High-proportion new energy power distribution network optimization scheduling method based on mobile energy storage and flexible direct current interconnection

Abstract

The invention discloses a high-proportion new energy power distribution network optimization scheduling method based on mobile energy storage and flexible direct current interconnection. The method comprises the following steps: 1, establishing a reconfigurable intelligent soft switch (R-SOP) model; 2, establishing a mobile energy storage vehicle (MESS) operation model; 3, constructing a flexible power distribution network comprehensive optimization model; and 4, solving the optimization operation model to obtain an optimal operation strategy of the power distribution system. The space adjustability of mobile energy storage and the power flow regulation and control capacity of flexible interconnection are fully combined, the problems of voltage out-of-limit, light abandoning and line overload caused by high-permeability distributed photovoltaic access can be effectively relieved, the new energy consumption level and the voltage stability of the system can be remarkably improved, the network loss and the operation cost are reduced, and the power consumption of the system is reduced. Therefore, the economical efficiency, safety and flexibility of operation of the power distribution network can be improved, and the method is suitable for active power distribution network regulation and control scenes with high-proportion renewable energy access.

Description

Technical Field

[0001] This invention belongs to the field of power distribution system operation optimization, specifically a high-proportion new energy power distribution network optimization and scheduling method based on mobile energy storage and flexible DC interconnection. Background Technology

[0002] With the large-scale integration of distributed photovoltaic (PV) and other renewable energy sources into distribution networks, the operational safety and control flexibility of active distribution networks face new challenges. Due to the significant intermittency and randomness of PV power output, its generation periods often mismatch with peak and off-peak load periods. Coupled with factors such as limited local line capacity and insufficient equipment operating margins, this easily leads to problems such as power backfeeding, feeder overload, voltage exceeding limits, and curtailment. These phenomena not only restrict the safe and stable operation of the distribution network but also affect the efficient utilization and economic viability of clean energy.

[0003] Mobile Energy Storage Systems (MESS) are flexible resources with high power output capabilities, enabling rapid deployment and operation. They can be flexibly connected to different distribution nodes based on the real-time operation of the power grid and regional power demand, providing charging and discharging regulation and emergency support. By absorbing excess energy in areas with surplus power and transferring it to areas with insufficient power, MESS can effectively alleviate local power flow congestion, reduce peak-valley differences, and improve system resource sharing efficiency and operational resilience.

[0004] Reconfigurable Soft Open Point (R-SOP) is an important device in flexible distribution networks. It can improve the resilience and dynamic response of the distribution network by coordinating the regulation of active and reactive power. However, R-SOP only has the ability to transfer energy across space and lacks the function of energy storage and regulation in the time dimension. Mobile energy storage devices (MESS) have a compact structure and fast response speed, which is of great significance for ensuring the safe, stable and economical operation of distribution networks. However, considering objective factors such as traffic congestion, dispatching paths and access time, the efficiency of mobile energy storage devices in performing energy transfer within a spatial range is relatively low.

[0005] To address this, a reconfigurable energy storage open point (R-ESOP) system can be constructed by connecting the mobile energy storage system (MESS) to a reconfigurable smart open point (R-SOP) via a DC link. This system integrates the time-series energy regulation capabilities of the MESS with the spatial power flow control capabilities of the R-SOP, enabling their coordinated operation and optimized control in the distribution network, thereby effectively improving the dynamic allocation efficiency of electricity. This method provides a new solution to the voltage overrun, line overload, and curtailment problems caused by high-proportion distributed photovoltaic (PV) grid integration, achieving efficient allocation and stable supply of electricity in both time and space dimensions.

[0006] However, existing research largely focuses on the independent operation optimization of R-SOP and mobile energy storage systems, lacking a unified collaborative scheduling model and dynamic optimization strategy, making it difficult to fully leverage their synergistic potential. Furthermore, under high-penetration photovoltaic (PV) grid integration conditions, traditional static scheduling methods struggle to cope with real-time load fluctuations and the uncertainty of renewable energy output, leading to decreased system operational economy and stability. Therefore, there is an urgent need to propose a flexible control method integrating R-ESOP and mobile energy storage systems, achieving multi-timescale rolling optimization and coordinated energy scheduling through model predictive control, thereby improving the safety, economy, and flexibility of high-proportion renewable energy distribution networks. Summary of the Invention

[0007] The purpose of this invention is to address the problems of power reversal, voltage overrun, line overload, and curtailment in distribution networks under high-proportion distributed photovoltaic (PV) grid access conditions. It proposes an optimized scheduling method for high-proportion renewable energy distribution networks based on mobile energy storage and flexible DC interconnection. The method aims to integrate the spatial power flow regulation capability of the reconfigurable intelligent soft switch (R-SOP) with the temporal energy regulation capability of mobile energy storage (MESS) to construct an energy storage-enhanced reconfigurable intelligent soft switch system (R-ESOP). This enables coordinated and optimized allocation of energy in both time and space dimensions of the distribution network, improving the system's safety, stability, and economy, and enhancing the absorption capacity of renewable energy.

