A rural station area flexible interconnection and charging and storage station double-layer collaborative optimization scheduling method and device

Abstract

The application discloses a kind of rural station area flexible interconnection and filling and storage power station double-layer collaborative optimization scheduling method and device, this method is based on the time-space complementary characteristics of rural commercial and residential station area source and load, builds flexible interconnection system architecture, constructs double-layer game optimization model.Lower model introduces weber-fishina law to build user response model to complete optimization, first particle swarm optimization is solved to obtain intelligent soft switch interaction power instruction, which is passed into lower model as boundary condition, then genetic algorithm is solved to lower model, and actual response power and service cost of filling and storage power station are obtained and fed back to upper layer.Two-layer model is solved by repeated iteration, until actual response power and upper target power deviation is less than set threshold for three times in succession, system reaches game equilibrium state, i.e.global optimal scheduling scheme can be obtained.The application solves the problems of insufficient scheduling accuracy, limited new energy consumption capacity and other problems in the prior art.

Description

Technical Field

[0001] This invention relates to the field of power system optimization scheduling technology, specifically to a method and apparatus for flexible interconnection of rural distribution areas and dual-layer collaborative optimization scheduling of charging and storage power stations. Background Technology

[0002] Currently, the penetration rate of distributed photovoltaic (PV) power and electric vehicles in rural power distribution networks continues to increase. Among existing technologies, smart soft switches (SOPs) have been widely applied to the flexible interconnection of commercial and residential transformer substations in rural areas, enabling bidirectional transmission of active power and dynamic power flow regulation between substations. Simultaneously, electric vehicle charging and storage stations participate in load balancing of the distribution network through technologies such as orderly charging and V2G interaction. These two technologies have formed mature scheduling schemes in the dimensions of spatial power balance and temporal energy regulation, respectively, alleviating to some extent the problems of large peak-valley differences and insufficient PV absorption in rural power distribution networks.

[0003] However, existing technologies still have significant limitations: On the one hand, smart soft switches and charging / storage stations are mostly controlled independently, failing to incorporate the spatiotemporal complementary characteristics of rural commercial power generation ("high during the day and low at night") and residential power generation ("duck curve") to build a collaborative scheduling mechanism. This results in the underutilization of the potential for power mutual assistance between distribution stations and energy regulation on the user side. On the other hand, existing charging / storage station scheduling models mostly adopt rigid electricity price guidance strategies, failing to introduce the Weber-Fechner law to quantify the impact of user psychological thresholds and saturation effects on charging and discharging willingness. This leads to significant deviations in user response prediction, making it difficult to support the precise control needs of the distribution network. Ultimately, this restricts further improvements in the operating efficiency of rural distribution networks and the capacity for renewable energy absorption. Therefore, there is an urgent need for a flexible interconnection method for rural power distribution areas and a two-layer collaborative optimization scheduling method for charging and storage power stations to solve the problems of insufficient scheduling accuracy and limited renewable energy consumption capacity in existing technologies. Summary of the Invention

[0004] To address these issues, this invention provides a method and apparatus for flexible interconnection of rural distribution areas and dual-layer collaborative optimization scheduling of charging and storage power stations. This method solves problems such as independent control of smart soft switches and charging and storage power stations in existing rural distribution networks, failure to utilize the spatiotemporal complementarity of source and load, insufficient scheduling accuracy due to user response models not considering psychological thresholds and saturation effects, and limited renewable energy absorption capacity.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for dual-layer collaborative optimization scheduling of flexible interconnection in rural power distribution areas and charging / storage power stations, characterized in that it includes: Based on the spatiotemporal complementarity of source and load between rural commercial and residential transformer substations, a flexible interconnected system architecture including intelligent soft switches and charging and storage stations is constructed. Based on the aforementioned flexible interconnected system architecture, a two-layer game optimization model is constructed, encompassing distribution network operators, charging and storage station operators, and electric vehicle users. The two-layer game optimization model includes an upper-layer distribution network operator optimization scheduling model and a lower-layer charging and storage station operator optimization scheduling model. The lower-layer charging and storage station operator optimization scheduling model is obtained by introducing the Weber-Fechner law to construct a user response model. The upper-level distribution network operator's optimal scheduling model is solved using the particle swarm optimization algorithm to obtain the interactive power command of the smart soft switch; the interactive power command is then passed as a boundary condition to the lower-level charging and storage station operator's optimal scheduling model. Based on the interactive power command, the optimized scheduling model of the lower-level charging and storage power station operator is solved by a genetic algorithm to obtain the actual response power and service cost of the charging and storage power station, and then fed back to the upper-level distribution network operator's optimized scheduling model. Based on the actual response power and the service cost, the upper-level distribution network operator's optimized scheduling model and the optimized lower-level charging and storage station operator's optimized scheduling model are repeatedly solved until the deviation between the actual response power and the target power of the upper-level distribution network operator's optimized scheduling model is less than a set threshold three times in a row. At this point, it is determined that the flexible interconnection system has reached a game equilibrium state and the globally optimal scheduling scheme is obtained.

[0006] As a preferred scheme for a two-layer collaborative optimization scheduling method for flexible interconnection of rural distribution areas and charging and storage power stations, the objective function of the upper-level distribution network operator's optimization scheduling model is a multi-objective collaborative optimization function; the multi-objective collaborative optimization function includes: distribution network operating cost function, voltage offset function, and load balancing function; The expression for the multi-objective collaborative optimization function is: ; In the formula, The objective function for the upper-level distribution network operator; , , All are weighted coefficients; , , The objective function after dimensionless processing , and ; The expression for the distribution network operation cost function is as follows: ; In the formula, The total operating cost of the distribution network; and These are the unit electricity costs for photovoltaic power generation and curtailed solar power, respectively. for t The service fees that are constantly paid to the charging and storage power station; The operating and maintenance cost per unit of electricity for SOP; for t The electricity price charged by the power station for charging and storage to the distribution network; for t The electricity price that the DSO in the time zone purchases from the upper-level power grid; for t The subsidy fee for the power consumption of the DSO in the time zone for SOP interaction; for t The electricity price that the power station purchases from the distribution network; for t The electricity price sold to users in both areas at all times; and They are respectively t Active power flowing into and out of the charging and storage power station at all times through SOP; for t Total interactive power at each SOP; for t The flexible interconnected system purchases power from the upper-level power grid. for t User demand load in the two transformer areas at any given time; , These are the photovoltaic power generation and curtailment within the transformer substation, respectively. T This represents the total number of time periods in the scheduling cycle. l ∈{A,B} represents commercial station area A and residential station area B; The expression for the voltage offset function is: ; ; In the formula, The objective function is the voltage offset. , and feeders Corresponding common connection point The set voltage reference value and upper and lower voltage limits at the location; for t Time about the feeder end Voltage exceeding limit at the location; The expression for the load balancing function is: ; ; ; ; ; In the formula, for t Time zone transformer The actual active load; for t Time Transformer Internal active load power demand; for t Timing feeder Internal photovoltaic grid-connected power; for t Timing feeder Inflow into SOP The active power; Transformer for the distribution area The maximum amount of active power that can be provided; for t Time zone transformer Load rate; for t Time zone transformer The load rate offset penalty function; Transformer for the distribution area The load factor variance; Transformer for the distribution area Based on the variance of the mean load rate of the two zones.

[0007] As a preferred scheme for a two-layer collaborative optimization scheduling method for flexible interconnection of rural power distribution areas and charging / storage stations, the objective function expression of the lower-layer charging / storage station operator optimization scheduling model is: ; In the formula, Let the objective function be about the cost of the charging and storage power station; for t The electricity price that the power station purchases from the grid; Costs associated with the operation and maintenance of energy storage systems; for t The electricity price for the EV; for t The power storage station provides a penalty for power offset at all times; for t Electricity purchase price for EVs; and for t The charging and discharging power of the EV group at any given time; yes t The electricity price at which a power station sells electricity to the grid; for t The difference between the power supplied by the charging and storage power station and the power demanded by the grid is always present; , They are respectively t Active power flowing into and out of the charging and storage power station at all times through SOP; for t Total power of energy storage and charging at all times.

[0008] As a preferred scheme for a two-layer collaborative optimization scheduling method for flexible interconnection of rural power distribution areas and charging and storage power stations, the expression of the user response model is: ; ; In the formula, and They are respectively t Time of the first n The charging and discharging response of an EV; and These are the sensitivity coefficients of EVs to charging and discharging electricity prices, respectively. and They are respectively t The charging and discharging electricity price of EVs at any given time; and This represents the user's sensitivity to the SOC. For the first n EVs in t The state of charge at any given moment; and These are the user's charging and discharging response constants, respectively.

[0009] As a preferred scheme for a two-layer collaborative optimization scheduling method for flexible interconnection of rural distribution areas and charging and storage power stations, the constraints of the upper-level distribution network operator's optimization scheduling model include: voltage constraints at the point of common coupling of the distribution area, photovoltaic grid-connected power constraints, and active and reactive power balance constraints of smart soft switching. The expression for the voltage constraint at the common coupling point of the transformer area is: ; In the formula, Taiwan District Voltage at the point; for Lower limit of operating voltage; for Upper limit of operating voltage; The expression for the photovoltaic grid-connected power constraint is: ; ; In the formula, This refers to the grid-connected power of photovoltaic power. , and They are respectively Taiwan Transformer The amount of photovoltaic power generation, curtailment, and installed capacity within the region; The expression for the active and reactive power balance constraint of the intelligent soft switch is: ; ; ; ; ; ; ; In the formula, and for t time i Active and reactive power injected into the node; and for t time j Active and reactive power injected into the node; and for t time k The energy storage power injected into the node and the total EV power; and for i Nodes and j The loss factor of the converter at the node; and for t Always connected to the node i and nodes j The losses of the SOP converters at both ends; , and , Access nodes i and j Upper and lower limits of reactive power of the converter at the node; and Access nodes i and j The capacity of the converter in the node.

[0010] As a preferred scheme of a two-layer collaborative optimization scheduling method for flexible interconnection of rural power distribution areas and charging and storage power stations, the constraints of the lower-level charging and storage power station operator optimization scheduling model include: SOC operation constraints of the energy storage system and energy storage charging and discharging power constraints. The expression for the SOC operating constraint of the energy storage system is: ; ; In the formula, For energy storage systems t State of charge at any given moment; For ESS system t State of charge at time -1; and These are the energy storage charging and discharging efficiencies, respectively. and for t The charging and discharging power of energy stored at all times; This refers to the rated capacity of the energy storage. and These represent the minimum and maximum values ​​of the energy storage SOC. The expression for the energy storage charging and discharging power constraint is: ; In the formula, , , , These represent the upper and lower limits of energy storage charging and discharging power, respectively.

