Power distribution network reconstruction method and system considering intelligent residence demand response, and medium

Abstract

The invention provides a power distribution network reconstruction method and system considering intelligent residence demand response and a medium, and belongs to the technical field of power distribution network safe operation. The method comprises the following steps of: firstly, analyzing a demand response behavior of a user based on an evolutionary game model on a social network by considering that the intelligent residential user has finite rationality, information interaction, strategy learning and updating characteristics when responding to an electricity price incentive strategy; secondly, with power distribution network load fluctuation minimization as a target, time-of-use electricity price is optimized in combination with finite rational response behaviors of intelligent residence users, and residence user loads are reasonably distributed on the time scale; furthermore, the influence of the load space difference of the residential users on the power flow and voltage distribution is considered, and the power distribution network is reconstructed through a flexible switch to transfer the power flow and improve the voltage quality. The method can give full play to the flexibility of the intelligent residence load, and improves the voltage quality of the power distribution network while reducing the load peak-valley difference and network loss of the power distribution network.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of safe operation of power distribution network, and particularly relates to a power distribution network reconstruction method and system considering demand response of smart home and a medium. BACKGROUND

[0002] As an important part of smart grid construction, smart power utilization is receiving more and more attention. Among them, the rapid development of residential smart power utilization has significantly increased the flexible load that can be adjusted by users, which has intensified the uncertainty and complexity of the operation of the power distribution network. At the same time, a large number of residential power utilization loads have certain concentration and volatility, which has significantly increased the peak-valley difference of the power distribution network load, seriously affecting the safety and economy of the operation of the power distribution network. Considering that there are transferable and interruptible loads in the smart home power utilization load, the transfer of power load can be realized through flexible load scheduling. Under the background of the current power system vigorously promoting demand side management and optimizing power resource allocation, the use of demand response resources of smart home power utilization load and the reasonable allocation of power utilization load can help to alleviate the contradiction between power supply and demand. In addition, with the increase of the scale of power system load, the existing power distribution network structure is difficult to adapt to the uncertain load that changes over time and space, which will lead to increased network loss and decreased power quality. By changing the flexible switch in the power distribution network to optimize the power flow distribution, not only the network loss can be reduced, but also the power quality can be improved. Therefore, it is of great significance to carry out power distribution network load management by using the flexibility of smart home users and the improvement of power flow through power distribution network reconstruction.

[0003] In the research on the reconstruction of power distribution networks containing smart home loads at home and abroad, the coordination and improvement effect of demand response management and power distribution network reconstruction is not considered. In the optimization and scheduling of smart home user load, the influence of user bounded rationality and social attributes on user response characteristics is not effectively considered. When flexible load participates in the regulation of power grid operation, there is a lack of coordination and cooperation between load and power grid, which may lead to low power supply quality. SUMMARY

[0004] The technical problem to be solved by the embodiments of the present application is to provide a power distribution network reconstruction method considering demand response of smart home, an analysis system and a storage medium. Compared with the existing power grid load scheduling method considering user load flexibility, the present application fully takes into account the influence of user bounded rationality and social attributes on user response characteristics, and is more reasonable. Compared with the research on improving the safety and economy of power distribution network operation from the single perspective of demand response or power distribution network reconstruction, the present application fully combines the optimization advantages of demand response strategy and power grid reconstruction on the time and space distribution of smart home user load, and can more effectively improve the economy and safety of power distribution network operation.

[0005] In a first aspect, the application provides a power distribution network reconstruction method considering demand response of smart homes, comprising: obtaining social relationships among smart home users, establishing a user social network according to the social relationships among the smart home users, and establishing an evolutionary game model for analyzing demand response characteristics of the smart home users on the user social network to obtain response levels of the users under different electricity prices or incentive levels; based on the demand response characteristics and the response levels of the users, constructing a time-of-use (TOU) price optimization model of the power distribution network with the TOU price of the power distribution network as an optimization object to obtain an optimized price strategy, and based on the optimized price strategy, constructing a smart home user load distribution strategy optimization model with time distribution of the smart home user load as an optimization object to obtain an optimized load distribution strategy; based on spatial distribution characteristics of the optimized load distribution strategy, establishing a power distribution network reconstruction model with voltage distribution of the power distribution network as an optimization object, and solving the model by using a second-order cone programming method to obtain a globally optimal solution of the power distribution network reconstruction.

[0006] Preferably, the obtaining of the social relationships among the smart home users, the establishment of the user social network according to the social relationships among the smart home users, and the establishment of the evolutionary game model for analyzing the demand response characteristics of the smart home users on the user social network to obtain the response levels of the users under different electricity prices or incentive levels comprise: obtaining social relationships among smart home users, constructing a smart home user social network relationship based on graph theory, taking the smart home users as points and the existing social relationships among the users as edges, and statistically analyzing average path length and clustering coefficient of the social network, and generating a smart home user social network based on a scale-free community network model; based on the smart home user social network, constructing a game model for response of the smart home users to the electricity price or incentive, taking all the smart home users as decision makers, taking the proportion of actual electricity consumption of the users to maximum electricity consumption as a decision space, and taking a revenue function of the users under different decision spaces as an utility function; by setting an initial response value for each user and calculating the utility function thereof, comparing the utility function of each user with neighbors on the social network thereof, taking a decision of a neighbor with maximum utility as a game strategy of the user in the next round, updating the decision of each user, and through repeated iteration until the decisions of all the users are no longer updated, obtaining the demand response characteristics of the smart home users under stable utility functions.

[0007] Preferably, the user-based demand response characteristics and response level are used to construct a distribution network time-of-use price optimization model with the distribution network time-of-use price as the optimization object to obtain an optimized price strategy, and based on the optimized price strategy, a smart residential user load distribution strategy optimization model is constructed with the time distribution of the smart residential user load as the optimization object to obtain an optimized load distribution strategy, including: Based on the user-based demand response characteristics and response level, a distribution network time-of-use price optimization model is constructed with the minimum distribution network load variance as the target under the conditions of meeting the upper and lower limits of the smart residential user load, the energy demand constraints of the electrical equipment, and the upper and lower limits of the price, and the relationship between the user response level and the price is fitted by using a linear function as the user demand response constraint to obtain an optimized time-of-use price strategy; With the minimum total electricity cost of the smart residential user under the optimized time-of-use price strategy as the target, a smart residential user load distribution strategy optimization model is constructed under the conditions of meeting the residential user electrical equipment start-stop state constraint, the power upper and lower limit constraint, the non-interruptible load operation constraint, the translatable load operation constraint, the translatable and interruptible load operation constraint, and the time sequence constraint relationship between the electrical tasks to obtain an optimized load time distribution strategy.