[0008] To achieve the above-mentioned objectives, the present invention adopts the following technical solution: The present invention provides a method for optimizing the scheduling of high-proportion renewable energy distribution networks based on mobile energy storage and flexible DC interconnection, characterized by the following steps: Step 1: Construct the operational constraints of the reconfigurable smart soft switch R-SOP; Step 2: Construct the operational constraints for the Mobile Energy Storage Vehicle (MESS); Step 3: Construct the operating constraints of controllable equipment in the distribution network, including: operating constraints of on-load tap-changing transformers (OLTC), operating constraints of switchable capacitor banks (CBs), and operating constraints of energy storage systems (ESS). Step 4: Construct the operational constraints of the distributed generation (DG); Step 5: Construct operational constraints for the distribution network that coordinates the mobile energy storage vehicle (MESS) and the intelligent soft switch (R-SOP). Step 6: Construct the objective function of the high-proportion renewable energy distribution network optimization scheduling model. ; Step 7: The operational constraints of the high-proportion renewable energy distribution network optimization scheduling model are constructed from the operational constraints of the reconfigurable smart soft switch R-SOP, the mobile energy storage vehicle MESS, the controllable equipment, the distributed generation (DG), and the distribution network. The distribution network's operational constraints are then transformed into mixed-integer second-order cone programming constraints, and then combined with the objective function. A high-proportion renewable energy distribution network optimization scheduling model was constructed and solved to obtain the distribution network operation scheme, which includes the operation of intelligent soft switches, on-load tap-changing transformers, switchable capacitors, and mobile energy storage vehicle systems.

[0009] The high-proportion renewable energy distribution network optimization scheduling method based on mobile energy storage and flexible DC interconnection described in this invention is also characterized in that step 1 includes: Step 1.1: Construct the power balance constraints of R-SOP using equations (1)-(3): (1) (2) (3) In equations (1)-(3), for The h-th voltage source converter at time h Active power on the DC side; for The h-th voltage source converter at time h The actual active power transmitted; for The h-th voltage source converter at time h Active power loss during transmission; This represents the total number of voltage source converters (VSCs). For the h-th voltage source converter The loss coefficient; This is the set of VSC indexes; This indicates the h-th voltage source converter. The capacity; Step 1.2: Construct the capacity constraint of R-SOP using equations (4)-(7): (4) (5) (6) (7) In equations (4)-(7), Indicates the first connected to R-SOP Branch power transmission capacity; This indicates the h-th voltage source converter. The capacity; for The h-th voltage source converter at time h The actual reactive power transmitted; This indicates the h-th voltage source converter. With the The status of the switch on the branch line; For the h-th voltage source converter Maximum output reactive power; This represents the total number of branches connected to R-SOP.

[0010] Furthermore, in step 2, the operational constraints of the mobile energy storage vehicle MESS are given by equations (8) to (18): (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) In equations (8)-(18), This is a 0 / 1 variable indicating whether the mobile energy storage vehicle (MESS) is connected to the node. When =1, it means the first... MESS mobile energy storage vehicle Always access node ;when When =0, it means the first... MESS mobile energy storage vehicle Node not connected at any time ; t represents the total number of mobile energy storage vehicles (MESS); t represents time. To enable the node set to access the Mobile Energy Storage Vehicle (MESS); For nodes The maximum number of mobile energy storage vehicles that can be connected; The variable is 0 / 1 indicating whether the mobile energy storage vehicle (MESS) is in a driving state. When =1, it means the first... The mobile energy storage vehicle MESS is in a driving state at time t; when When =0, it means the first... The mobile energy storage vehicle MESS is stationary at time t; , These are the charging and discharging efficiencies of the MESS mobile energy storage vehicle, respectively. , They are respectively Time of the first The 0 / 1 variables of the charging and discharging states of a mobile energy storage vehicle (MESS) when... =1 or When =1, they represent respectively Time of the first The MESS mobile energy storage vehicle is currently performing charging or discharging operations; when =0 or When =0, they represent respectively Time of the first The MESS mobile energy storage vehicle did not perform charging or discharging operations. for Time of the first The capacity of the MESS mobile energy storage vehicle, , They are respectively Time of the first The charging and discharging power of a mobile energy storage vehicle (MESS); , These are the minimum and rated energy capacities of the MESS mobile energy storage vehicle, respectively. T0 indicates the rated power of the MESS charging and discharging of the mobile energy storage vehicle; T0 indicates the start time. This is the end time.