[0011] This invention also provides a rural transformer area flexible interconnection and charging / storage station dual-layer collaborative optimization scheduling device, employing the above-mentioned rural transformer area flexible interconnection and charging / storage station dual-layer collaborative optimization scheduling method, including: The flexible interconnected system architecture construction module is used to build a flexible interconnected system architecture including intelligent soft switches and charging and storage stations based on the spatiotemporal complementarity of source and load in rural commercial and residential transformer areas. A two-layer game optimization model construction module is used to construct a two-layer game optimization model covering distribution network operators, charging and storage station operators, and electric vehicle users based on the flexible interconnected system architecture. The two-layer game optimization model includes an upper-layer distribution network operator optimization scheduling model and a lower-layer charging and storage station operator optimization scheduling model. The lower-layer charging and storage station operator optimization scheduling model is obtained by constructing a user response model by introducing the Weber-Fechner law. The upper-level distribution network operator optimization scheduling model solution module is used to solve the upper-level distribution network operator optimization scheduling model using the particle swarm optimization algorithm to obtain the interactive power command of the smart soft switch; and to pass the interactive power command as a boundary condition to the lower-level charging and storage station operator optimization scheduling model. The lower-level charging and storage power station operator optimization scheduling model solution module is used to solve the optimized lower-level charging and storage power station operator optimization scheduling model based on the interactive power command using a genetic algorithm, to obtain the actual response power and service cost of the charging and storage power station, and feed it back to the upper-level distribution network operator optimization scheduling model. The upper and lower layer model iterative solution module is used to repeatedly solve the upper layer distribution network operator optimization scheduling model and the optimized lower layer charging and storage station operator optimization scheduling model based on the actual response power and the service cost, until the deviation between the actual response power and the target power of the upper layer distribution network operator optimization scheduling model is less than a set threshold three times in a row. At this point, it is determined that the flexible interconnection system has reached a game equilibrium state and the globally optimal scheduling scheme is obtained.

[0012] As a preferred scheme for a dual-layer collaborative optimization scheduling device for flexible interconnection of rural distribution areas and charging and storage power stations, in the dual-layer game optimization model construction module, the objective function of the upper-layer distribution network operator optimization scheduling model is a multi-objective collaborative optimization function; the multi-objective collaborative optimization function includes: distribution network operating cost function, voltage offset function, and load balancing function. The expression for the multi-objective collaborative optimization function is: ; In the formula, The objective function for the upper-level distribution network operator; , , All are weighted coefficients; , , The objective function after dimensionless processing , and ; The expression for the distribution network operation cost function is as follows: ; In the formula, The total operating cost of the distribution network; and These are the unit electricity costs for photovoltaic power generation and curtailed solar power, respectively. for t The service fees that are constantly paid to the charging and storage power station; The operating and maintenance cost per unit of electricity for SOP; for t The electricity price charged by the power station for charging and storage to the distribution network; for t The electricity price that the DSO in the time zone purchases from the upper-level power grid; for t The subsidy fee for the power consumption of the DSO in the time zone for SOP interaction; for t The electricity price that the power station purchases from the distribution network; for t The electricity price sold to users in both areas at all times; and They are respectively t Active power flowing into and out of the charging and storage power station at all times through SOP; for t Total interactive power at each SOP; for t The flexible interconnected system purchases power from the upper-level power grid. for t User demand load in the two transformer areas at any given time; , These are the photovoltaic power generation and curtailment within the transformer substation, respectively. T This represents the total number of time periods in the scheduling cycle. l ∈{A,B} represents commercial station area A and residential station area B; The expression for the voltage offset function is: ; ; In the formula, The objective function is the voltage offset. , and feeders Corresponding common connection point The set voltage reference value and upper and lower voltage limits at the location; for t Time about the feeder end Voltage exceeding limit at the location; The expression for the load balancing function is: ; ; ; ; ; In the formula, for t Time zone transformer The actual active load; for t Time Transformer Internal active load power demand; for t Timing feeder Internal photovoltaic grid-connected power; for t Timing feeder Inflow into SOP The active power; Transformer for the distribution area The maximum amount of active power that can be provided; for t Time zone transformer Load rate; for t Time zone transformer The load rate offset penalty function; Transformer for the distribution area The load factor variance; Transformer for the distribution area Based on the variance of the mean load rate of the two zones.

[0013] As a preferred scheme for a rural power distribution area flexible interconnection and charging / storage station dual-layer collaborative optimization scheduling device, the objective function expression of the lower-layer charging / storage station operator optimization scheduling model in the dual-layer game optimization model construction module is: ; In the formula, Let the objective function be about the cost of the charging and storage power station; for t The electricity price that the power station purchases from the grid; Costs associated with the operation and maintenance of energy storage systems; for t The electricity price for the EV; for t The power storage station provides a penalty for power offset at all times; for t Electricity purchase price for EVs; and for t The charging and discharging power of the EV group at any given time; yes t The electricity price at which a power station sells electricity to the grid; for t The difference between the power supplied by the charging and storage power station and the power demanded by the grid is always present; , They are respectively t Active power flowing into and out of the charging and storage power station at all times through SOP; for t Total power of energy storage and charging at all times.

[0014] As a preferred scheme for a rural power distribution area flexible interconnection and charging / storage station dual-layer collaborative optimization scheduling device, the expression of the user response model in the dual-layer game optimization model construction module is: ; In the formula, Taiwan District Voltage at the point; for Lower limit of operating voltage; for Upper limit of operating voltage; The expression for the photovoltaic grid-connected power constraint is: ; ; In the formula, This refers to the grid-connected power of photovoltaic power. , and They are respectively Taiwan Transformer The amount of photovoltaic power generation, curtailment, and installed capacity within the region; The expression for the active and reactive power balance constraint of the intelligent soft switch is: ; ; ; ; ; ; ; In the formula, and for t time i Active and reactive power injected into the node; and for t time j Active and reactive power injected into the node; and for t time k The energy storage power injected into the node and the total EV power; and for i Nodes and j The loss factor of the converter at the node; and for t Always connected to the node i and nodes j The losses of the SOP converters at both ends; , and , Access nodes i and j Upper and lower limits of reactive power of the converter at the node; and Access nodes i and j The capacity of the converter in the node.

[0015] As a preferred scheme for a rural power distribution area flexible interconnection and charging and storage station dual-layer collaborative optimization scheduling device, the constraints of the lower-layer charging and storage station operator optimization scheduling model in the dual-layer game optimization model construction module include: the SOC operation constraint of the energy storage system and the energy storage charging and discharging power constraint. The expression for the SOC operating constraint of the energy storage system is: ; ; In the formula, For energy storage systems t State of charge at any given moment; For ESS system t State of charge at time -1; and These are the energy storage charging and discharging efficiencies, respectively. and for t The charging and discharging power of energy stored at all times; This refers to the rated capacity of the energy storage. and These represent the minimum and maximum values ​​of the energy storage SOC. The expression for the energy storage charging and discharging power constraint is: ; In the formula, , , , These represent the upper and lower limits of energy storage charging and discharging power, respectively.

[0016] The present invention has the following advantages: First, high efficiency through multi-source collaboration: By integrating the spatial power mutual assistance capability of intelligent soft switches with the temporal energy regulation capability of charging and storage stations, the source-load spatiotemporal complementary characteristics of rural commercial and residential transformer areas are deeply explored, significantly improving the operating efficiency of the distribution network and the level of new energy consumption.

[0017] Second, the user response is highly accurate: the user response model built on the Weber-Fechner law accurately quantifies the impact of user psychological threshold and saturation effect on charging and discharging willingness, effectively reducing the prediction deviation of user response and supporting the accurate control of the distribution network.

[0018] Third, it balances operational stability and economy: through multi-objective collaborative optimization scheduling, it significantly reduces the operating costs of the distribution network and the operating costs of charging and storage stations while ensuring voltage stability and load balance, achieving a win-win situation for both the grid side and the user side.

[0019] Fourth, it has good scenario adaptability: Designed for the load characteristics of rural transformer areas, which are "high during the day and low at night" and "duck curve", it can be directly adapted to the transformation and upgrading of existing rural power distribution networks without large-scale hardware replacement. It is easy to implement and has high promotion value. Attached Figure Description

[0020] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0021] The structures, proportions, sizes, etc. illustrated in this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed herein, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0022] Figure 1 This is a flowchart illustrating a two-layer collaborative optimization scheduling method for flexible interconnection of rural power distribution areas and charging / storage power stations provided in Embodiment 1 of the present invention. Figure 2 This is a schematic diagram of a two-layer collaborative optimization scheduling model framework for flexible interconnection of rural power distribution areas and charging and storage power stations provided in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the flexible interconnection distribution network topology and equivalent circuit based on SOP in the dual-layer collaborative optimization scheduling method for flexible interconnection of rural distribution areas and charging and storage power stations provided in Embodiment 1 of the present invention. Figure 4 This is a schematic diagram of the B2B VSC type smart soft switch topology in a two-layer collaborative optimization scheduling method for flexible interconnection of rural distribution areas and charging and storage power stations provided in Embodiment 1 of the present invention; Figure 5 This is a schematic diagram of the algorithm for solving the two-layer optimization scheduling model in the two-layer collaborative optimization scheduling method for flexible interconnection of rural power distribution areas and charging and storage power stations provided in Embodiment 1 of the present invention; Figure 6 This is a schematic diagram comparing the power and capacity utilization results of various schemes in scenario one of the possible embodiments provided in Embodiment 1 of the present invention; Figure 7This is a schematic diagram of the electricity price, SOP, and power allocation results of the charging and storage power station under Scheme 3 in Scenario 1 of Embodiment 1 of the present invention; Figure 8 This is a schematic diagram comparing the terminal voltages of various schemes in scenario one of the possible embodiments provided in Embodiment 1 of the present invention; Figure 9 This is a schematic diagram comparing the power and capacity utilization results of various schemes in scenario two of a possible embodiment provided in Embodiment 1 of the present invention; Figure 10 This is a schematic diagram of the electricity price, SOP, and power allocation results of the charging and storage power station under Scheme 3 in Scenario 2 of Embodiment 1 of the present invention; Figure 11 This is a schematic diagram comparing the terminal voltages of various schemes in scenario two of a possible embodiment provided in Embodiment 1 of the present invention; Figure 12 This is a schematic diagram of the architecture of a rural transformer area flexible interconnection and charging / storage station dual-layer collaborative optimization scheduling device provided in Embodiment 2 of the present invention. Detailed Implementation