[0008] Preferably, the spatial distribution characteristics of the optimized load distribution strategy are used to establish a distribution network reconstruction model with the distribution network voltage distribution as the optimization object, and a second-order cone programming method is used for solving to obtain an optimal switch operation strategy to reconstruct the network topology, including: Based on the spatial distribution characteristics of the optimized load distribution strategy, a distribution network reconstruction strategy model of flexible switch state optimization in each time period is constructed with the minimum distribution network loss as the target, considering the distribution network active and reactive power balance constraint, the node voltage upper and lower limit constraint, the distribution network radial topology constraint, the line power transmission capacity constraint, and the switch action number limit constraint; The active and reactive power balance constraint of the distribution network is relaxed by using a second-order cone equation, a virtual voltage related to the line communication variable is introduced, the objective function and the constraint condition of the distribution network reconstruction model are linearly converted, a mixed integer second-order cone programming model of the distribution network reconstruction is constructed, a global optimal solution of the distribution network reconstruction is solved, and an optimal switch operation strategy is obtained to reconstruct the network topology.

[0009] In a second aspect, the application further provides a distribution network reconstruction system considering smart residential demand response, which applies the aforementioned method, including: A demand response characteristic analysis module is used to obtain the social relationship among smart residential users, establish a user social network according to the social relationship among the smart residential users, and establish an evolutionary game model for analyzing the demand response characteristics of the smart residential users on the user social network to obtain the response level of the users under different prices or incentive levels. The load optimization scheduling model construction module is configured to construct a time-of-use price optimization model of the distribution network based on the demand response characteristics and response level of the users and taking the time-of-use price of the distribution network as an optimization object, to obtain an optimized price strategy, and to construct a smart residential user load distribution strategy optimization model based on the optimized price strategy and taking the time distribution of the smart residential user load as an optimization object, to obtain an optimized load distribution strategy. The distribution network reconstruction model construction module is configured to establish a distribution network reconstruction model based on the spatial distribution characteristics of the optimized load distribution strategy and taking the voltage distribution of the distribution network as an optimization object, and to solve the model by using a second-order cone programming method to obtain a globally optimal solution of the distribution network reconstruction.

[0010] Preferably, the demand response characteristic analysis module is specifically configured to: Obtain the social relationship among the smart residential users, construct a smart residential user social network relationship based on graph theory and taking the smart residential users as points and the social relationship among the users as edges, and statistically obtain the average path length and clustering coefficient of the social network, and generate a smart residential user social network based on a scale-free community network model. Based on the smart residential user social network, construct a game model of the response of the smart residential users to the price or incentive by taking all the smart residential users as decision makers, taking the proportion of the actual electricity consumption of a user to the maximum electricity consumption as the decision space, and taking the revenue function of the user under different decision spaces as the utility function. By setting the response initial value for each user and calculating the utility function thereof, and comparing the utility function of each user with the neighbors on the social network thereof, the decision of the neighbor with the maximum utility is taken as the game strategy of the user in the next round, the decision of each user is updated, and the demand response characteristics of the smart residential users under the stable utility function are obtained through repeated iteration until the decisions of all the users are no longer updated.

[0011] Preferably, the load optimization scheduling model construction module is specifically configured to: Based on the demand response characteristics and response level of the users, the distribution network time-of-use price optimization model is constructed by taking the minimization of the load variance of the distribution network as the target, under the conditions of satisfying the upper and lower limit constraints of the smart residential user load, the energy demand constraints of the electricity equipment, and the upper and lower limit constraints of the price, and by using a linear function to fit the relationship between the user response level and the price as the user demand response constraint, to obtain the optimized time-of-use price strategy. The optimized time-of-use electricity price strategy is used to minimize the total electricity cost of the smart residential user, and under the conditions of meeting the start-stop state constraints of the residential user's electricity equipment, the upper and lower power constraints, the uninterrupted load operation constraints, the translatable load operation constraints, the translatable and interruptible load operation constraints and the time sequence constraints between electricity tasks, a smart residential user load distribution strategy optimization model is constructed to obtain the optimized load time distribution strategy.

[0012] Preferably, the power distribution network reconstruction model establishment module is specifically used for: Based on the spatial distribution characteristics of the optimized load distribution strategy, a power distribution network reconstruction strategy model of flexible switch state optimization in each time period is constructed with the minimization of power distribution network loss as the target, considering the active and reactive power balance constraints of the power distribution network, the upper and lower node voltage constraints, the radial topology constraints of the power distribution network, the line power transmission capacity constraints and the switch action quantity limit constraints. The active and reactive power balance constraints of the power distribution network are relaxed by using a second-order cone equation, and a virtual voltage related to the line communication variable is introduced to linearly convert the objective function and the constraint conditions of the power distribution network reconstruction model, a mixed integer second-order cone programming model of power distribution network reconstruction is constructed, the global optimal solution of power distribution network reconstruction is solved, and the optimal switch operation strategy is obtained to reconstruct the network topology.

[0013] In a third aspect, the present application also provides a computer readable storage medium, the computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize the steps of the power distribution network reconstruction method considering smart residential demand response.

[0014] In a third aspect, the present application also provides a computer readable storage medium, the computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize the steps of the power distribution network reconstruction method considering smart residential demand response.

[0015] The embodiment of the present application has the following beneficial effects: 1、The power distribution network reconstruction method considering smart residential demand response of the present application fully considers the bounded rationality and social attributes of smart residential users by establishing a user social network and an evolutionary game model, the response level of the user under different electricity prices / incentives obtained is more in line with the actual scene, the subsequent load distribution strategy is more reasonable, the execution deviation of the scheduling scheme caused by ignoring the real response characteristics of the user is avoided, and the utilization rate of demand response resources is improved.

[0016] 2. The distribution network reconfiguration method of this invention, which considers demand response in smart homes, differs from existing research that optimizes solely from the perspectives of demand response or distribution network reconfiguration. It guides load temporal distribution equilibrium through time-of-use pricing optimization, and then optimizes power flow through distribution network reconfiguration based on load spatial distribution characteristics, achieving dual optimization of load transfer in the temporal dimension and power flow regulation in the spatial dimension. This collaborative model not only utilizes demand response to reduce the impact of load temporal and spatial fluctuations on the power grid, but also further improves power flow distribution through reconfiguration. Compared to single optimization schemes, it can alleviate the operational pressure on the distribution network more effectively at its source. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, obtaining other drawings based on these drawings without creative effort still falls within the scope of the present invention.