[0011] Furthermore, step 3 includes: Step 3.1: Construct the operating constraints of the on-load tap-changing transformer (OLTC) using equations (19)-(22): (19) (20) (twenty one) (twenty two) In equations (19)-(22), for Time Node and nodes Branch road The voltage turns ratio of the OLTC at that location; Indicates a branch The initial voltage turns ratio of the OLTC at the location; for Time Branch The number of gears in the OLTC at this location; branch road The maximum number of operations allowed for the OLTC at a given time set T; branch road The maximum adjustable gear of the OLTC; Step 3.2: Construct the operating constraints of the capacitor bank CBs using equations (23)-(24): (twenty three) (twenty four) In equations (23)-(24), For nodes exist The capacity of capacitors constantly connected to the power distribution network. It refers to the capacitance of the capacitors in CBs. yes Time Node The number of capacitor banks (CBs) installed at the location. This is the maximum number of capacitor banks (CBs) that can be connected. Step 3.3: Construct the operating constraints of the energy storage system ESS using equations (25)-(29): (25) (26) (27) (28) (29) In equations (25)-(29), and They represent Time Node The charging and discharging power of the energy storage system ESS at the location; express Time Node The charging and discharging states of the energy storage system (ESS) Indicates charging status. Indicates the discharge state. express Time Node The remaining energy of the energy storage system ESS at the location; and They are nodes The charging and discharging efficiency of the energy storage system (ESS) and Representing nodes respectively The upper and lower limits of the energy storage system (ESS) capacity; and They are nodes The energy value of the energy storage system ESS at the initial time T0 and the energy value at the end time T; For nodes The rated capacity of the energy storage system (ESS).

[0012] Furthermore, in step 4, the operating constraints of the distributed generation (DG) are constructed using equations (30)-(32): (30) (31) (32) In equations (30)-(32), , These are the node sets for mobile energy storage vehicles (MESS) and photovoltaic systems, respectively. Representing distributed generation (DG) and nodes The connection status; and They are nodes The upper limits of active power output and reactive power output of distributed generation (DG); and They are nodes The upper and lower limits of the power factor of the distributed generation (DG).

[0013] Furthermore, in step 5, the operating constraints of the distribution network are constructed using equations (33) to (39): (33) (34) (35) (36) (37) (38) (39) In equations (33)-(39), and These represent the outflow nodes respectively. The set of nodes and the inflow nodes The set of nodes; , They represent Time Node and Nodes in Branch roads between and Nodes in With nodes Branch roads between The active power transmitted; , They represent Time Branch and branch roads Transmitted reactive power; , For nodes The upper limits of active power and reactive power of the connected load L; for Distributed generation (DG) at nodes The active power injected at the point; for Time Node Square of the voltage; and Representing branch roads Resistance and reactance; and For nodes Upper and lower limits of the squared value of the voltage; branch road The upper limit of the square of the current transmitted.

[0014] Further step 6 includes: Step 6.1: Construct the line loss cost of the distribution network using equation (41). : (40) In equation (35), The unit cost of line loss. A collection of branches in a power distribution network; Step 6.2: Construct the voltage over-limit cost of the distribution network using equations (42) and (43). : (41) (42) In equations (40)-(43), This refers to the energy loss caused by voltage exceeding the limit; For the set of nodes in the distribution network; for Time Node Energy loss due to voltage exceeding limits; for Time Node The power of the load L; for Time Node Injection power at the location; for Time Node Voltage at the point; For voltage regulation dead zone, , These are the lower and upper limits of voltage regulation, respectively; This is the minimum allowable voltage value for the distribution network; This represents the maximum allowable voltage for the distribution network. Step 6.3: Construct the objective function using equation (40). : (43)

[0015] Furthermore, in step 7, equation (44) is used to transform the nonlinear constraints between voltage, current, and power in the operation constraints of the distribution network into second-order cone constraints: (44)

[0016] The present invention provides an electronic device, including a memory and a processor, characterized in that the memory is used to store a program that supports the processor in executing the high-proportion renewable energy distribution network optimization scheduling method, and the processor is configured to execute the program stored in the memory.

[0017] The present invention discloses a computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, performs the steps of the high-proportion renewable energy distribution network optimization scheduling method.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Realized spatiotemporal dual-dimensional energy regulation: This invention combines the time energy regulation capability of the mobile energy storage system (MESS) with the spatial power flow regulation capability of the reconfigurable intelligent soft switch (R-SOP) to construct an energy storage enhanced reconfigurable intelligent soft switch system (R-ESOP), realizing the coordinated and optimized allocation of power distribution network energy in both time and space dimensions.

[0019] 2. Significantly improved renewable energy absorption capacity and voltage stability: By optimizing the regulation of photovoltaic output, reactive power support and energy storage coordination control, the system can effectively match photovoltaic power generation and load demand under various operating conditions, suppress voltage over-limit and power backflow phenomena, thereby significantly improving the system operation stability and renewable energy utilization rate in scenarios with a high proportion of renewable energy access.