[0023] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Example 1 See Figure 1 Embodiment 1 of the present invention provides a method for flexible interconnection of rural power distribution areas and dual-layer collaborative optimization scheduling of charging and storage power stations, comprising the following steps: S1. Based on the spatiotemporal complementary characteristics of source and load in rural commercial and residential transformer areas, a flexible interconnected system architecture including intelligent soft switches and charging and storage stations is constructed. S2. Based on the aforementioned flexible interconnected system architecture, a two-layer game optimization model is constructed, encompassing distribution network operators, charging and storage station operators, and electric vehicle users. The two-layer game optimization model includes an upper-layer distribution network operator optimization scheduling model and a lower-layer charging and storage station operator optimization scheduling model. The lower-layer charging and storage station operator optimization scheduling model is obtained by introducing the Weber-Fechner law to construct a user response model. S3. Solve the upper-level distribution network operator optimization scheduling model using the particle swarm optimization algorithm to obtain the interactive power command of the smart soft switch; pass the interactive power command as a boundary condition to the lower-level charging and storage station operator optimization scheduling model; S4. Based on the interactive power command, the optimized scheduling model of the lower-level charging and storage station operator is solved by a genetic algorithm to obtain the actual response power and service cost of the charging and storage station, and then fed back to the upper-level distribution network operator optimization scheduling model. S5. Based on the actual response power and the service cost, repeatedly solve the upper-level distribution network operator optimization scheduling model and the optimized lower-level charging and storage station operator optimization scheduling model until the deviation between the actual response power and the target power of the upper-level distribution network operator optimization scheduling model is less than a set threshold three times in a row. Then, determine that the flexible interconnection system has reached a game equilibrium state and obtain the global optimal scheduling scheme.

[0025] like Figure 2 As shown, this invention selects a typical rural flexible interconnection transformer area with significant source-load heterogeneity as the research object, covering commercial transformer areas and residential transformer areas.

[0026] In the commercial area, load fluctuations are highly coupled with the business cycle, exhibiting a significant "high during the day and low at night" characteristic. Although distributed photovoltaic systems alleviate some power supply pressure at midday, the disorderly connection of EVs in the evening can easily trigger a "source-load superposition" effect, leading to a surge in peak load.

[0027] Residential transformer substations: Due to the influence of specific loads such as agricultural irrigation, the morning peak shifts earlier. On an intraday scale, the high proportion of photovoltaic backflow at noon and the concentrated charging of EVs at night cause the net load of the substation to exhibit a typical "duck curve" characteristic, that is, the net load "valley" caused by the large photovoltaic power generation at noon and the "peak" of the evening peak load coexist.

[0028] From a spatiotemporal complementarity perspective: Temporally, although both distribution areas are at their lowest point in the early morning, the significant asynchronicity of their intraday load characteristics creates substantial capacity complementarity. Spatially, the natural misalignment of peak and off-peak load times between the two distribution areas provides a physical basis for utilizing Standard Operating Procedures (SOPs) to achieve cross-area power support and capacity sharing. In summary, the two distribution areas possess significant spatiotemporal complementarity potential. By constructing a flexible interconnected system including SOPs and charging / storage stations, physical isolation can be effectively broken down, enabling dynamic spatiotemporal energy transfer and resource sharing.

[0029] To address the challenges of unbalanced spatiotemporal load distribution in rural power distribution networks and the complex inter-stakeholder interests among network, substation, and vehicle operators, this invention proposes a two-tiered optimized scheduling method for flexible interconnected rural distribution areas, with the overall architecture as follows: Figure 2 As shown.

[0030] Upper-level model (DSO perspective): Represents the interests of the Distribution System Operator (DSO). The objectives are optimal system operation economy, voltage safety margin, and load balance across distribution areas. The main decision variables are the power flow transmitted by the State Operation Point (SOP) and the amount of photovoltaic (PV) reduction. Based on the global optimization results, the DSO transmits SOP interactive power commands as boundary conditions to the lower level.

[0031] Lower-level model (CSSO perspective): Represents the interests of the Charging and Storage Station Operator (CSSO). A user price response mechanism is built based on the Weber-Fechner law to guide EVs in orderly charging and discharging. With the goal of minimizing station operating costs, the charging and discharging pricing strategy and internal scheduling plan are optimized, and the actual response power and service cost information of the EV cluster are fed back to the upper layer.

[0032] Through iterative game and information interaction between upper and lower layers, the system seeks the Nash equilibrium point to achieve a balance of interests and resource coordination among multiple stakeholders, including the power distribution network, charging and storage stations, and EV users. This fundamentally alleviates the problem of uneven spatial and temporal distribution of load in rural areas while meeting the demands of all parties.

[0033] In this embodiment, in step S1, based on the spatiotemporal complementary characteristics of source and load in rural commercial transformer areas and residential transformer areas, a flexible interconnected system architecture including intelligent soft switches and charging and storage stations is constructed.

[0034] Specifically, based on the analysis from a spatiotemporal complementary perspective, a flexible interconnected system architecture incorporating intelligent soft switching and charging / storage stations is constructed, such as... Figure 3 As shown, this system can be equivalent to a low-voltage active distribution network that achieves flexible closed-loop operation through a SOP DC link. In terms of physical architecture, distributed photovoltaic (PV) systems, charging and storage stations, and various loads are all connected to the Point of Common Coupling (PCC). Regarding operational characteristics, rural distribution networks exhibit a significant high impedance ratio due to their long and thin feeders. Under normal operating conditions (i.e., PV power generation can be fully absorbed locally), the power flow is from the beginning to the end of the feeder, with the voltage gradually decreasing along the line. When PV output exceeds local absorption capacity, the PCC node becomes a power injection source, and excess power is fed back to the beginning of the feeder via line impedance. Due to the voltage drop mechanism dominated by line resistance, this leads to a significant voltage increase at the PCC and the end of the feeder. Without effective flexible regulation, this can easily lead to voltage exceeding the upper limit and the risk of curtailment.

[0035] In a flexible interconnection system between two distribution areas, feeder ( ) represents any feeder in either commercial distribution area A or rural residential distribution area B. Let the voltage at the feeder's starting end (low-voltage side outlet of the distribution transformer) be and the voltage at the end be ∠0°. Define the active and reactive power injected into the grid side as and , respectively, the line impedance as (the resistive component of the line impedance in a rural low-voltage distribution network cannot be ignored), and the active and reactive power of the user load as and .

[0036] Assuming the photovoltaic system operates at unity power factor, its output active power is given by ; the active and reactive power flowing into the SOP are defined as and , respectively. Based on the power flow relationship of the distribution network, the feeder voltage drop can be expressed as: (1) Given the short power supply radius of low-voltage distribution substations and the extremely small voltage phase angle difference between the first and last nodes under normal operating conditions, based on engineering approximations, the transverse component of voltage drop can be ignored. The voltage at that point can be expressed as: (2) The inflow from the grid side in equation (2) Replace the active and reactive power at the point with the actual active and reactive power load demand of the users in the system. and Photovoltaic grid-connected power And the flow into SOP active power and reactive power It indicates that the Taiwan area The voltage at that point is: (3) In addition, to ensure the safe operation of the system, the transformer substation... voltage at The grid-connected power of photovoltaic systems must also meet the following physical constraints: (4) (5) (6) In the formula, for The lower limit of operation; for Limit the operation; , and They are respectively Taiwan Transformer The amount of photovoltaic power generation, curtailment, and installed capacity within the region.

[0037] In the rural flexible interconnection system constructed in this invention, the SOP (Standard Operating Position) serves as a novel fully controlled power electronic device, replacing traditional mechanical tie switches, and is the core execution unit for realizing spatiotemporal energy mutual assistance among distribution areas. Unlike the traditional rigid tie mode, which is constrained by "closed-loop design and open-loop operation," the SOP, with its millisecond-level response speed and continuous adjustment characteristics, can achieve precise migration of active power flow between feeders and dynamic support of reactive voltage at the ports, thereby significantly enhancing the operational flexibility and dispatchability of the distribution system. Figure 4 As shown, this invention takes a typical back-to-back voltage converter type two-port SOP as an example. Its symmetrical topology consists of two AC-DC converters connected by a DC capacitor, which has bidirectional active power transmission and four-quadrant operation capabilities.

[0038] SOPs typically employ a master-slave control mode (PQ-V) dc Q) To achieve independent decoupling control of active and reactive power at both ports. Assume the converters on both sides of SOP are connected to node A of the distribution area. i Hetai District B Node j The DC link passes through the node k Coupled with charging and storage power stations, such as Figure 3 As shown, the power injection direction of the SOP is defined as positive. The SOP, as the system's power exchange hub, must satisfy the following active power balance constraints during operation: (7) (8) (9) In the formula, and for t time i Active and reactive power injected into the node; and for t time j Active and reactive power injected into the node; and for t time k The energy storage power injected into the node and the total EV power; and for i Nodes and j The loss factor of the converter at the node; and for t Always connected to the node i and nodes j The losses of the SOP converters at both ends.

[0039] (10) (11) (12) (13) , and , Access nodes i and j Upper and lower limits of reactive power of the converter at the node; and Access nodes i and j The capacity of the converter in the node.

[0040] In this embodiment, in step S2, based on the flexible interconnected system architecture, a two-layer game optimization model covering distribution network operators, charging and storage station operators, and electric vehicle users is constructed. The two-layer game optimization model includes an upper-layer distribution network operator optimization scheduling model and a lower-layer charging and storage station operator optimization scheduling model. The lower-layer charging and storage station operator optimization scheduling model is obtained by introducing the Weber-Fechner law to construct a user response model.

[0041] Specifically, this paper first analyzes the characteristics of EV travel and charging in rural scenarios. The target users of the charging and storage stations selected in this invention are private cars belonging to permanent rural residents and those passing through or briefly stopping at scenic spots. Considering that rural travel radii are relatively fixed, the behavior of these users exhibits strong regularity. This invention modifies and calibrates the classical probability distribution function based on statistical data to accurately characterize the differentiated travel characteristics of the two areas. Assuming the daily mileage of EVs... x It follows a logarithmic distribution, and its probability density function is: (14) In the formula, and These represent the expectation and standard deviation of the probability density function, respectively. For different transformer substation characteristics, in commercial transformer substations... , In rural residential areas , .

