0072]
[0073] =
[0074] in, , Respectively represent resource subjects Demand response costs and demand response subsidy benefits.
[0075] Step 4: Determine feasible solutions for different flexibility resources to participate in demand response by optimizing and solving the user response optimization objective function; based on the feasible solutions for different flexibility resources to participate in demand response, determine the flexibility resources to participate in demand response optimization scheduling by maximizing the operating income of the virtual power plant operator; based on the scheduling priorities of different flexibility resources, determine the order and scope of flexibility resources to participate in optimized scheduling, and realize the optimized scheduling of rural virtual power plants in demand response.
[0076] In specific implementation, the goal pursued by the virtual power plant operator is to maximize operating revenue, and its daily revenue function can be expressed as follows:
[0077]
[0078]
[0079] in, represents the total daily revenue of the virtual power plant operator when participating in the demand response process; It represents the compensation fee for electricity demand response provided by the power grid company; It represents the compensation paid by the virtual power plant to the resource entity for participating in demand response; represents the system maintenance cost; M represents the number of resource entities participating in demand response; Indicates The flexibility resource subject The amount of electricity participating in power demand response during the period; Indicated in The demand response subsidy price paid by the grid to the virtual power plant operator during the time period; Indicates that virtual power plant operators The demand response subsidy price paid to flexibility resource entities during the period.
[0080] In one or more implementations, corresponding to the above method, this embodiment provides a rural virtual power plant demand response optimization scheduling system taking into account bounded rationality, including: A data acquisition unit, which is used to acquire flexibility resources in a preset rural area; A scheduling priority determination unit, which is used to determine the scheduling priorities of different flexibility resources based on the indicator characteristics corresponding to the flexibility resources; wherein the indicator characteristics include but are not limited to the kilowatt-hour output value, seasonal sensitivity, and the average age of the main members of the user; A response optimization objective function construction unit, which is used to construct a user response optimization objective function by minimizing the gap between the response benefit of the flexible resources participating in the optimized scheduling and the expected benefit; wherein the user response optimization objective function quantitatively characterizes the limited rational behavior of the flexible resources by introducing prospect theory and conformity coefficient; The optimization scheduling unit is used to determine feasible solutions for different flexibility resources to participate in demand response by optimizing and solving the user response optimization objective function; based on the feasible solutions for different flexibility resources to participate in demand response, the flexibility resources participating in the demand response optimization scheduling are determined by maximizing the operating income of the virtual power plant operator; based on the scheduling priorities of different flexibility resources, the order and scope of the flexibility resources participating in the optimization scheduling are determined to realize the demand response optimization scheduling of rural virtual power plants.
[0081] It can be understood that the system described in this embodiment corresponds to the method described in the above embodiment, and its technical details have been described in detail in Embodiment 1 and will not be repeated here.
[0082] In further embodiments, there is also provided: An electronic device includes a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in Embodiment 1 is performed. For the sake of brevity, no further description is given here.
[0083] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.
[0084] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.
[0085] A computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the method described in embodiment 1 is completed.
[0086] The method in the first embodiment can be directly embodied as a hardware processor, or a combination of hardware and software modules in the processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.
[0087] A computer program product includes a computer program. When the computer program is executed by a processor, the method described in the above-mentioned embodiment 1 is implemented.
[0088] Those skilled in the art will appreciate that the units, i.e., algorithm steps, of the various examples described in the present embodiment can be implemented in electronic hardware or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.
[0089] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.
Claims
1. A rural virtual power plant demand response optimization scheduling method considering bounded rationality, characterized in that: include: Access to flexibility resources within pre-defined rural areas; Determine the scheduling priority of different flexibility resources based on the indicator characteristics corresponding to the flexibility resources; wherein the indicator characteristics include but are not limited to the value of electricity output per kilowatt-hour, seasonal sensitivity, and the average age of the main members of the user; The user response optimization objective function is constructed to minimize the gap between the response benefit of the flexible resources participating in the optimal scheduling and the expected benefit; wherein the user response optimization objective function quantitatively characterizes the limited rational behavior of the flexible resources by introducing prospect theory and conformity coefficient; By optimizing and solving the user response optimization objective function, feasible solutions for different flexibility resources to participate in demand response are determined; Based on the feasible solutions for different flexibility resources to participate in demand response, the flexibility resources that participate in the optimal dispatch of demand response are determined by maximizing the operating income of the virtual power plant operator; Based on the dispatch priorities of different flexibility resources, the order and scope of the flexibility resources participating in the optimized dispatch are determined to achieve demand response optimized dispatch of rural virtual power plants.