[0018] Figure 1 A flowchart of a power distribution network reconfiguration method considering smart home demand response is provided in an embodiment of the present invention.

[0019] Figure 2 This is a schematic diagram of a power distribution network reconfiguration system that takes into account the demand response of smart homes, provided as an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings.

[0021] like Figure 1 As shown, an embodiment of the present invention provides a power distribution network reconfiguration method considering smart home demand response, comprising: Step S101: Obtain the social relationships among smart home users, establish a user social network based on the social relationships among smart home users, and establish an evolutionary game model on the user social network to analyze the demand response characteristics of smart home users in order to obtain the user response level under different electricity prices or incentive levels. Since the grid's incentive pricing is related to the overall demand response level of users—meaning changes in the response decisions of individual users affect the grid's incentive pricing, and consequently influence the interests and response strategies of other users—the demand response behavior of smart home users constitutes a game between individuals and the collective user base. Furthermore, due to limitations in information acquisition and computational capabilities, real-world users exhibit bounded rationality and cannot obtain the optimal solution through a single game. In practice, users typically exchange information with users they have social relationships with and learn better response strategies. Therefore, the demand response behavior of smart home users can be characterized as an evolutionary game process on a social network.

[0022] Social relationships and information exchanges among smart home users influence their response to price incentives. The networks that reflect these social relationships and information exchanges are called user social networks, which are a typical type of complex network.

[0023] First, a user social network model is established using graph theory. The basic elements of a social network are nodes and edges, where different nodes represent different smart home users, and edges represent social relationships between users; that is, if a social relationship exists between users, there is an edge connecting the two nodes. The small-world property and scale-free property are two main characteristics of social networks. Scale-free means that the degree distribution of nodes follows a power-law distribution. The small-world property of complex networks indicates that the network simultaneously has a short average path length and a high clustering coefficient. The average path length is the average of the shortest path lengths between any two nodes in the network. The clustering coefficient describes the probability that two nearest neighbors of a node are themselves adjacent; that is, how many of the neighbors of each connected group are common neighbors.

[0024] The network clustering coefficient is calculated as follows: Assume nodes i have l Let there be adjacent nodes, and let C i This represents the actual number of connections between these adjacent nodes. Clustering coefficient. q i It can be represented as: (1) Clustering coefficient of all nodes in the network q i By averaging the results, we can obtain the clustering coefficient of the entire network. (2) Therefore, considering the characteristics of the social network of smart home users, this invention uses a scale-free community network to establish a user social network model. This model has a power-law degree distribution, small-world properties, and includes a community structure, which can effectively simulate the information exchange structure among smart home users.

[0025] The specific network generation process is as follows: Step 1: Set the initial number of communities to M Generate m 0( m 0≥ M ) initial nodes, and ensure that each community contains at least one node. Connect all initial nodes, and set the number of newly generated nodes to t 0, t =1.

[0026] Step 2: Add a new node to the network, randomly select a community from M communities with equal probability, and record it as community j . The new node is connected to j ( m ≤ m 0) nodes in community m (If the number of nodes in community j is less than m , connect to all nodes in the community). The connection rule is: the probability of the new node connecting to node j in community i is: (3) where S ij represents the connectivity between node i and other nodes in community j , i.e., the number of established connections; k represents the total number of nodes in community j .

[0027] Step 3: Determine whether the new node is connected to an external community, with a probability of p (0< p <1). If not, go directly to Step 4; if so, the connection rule is: the number of connections is n , and the probability of the new node connecting to node h ( h ≠ j ) in external community i is: (4) Step 4: Update t = t +1. When t reaches the preset total number of nodes t 0, the generation process stops; otherwise, return to Step 2 and repeat the above process.

[0028] By the above steps, the scale-free community network is generated, and different clustering characteristics of the user social network can be obtained. p determined by the parameter p , q The greater the value, the more obvious the clustering characteristics of the social network, and the greater the corresponding clustering coefficient .

[0029] The intelligent residential users in the transformer area can be regarded as "prosumers", who not only need to meet the basic electricity demand, but also reduce electricity costs or obtain additional benefits through load reduction and transfer. As a centralized management unit of users, the transformer area purchases electricity from the distribution network to meet the load demand, and guides residents to change their electricity habits, realizing the mutual benefit and win-win of the power grid and users. As a centralized unit, the retail electricity price strategy of the transformer area is affected by its overall electricity purchase, while the electricity purchase cost is directly related to the electricity behavior of each user. Therefore, the electricity decision of each user will affect the overall electricity purchase price, thereby determining the electricity cost of the user. Based on this, the evolution game model of the demand response decision of residential users is constructed.

[0030] When the transformer area participates in the operation regulation of the distribution network in the form of the whole, the power grid will implement time-of-use electricity price to guide the user electricity behavior to meet the operation demand. In the user community system, there is a mutual learning mechanism in information transmission and decision-making, and each user evaluates and learns the strategy of the user with social relationship to pursue the maximization of self-interest and satisfaction. Therefore, the strategy selection of each user is significantly affected by the social relationship users, which means that the social network reflects the game relationship between users.

[0031] The user decision game model is constructed , which includes the following three main components: 1) Decision subject: intelligent residential users in the transformer area N .

[0032] 2) Decision space: , where represents the electricity level of the user i , defined as the ratio of the actual electricity consumption of the user to the maximum electricity consumption, denoted as .

[0033] (5) wherein represents the actual electricity consumption of the user i ; represents the maximum electricity consumption of the user i , i.e. the maximum demand without constraints; according to the range of , it can be known that .

[0034] According to the electricity price theory and the supply and demand relationship in the market, the electricity price is affected by the overall electricity consumption of users. When the overall electricity consumption is high, the electricity price will rise to curb user demand; on the contrary, when the overall electricity demand is low, the electricity price will fall.

[0035] 3) Utility function: user i The electricity utility of user is composed of two parts, i.e. electricity purchase cost and electricity satisfaction, which can be expressed as: (6) Wherein, Q -i represents the electricity decision of other users except user i It can be seen that the decision utility of a single user is affected by the collective decision of the user group. The electricity satisfaction level of user i is determined by the following quadratic utility function: (7) Wherein, and α are preset parameters reflecting the utility level brought by power consumption. It can be seen that higher power consumption corresponds to higher power utility; however, there is an upper limit, and when it is exceeded, further increase in power consumption will no longer bring higher satisfaction.