[0042] EV grid connection and disconnection times and It follows a piecewise normal distribution, and its probability density distribution is as follows: (15) (16) In the formula, and The expected times for EVs to connect to and disconnect from the grid; and This represents the standard deviation of the times of grid connection and grid disconnection. In commercial transformer substations... , , , In rural residential areas , , , .

[0043] In this embodiment, traditional demand response models are typically based on the assumption of "perfectly rational economic man" and use a linear price elasticity of demand matrix to describe user behavior. However, this assumption ignores the bounded rationality and psychological perception biases of users in the decision-making process. Especially in rural power grids where charging facilities are relatively scarce, user decisions are affected by the dual nonlinear coupling of economic incentives (electricity price) and range anxiety (SOC).

[0044] To this end, this invention introduces the Weber-Fechner law from psychophysics. This law reveals a logarithmic nonlinear mapping mechanism between psychological perception and physical stimulus: the degree to which a person perceives a change in stimulus is proportional to the logarithm of the stimulus quantity, meaning that a user's sensitivity to price changes depends on the relative rate of change rather than the absolute difference, and there exists a perception threshold and a saturation effect. This invention utilizes this law to construct a user response model under multidimensional stimuli, defining real-time electricity price and battery SOC as physical stimulus sources, defining the probability of a user's charging / discharging intention as a psychological perception quantity, and constructing the response function as follows: (17) (18) In the formula, and They are respectively t Time of the first n The charging and discharging response of an EV; and These are the sensitivity coefficients of EVs to charging and discharging electricity prices, respectively. and They are respectively t The charging and discharging electricity price of EVs at any given time; and This represents the user's sensitivity to the SOC. For the first n EVs in t The state of charge at any given moment; and These are the user's charging and discharging response constants, respectively.

[0045] Considering the differences in user participation willingness, the EV group is divided into two categories: Category I is unidirectional ordered charging type, only responding to charging guidance, with a constant discharge response of 0; Category II is bidirectional V2G interactive type, deeply participating in charge and discharge scheduling. To avoid battery degradation, the model constrains the vehicle to be in only a single charging / discharging state at any given time. Based on this, t Time of the first n The state-of-charge evolution equation for an EV is: (19) In the formula, Δ t The scheduling time interval; and The first n EVs in t The charging and discharging power at any given moment; Improve battery charging and discharging efficiency; For the first n Battery capacity of an EV; and for t Time of the first n The charging / discharging status value of the EV indicates that the EV is not charging / discharging at the current moment, while 1 indicates that the EV is charging / discharging at the current moment.

[0046] During the response process, the EV still needs to meet its own energy conservation constraint, that is, the sum of its charging and discharging capacity during the scheduling cycle should equal the vehicle's daily charging demand. The daily charging demand constraint generated based on Monte Carlo sampling is as follows: (20) In addition, EVs must also meet individual physical constraints (power, SOC boundaries) and charging / storage station group control constraints (pile group capacity limitations) during charging and discharging: (twenty one) (twenty two) (twenty three) (twenty four) In the formula, and These are the upper and lower safety limits of the battery's state of charge (SOC). , , and These are the upper limits of charging and discharging power for a single vehicle; and for t The total number of vehicles participating in charging and discharging at any given time; The total number of charging piles in the charging and energy storage station.

[0047] The user response model constructed in this invention successfully maps the economic incentives of electricity price signals and the physical constraints of State of Charge (SOC) into the probability of users' psychological willingness by introducing a logarithmic function. In the subsequent two-level game model, this model will serve as a key link connecting the upper-level power command and the lower-level user behavior, enabling precise guidance of flexible loads by electricity prices while taking into account users' psychological comfort zones.

[0048] In this embodiment, the objective function of the upper-level distribution network operator's optimized scheduling model is: the upper-level model represents the interests of the DSO and aims to achieve multi-objective collaborative optimization of system operation economy, voltage security and spatiotemporal load balance in the distribution area.

[0049] The objective function of the upper-level distribution network operator's optimized scheduling model is a multi-objective collaborative optimization function; the multi-objective collaborative optimization function includes: distribution network operation cost function, voltage offset function, and load balancing function.

[0050] Among them, the distribution network operation cost function: To improve the economic efficiency of distribution network operation, a comprehensive cost function is constructed that includes electricity purchase cost, photovoltaic consumption cost, SOP operation and maintenance cost, and flexibility service fee. (25) (26) (27) (28) In the formula, The total operating cost of the distribution network; and These are the unit electricity costs for photovoltaic power generation and curtailed solar power, respectively. for t The service fees that are constantly paid to the charging and storage power station; The operating and maintenance cost per unit of electricity for SOP; for t The electricity price charged by the power station for charging and storage to the distribution network; for t The electricity price that the DSO in the time zone purchases from the upper-level power grid; for t The subsidy fee for the power consumption of the DSO in the time zone for SOP interaction; for t The electricity price that the power station purchases from the distribution network; for t The electricity price sold to users in both areas at all times; and They are respectively t Active power flowing into and out of the charging and storage power station at all times through SOP; for tTotal interactive power at each SOP; for t The flexible interconnected system purchases power from the upper-level power grid. for t User demand load in the two transformer areas at any given time; , These are the photovoltaic power generation and curtailment within the transformer substation, respectively. T This represents the total number of time periods in the scheduling cycle. l ∈{A,B} represents commercial station area A and residential station area B.

[0051] Voltage offset function: To improve voltage quality in rural weak power grids, a safety function is constructed with the goal of minimizing node voltage offset, aiming to mitigate the risk of voltage exceedance caused by source-load fluctuations. (29) (30) In the formula, The objective function is the voltage offset. , and feeders Corresponding common connection point The set voltage reference value and upper and lower voltage limits at the location; for t Time about the feeder end Voltage exceeding limits at the location.

[0052] Load Balancing Function: Addressing the issues of uneven power distribution and polarization between light and heavy loads in rural flexible interconnection systems, this invention achieves dynamic power balancing between transformers through Standard Operating Procedures (SOPs). To this end, a load balancing objective function based on load factor variance is constructed: (31) (32) (33) (34) (35) (36) In the formula, for t Time zone transformer The actual active load; for t Time Transformer Internal active load power demand; for t Timing feeder Internal photovoltaic grid-connected power; for t Timing feeder Inflow into SOP The active power; Transformer for the distribution area The maximum amount of active power that can be provided; for t Time zone transformer Load rate; for t Time zone transformer The load rate offset penalty function; Transformer for the distribution area The load factor variance; Transformer for the distribution area Based on the variance of the mean load rate of the two zones.

[0053] In this embodiment, the constructed upper-level optimization model involves the SOP power flow equation and voltage safety constraints, which is a typical high-dimensional non-convex nonlinear programming problem. The upper-level objectives cover three dimensions: operational economy, voltage over-limit risk, and load balancing. Given the significant heterogeneity of each indicator in terms of dimensions and numerical scale, direct weighted solution can easily lead to an imbalance in optimization preferences. Therefore, the Max-Min Normalization method is used to make each sub-objective dimensionless, and then a linear weighting method is used to construct an equivalent single-objective function.

[0054] Based on this, The objective function for the top-level leader (DSO); This is a strategy set for the amount of photovoltaic curtailment and the interaction power of SOP in each time period of DSO. Among them, the power of the interaction between SOP and charging and storage station will be transmitted to the lower layer as the target instruction. After optimization, the lower layer will feed back the actual response power and service fee to the upper layer to realize the iterative solution of the two-layer model. In terms of the constraint system, the upper layer model mainly considers the core physical constraints of distribution network operation, including system power flow and voltage constraints (1)-(4), photovoltaic output limit (5)-(6), and SOP-related power balance and capacity limit formulas (7)-(13), etc. Based on this, the upper layer model can be finally expressed as: The expression for the multi-objective collaborative optimization function is: (37) In the formula, The objective function for the upper-level distribution network operator; , , All are weighted coefficients; , , The objective function after dimensionless processing , and .

[0055] In this embodiment, to enhance the enthusiasm of charging and storage power stations to participate in grid interaction, the lower-level model focuses on CSSO benefits, with the goal of minimizing the operating costs of charging and storage power stations.

[0056] The objective function expression for the optimized scheduling model of the lower-level charging and storage power station operator is: (38) (39) (40) (41) In the formula, Let the objective function be about the cost of the charging and storage power station; for t The electricity price that the power station purchases from the grid; Costs associated with the operation and maintenance of energy storage systems; for t The electricity price for the EV; for t The power storage station provides a penalty for power offset at all times; for t Electricity purchase price for EVs; and for t The charging and discharging power of the EV group at any given time; yes t The electricity price at which a power station sells electricity to the grid; for t The difference between the power supplied by the charging and storage power station and the power demanded by the grid is always present; , They are respectively t Active power flowing into and out of the charging and storage power station at all times through SOP; for t Total power of energy storage and charging at all times.

[0057] The constraints of the lower-level charging and storage power station operator's optimized scheduling model include: the SOC operation constraint of the energy storage system and the energy storage charging and discharging power constraint; The expressions for the SOC dynamic equations and operating constraints of the energy storage system are as follows: (42) In the formula, For energy storage systems t State of charge at any given moment; For ESS systemt State of charge at time -1; and These are the energy storage charging and discharging efficiencies, respectively. and for t The charging and discharging power of energy stored at all times; This refers to the rated capacity of the energy storage.

[0058] The SOC of energy storage should meet the following constraints: (43) and These represent the minimum and maximum values ​​of the energy storage SOC.

[0059] The expression for the energy storage charging and discharging power constraint is: (44) In the formula, , , , These represent the upper and lower limits of energy storage charging and discharging power, respectively.