2. A rural virtual power plant demand response optimization scheduling method taking into account bounded rationality as claimed in claim 1, characterized in that: The user response optimization objective function quantitatively characterizes the limited rational behavior of the flexibility resources by introducing the prospect theory and the herd coefficient, specifically: the response power of the flexibility resources is corrected by introducing the herd coefficient; the prospect theory utility function is characterized by adopting the value function and the weight function; The user response optimization objective function is constructed based on the correction results of the response power and the prospect theory utility function.
3. A rural virtual power plant demand response optimization scheduling method taking into account bounded rationality as claimed in claim 1, characterized in that: The user response optimization objective function is specifically expressed as follows: = in, , Respectively represent resource subjects actual and expected returns; , are the risk preference coefficient and the risk aversion coefficient respectively; is the loss aversion coefficient, which indicates the degree of loss aversion of the resource subject; Positively correlated with electricity demand, i.e. ,in is the expected profit per unit of electricity; , are the risk attitude coefficients corresponding to gains and losses respectively; is the actual probability of the event occurring, is the distribution probability of daily random electricity demand; , Respectively represent resource subjects Demand response costs and demand response subsidy benefits.
4. A rural virtual power plant demand response optimization scheduling method taking into account bounded rationality as claimed in claim 1, characterized in that: The scheduling priorities of different flexibility resources are determined based on the indicator characteristics corresponding to the flexibility resources, specifically: based on the indicator characteristics corresponding to the flexibility resources, normalization processing is performed and weighting processing is performed using the AHP-entropy weight method; based on the normalization processing results of each indicator characteristic and the weights corresponding to the obtained different indicators, the comprehensive scores of different flexibility resources are calculated using the TOPSIS method; The scheduling priorities of different flexibility resources are determined based on the obtained comprehensive scores.
5. The method for optimizing the dispatching of rural virtual power plant demand response taking into account bounded rationality according to claim 1, characterized in that: The electricity output value is specifically expressed as: Among them, VOE represents the value of electricity output of flexible resources; represents the total value of production of flexible resources; represents the total electricity consumption of the flexibility resources; The seasonal sensitivity is specifically expressed as: Among them, SE represents the seasonal sensitivity parameter of the flexibility resource; Indicates the maximum power load of the flexible resources in each month; Represents the annual maximum electricity load of the flexibility resource.
6. A rural virtual power plant demand response optimization scheduling method taking into account bounded rationality as claimed in claim 1, characterized in that: The indicator characteristics also include response capacity, duration and effective response rate.
7. A rural virtual power plant demand response optimization scheduling system taking into account bounded rationality, characterized in that: include: A data acquisition unit, which is used to acquire flexibility resources in a preset rural area; A scheduling priority determination unit, which is used to determine the scheduling priorities of different flexibility resources based on the indicator characteristics corresponding to the flexibility resources; wherein the indicator characteristics include but are not limited to the kilowatt-hour output value, seasonal sensitivity, and the average age of the main members of the user; A response optimization objective function construction unit, which is used to construct a user response optimization objective function by minimizing the gap between the response benefit of the flexible resources participating in the optimized scheduling and the expected benefit; wherein the user response optimization objective function quantitatively characterizes the limited rational behavior of the flexible resources by introducing prospect theory and conformity coefficient; The optimization scheduling unit is used to determine feasible solutions for different flexibility resources to participate in demand response by optimizing and solving the user response optimization objective function; based on the feasible solutions for different flexibility resources to participate in demand response, the flexibility resources participating in the demand response optimization scheduling are determined by maximizing the operating income of the virtual power plant operator; based on the scheduling priorities of different flexibility resources, the order and scope of the flexibility resources participating in the optimization scheduling are determined to realize the demand response optimization scheduling of rural virtual power plants.
8. An electronic device comprising a memory, a processor and a computer program stored and running on the memory, characterized in that: When the processor executes the program, it implements a rural virtual power plant demand response optimization scheduling method taking into account bounded rationality as described in any one of claims 1-6.
9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, it implements a rural virtual power plant demand response optimization scheduling method taking into account bounded rationality as described in any one of claims 1-6.
10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, it implements a rural virtual power plant demand response optimization scheduling method taking into account bounded rationality as described in any one of claims 1-6.