[0020] 3. Reduced system operating costs and network losses: While optimizing power flow distribution, this invention minimizes system operating costs, achieves coordinated control of energy storage and voltage regulation equipment, reduces network active power losses, and significantly improves the economic operation level of the distribution network.

[0021] 4. Enhanced flexibility and security of distribution network operation: Through the flexible access of mobile energy storage vehicles and the rapid power regulation capability of R-ESOP, the response speed and risk resistance of the distribution network under load fluctuations, uncertainties in photovoltaic output and fault disturbances are improved, ensuring the safe and stable operation of the distribution network. Attached Figure Description

[0022] Figure 1 This is the R-SOP topology diagram; Figure 2 This is a topology diagram of an IEEE 33-node system that includes mobile energy storage vehicles and R-SOPs; Figure 3 This is a dynamic scheduling diagram of mobile energy storage vehicles; Figure 4 This diagram illustrates the power exchange between the nodes connected to the mobile energy storage vehicle and the mobile energy storage vehicle itself. Figure 5 This is the operation diagram of the feeder selection switch in R-SOP; Figure 6 This is a diagram showing the operating strategies of controllable equipment installed in the power distribution system (on-load tap-changing transformer, switchable capacitor bank); Figure 7 It is a three-dimensional voltage diagram of the IEEE 33-node system containing mobile energy storage vehicles and R-SOPs; Figure 8This is a comparison of the active power diagrams of adding a four-port SOP to the original IEEE 33-node system, connecting the four ports to nodes 12, 18, 22, and 33, and adding fixed energy storage on the DC side of the SOP (Case 2) versus replacing the fixed energy storage with a mobile energy storage vehicle in Case 2 (Case 3) and transmitting it through feeders 12, 18, 22, and 33. Figure 9 This is a flowchart illustrating the implementation of the method described in the invention. Detailed Implementation