[0036] Considering the information transmission in the social network and the mutual learning between users, users will continuously update the strategy to maximize their own interests. The present invention adopts the best response learning algorithm to simulate the dynamic process of this strategy adjustment. The basic principle is that individual users obtain utility through game interaction with other users in the social network, i.e. under the premise of knowing the results of the previous round of game, each game participant can identify the optimal response strategy. The specific implementation includes: when a user updates the game strategy, the user's utility is compared with the utility of all users with which the user has social relations, and then the strategy with the highest utility is selected as the next round of game strategy. The process is continuously iterated until all users do not change their strategies.

[0037] In step S102, based on the demand response characteristics and response level of the user, a time-of-use electricity price optimization model of the distribution network is constructed with the time-of-use electricity price of the distribution network as the optimization object, to obtain an optimized electricity price strategy. Based on the optimized electricity price strategy, a smart residential user load distribution strategy optimization model is constructed with the time distribution of the smart residential user load as the optimization object, to obtain an optimized load distribution strategy. When the smart residence distributes electricity tasks, the electricity cost needs to be considered. The distribution network can guide the smart residential electricity load distribution by formulating a time-of-use electricity price to reduce the load peak-valley difference and achieve the purpose of reducing network loss.

[0038] ​The objective function for setting time-of-use pricing in the distribution network is: (8) in, D L For the distribution network load variance; T The scheduling period; P L,t for t Average base load over the period; P R,t for t All smart home electricity loads within the specified time period.

[0039] Equation (8) also needs to satisfy the following constraints: 1) Limitation of Electricity Load in Smart Homes If the electrical load of a smart home cannot exceed its upper limit at any time, then... (9) in, p up R, t for t The upper limit of the power load of smart homes during the specified time period. Since users need to consider electricity costs, all smart home electricity consumption is affected by electricity prices, and it can be assumed that smart home electricity consumption is subject to grid dispatch.

[0040] (10) in, p up R is the upper limit of the power transmission capacity of the smart home that the power distribution network can withstand.

[0041] 2) Energy demand constraints for smart home electrical equipment (11) in, E r,1 and E r,2 Smart Home r Minimum and maximum electricity demand limits during the dispatch period; t 0 represents the initial scheduling time; ∆t The scheduling interval.

[0042] 3) Electricity price upper and lower limits constraints The setting of time-of-use electricity pricing needs to be within a certain range. (12) in, π up b and π lowb represent the upper and lower limits of the distribution network electricity price, respectively; π b,t express The electricity price of the power distribution network to the smart home in a time period.

[0043] 4) Power demand response constraint Based on the evolutionary game model of smart home user limited rationality demand response, the response level of the user under different electricity prices or incentive levels can be obtained. Since there is a complex relationship between the electricity price and the response level, the present invention uses a linear function to fit the functional relationship between the user response level and the electricity price, as shown in equation (13): (13) Wherein, π 0 is the reference electricity price; P r0,t is the power load of the smart home under the reference electricity price. r

[0044] The purpose of the smart home load adjustment is to reduce its electricity cost, so the smart home power load distribution objective function can be expressed as: (14) Wherein, p x,t is the actual power of the device at time t. t x

[0045] Introducing 0-1 variable S x,t represents the running state of the power equipment in the smart home at time t, x t when =1, the device is in the start state, S x,t when =0, the device is in the stop state; equation (15) and equation (16) are the actual power of the constant power load and the variable power load respectively. x S x,t x N is the rated power of the constant power device;

[0046] min and max are the power upper and lower limit constraints of the variable power device respectively. p x x p x p x x

[0047] ​​​​​​​​​​​​For non-interruptible load devices, they must run continuously after starting until the end of the power consumption task, so their running state meets the constraint of equation (17).

[0048] (17) For translatable load devices, their starting time can be flexibly selected within the scheduling period, and their running constraints can be described by equation (18).

[0049] (18) In equation (17) and equation (18), Tstart x 、 Tend x 、 Tlast x and Tdelay x are the upper and lower limits, the duration and the delay time of the period in which the device x 1 completes the power consumption task.

[0050] When the device load is translatable and interruptible, it only needs to meet equation (18); when it is a translatable load but not interruptible, it needs to meet both equation (17) and equation (18); when it is a non-translatable and non-interruptible load, it only needs to meet equation (17).

[0051] Since there is a constraint relationship between the start and stop times of some power consumption tasks, such as the start of the dryer needs to be after the end of the washing machine power consumption task, equation (19) is used to describe this constraint relationship.

[0052] (19) where, T total x 1 is the time required for the device x 1 to complete the power consumption task, and the device x 2 needs to start after the device x 1 completes the power consumption task.

[0053] In addition, there are variable power devices such as air conditioners and refrigerators in residential power consumption, mainly including refrigerators, air conditioners, etc., whose power consumption is related to the set stable and environmental temperature. The air conditioner and refrigerator load model in classical thermodynamics is introduced to describe their power characteristics.

[0054] (20) (21) (22) (23) In equation (20)-(23), p AC,t and p fr,tFor air conditioners and refrigerators respectively t Power requirements at any given time; p Nfr is the rated power for the refrigerator to start; θ in,t , θ out,t , θ fr,t For ambient temperature, indoor temperature, and refrigerator internal temperature; θ max in、 θ minisum θ max fr、 θ min fr are respectively θ in,t and θ fr,t The upper and lower limits; ε air and κ air These represent the inertia factor and thermal conductivity of air, respectively. A fr,t for t The refrigerator's operating level at any given time; ψ fr The coefficient representing the influence of the refrigerator's operating level on temperature; ϕ fr The temperature drop inside the refrigerator per unit time when the refrigerator compressor is working; Ω fr This represents the temperature rise of the refrigerator per unit time due to cold loss.

[0055] Step S103: Based on the spatial distribution characteristics of the optimized load allocation strategy, taking the voltage distribution of the distribution network as the optimization object, a distribution network reconfiguration model is established, and the second-order cone programming method is used to solve it to obtain the global optimal solution for distribution network reconfiguration.

[0056] Distribution network reconfiguration optimizes power flow distribution by optimizing the state of flexible switches in each time period, reducing the impact of load spatial differences on power flow and voltage distribution, and seeking further reduction in distribution network losses.

[0057] The objective function for distribution network reconfiguration is: (twenty four) in, I Indicates the number of load nodes in the distribution network; C ( i ) indicates the relationship with the node j A connected set of nodes; L For the set of routes; P ij,t Indicates the line ij Network loss between them.