[0060] In this embodiment, The objective function for the lower-level follower CSSO; This is a strategy set for the sales and purchase prices of CSSO electricity and the charging and discharging power of EVs and energy storage in each time period. The solution space of the lower-level model is mainly constrained by the dual constraints of EV user behavior characteristics and energy storage system physical characteristics. To characterize the EV response power, firstly, the random distribution characteristics of EV intraday travel are fitted according to Equations (14)-(16) to define the spatiotemporal window and capacity boundary of vehicles that can participate in scheduling; on this basis, combined with the electricity price strategy of charging and storage stations, the real-time charging and discharging response probability of users is quantified by Equations (17)-(18), and the actual aggregate response power of the EV group is calculated under the power and energy constraints defined by Equations (19)-(24). At the same time, the energy storage system needs to meet the physical conditions such as capacity limit, charging and discharging power limit and energy balance given by Equations (42)-(44) in the process of participating in the response to ensure the stability and feasibility of the energy storage system throughout the entire cycle. Under the premise of satisfying the above-mentioned multiple coupling constraints of EVs and energy storage systems, the lower-level optimization problem can be expressed as: (45) In the formula, Let be the objective function for the cost of the charging and storage power station.

[0061] In this embodiment, in step S3, the upper-level distribution network operator optimization scheduling model is solved by particle swarm optimization to obtain the interactive power command of the smart soft switch; the interactive power command is then passed as a boundary condition to the lower-level charging and storage station operator optimization scheduling model. Specifically, such as Figure 5 As shown, for optimization problems in the upper-level DSO model that include continuous decision variables such as SOP interaction power and light waste and have nonlinear constraint relationships, the Particle Swarm Optimization (PSO) algorithm, which has fast convergence speed and flexible parameter adjustment mechanism, is adopted.

[0062] The upper-level model, acting as the leader, optimizes the SOP interactive power and curtailment amount at each time point based on the distribution network operation objective function (25)-(35) and constraints (1)-(13), and transmits the target interactive power of the charging and storage power station as a strategy signal to the lower level.

[0063] In this embodiment, in step S4, based on the interactive power command, the optimized scheduling model of the lower-level charging and storage power station operator is solved by a genetic algorithm to obtain the actual response power and service cost of the charging and storage power station, and then fed back to the upper-level distribution network operator's optimized scheduling model.

[0064] Specifically, such as Figure 5 As shown, the lower-level CSSO model not only includes continuous variables (pricing strategies) but also involves a large number of discrete variables and a strongly nonlinear psychological response element introduced by the Weber-Fechner law, resulting in a solution space exhibiting multi-peak and discontinuous characteristics. A genetic algorithm (GA), which is highly adaptable and adept at handling discrete encoding, is employed for solving the problem. Leveraging its superior global search capabilities and adaptability to discrete / hybrid encoding, it effectively avoids local optima traps and accurately captures user response behavior.

[0065] After receiving the instruction, the lower-level model, as a follower, optimizes the objective function (38) of the charging and storage power station based on the user response model (24)-(27) and energy storage-related constraints (42)-(44), and feeds back the calculated actual response power deviation and service cost to the upper level.

[0066] In this embodiment, in step S5, based on the actual response power and the service cost, the upper-level distribution network operator's optimized scheduling model and the optimized lower-level charging and storage station operator's optimized scheduling model are repeatedly solved until the deviation between the actual response power and the target power of the upper-level distribution network operator's optimized scheduling model is less than a set threshold three times in a row. Then, it is determined that the flexible interconnection system has reached a game equilibrium state and the globally optimal scheduling scheme is obtained.

[0067] Specifically, such as Figure 5 As shown, through repeated iterations and corrections of the upper and lower layer strategies, until the deviation between the actual power and the upper layer target power is less than the set threshold three times in a row, the system is determined to have reached a game equilibrium state and obtained the globally optimal two-layer scheduling scheme.

[0068] In one possible embodiment, the verification algorithm is provided as follows: I. Example Setup This invention selects a typical rural two-unit flexible interconnection system for simulation analysis, with the topology as follows: Figure 3 As shown in Table 1, the system encompasses a commercial transformer substation A and a residential transformer substation B with complementary load characteristics. The two substations achieve flexible power exchange through a Standard Operating Procedure (SOP), and a charging / storage station is coupled to the DC link of the SOP to enhance the system's dynamic capacity expansion capability and energy coordination level. The main system parameters are shown in Table 1. Table 1 Parameters of Rural Flexible Interconnection System

[0069] To deeply analyze the carrying capacity limit and strategy robustness of rural areas under limited charging resources, this invention fixes the number of charging piles in the charging and energy storage station at 20 (simulating a transformer capacity-limited scenario) and constructs two scenarios with significantly different EV penetration rates to simulate the access pressure at different development stages: Scenario 1 (Regular Penetration Rate / Benchmark Scenario): The total number of EVs is set at 80, with a vehicle-to-charging-pile ratio of 4:1. This aims to simulate the system operation benchmark characteristics during the current regular development stage of EVs in rural areas and verify the economic efficiency and balance effect of the strategy under normal conditions.

[0070] Scenario 2 (High Penetration / Stress Test Scenario): The total number of EVs is set to increase to 160, with a vehicle-to-charging-pile ratio of 8:1. This simulates the extreme condition of resource scarcity due to a future explosive growth in EVs and a shortage of charging piles. The focus is on examining the extreme performance of the strategy of this invention in alleviating charging congestion and improving the system's resilience.

[0071] In addition, to decouple and quantify the marginal contribution of different levels of scheduling methods, three operating schemes were set up for comparison in each of the above scenarios: Option 1 (Traditional Rigid Operation): This option adopts a radial operation mode, with independent distribution substations and no SOP interconnection, and unordered EV charging. It serves as a control group to reveal the physical limitations and bottlenecks of traditional distribution networks when dealing with a high proportion of heterogeneous load sources.

[0072] Option 2 (SOP Physical Interconnection): Only SOPs are introduced to achieve power interconnection between distribution stations, but EVs still operate in an unordered mode. The aim is to quantify the fundamental benefits of SOP physical access in alleviating distribution station imbalance and voltage exceedance.

[0073] Option 3 (SOP-Recharge-Storage Game-Based Collaborative Scheduling): Building upon Option 2, this invention employs a two-layer collaborative scheduling strategy based on master-slave game theory. As an experimental group, it aims to highlight the value of this invention in addressing spatiotemporal imbalances and improving economic efficiency through comparison with Option 2.

[0074] II. Results Analysis Scenario 1: Operational Characteristics Analysis under Conventional Penetration Rates like Figure 6 As shown, the net load power curves of the transformer substations and the time-series distribution of transformer capacity utilization are displayed for the three operating schemes in Scenario 1.

[0075] Option 1: Due to the concentrated output of photovoltaic power during midday and the characteristics of commercial / residential loads, transformer substations A and B experience significant net load troughs during the 5:00-9:00 and 11:00-14:00 periods, respectively, forming "light load periods." Conversely, during the 18:00-21:00 period, the disorderly charging load of EVs and the evening peak load create a strong "source-load superposition" effect, causing the capacity utilization rate to surge to over 75%. This spatiotemporal mismatch leads to a sharp increase in the system peak-to-valley difference, resulting in a high risk of voltage exceeding limits.

[0076] Option 2: After introducing SOP, the two distribution areas achieve power mutual assistance through the DC link, which to some extent fills the trough of distribution area A during the peak photovoltaic power generation period at noon (11:00-14:00). However, due to the physical characteristics of SOP, which can only perform spatial transmission rather than temporal storage, and the lack of active coordination with EVs, the system cannot effectively cope with the absolute rigid increase in load during the evening peak, and the improvement of load imbalance is passive and limited.

[0077] Option 3: In comparison, after adopting the strategy proposed in this invention, the peak power of transformer area A and transformer area B is reduced by about 30% and 20% respectively, and the capacity utilization rate is effectively controlled below 70%. At the same time, the power level during off-peak hours is significantly increased by about 100%, and the corresponding capacity utilization rate is increased from about 10% to 30%. This achieves dynamic balance of power in the time dimension (peak shaving and valley filling) and the spatial dimension (dynamic balance), significantly alleviating the operating pressure of the distribution network.

[0078] The SOP and power allocation of the charging and storage power station in Scheme 3, as well as the corresponding electricity pricing strategy, are as follows: Figure 7 As shown. Combined with Figure 6Analysis shows that this invention, through the Weber-Fechner response model, accurately quantifies the nonlinear psychological perception threshold of users to price stimuli, thereby generating a highly sensitive dynamic time-of-use electricity price signal. This signal effectively guides EV users to proactively shift their charging behavior from evening peak hours to nighttime off-peak hours or midday peak photovoltaic power generation periods. During this process, the SOP-optimized transmission power between distribution stations effectively fills the load gap, maintaining high-power operation during periods of significant load heterogeneity in the distribution area; simultaneously, the charging and storage stations, in coordination with the electricity price signal, further smooth out net load fluctuations.

[0079] like Figure 8 As shown, the voltage timing distribution evolution of key nodes at the feeder end is illustrated under various schemes. Combined with... Figure 6 Analysis reveals that in Schemes 1 and 2, the lack of effective guidance for user-side resources leads to a surge in line voltage drop due to the concentrated charging load of EVs during the evening peak hours. The voltage at the ends of both substations experiences a severe drop during the heavy load period (18:00-20:00), falling below the safe lower limit of 0.93 pu, posing a significant voltage safety risk to the system. In contrast, Scheme 3, by introducing a source-load coordinated control mechanism, reduces the power flow pressure during peak hours at the source. While achieving dynamic power balance between substations, it successfully maintains the voltage at the ends of both substations above the safe lower limit throughout the entire period. This demonstrates that the present invention can effectively solve the problem of polarization between light and heavy loads, ensuring voltage safety throughout the entire period.

[0080] As shown in Table 2, the operational indicators of the three schemes were further quantitatively compared using multi-dimensional metrics. In Scheme 1, both transformer substations exhibited significant peak-to-valley differences and utilization variance. Scheme 2, relying solely on Standard Operating Procedures (SOPs) for flexible interconnection between the two substations, reduced the load variance of substation A to below 70%, but had limited improvement on substation B and did not significantly reduce the peak-to-valley difference between the two substations. In contrast, Scheme 3, by introducing game-theoretic collaboration between charging and energy storage stations, further converged the peak-to-valley differences between the two substations to 61.5% and 56.8% of their initial values, respectively, and controlled the load variance to within 1%. More importantly, EV users achieved a significant reduction in charging costs, even turning from paying to profiting from negative costs, through response-optimized electricity pricing. This fully verifies that the two-layer game mechanism can effectively balance the safety constraints of the grid side and the economic incentives of the user side, achieving a synergistic improvement in grid security and user benefits.