[0023] In this embodiment, a high-power mobile energy storage vehicle with optimized configuration for multiple scenarios and its intelligent soft-switching control method are described, such as... Figure 9 As shown, the specific steps are as follows: Step 1, as follows Figure 1 As shown, the operational constraints for constructing the reconfigurable intelligent soft switch R-SOP are defined. Step 1.1: Construct the power balance constraints of R-SOP using equations (1)-(3): (1) (2) (3) In equations (1)-(3), for The h-th voltage source converter at time h Active power on the DC side; for The h-th voltage source converter at time h The actual active power transmitted; for The h-th voltage source converter at time h Active power loss during transmission; This represents the total number of voltage source converters (VSCs). For the h-th voltage source converter The loss coefficient; This is the set of VSC indexes; This indicates the h-th voltage source converter. The capacity; Step 1.2: Construct the capacity constraint of R-SOP using equations (4)-(7): (4) (5) (6) (7) In equations (4)-(7), Indicates the first connected to R-SOP Branch power transmission capacity; This indicates the h-th voltage source converter. The capacity; for The h-th voltage source converter at time h The actual reactive power transmitted; This indicates the h-th voltage source converter. With the The status of the switch on the branch line; For the h-th voltage source converter Maximum output reactive power; The total number of branches connected to R-SOP; Step 2: Construct the operational constraints of the mobile energy storage vehicle (MESS) using equations (8)-(18): (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) In equations (8)-(18), This is a 0 / 1 variable indicating whether the mobile energy storage vehicle (MESS) is connected to the node. When =1, it means the first... MESS mobile energy storage vehicle Always access node ;when When =0, it means the first... MESS mobile energy storage vehicle Node not connected at any time ; t represents the total number of mobile energy storage vehicles (MESS); t represents time. To enable the node set to access the Mobile Energy Storage Vehicle (MESS); For nodes The maximum number of mobile energy storage vehicles that can be connected; The variable is 0 / 1 indicating whether the mobile energy storage vehicle (MESS) is in a driving state. When =1, it means the first... The mobile energy storage vehicle MESS is in a driving state at time t; when When =0, it means the first... The mobile energy storage vehicle MESS is stationary at time t; , These are the charging and discharging efficiencies of the MESS mobile energy storage vehicle, respectively. , They are respectively Time of the first The 0 / 1 variables of the charging and discharging states of a mobile energy storage vehicle (MESS) when... =1 or When =1, they represent respectively Time of the first The MESS mobile energy storage vehicle is currently performing charging or discharging operations; when =0 or When =0, they represent respectively Time of the first The MESS mobile energy storage vehicle did not perform charging or discharging operations. for Time of the first The capacity of the MESS mobile energy storage vehicle, , They are respectively Time of the first The charging and discharging power of a mobile energy storage vehicle (MESS); , These are the minimum and rated energy capacities of the MESS mobile energy storage vehicle, respectively. T0 indicates the rated power of the MESS charging and discharging of the mobile energy storage vehicle; T0 indicates the start time. The end time; Step 3: Construct operational constraints for controllable equipment in the distribution network, including: operational constraints for on-load tap-changing transformers (OLTCs), operational constraints for switchable capacitor banks (CBs), and operational constraints for energy storage systems (ESSs). Step 3.1: Construct the operating constraints of the on-load tap-changing transformer (OLTC) using equations (19)-(22): (19) (20) (twenty one) (twenty two) In equations (19)-(22), for Time Node and nodes Branch road The voltage turns ratio of the OLTC at that location; Indicates a branch The initial voltage turns ratio of the OLTC at the location; for Time Branch The number of gears in the OLTC at this location; branch road The maximum number of operations allowed for the OLTC at a given time set T; branch road The maximum adjustable gear of the OLTC; Step 3.2: Construct the operating constraints of the capacitor bank CBs using equations (23)-(24): (twenty three) (twenty four) In equations (23)-(24), For nodes exist The capacity of capacitors constantly connected to the power distribution network. It refers to the capacitance of the capacitors in CBs. yes Time Node The number of capacitor banks (CBs) installed at the location. This is the maximum number of capacitor banks (CBs) that can be connected. Step 3.3: Construct the operating constraints of the energy storage system ESS using equations (25)-(29): (25) (26) (27) (28) (29) In equations (25)-(29), and They represent Time Node The charging and discharging power of the energy storage system ESS at the location; express Time Node The charging and discharging states of the energy storage system (ESS) Indicates charging status. Indicates the discharge state. express Time Node The remaining energy of the energy storage system ESS at the location; and They are nodes The charging and discharging efficiency of the energy storage system (ESS) and Representing nodes respectively The upper and lower limits of the energy storage system (ESS) capacity; and They are nodes The energy value of the energy storage system ESS at the initial time T0 and the energy value at the end time T; For nodes The rated capacity of the energy storage system (ESS); Step 4: Construct the operating constraints of the distributed generation (DG) using equations (30)-(32): (30) (31) (32) In equations (30)-(32), , These are the node sets for mobile energy storage vehicles (MESS) and photovoltaic systems, respectively. Representing distributed generation (DG) and nodes The connection status; and They are nodes The upper limits of active power output and reactive power output of distributed generation (DG); and They are nodes The upper and lower limits of the power factor of the distributed generation (DG); Step 5: Construct the operational constraints of the distribution network for the coordinated operation of the mobile energy storage vehicle MESS and the intelligent soft switch R-SOP using equations (33)-(39): (33) (34) (35) (36) (37) (38) (39) In equations (33)-(39), and These represent the outflow nodes respectively. The set of nodes and the inflow nodes The set of nodes; , They represent Time Node and Nodes in Branch roads between and Nodes in With nodes Branch roads between The active power transmitted; , They represent Time Branch and branch roads Transmitted reactive power; , For nodes The upper limits of active power and reactive power of the connected load L; for Distributed generation (DG) at nodes The active power injected at the point; for Time Node Square of the voltage; and Representing branches Resistance and reactance; and For nodes Upper and lower limits of the squared value of the voltage; branch road The upper limit of the square of the current transmitted upwards; Step 6: Construct the objective function of the high-proportion renewable energy distribution network optimization scheduling model. : Step 6.1: Construct the line loss cost of the distribution network using equation (41). : (40) In equation (35), The unit cost of line loss. A collection of branches in a power distribution network; Step 6.2: Construct the voltage over-limit cost of the distribution network using equations (42) and (43). : (41) (42) In equations (40)-(43), This refers to the energy loss caused by voltage exceeding the limit; For the set of nodes in the distribution network; for Time Node Energy loss due to voltage exceeding limits; for Time Node The power of the load L; for Time Node Injection power at the location; for Time Node Voltage at the point; For voltage regulation dead zone, , These are the lower and upper limits of voltage regulation, respectively; This is the minimum allowable voltage value for the distribution network; This represents the maximum allowable voltage for the distribution network. Step 6.3: Construct the objective function using equation (40). : (43) Step 7: The operational constraints of the high-proportion new energy distribution network optimization scheduling model are composed of the operational constraints of the reconfigurable intelligent soft switch R-SOP, the operational constraints of the mobile energy storage vehicle MESS, the operational constraints of the controllable equipment, the operational constraints of the distributed generation DG, and the operational constraints of the distribution network. Then, using Equation (44), the nonlinear constraints between voltage, current, and power in the operational constraints of the distribution network are transformed into mixed integer second-order cone programming constraints, and then combined with the objective function. A high-proportion renewable energy distribution network optimization scheduling model was constructed and solved to obtain the distribution network operation scheme, which includes the operation of intelligent soft switches, on-load tap-changing transformers, switchable capacitors, and mobile energy storage vehicle systems.