[0058] (25) in, G ij branch road ij The electrical conductivity; V i,t Represents a node i exist t Voltage during a given period; θ ij,t Indicates a branch ij exist t The difference in work angle during different time periods, α ij,t Indicates a branch ij exist t Connectivity status during a time period α ij,t = 1 represents a node i, j Connected.

[0059] The objective function (24) needs to satisfy constraints such as node active and reactive power constraints, voltage upper and lower limits constraints, distribution network radiation topology constraints, line transmission capacity constraints, and the number of switch actions.

[0060] 1) Active and reactive power balance constraints at nodes (26) in, Pi L, t, Qi L ,t Represents a node i exist t The magnitude of active and reactive load at any given moment; P ij,t 、Q ij,t for t Time Node j Injection Node i The active and reactive power.

[0061] 2) Node voltage upper and lower limit constraints (27) in, V imin , V imax Representing nodes respectively i The upper and lower limits of the voltage amplitude.

[0062] 3) Radial topology constraints of distribution networks (28) in, β ij,t For nodes i With nodesj The hierarchical relationship between them, the value of 1 indicates i is j The parent node of the balanced node must not be the parent node.

[0063] 4) Line l Power transmission capacity constraints (29) Where, P l is the line l Maximum transmission capacity.

[0064] 5) Switch action number limit (30) Where, N m is the maximum number of switches.

[0065] Relax the power flow equation using the second-order cone, replace the original power flow equation with equation (31): (31) Introduce a virtual voltage related to the line communication variable u a i,t and u a j,t , α n =1 u a i,t = u i,t , u a j,t = i,t = u u j,t ; α n =0, u a Figure 2 a Tstart x 0 has: (32) At this time, the original objective function (24) is converted to equation (33): (33) The node active and reactive power balance constraints are converted to equation (34): (34) The line l Power transmission capacity constraints are converted to equation (35): (35) After transformation, they are all linear expressions, but the redundant variables are introduced in formula (31), and the original variables can be constrained by formula (36) to ensure the accuracy of variable replacement.

[0066] (36) The corresponding solution space of formula (36) is a cone surface, so that the problem is non-convex, the solution space is relaxed into a cone body by formula (37), the objective function has sufficient gradient to make the optimal solution return to the cone surface, the power distribution network reconstruction problem is converted into a mixed integer second-order cone programming, and the global optimal solution is ensured while the rapidity is ensured.

[0067] (37) The power distribution network reconstruction method considering the demand response of intelligent residences provided by the application fully considers the bounded rationality and social attributes of intelligent residence users by establishing a user social network and an evolutionary game model, the response level of the users under different electricity prices / incentives obtained is more in line with the actual scene, the subsequent load distribution strategy is more reasonable, the execution deviation of the scheduling scheme caused by ignoring the real response characteristics of the users is avoided, and the utilization rate of the demand response resources is improved. Meanwhile, the power distribution network reconstruction method provided by the application is different from the existing researches which optimize from the demand response or the power distribution network reconstruction, the time distribution of the load is balanced by optimizing the time-of-use electricity price, and then the power flow is optimized based on the spatial distribution characteristics of the load by the power distribution network reconstruction, so that the double optimization of the load transfer in the time dimension and the power flow adjustment in the space dimension is realized. Based on the cooperative mode, the impact of the load time and space fluctuation on the power grid is reduced by the demand response, and the power flow distribution is further improved by the reconstruction, compared with the single optimization scheme, the operation pressure of the power distribution network can be relieved from the root. The load distribution strategy after optimization reduces the impact of the concentrated and fluctuating load on the power distribution network, and reduces the overload risk caused by the excessively large peak-valley difference; the power distribution network reconstruction model solved based on the second-order cone programming can adjust and optimize the voltage distribution by the flexible switch, and effectively solves the problems that the existing power distribution network is difficult to adapt to the increased network loss and voltage deviation caused by uncertain load. The double optimization jointly acts, significantly reduces the network loss of the power distribution network, improves the power quality, and ensures the safety of the power grid operation.

[0068] As Tend x shown, the embodiment of the application further provides a power distribution network reconstruction analysis system considering the demand response of intelligent residences, which comprises a demand response characteristic analysis module 21, a load optimization scheduling model establishment module 22 and a power distribution network reconstruction model establishment module 23.

[0069] The demand response characteristic analysis module 21 obtains the response level of the users under different electricity prices or incentive levels by analyzing the demand response characteristics of the intelligent residence users, and is used for: A user social network model is constructed using graph theory. The basic elements of a social network are nodes and edges, where different nodes represent different smart home users, and edges represent social relationships between users; that is, if a social relationship exists between users, there is an edge connecting the two nodes. The small-world property and scale-free property are two key characteristics of social networks.

[0070] The network clustering coefficient is calculated as follows: Assume nodes i have l Let there be adjacent nodes, and let C i This represents the actual number of connections between these adjacent nodes. Clustering coefficient. q i It can be represented as: (38) Clustering coefficient of all nodes in the network q i By averaging the results, we can obtain the clustering coefficient of the entire network. (39) Therefore, considering the characteristics of the smart home user social network, a scale-free community network is adopted to establish the user social network model.

[0071] The specific network generation process is as follows: Step 1: Set the initial number of communities to M ,generate m 0( m 0≥ M ) initial nodes, ensuring each community contains at least one node, fully connect the initial nodes, and set the number of newly generated nodes to . t 0, t =1.

[0072] Step 2: Add a new node to the network with equal probability from M One community is randomly selected from the given communities to join, denoted as community A. j New Nodes and the Community j In m ( m ≤ m 0) nodes establish connections (if the community) j The number of nodes in the middle is less than m If a new node connects to the community, then all nodes within the community are connected. The connection rule is: a new node connects to the community. j Middle node i The probability is: (40) in, S ij Represents a nodei With the community j The connectivity between other nodes, i.e., the number of established connections; k Indicates community j The total number of nodes in the system.

[0073] Step 3: Determine whether the new node has established a connection with the external community, the probability of which is... p (0< p <1). If no connection is established, proceed directly to step 4; if a connection is established, the connection rule is: the number of connections is... n The new node connects to the external community. h ( h ≠ j ) nodes i The probability is: (41) Step 4: Update t = t +1. When t Reach the preset total number of nodes t If the value is 0, the generation process stops; otherwise, return to step 2 and repeat the above process.