[0081] Table 2 Comparison of Results of Different Solutions in Scenario 1

[0082] Scenario 2: Operational Characteristics Analysis under High Penetration Rates like Figure 9 As shown, the net load power curves of the distribution area and the time-series distribution of transformer capacity utilization are presented for the three operating schemes in Scenario 2. Combined with... Figure 6 The results of Scenario 1 are used to compare and analyze the results of different solutions in the two scenarios: Option 1: Similar to Scenario 1, due to the concentrated output of photovoltaic power at noon and the characteristics of commercial / residential loads, the light load periods of the two transformer areas are basically the same in both scenarios; however, with the increase in the scale of EV access, the "source-load superposition" effect is more prominent during the period of 18:00-21:00, resulting in a higher peak load than in Scenario 1, and the capacity utilization rate rapidly climbs to over 80%, further widening the system peak-valley difference and significantly increasing the risk of voltage exceeding limits.

[0083] Option 2: In Scenario 2, after introducing SOP to achieve flexible interconnection of transformer substations, its effect on alleviating midday light load troughs is basically the same as in Scenario 1; however, it shows more obvious limitations in peak shaving capabilities during the evening peak hours. Since SOP mainly functions through spatial power balancing and lacks energy regulation capabilities in the time dimension, it is difficult to fundamentally improve the load imbalance between the two transformer substations.

[0084] Option 3: In comparison, by applying the strategy of this invention, the peak power of transformer area A and transformer area B is effectively reduced by approximately 40% and 34% respectively, and the capacity utilization rate is smoothed out to around 60%. At the same time, the power during light load periods (2:00-8:00) is increased by approximately 100%, corresponding to an increase in capacity utilization rate to approximately 40%. This further achieves peak shaving and valley filling of power in the time dimension and dynamic balance in the spatial dimension, thereby significantly improving the balance and safety margin of system operation.

[0085] The SOP, power allocation of charging and storage power stations, and corresponding electricity pricing strategies in Scheme 3 are as follows: Figure 10 As shown. Combined with Figure 7 It can be seen that the optimized electricity pricing strategies under the two scenarios exhibit a high degree of consistency, indicating that under conditions of high EV penetration, the constructed Weber-Fechner response model can stably characterize the nonlinear psychological perception mechanism of users in response to electricity price stimuli. Guided by price signals, users can still actively shift their charging behavior from evening peak hours to nighttime off-peak hours or midday when photovoltaic output is abundant, demonstrating the model's good stability and adaptability under different operating scenarios. During this process, the SOP equipment utilization rate increased by approximately 100% compared to scenario one, effectively balancing the power distribution between the two areas.

[0086] like Figure 11 As shown, the voltage timing distribution evolution of key nodes at the feeder end is presented under various schemes in Scenario 2. Combined with... Figure 8Analysis shows that with the increasing penetration rate of electric vehicles, without effective regulation (Schemes 1 and 2), the period during which the terminal voltage is below the safe lower limit of 0.93 pu extends to 4-5 hours, greatly increasing the voltage safety risk of the distribution network. In contrast, Scheme 3 achieves dynamic spatiotemporal power balance through multi-energy coordinated scheduling, enabling the terminal voltage to remain within the safe operating range throughout the entire period even in high penetration scenarios, further verifying the good adaptability of this invention under different penetration scenarios.

[0087] Table 3 further illustrates the comparison of operational indicators for different schemes under high penetration scenarios. Comparing the results with Table 2, it can be seen that as the scale of EV access expands, all operational indicators in Scheme 1 deteriorate to varying degrees; while Scheme 2, after introducing SOP, reduces the utilization variance of transformer area A to 68.2%, its improvement on transformer area B is limited; in contrast, Scheme 3, by introducing a collaborative game mechanism, demonstrates superior control performance under the stringent conditions of larger-scale EV access: the peak-valley difference between the two transformer areas is significantly reduced to 39.5% and 30.3% of the initial values, and the load factor variance is controlled within 0.5%. This scheme supports double the charging load while increasing user-side revenue to 109.6% of the original level through electricity price incentives. This not only verifies the effectiveness of the two-layer game mechanism in smoothing load fluctuations but also demonstrates its superiority in balancing grid steady-state operation and the interests of multiple stakeholders under different EV access environments.

[0088] Table 3 Comparison of Results for Different Solutions in Scenario 2

[0089] In summary, this invention addresses the practical challenges of spatiotemporal mismatch between power sources and loads and difficulties in transformer capacity expansion in rural power distribution networks. It proposes a two-layer collaborative scheduling strategy for flexible interconnected distribution areas and charging / storage stations based on the Weber-Fechner law. Simulation verification using a typical rural distribution area example yields the following main conclusions: First, compared with using only SOP (Standard Operating Procedure) for flexible interconnection, the proposed strategy can significantly alleviate the uneven spatial distribution of power between the two transformer substations and achieve dynamic power smoothing over time, maintaining the load rate steadily within the ideal range of 20% to 70%. While rigidly guaranteeing the charging needs of large-scale EVs, this strategy increases user-side revenue to 109.6% of the initial level. It deeply promotes the local consumption of distributed photovoltaic power while synergistically improving the economic efficiency and safety margin of transformer substation operation, enhancing the adaptability and sustainable development capabilities of the distribution network in photovoltaic and EV integration scenarios.

[0090] II. Comparing the optimization results of EV access scenarios with conventional and high penetration rates reveals that the introduction of the two-layer game strategy enables the flexible interconnection system to deeply explore the spatiotemporal complementarity potential between distribution substations, significantly improving their carrying capacity. Without requiring distribution network hardware upgrades, EVs can participate in dispatch as mobile energy storage. The increased number of EVs further releases the spatiotemporal complementarity potential between substations, effectively improving the shared capacity of the two substations and achieving deep mutual support of distribution transformer resources. Simulation results show that this strategy can ensure that the terminal voltage remains stable within the acceptable range; while supporting twice the EV charging demand, it reduces the peak-valley difference to below 40% of its original value, and the variance of substation capacity utilization rate drops to below 0.5%, effectively alleviating the uneven spatiotemporal power distribution and the polarization between light and heavy loads caused by photovoltaic and EV access. This further verifies the robustness and scalability of the strategy under high load and high volatility environments. Research indicates that this model is expected to become a key technical path for improving the flexibility of distribution networks and the ability to accommodate new energy sources.

[0091] The application scenarios of this invention are as follows: In the scenario of flexible interconnection between rural commercial and residential distribution networks, this invention can realize dual-layer collaborative optimization scheduling of intelligent soft switches and charging and storage stations, and carry out power grid regulation and control in accordance with the spatiotemporal complementary characteristics of source and load in rural distribution areas.

[0092] In rural power distribution networks with high penetration of distributed photovoltaic power, this invention can improve the capacity for renewable energy absorption through multi-party game optimization, thereby alleviating the problems of large peak-valley differences and photovoltaic curtailment in the power distribution network.

[0093] In scenarios where a large number of electric vehicles are connected to rural power distribution networks, this invention can conduct scheduling based on the user's charging and discharging response characteristics, coordinating the operational needs of the power grid side, the charging and storage station side, and the user side.

[0094] In rural power distribution networks where economic and safe operation is sought, this invention can balance the control of power distribution network operating costs with the stable management of voltage and load, thereby improving overall operating efficiency.

[0095] In the context of intelligent transformation and upgrading of rural power distribution networks, this invention can rely on existing power grid hardware facilities to implement optimized scheduling without large-scale hardware replacement, thus adapting to the transformation needs of rural power grids.

[0096] It should be noted that the method of this disclosure embodiment can be executed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method of this disclosure embodiment, and the multiple devices will interact with each other to complete the method described.

[0097] It should be noted that the above description describes some embodiments of this disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0098] Example 2 See Figure 12 Embodiment 2 of the present invention also provides a rural transformer area flexible interconnection and charging and storage power station dual-layer collaborative optimization scheduling device, comprising: Flexible interconnected system architecture construction module 001 is used to construct a flexible interconnected system architecture including intelligent soft switches and charging and storage stations based on the spatiotemporal complementarity of source and load in rural commercial and residential transformer areas. The two-layer game optimization model construction module 002 is used to construct a two-layer game optimization model covering distribution network operators, charging and storage station operators, and electric vehicle users based on the flexible interconnected system architecture. The two-layer game optimization model includes an upper-layer distribution network operator optimization scheduling model and a lower-layer charging and storage station operator optimization scheduling model. The lower-layer charging and storage station operator optimization scheduling model is obtained by introducing the Weber-Fechner law to construct a user response model. The upper-level distribution network operator optimization scheduling model solution module 003 is used to solve the upper-level distribution network operator optimization scheduling model using the particle swarm optimization algorithm to obtain the interactive power command of the smart soft switch; and to pass the interactive power command as a boundary condition to the lower-level charging and storage station operator optimization scheduling model. The lower-level charging and storage power station operator optimization scheduling model solution module 004 is used to solve the optimized lower-level charging and storage power station operator optimization scheduling model based on the interactive power command using a genetic algorithm, to obtain the actual response power and service cost of the charging and storage power station, and to feed it back to the upper-level distribution network operator optimization scheduling model. The upper and lower layer model iterative solution module 005 is used to repeatedly solve the upper layer distribution network operator optimization scheduling model and the optimized lower layer charging and storage station operator optimization scheduling model based on the actual response power and the service cost, until the deviation between the actual response power and the target power of the upper layer distribution network operator optimization scheduling model is less than a set threshold three times in a row. Then, it is determined that the flexible interconnection system has reached the game equilibrium state and the globally optimal scheduling scheme is obtained.