[0024] (44) Using the above method, the operational constraints of the power distribution system based on the collaboration of mobile energy storage vehicles and intelligent soft switches are transformed into a mixed integer second-order cone programming problem. By solving the problem with the help of a solver, the operation strategy of the power distribution system, including the operation of intelligent soft switches, on-load tap-changing transformers, switchable capacitors, and mobile energy storage vehicles, is obtained.

[0025] In this embodiment, an electronic device includes a memory and a processor. The memory stores a program that supports the processor in executing the above-described method, and the processor is configured to execute the program stored in the memory.

[0026] In this embodiment, a computer-readable storage medium stores a computer program, which is executed by a processor to perform the steps of the above method.

[0027] To enable those skilled in the art to better understand the present invention, the numerical example analysis includes the following components: I. Case Description and Simulation Result Analysis To verify its effectiveness, the present invention employs, as follows: Figure 2 The IEEE 33-node system shown, which includes a 4-port R-SOP and a mobile energy storage vehicle, serves as a test system, and a case study analysis is conducted. Figure 2 In the test system shown, the active and reactive power requirements of each node load are set according to the standard IEEE 33-node test system. The system reference voltage is set to 12.66kV, and the voltage safety range is set to 0.95pu-1.05pu. The total capacity of the equipped 4-port R-SOP is 1MVA, and the nodes selectable by the feeder selector switch are set at nodes 12, 18, 22, and 33. The system additionally deploys photovoltaic systems at nodes 7, 9, 13, 15, and 27. CBs are set at node 29, with each CB having a capacity of 0.15MVar, and a maximum of 4 capacitor banks can be connected. The simulation timescale is set to 1 hour, and the simulation time is from 1:00 to 24:00.

[0028] To fully demonstrate the effectiveness of the proposed distribution network optimization operation method based on the collaboration of mobile energy storage vehicles and intelligent soft switches, three schemes, Case 1, Case 2, and Case 3, are set up for comparison in the case study. Case 1 is based on the original distribution network of IEEE 33 nodes. Case 2 adds a four-port SOP to Case 1 and connects the four ports to nodes 12, 18, 22, and 33, and adds fixed energy storage on the DC side of the SOP. Case 3 replaces the fixed energy storage in Case 2 with a mobile energy storage vehicle and changes the SOP to an R-SOP.

[0029] Case 1: Based on the original distribution network of the IEEE 33-node network; Case 2: Add a four-port SOP to Case 1, connect the four ports to nodes 12, 18, 22 and 33, and add fixed energy storage on the DC side of the SOP; Case 3: A control model that coordinates R-SOP with mobile energy storage vehicles; All numerical simulations in the examples section were performed in MATLAB 2020a and solved using the YALMIP toolbox and Gurobi solver in a 64-bit Windows environment.

[0030] exist Figure 3 In the IEEE 33-node test system shown, both schemes were executed simultaneously. The light loss, network loss, and voltage limit exceedance rates of Case 1, Case 2, and Case 3 are shown in Table 1.

[0031] Table 1

[0032] Table 1 shows the simulation results of the three cases. It can be seen that the optimization model proposed in this paper (Case 3) performs best in terms of photovoltaic absorption capacity, network loss reduction and voltage quality improvement. Its overall operation effect is significantly better than the other two schemes, which further verifies the effectiveness and advancement of the proposed model in multi-objective coordinated optimization.

[0033] like Figure 3 As shown, part (a) represents the dispatching status of mobile energy storage vehicles and the active power of each mobile energy storage vehicle. Figure 3 Part (b) represents the active power of each mobile energy storage vehicle's discharge. Figure 3 Section (c) represents the remaining electrical energy of each mobile energy storage vehicle. The mobile energy storage vehicles are concentrated on charging during peak photovoltaic output periods (12:00-15:00) to store electrical energy and prevent curtailment. They are also concentrated on discharging during peak morning and evening electricity consumption periods (8:00-10:00 and 19:00-21:00) to prevent the grid voltage from falling below the lower limit due to excessive load. Simultaneously, the mobile energy storage transfers electrical energy between nodes 4, 9, 13, 15, 20, 25, 32, and 34 24 hours a day. Furthermore, the mobile energy storage interacts with the DC side of the reconfigurable smart soft switch R-SOP, which then transmits the electrical energy to nodes 12, 18, 22, and 33 via feeders. This significantly expands the positive benefits of the R-SOP in the power grid.

[0034] like Figure 4 The diagram shows the charging and discharging data of the mobile energy storage vehicle at the accessible nodes. It can be seen that the mobile energy storage vehicle charges during periods of high photovoltaic output and discharges during peak electricity consumption in the morning and evening, which improves the absorption of new energy and avoids voltage exceeding limits.