[0074] By generating a scale-free community network through the above steps, different clustering characteristics of user social networks can be obtained. Here, the clustering characteristics of user social networks are determined by parameters. p Decide, p The higher the value, the more pronounced the clustering characteristics of the social network, and the higher the corresponding clustering coefficient. q Also bigger.

[0075] Constructing a user decision-making game model It comprises the following three main components: 1) Decision-making body: within the transformer substation area N One smart home user.

[0076] 2) Decision space: ,in Indicates user i The electricity consumption level is defined as the ratio of a user's actual electricity consumption to their maximum electricity consumption, denoted as _____. .

[0077] (42) in, Indicates user i The actual electricity consumption; Indicates user i The maximum power consumption, i.e., the maximum unconstrained demand; according to The range can be known .

[0078] According to electricity pricing theory and the supply and demand relationship in the market, electricity prices are influenced by the overall electricity consumption of users. When overall electricity consumption is high, electricity prices will rise to curb user demand; conversely, when overall electricity demand is low, electricity prices will fall.

[0079] 3) Utility function: User i The utility of electricity consumption consists of two parts: the cost of purchasing electricity and the satisfaction with electricity consumption, which can be expressed as: (43) in, Q -i Indicates excluding users i The electricity consumption decisions of other users show that the utility of an individual user's decision is influenced by the collective decision-making of the user group. i Electricity satisfaction level Determined by the following quadratic utility function: (44) in, and α These are preset parameters that reflect the utility level derived from electricity consumption. It can be seen that higher electricity consumption corresponds to higher electricity utility; however, there is an upper limit, and once this limit is exceeded, further increases in electricity consumption will no longer bring higher satisfaction.

[0080] Considering information dissemination and mutual learning among users in social networks, users continuously update their strategies to maximize their own interests. An optimal response learning algorithm is employed to simulate this dynamic process of strategy adjustment. Its basic principle is that individual users gain utility through game interaction with other users in the social network; that is, given the outcome of the previous round of the game, each participant can identify the optimal response strategy. Specifically, when a user updates their game strategy, they compare their utility with the utilities of all users with whom they have social relationships, and then select the strategy with the highest utility as the strategy for the next round. This process iterates until all users no longer change their strategies.

[0081] Among them, the load optimization scheduling model establishment module 22 optimizes the time-of-use pricing of the distribution network and the time distribution of loads in smart homes for the following purposes: The objective function for setting time-of-use pricing in the distribution network is: (45) in, D L For the distribution network load variance; T The scheduling period; P L,t for tAverage base load in the period; P R,t For t All smart residential electricity load in the period.

[0082] Formula (45) also needs to meet the following constraints: 1) Smart residential electricity load upper limit constraint The smart residential electricity load cannot exceed its upper limit value in any period, that is, (46) Where, p up R, t For t The smart residential electricity load power upper limit in the period. Because users need to consider the cost of electricity, all smart residential electricity behaviors are affected by electricity prices, and it can be considered that smart residential electricity is subject to grid dispatching.

[0083] (47) Where, p up R is the upper limit of the smart residential electricity power transmission that the distribution network can withstand.

[0084] 2) Smart residential electricity equipment energy demand constraint (48) Where, E r,1 And E r,2 are the minimum and maximum electricity demand limits of the smart residential r in the dispatching period; t 0 is the starting dispatching time; ∆t is the dispatching interval.

[0085] 3) Upper and lower limit constraints of electricity price The development of time-of-use electricity price needs to be within a certain range, that is, (49) Where, π up b and π lowb represent the upper and lower limits of the distribution network electricity price, respectively; π b,t represents the distribution network electricity price to the smart residential in the period.

[0086] 4) Electricity demand response constraint Based on the evolutionary game model of smart residential user bounded rationality demand response, the response level of users under different electricity prices or incentive levels can be obtained. A linear function is used to fit the functional relationship between user response level and electricity price, as shown in formula (50): (50) wherein, π 0 is the reference electricity price; P r0,t is the electricity load of the smart home under the reference electricity price. r

[0087] The electricity load distribution objective function of the smart home can be expressed as: (51) wherein, p x,t is the actual power of the device at the moment t. t x The 0-1 variable

[0088] is introduced. S x,t The running state of the electricity device in the smart home at the moment t, x t = 1 means that the device is in the on state, S x,t = 0 means that the device is in the off state; formula (52) and formula (53) are the actual powers of the constant power load and the variable power load, respectively. x S x,t x

[0089] (52) (53) In formula (52) and formula (53), p N x is the rated power of the constant power device; x min p and x max p are the upper and lower power constraints of the variable power device, respectively. x x For the uninterruptible load device, it must be continuously operated after being turned on until the end of the electricity task, so its running state satisfies the constraint of formula (54).

[0090]

[0091] (54) For the shiftable load device, its on moment can be flexibly selected within the scheduling period, and its running constraint can be described by formula (55).

[0092] (55) In formula (54) and formula (55),​​​​​​​​Tlast x , Tdelay x , θ and θ are the upper and lower bounds, the duration and the delay time of the period in which the device x performs the electricity task.

[0093] When the device load is both shiftable and interruptible, it only needs to satisfy equation (55); when it is shiftable but not interruptible, it needs to satisfy both equation (54) and equation (55); when it is neither shiftable nor interruptible, it only needs to satisfy equation (54).

[0094] Since there is a constraint relationship between the start and stop times of some electricity tasks, such as the start of the dryer needs to be after the end of the washing machine electricity task, equation (56) is used to describe this constraint relationship.

[0095] (56) where, T total x 1 is the time required for the device x 1 to complete the electricity task, and the device x 2 needs to start after the device x 1 completes the electricity task.

[0096] In addition, there are variable power devices such as air conditioners and refrigerators in residential electricity, mainly including refrigerators, air conditioners, etc., whose electricity power is related to the set stable and environmental temperature, and the air conditioner and refrigerator load model in classical thermodynamics is introduced to describe their power characteristics.

[0097] (57) (58) (59) (60) In equations (57)-(60), p AC,t and p fr,t are the power requirements of the air conditioner and the refrigerator at t time, respectively; p N fr is the rated power of the refrigerator start; θ in,t , θ out,t , θ fr,t are the environmental temperature, indoor temperature, and refrigerator internal temperature; θ max in, θ minin and θ max fr,θ min fr respectively ε in,t with κ fr,t upper and lower limits of ψ air and ϕ air respectively represent the inertia factor and the thermal conductivity of air; A fr,t is t the running level of the refrigerator at the moment; ij fr is the influence coefficient of the running level of the refrigerator on temperature; ij fr is the temperature drop of the refrigerator per unit time when the compressor of the refrigerator is working; Ω fr is the temperature rise of the refrigerator per unit time due to cold loss.