[0099] In this embodiment, in the two-layer game optimization model construction module 002, the objective function of the upper-layer distribution network operator optimization scheduling model is a multi-objective collaborative optimization function; the multi-objective collaborative optimization function includes: distribution network operating cost function, voltage offset function, and load balancing function; The expression for the multi-objective collaborative optimization function is: ; In the formula, The objective function for the upper-level distribution network operator; , , All are weighted coefficients; , , The objective function after dimensionless processing , and ; The expression for the distribution network operation cost function is as follows: ; In the formula, The total operating cost of the distribution network; and These are the unit electricity costs for photovoltaic power generation and curtailed solar power, respectively. for t The service fees that are constantly paid to the charging and storage power station; The operating and maintenance cost per unit of electricity for SOP; for t The electricity price charged by the power station for charging and storage to the distribution network; for t The electricity price that the DSO in the time zone purchases from the upper-level power grid; for t The subsidy fee for the power consumption of the DSO in the time zone for SOP interaction; for t The electricity price that the power station purchases from the distribution network; for t The electricity price sold to users in both areas at all times; and They are respectively t Active power flowing into and out of the charging and storage power station at all times through SOP; for t Total interactive power at each SOP; for t The flexible interconnected system purchases power from the upper-level power grid. for t User demand load in the two transformer areas at any given time; , These are the photovoltaic power generation and curtailment within the transformer substation, respectively. T This represents the total number of time periods in the scheduling cycle. l ∈{A,B} represents commercial station area A and residential station area B; The expression for the voltage offset function is: ; ; In the formula, The objective function is the voltage offset. , and feeders Corresponding common connection point The set voltage reference value and upper and lower voltage limits at the location; for t Time about the feeder end Voltage exceeding limit at the location; The expression for the load balancing function is: ; ; ; ; ; In the formula, for t Time zone transformer The actual active load; for t Time Transformer Internal active load power demand; for t Timing feeder Internal photovoltaic grid-connected power; for t Timing feeder Inflow into SOP The active power; Transformer for the distribution area The maximum amount of active power that can be provided; for t Time zone transformer Load rate; for t Time zone transformer The load rate offset penalty function; Transformer for the distribution area The load factor variance; Transformer for the distribution area Based on the variance of the mean load rate of the two zones.

[0100] In this embodiment, in the two-layer game optimization model construction module 002, the objective function expression of the lower-layer charging and storage power station operator optimization scheduling model is: ; In the formula, Let the objective function be about the cost of the charging and storage power station; for t The electricity price that the power station purchases from the grid; Costs associated with the operation and maintenance of energy storage systems; for t The electricity price for the EV; for t The power storage station provides a penalty for power offset at all times; for t Electricity purchase price for EVs; and for t The charging and discharging power of the EV group at any given time; yes t The electricity price at which a power station sells electricity to the grid; for t The difference between the power supplied by the charging and storage power station and the power demanded by the grid is always present; , They are respectively t Active power flowing into and out of the charging and storage power station at all times through SOP; for t Total power of energy storage and charging at all times.

[0101] In this embodiment, in the two-layer game optimization model construction module 002, the expression of the user response model is: ; ; In the formula, and They are respectively t Time of the first n The charging and discharging response of an EV; and These are the sensitivity coefficients of EVs to charging and discharging electricity prices, respectively. and They are respectively t The charging and discharging electricity price of EVs at any given time; and This represents the user's sensitivity to the SOC. For the first n EVs in t The state of charge at any given moment; and These are the user's charging and discharging response constants, respectively.

[0102] In this embodiment, the constraints of the upper-level distribution network operator's optimized scheduling model in the two-layer game optimization model construction module 002 include: voltage constraints at the common connection point of the distribution area, photovoltaic grid-connected power constraints, and active and reactive power balance constraints of smart soft switching. The expression for the voltage constraint at the common coupling point of the transformer area is: ; In the formula, Taiwan District Voltage at the point; for Lower limit of operating voltage; for Upper limit of operating voltage; The expression for the photovoltaic grid-connected power constraint is: ; ; In the formula, This refers to the grid-connected power of photovoltaic power. , and They are respectively Taiwan Transformer The amount of photovoltaic power generation, curtailment, and installed capacity within the region; The expression for the active and reactive power balance constraint of the intelligent soft switch is: ; ; ; ; ; ; ; In the formula, and for t time i Active and reactive power injected into the node; and for t time j Active and reactive power injected into the node; and for t time k The energy storage power injected into the node and the total EV power; and for i Nodes and j The loss factor of the converter at the node; and for t Always connected to the node i and nodes j The losses of the SOP converters at both ends; , and , Access nodes i and j Upper and lower limits of reactive power of the converter at the node; and Access nodes i and j The capacity of the converter in the node.

[0103] In this embodiment, the constraints of the lower-level charging and storage power station operator optimization scheduling model in the two-layer game optimization model construction module 002 include: SOC operation constraints of the energy storage system and energy storage charging and discharging power constraints. The expression for the SOC operating constraint of the energy storage system is: ; ; In the formula, For energy storage systems t State of charge at any given moment; For ESS system t State of charge at time -1; and These are the energy storage charging and discharging efficiencies, respectively. and for t The charging and discharging power of energy stored at all times; This refers to the rated capacity of the energy storage. and These represent the minimum and maximum values ​​of the energy storage SOC. The expression for the energy storage charging and discharging power constraint is: ; In the formula, , , , These represent the upper and lower limits of energy storage charging and discharging power, respectively.

[0104] It should be noted that the information interaction and execution process between the modules of the above system are based on the same concept as the method embodiment in Embodiment 1 of this application, and the resulting technical effects are the same as those in the method embodiment of this application. For details, please refer to the description in the method embodiment shown above in this application, and it will not be repeated here.

[0105] Example 3 Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium storing program code for a two-layer collaborative optimization scheduling method for flexible interconnection of rural power distribution areas and charging and storage power stations. The program code includes instructions for executing the two-layer collaborative optimization scheduling method for flexible interconnection of rural power distribution areas and charging and storage power stations in Embodiment 1 or any possible implementation thereof.

[0106] Computer-readable storage media can be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0107] Example 4 Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor; The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor can call the program instructions to execute the rural power distribution area flexible interconnection and charging and storage power station dual-layer collaborative optimization scheduling method of Embodiment 1 or any possible implementation thereof.

[0108] Specifically, a processor can be implemented in hardware or software. When implemented in hardware, the processor can be a logic circuit, an integrated circuit, etc. When implemented in software, the processor can be a general-purpose processor that reads software code stored in memory. This memory can be integrated into the processor or located outside the processor and exist independently.

[0109] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable system. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0110] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing systems. They can be centralized on a single computing system or distributed across a network of multiple computing systems. Optionally, they can be implemented using program code executable by a computing system, thereby storing them in a storage system for execution by the computing system. In some cases, the steps shown or described can be performed in a different order than those presented herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0111] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A method for flexible interconnection of rural power distribution areas and dual-layer collaborative optimization scheduling of charging and storage power stations, characterized in that, include: Based on the spatiotemporal complementarity of source and load between rural commercial and residential transformer substations, a flexible interconnected system architecture including intelligent soft switches and charging and storage stations is constructed. Based on the aforementioned flexible interconnected system architecture, a two-layer game optimization model is constructed, encompassing distribution network operators, charging and storage station operators, and electric vehicle users. The two-layer game optimization model includes an upper-layer distribution network operator optimization scheduling model and a lower-layer charging and storage station operator optimization scheduling model. The lower-layer charging and storage station operator optimization scheduling model is obtained by introducing the Weber-Fechner law to construct a user response model. The upper-level distribution network operator's optimal scheduling model is solved using the particle swarm optimization algorithm to obtain the interactive power command of the smart soft switch; the interactive power command is then passed as a boundary condition to the lower-level charging and storage station operator's optimal scheduling model. Based on the interactive power command, the optimized scheduling model of the lower-level charging and storage power station operator is solved by a genetic algorithm to obtain the actual response power and service cost of the charging and storage power station, and then fed back to the upper-level distribution network operator's optimized scheduling model. Based on the actual response power and the service cost, the upper-level distribution network operator's optimized scheduling model and the optimized lower-level charging and storage station operator's optimized scheduling model are repeatedly solved until the deviation between the actual response power and the target power of the upper-level distribution network operator's optimized scheduling model is less than a set threshold three times in a row. At this point, it is determined that the flexible interconnection system has reached a game equilibrium state and the globally optimal scheduling scheme is obtained.

2. The method for flexible interconnection of rural power distribution areas and dual-layer collaborative optimization scheduling of charging and storage power stations according to claim 1, characterized in that, The objective function of the upper-level distribution network operator's optimized scheduling model is a multi-objective collaborative optimization function; the multi-objective collaborative optimization function includes: distribution network operating cost function, voltage offset function, and load balancing function; The expression for the multi-objective collaborative optimization function is: ； In the formula, The objective function for the upper-level distribution network operator; , , All are weighted coefficients; , , The objective function after dimensionless processing , and ; The expression for the distribution network operation cost function is as follows: ； In the formula, The total operating cost of the distribution network; and These are the unit electricity costs for photovoltaic power generation and curtailed solar power, respectively. for t The service fees that are constantly paid to the charging and storage power station; The operating and maintenance cost per unit of electricity for SOP; for t The electricity price charged by the power station for charging and storage to the distribution network; for t The electricity price that the DSO in the time zone purchases from the upper-level power grid; for t The subsidy fee for the power consumption of the DSO in the time zone for SOP interaction; for t The electricity price that the power station purchases from the distribution network; for t The electricity price sold to users in both areas at all times; and They are respectively t Active power flowing into and out of the charging and storage power station at all times through SOP; for t Total interactive power at each SOP; for t The flexible interconnected system purchases power from the upper-level power grid. for t User demand load in the two transformer areas at any given time; , These are the photovoltaic power generation and curtailment within the transformer substation, respectively. T This represents the total number of time periods in the scheduling cycle. l ∈{A,B} represents commercial station area A and residential station area B; The expression for the voltage offset function is: ； ； In the formula, The objective function is the voltage offset. , and feeders Corresponding common connection point The set voltage reference value and upper and lower voltage limits at the location; for t Time about the feeder end Voltage exceeding limit at the location; The expression for the load balancing function is: ； ； ； ； ； In the formula, for t Time zone transformer The actual active load; for t Time Transformer Internal active load power demand; for t Timing feeder Internal photovoltaic grid-connected power; for t Timing feeder Inflow into SOP The active power; Transformer for the distribution area The maximum amount of active power that can be provided; for t Time zone transformer Load rate; for t Time zone transformer The load rate offset penalty function; Transformer for the distribution area The load factor variance; Transformer for the distribution area Based on the variance of the mean load rate of the two zones.

3. The method for flexible interconnection of rural power distribution areas and dual-layer collaborative optimization scheduling of charging and storage power stations according to claim 2, characterized in that, The objective function expression of the lower-level charging and storage power station operator's optimized scheduling model is: ； In the formula, Let the objective function be about the cost of the charging and storage power station; for t The electricity price that the power station purchases from the grid; Costs associated with the operation and maintenance of energy storage systems; for t The electricity price for the EV; for t The power storage station provides a penalty for power offset at all times; for t Electricity purchase price for EVs; and for t The charging and discharging power of the EV group at any given time; yes t The electricity price at which a power station sells electricity to the grid; for t The difference between the power supplied by the charging and storage power station and the power demanded by the grid is always present; , They are respectively t Active power flowing into and out of the charging and storage power station at all times through SOP; for t Total power of energy storage and charging at all times.