[0035] like Figure 5 The diagram shows the operation of the energy storage-type reconfigurable intelligent soft switch feeder selector switch. Figure 5 Parts (a) to (d) of the diagram illustrate the switching operations of the feeders containing each voltage source converter (VSC) in the R-SOP: Figure 5 Part (a) in the diagram refers to the feeder switch VSC1 connected to node 12. Figure 5 Part (b) in the diagram refers to the feeder switch VSC2 connected to node 18. Figure 5 Part (c) in the diagram refers to the feeder switch VSC3 connected to node 22. Figure 5 Part (d) in the diagram refers to the feeder switch VSC4 connected to node 33. At any given time, R-SOP selects the optimal feeder switch operation to transmit power with maximum efficiency.

[0036] Figure 6 It is the number of battery blocks configured on the nodes that can be connected to the mobile energy storage vehicle at any given time; Figure 7 It refers to the changes in OLTC gear position and the number of capacitor banks connected to the CBs during the entire optimization process.

[0037] like Figure 7 As shown, the per-unit voltage values ​​of most nodes in Case 3 remain within the expected range of 0.98 pu to 1.03 pu, which meets the voltage safety standards for flexible power distribution systems.

[0038] Figure 8 As can be seen, compared to Case 2, the power transmission of the smart soft-switching feeder was significantly enhanced in Case 3 after the introduction of the mobile energy storage vehicle. Simultaneously, the operating period of the smart soft switch was extended from primarily concentrated during peak photovoltaic output (12:00–15:00) to the entire day, achieving more continuous and balanced operation. This indicates that the mobile energy storage system plays a positive role in optimizing spatiotemporal energy allocation and improving the system's flexible regulation capabilities, effectively enhancing the operating efficiency and resource coordination capabilities of the smart soft switch.

[0039] In this specification, the illustrative descriptions of the invention are not necessarily directed at the same embodiments or examples. Those skilled in the art can combine and integrate the different embodiments or examples described in this specification. Furthermore, the embodiments in this specification are merely enumerations of implementation forms of the inventive concept, and the scope of protection of the invention should not be considered as limited to the specific forms stated in the embodiments. The scope of protection of the invention also includes equivalent technical means that can be conceived by those skilled in the art based on the inventive concept.

Claims

1. A method for optimizing the scheduling of high-proportion renewable energy distribution networks based on mobile energy storage and flexible DC interconnection, characterized in that, Includes the following steps: Step 1: Construct the operational constraints of the reconfigurable smart soft switch R-SOP; Step 2: Construct the operational constraints for the Mobile Energy Storage Vehicle (MESS); Step 3: Construct the operating constraints of controllable equipment in the distribution network, including: operating constraints of on-load tap-changing transformers (OLTC), operating constraints of switchable capacitor banks (CBs), and operating constraints of energy storage systems (ESS). Step 4: Construct the operational constraints of the distributed generation (DG); Step 5: Construct operational constraints for the distribution network that coordinates the mobile energy storage vehicle (MESS) and the intelligent soft switch (R-SOP). Step 6: Construct the objective function of the high-proportion renewable energy distribution network optimization scheduling model. ; Step 7: The operational constraints of the high-proportion renewable energy distribution network optimization scheduling model are constructed from the operational constraints of the reconfigurable smart soft switch R-SOP, the mobile energy storage vehicle MESS, the controllable equipment, the distributed generation (DG), and the distribution network. The distribution network's operational constraints are then transformed into mixed-integer second-order cone programming constraints, and then combined with the objective function. A high-proportion renewable energy distribution network optimization scheduling model was constructed and solved to obtain the distribution network operation scheme, which includes the operation of intelligent soft switches, on-load tap-changing transformers, switchable capacitors, and mobile energy storage vehicle systems.

2. The method for optimizing and scheduling a high-proportion renewable energy distribution network based on mobile energy storage and flexible DC interconnection as described in claim 1, characterized in that, Step 1 includes: Step 1.1: Construct the power balance constraints of R-SOP using equations (1)-(3): (1) (2) (3) In equations (1)-(3), for The h-th voltage source converter at time h Active power on the DC side; for The h-th voltage source converter at time h The actual active power transmitted; for The h-th voltage source converter at time h Active power loss during transmission; This represents the total number of voltage source converters (VSCs). For the h-th voltage source converter The loss coefficient; This is the set of VSC indexes; This indicates the h-th voltage source converter. The capacity; Step 1.2: Construct the capacity constraint of R-SOP using equations (4)-(7): (4) (5) (6) (7) In equations (4)-(7), Indicates the first connected to R-SOP Branch power transmission capacity; This indicates the h-th voltage source converter. The capacity; for The h-th voltage source converter at time h The actual reactive power transmitted; This indicates the h-th voltage source converter. With the The status of the switch on the branch line; For the h-th voltage source converter Maximum output reactive power; This represents the total number of branches connected to R-SOP.