[0098] Among them, the power distribution network reconstruction model establishment module optimizes the soft switch state of the power distribution network and the spatial distribution of the intelligent residential user load, and is used for: The power distribution network reconstruction objective function is: (61) wherein, I represents the number of load nodes of the power distribution network; C i represents the node set connected with node j ; L is a line set; P ij,t represents the network loss between lines θ .

[0099] (62) wherein, G ij is the conductance of branch ij ; V i,t represents the voltage of node i in t time period; ij ij,t represents the power angle difference of branch i, j in t time period, α ij,t represents the connectivity state of branch Pi in t time period, α ij,t = 1 represents that node t, Qi is connected.

[0100] ​The objective function (61) needs to satisfy constraints such as node active and reactive power constraints, voltage upper and lower limits constraints, distribution network radiation topology constraints, line transmission capacity constraints, and the number of switch actions.

[0101] 1) Active and reactive power balance constraints at nodes (63) in, i,t L, j,t L ,t Represents a node i exist t The magnitude of active and reactive load at any given moment; P ij,t 、Q ij,t for t Time Node j Injection Node i The active and reactive power.

[0102] 2) Node voltage upper and lower limit constraints (64) in, V imin , V imax Representing nodes respectively i The upper and lower limits of the voltage amplitude.

[0103] 3) Radial topology constraints of distribution networks (65) in, β ij,t For nodes i With nodes j The hierarchical relationship between them, with a value of 1 indicating i yes j The parent node of a balanced node is not the parent node.

[0104] 4) Route l Power transmission capacity constraints (66) in, P l For the line l Maximum transmission capacity.

[0105] 5) Limitation on the number of switch actions (67) in, N m This represents the maximum number of active switches.

[0106] The second-order cone is used to relax the tide flow equation, and the original tide flow equation is replaced by formula (31) : (68) The virtual voltage related to the line communication variable is introduced u a i,t = u With u a j,t = i,t = u , α n =1 u a ​ i,t 、 u a ​ u j,t ; α n =0, u a ​ a ​ 0 have: (69) At this time, the original objective function (61) is converted into formula (70) : (70) The node active and reactive power balance constraint is converted into formula (71) : (71) The line l Power transmission capacity constraint is converted into formula (72) : (72) After conversion, they are all linear expressions, but formula (68) introduces redundant variables, which can be used to constrain the original variables to ensure the accuracy of variable replacement.

[0107] (73) The corresponding solution space of formula (73) is a cone surface, so that the problem is non-convex, and formula (74) is used to relax the solution space into a cone, the objective function has enough gradient to make the optimal solution return to the cone surface, and the power distribution network reconstruction problem is converted into a mixed integer second-order cone programming, which guarantees the rapidity of solving while obtaining the global optimal solution.

[0108] (74) The embodiment of the application also provides a computer readable storage medium, the computer readable storage medium stores a computer program, the computer program is executed by the processor to realize the steps of the power distribution network reconstruction method considering the smart home demand response as described above.

[0109] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be realized in the form of hardware or software function unit. In addition, the specific name of each functional unit and module is only for the convenience of mutual distinction, and does not limit the protection scope of the present application. The specific working process of the unit and module in the above-mentioned system can refer to the corresponding process in the foregoing method embodiment, which will not be described here.

[0110] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described or recorded in detail in a certain embodiment can be referred to the related description of other embodiments.

[0111] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the disclosed embodiments of the application can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0112] In the embodiments provided by the present application, it should be understood that the disclosed method and system can be implemented by other ways. For example, the system embodiments described above are only schematic, for example, the division of the modules or units is only a logical function division, and there can be another division way in actual implementation, for example, a plurality of units or modules can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection between each other can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0113] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0114] In addition, each function unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software function unit.

[0115] The integrated module / unit, if realized in the form of a software function unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of each of the above-mentioned power distribution network reconstruction methods considering the demand response of intelligent residences can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer-readable medium can include any entity or device capable of carrying the computer program code, a recording medium, a U disk, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0116] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present application, and should be included in the protection scope of the present application.

Claims

1. A power distribution network reconfiguration method considering demand response in smart homes, characterized in that, include: The social relationships among smart home users are obtained, a user social network is established based on the social relationships among smart home users, and an evolutionary game model is established on the user social network to analyze the demand response characteristics of smart home users in order to obtain the user response level under different electricity prices or incentive levels. Based on the user's demand response characteristics and response level, a time-of-use pricing optimization model for the distribution network is constructed with the time-of-use pricing of the distribution network as the optimization object, and an optimized pricing strategy is obtained. Based on the optimized pricing strategy, an optimization model for the load allocation strategy of smart residential users is constructed with the time distribution of smart residential user load as the optimization object, and an optimized load allocation strategy is obtained. Based on the spatial distribution characteristics of the optimized load allocation strategy, a distribution network reconfiguration model is established with the distribution network voltage distribution as the optimization object, and the second-order cone programming method is used to solve it to obtain the global optimal solution for distribution network reconfiguration.

2. The power distribution network reconfiguration method considering smart home demand response as described in claim 1, characterized in that, The process of acquiring social relationships among smart home users, establishing a user social network based on these relationships, and building an evolutionary game model on this user social network to analyze the demand response characteristics of smart home users, in order to obtain user response levels under different electricity prices or incentive levels, includes: To obtain the social relationships among smart home users, based on graph theory, a social network relationship of smart home users is constructed with smart home users as nodes and the social relationships between users as edges. The average path length and clustering coefficient of the social network are calculated, and a social network of smart home users is generated based on a scale-free community network model. Based on the aforementioned smart home user social network, with all smart home users as decision-makers, the proportion of a user's actual electricity consumption to the maximum electricity consumption as the decision space, and the payoff function under different decision spaces as the utility function, a game model is constructed to assess the response of smart home users to electricity prices or incentives. By setting initial response values ​​for each user and calculating their utility function, and comparing each user's utility function with their neighbors on their social network, the decision of the neighbor with the highest utility is used as the user's next game strategy. The decisions of each user are updated, and after repeated iterations until all user decisions are no longer updated, the user demand response characteristics of smart homes under stable utility functions are obtained.