4. The method for flexible interconnection of rural power distribution areas and dual-layer collaborative optimization scheduling of charging and storage power stations according to claim 3, characterized in that, The expression for the user response model is: ； ； In the formula, and They are respectively t Time of the first n The charging and discharging response of an EV; and These are the sensitivity coefficients of EVs to charging and discharging electricity prices, respectively. and They are respectively t The charging and discharging electricity price of EVs at any given time; and This represents the user's sensitivity to the SOC. For the first n EVs in t The state of charge at any given moment; and These are the user's charging and discharging response constants, respectively.

5. The method for flexible interconnection of rural power distribution areas and dual-layer collaborative optimization scheduling of charging and storage power stations according to claim 4, characterized in that, The constraints of the upper-level distribution network operator's optimized scheduling model include: voltage constraints at the point of common coupling of the transformer substations, photovoltaic grid-connected power constraints, and active and reactive power balance constraints of smart soft switching. The expression for the voltage constraint at the common coupling point of the transformer area is: ； In the formula, Taiwan District Voltage at the point; for Lower limit of operating voltage; for Upper limit of operating voltage; The expression for the photovoltaic grid-connected power constraint is: ； ； In the formula, This refers to the grid-connected power of photovoltaic power. , and They are respectively Taiwan Transformer The amount of photovoltaic power generation, curtailment, and installed capacity within the region; The expression for the active and reactive power balance constraint of the intelligent soft switch is: ； ； ； ； ； ； ； In the formula, and for t time i Active and reactive power injected into the node; and for t time j Active and reactive power injected into the node; and for t time k The energy storage power injected into the node and the total EV power; and for i Nodes and j The loss factor of the converter at the node; and for t Always connected to the node i and nodes j The losses of the SOP converters at both ends; , and , Access nodes i and j Upper and lower limits of reactive power of the converter at the node; and Access nodes i and j The capacity of the converter in the node.

6. The method for flexible interconnection of rural power distribution areas and dual-layer collaborative optimization scheduling of charging and storage power stations according to claim 5, characterized in that, The constraints of the lower-level charging and storage power station operator optimization scheduling model include: SOC operation constraints of the energy storage system and energy storage charging and discharging power constraints. The expression for the SOC operating constraint of the energy storage system is: ； ； In the formula, For energy storage systems t State of charge at any given moment; For ESS system t State of charge at time -1; and These are the energy storage charging and discharging efficiencies, respectively. and for t The charging and discharging power of energy stored at all times; This refers to the rated capacity of the energy storage. and These represent the minimum and maximum values ​​of the energy storage SOC. The expression for the energy storage charging and discharging power constraint is: ； In the formula, , , , These represent the upper and lower limits of energy storage charging and discharging power, respectively.

7. A rural transformer area flexible interconnection and charging / storage station dual-layer collaborative optimization scheduling device, employing the rural transformer area flexible interconnection and charging / storage station dual-layer collaborative optimization scheduling method as described in any one of claims 1-6, characterized in that, include: The flexible interconnected system architecture construction module is used to build a flexible interconnected system architecture including intelligent soft switches and charging and storage stations based on the spatiotemporal complementarity of source and load in rural commercial and residential transformer areas. A two-layer game optimization model construction module is used to construct a two-layer game optimization model covering distribution network operators, charging and storage station operators, and electric vehicle users based on the flexible interconnected system architecture. The two-layer game optimization model includes an upper-layer distribution network operator optimization scheduling model and a lower-layer charging and storage station operator optimization scheduling model. The lower-layer charging and storage station operator optimization scheduling model is obtained by constructing a user response model by introducing the Weber-Fechner law. The upper-level distribution network operator optimization scheduling model solution module is used to solve the upper-level distribution network operator optimization scheduling model using the particle swarm optimization algorithm to obtain the interactive power command of the smart soft switch; and to pass the interactive power command as a boundary condition to the lower-level charging and storage station operator optimization scheduling model. The lower-level charging and storage power station operator optimization scheduling model solution module is used to solve the optimized lower-level charging and storage power station operator optimization scheduling model based on the interactive power command using a genetic algorithm, to obtain the actual response power and service cost of the charging and storage power station, and feed it back to the upper-level distribution network operator optimization scheduling model. The upper and lower layer model iterative solution module is used to repeatedly solve the upper layer distribution network operator optimization scheduling model and the optimized lower layer charging and storage station operator optimization scheduling model based on the actual response power and the service cost, until the deviation between the actual response power and the target power of the upper layer distribution network operator optimization scheduling model is less than a set threshold three times in a row. At this point, it is determined that the flexible interconnection system has reached a game equilibrium state and the globally optimal scheduling scheme is obtained.

8. The rural transformer area flexible interconnection and charging / storage station dual-layer collaborative optimization scheduling device according to claim 7, characterized in that, In the two-layer game optimization model construction module, the objective function of the upper-layer distribution network operator optimization scheduling model is a multi-objective collaborative optimization function; the multi-objective collaborative optimization function includes: distribution network operation cost function, voltage offset function, and load balancing function; The expression for the multi-objective collaborative optimization function is: ； In the formula, The objective function for the upper-level distribution network operator; , , All are weighted coefficients; , , The objective function after dimensionless processing , and ; The expression for the distribution network operation cost function is as follows: ； In the formula, The total operating cost of the distribution network; and These are the unit electricity costs for photovoltaic power generation and curtailed solar power, respectively. for t The service fees that are constantly paid to the charging and storage power station; The operating and maintenance cost per unit of electricity for SOP; for t The electricity price charged by the power station for charging and storage to the distribution network; for t The electricity price that the DSO in the time zone purchases from the upper-level power grid; for t The subsidy fee for the power consumption of the DSO in the time zone for SOP interaction; for t The electricity price that the power station purchases from the distribution network; for t The electricity price sold to users in both areas at all times; and They are respectively t Active power flowing into and out of the charging and storage power station at all times through SOP; for t Total interactive power at each SOP; for t The flexible interconnected system purchases power from the upper-level power grid. for t User demand load in the two transformer areas at any given time; , These are the photovoltaic power generation and curtailment within the transformer substation, respectively. T This represents the total number of time periods in the scheduling cycle. l ∈{A,B} represents commercial station area A and residential station area B; The expression for the voltage offset function is: ； ； In the formula, The objective function is the voltage offset. , and feeders Corresponding common connection point The set voltage reference value and upper and lower voltage limits at the location; for t Time about the feeder end Voltage exceeding limit at the location; The expression for the load balancing function is: ； ； ； ； ； In the formula, for t Time zone transformer The actual active load; for t Time Transformer Internal active load power demand; for t Timing feeder Internal photovoltaic grid-connected power; for t Timing feeder Inflow into SOP The active power; Transformer for the distribution area The maximum amount of active power that can be provided; for t Time zone transformer Load rate; for t Time zone transformer The load rate offset penalty function; Transformer for the distribution area The load factor variance; Transformer for the distribution area Based on the variance of the mean load rate of the two zones.

9. A rural transformer area flexible interconnection and charging / storage station dual-layer collaborative optimization scheduling device according to claim 8, characterized in that, In the two-layer game optimization model construction module, the objective function expression of the lower-layer charging and storage power station operator optimization scheduling model is: ； In the formula, Let the objective function be about the cost of the charging and storage power station; for t The electricity price that the power station purchases from the grid; Costs associated with the operation and maintenance of energy storage systems; for t The electricity price for the EV; for t The power storage station provides a penalty for power offset at all times; for t Electricity purchase price for EVs; and for t The charging and discharging power of the EV group at any given time; yes t The electricity price at which a power station sells electricity to the grid; for t The difference between the power supplied by the charging and storage power station and the power demanded by the grid is always present; , They are respectively t Active power flowing into and out of the charging and storage power station at all times through SOP; for t Total power of energy storage and charging at all times.

10. A rural transformer area flexible interconnection and charging / storage station dual-layer collaborative optimization scheduling device according to claim 9, characterized in that, In the two-layer game optimization model construction module, the expression of the user response model is: ； ； In the formula, and They are respectively t Time of the first n The charging and discharging response of an EV; and These are the sensitivity coefficients of EVs to charging and discharging electricity prices, respectively. and They are respectively t The charging and discharging electricity price of EVs at any given time; and This represents the user's sensitivity to the SOC. For the first n EVs in t The state of charge at any given moment; and These are the user's charging and discharging response constants, respectively; In the two-layer game optimization model construction module, the constraints of the upper-layer distribution network operator's optimization scheduling model include: voltage constraints at the common connection point of the transformer area, photovoltaic grid-connected power constraints, and active and reactive power balance constraints of smart soft switching. The expression for the voltage constraint at the common coupling point of the transformer area is: ； In the formula, Taiwan District Voltage at the point; for Lower limit of operating voltage; for Upper limit of operating voltage; The expression for the photovoltaic grid-connected power constraint is: ； ； In the formula, This refers to the grid-connected power of photovoltaic power. , and They are respectively Taiwan Transformer The amount of photovoltaic power generation, curtailment, and installed capacity within the region; The expression for the active and reactive power balance constraint of the intelligent soft switch is: ； ； ； ； ； ； ； In the formula, and for t time i Active and reactive power injected into the node; and for t time j Active and reactive power injected into the node; and for t time k The energy storage power injected into the node and the total EV power; and for i Nodes and j The loss factor of the converter at the node; and for t Always connected to the node i and nodes j The losses of the SOP converters at both ends; , and , Access nodes i and j Upper and lower limits of reactive power of the converter at the node; and Access nodes i and j The capacity of the node's converter; In the two-layer game optimization model construction module, the constraints of the lower-layer charging and storage power station operator optimization scheduling model include: the SOC operation constraint of the energy storage system and the energy storage charging and discharging power constraint. The expression for the SOC operating constraint of the energy storage system is: ； ； In the formula, For energy storage systems t State of charge at any given moment; For ESS system t State of charge at time -1; and These are the energy storage charging and discharging efficiencies, respectively. and for t The charging and discharging power of energy stored at all times; This refers to the rated capacity of the energy storage. and These represent the minimum and maximum values ​​of the energy storage SOC. The expression for the energy storage charging and discharging power constraint is: ； In the formula, , , , These represent the upper and lower limits of energy storage charging and discharging power, respectively.

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