3. The power distribution network reconfiguration method considering smart home demand response as described in claim 1, characterized in that, Based on user demand response characteristics and response levels, a time-of-use pricing optimization model for the distribution network is constructed, using the distribution network's time-of-use pricing as the optimization object, to obtain an optimized pricing strategy. Based on this optimized pricing strategy, a load allocation strategy optimization model for smart residential users is constructed, using the time distribution of smart residential user load as the optimization object, to obtain an optimized load allocation strategy, including: Based on the user's demand response characteristics and response level, with the goal of minimizing the distribution network load variance, and under the conditions of satisfying the upper and lower limits of smart home user load, the energy demand of electrical equipment, and the upper and lower limits of electricity price, and using the relationship between user response level and electricity price as a user demand response constraint by fitting a linear function, a time-of-use electricity price optimization model for the distribution network is constructed to obtain the optimized time-of-use electricity price strategy. With the goal of minimizing the total electricity cost for smart home users under the optimized time-of-use pricing strategy, an optimization model for the load allocation strategy of smart home users is constructed under the conditions of satisfying the constraints of the start-stop status of residential users' electrical equipment, the upper and lower limits of power, the constraints of uninterrupted load operation, the constraints of movable load operation, the constraints of movable and interruptible load operation, and the temporal constraints between electricity tasks, so as to obtain the optimized load time distribution strategy.

4. The power distribution network reconfiguration method considering smart home demand response as described in claim 1, characterized in that, Based on the spatial distribution characteristics of the optimized load allocation strategy, and taking the voltage distribution of the distribution network as the optimization object, a distribution network reconfiguration model is established and solved using a second-order cone programming method to obtain the optimal switching operation strategy for reconfiguring the network topology, including: Based on the spatial distribution characteristics of the optimized load allocation strategy, and with the goal of minimizing distribution network losses, this paper constructs a distribution network reconfiguration strategy model for optimizing the flexible switch state in each time period, taking into account the active and reactive power balance constraints of the distribution network, the upper and lower limits of node voltage, the radial topology constraints of the distribution network, the line power transmission capacity constraints, and the number of switch actions constraints. The active and reactive power balance constraints of the distribution network are relaxed by using second-order cone equations, and virtual voltages related to line connectivity variables are introduced. The objective function and constraints of the distribution network reconfiguration model are linearly transformed to construct a mixed-integer second-order cone programming model for distribution network reconfiguration. The global optimal solution for distribution network reconfiguration is solved to obtain the optimal switching operation strategy for reconfiguring the network topology.

5. A power distribution network reconfiguration system considering demand response in smart homes, characterized in that, The method described in any one of claims 1-4 includes: The demand response feature analysis module is used to obtain the social relationships among smart home users, establish a user social network based on the social relationships among smart home users, and establish an evolutionary game model on the user social network to analyze the demand response features of smart home users in order to obtain the user response level under different electricity prices or incentive levels. The load optimization scheduling model construction module is used to construct a distribution network time-of-use electricity price optimization model based on the user's demand response characteristics and response level, taking the distribution network time-of-use electricity price as the optimization object, and obtain the optimized electricity price strategy. Based on the optimized electricity price strategy, the module constructs a smart residential user load allocation strategy optimization model with the time distribution of smart residential user load as the optimization object, and obtains the optimized load allocation strategy. The distribution network reconfiguration model construction module is used to establish a distribution network reconfiguration model based on the spatial distribution characteristics of the optimized load allocation strategy, with the distribution network voltage distribution as the optimization object, and to solve it using the second-order cone programming method to obtain the global optimal solution for distribution network reconfiguration.

6. A power distribution network reconfiguration system considering smart home demand response as described in claim 5, characterized in that, The demand response feature analysis module is specifically used for: To obtain the social relationships among smart home users, based on graph theory, a social network relationship of smart home users is constructed with smart home users as nodes and the social relationships between users as edges. The average path length and clustering coefficient of the social network are calculated, and a social network of smart home users is generated based on a scale-free community network model. Based on the aforementioned smart home user social network, with all smart home users as decision-makers, the proportion of a user's actual electricity consumption to the maximum electricity consumption as the decision space, and the payoff function under different decision spaces as the utility function, a game model is constructed to assess the response of smart home users to electricity prices or incentives. By setting initial response values ​​for each user and calculating their utility function, and comparing each user's utility function with their neighbors on their social network, the decision of the neighbor with the highest utility is used as the user's next game strategy. The decisions of each user are updated, and after repeated iterations until all user decisions are no longer updated, the user demand response characteristics of smart homes under stable utility functions are obtained.

7. A power distribution network reconfiguration system considering smart home demand response as described in claim 5, characterized in that, The load optimization scheduling model establishment module is specifically used for: Based on the user's demand response characteristics and response level, with the goal of minimizing the distribution network load variance, and under the conditions of satisfying the upper and lower limits of smart home user load, the energy demand of electrical equipment, and the upper and lower limits of electricity price, and using the relationship between user response level and electricity price as a user demand response constraint by fitting a linear function, a time-of-use electricity price optimization model for the distribution network is constructed to obtain the optimized time-of-use electricity price strategy. With the goal of minimizing the total electricity cost for smart home users under the optimized time-of-use pricing strategy, an optimization model for the load allocation strategy of smart home users is constructed under the conditions of satisfying the constraints of the start-stop status of residential users' electrical equipment, the upper and lower limits of power, the constraints of uninterrupted load operation, the constraints of movable load operation, the constraints of movable and interruptible load operation, and the temporal constraints between electricity tasks, so as to obtain the optimized load time distribution strategy.

8. A power distribution network reconfiguration system considering smart home demand response as described in claim 5, characterized in that, The power distribution network reconfiguration model establishment module is specifically used for: Based on the spatial distribution characteristics of the optimized load allocation strategy, and with the goal of minimizing distribution network losses, this paper constructs a distribution network reconfiguration strategy model for optimizing the flexible switch state in each time period, taking into account the active and reactive power balance constraints of the distribution network, the upper and lower limits of node voltage, the radial topology constraints of the distribution network, the line power transmission capacity constraints, and the number of switch actions constraints. The active and reactive power balance constraints of the distribution network are relaxed by using second-order cone equations, and virtual voltages related to line connectivity variables are introduced. The objective function and constraints of the distribution network reconfiguration model are linearly transformed to construct a mixed-integer second-order cone programming model for distribution network reconfiguration. The global optimal solution for distribution network reconfiguration is solved to obtain the optimal switching operation strategy for reconfiguring the network topology.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of a power distribution network reconfiguration method considering smart home demand response as described in any one of claims 1 to 4.

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