3. The method for optimizing and scheduling a high-proportion renewable energy distribution network based on mobile energy storage and flexible DC interconnection as described in claim 2, is characterized in that... Step 2 consists of the operational constraints of the mobile energy storage vehicle MESS, derived from equations (8) to (18): (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) In equations (8)-(18), This is a 0 / 1 variable indicating whether the mobile energy storage vehicle (MESS) is connected to the node. When =1, it means the first... MESS mobile energy storage vehicle Always access node ;when When =0, it means the first... MESS mobile energy storage vehicle Node not connected at any time ; t represents the total number of mobile energy storage vehicles (MESS); t represents time. To enable the node set to access the Mobile Energy Storage Vehicle (MESS); For nodes The maximum number of mobile energy storage vehicles that can be connected; The variable is 0 / 1 indicating whether the mobile energy storage vehicle (MESS) is in a driving state. When =1, it means the first... The mobile energy storage vehicle MESS is in a driving state at time t; when When =0, it means the first... The mobile energy storage vehicle MESS is stationary at time t; , These are the charging and discharging efficiencies of the MESS mobile energy storage vehicle, respectively. , They are respectively Time of the first The 0 / 1 variables of the charging and discharging states of a mobile energy storage vehicle (MESS) when... =1 or When =1, they represent respectively Time of the first The MESS mobile energy storage vehicle is currently performing charging or discharging operations; when =0 or When =0, they represent respectively Time of the first The MESS mobile energy storage vehicle did not perform charging or discharging operations. for Time of the first The capacity of the MESS mobile energy storage vehicle, , They are respectively Time of the first The charging and discharging power of a mobile energy storage vehicle (MESS); , These are the minimum and rated energy capacities of the MESS mobile energy storage vehicle, respectively. T0 indicates the rated power of the MESS charging and discharging of the mobile energy storage vehicle; T0 indicates the start time. This is the end time.

4. The method for optimizing and scheduling a high-proportion renewable energy distribution network based on mobile energy storage and flexible DC interconnection as described in claim 3, is characterized in that, Step 3 includes: Step 3.1: Construct the operating constraints of the on-load tap-changing transformer (OLTC) using equations (19)-(22): (19) (20) (21) (22) In equations (19)-(22), for Time Node and nodes Branch road The voltage turns ratio of the OLTC at that location; Indicates a branch The initial voltage turns ratio of the OLTC at the location; for Time Branch The number of gears in the OLTC at this location; branch road The maximum number of operations allowed for the OLTC at a given time set T; branch road The maximum adjustable gear of the OLTC; Step 3.2: Construct the operating constraints of the capacitor bank CBs using equations (23)-(24): (23) (24) In equations (23)-(24), For nodes exist The capacity of capacitors constantly connected to the power distribution network. It refers to the capacitance of the capacitors in CBs. yes Time Node The number of capacitor banks (CBs) installed at the location. This is the maximum number of capacitor banks (CBs) that can be connected. Step 3.3: Construct the operating constraints of the energy storage system ESS using equations (25)-(29): (25) (26) (27) (28) (29) In equations (25)-(29), and They represent Time Node The charging and discharging power of the energy storage system ESS at the location; express Time Node The charging and discharging states of the energy storage system (ESS) Indicates the charging status. Indicates the discharge state. express Time Node The remaining energy of the energy storage system ESS at the location; and They are nodes The charging and discharging efficiency of the energy storage system (ESS) and Representing nodes respectively The upper and lower limits of the energy storage system (ESS) capacity; and They are nodes The energy value of the energy storage system ESS at the initial time T0 and the energy value at the end time T; For nodes The rated capacity of the energy storage system (ESS).

5. The high-proportion renewable energy distribution network optimization scheduling method based on mobile energy storage and flexible DC interconnection according to claim 4, characterized in that, In step 4, the operating constraints of the distributed generation (DG) are constructed using equations (30) to (32): (30) (31) (32) In equations (30)-(32), , These are the node sets for mobile energy storage vehicles (MESS) and photovoltaic systems, respectively. Represents distributed generation (DG) and nodes The connection status; and They are nodes The upper limits of active power output and reactive power output of distributed generation (DG); and They are nodes The upper and lower limits of the power factor of the distributed generation (DG).

6. The method for optimizing and scheduling a high-proportion renewable energy distribution network based on mobile energy storage and flexible DC interconnection as described in claim 5, is characterized in that... In step 5, the operating constraints of the distribution network are constructed using equations (33) to (39): (33) (34) (35) (36) (37) (38) (39) In equations (33)-(39), and These represent the outflow nodes respectively. The set of nodes and the inflow nodes The set of nodes; , They represent Time Node and Nodes in Branch roads between and Nodes in With nodes Branch roads between Transmitted active power; , They represent Time Branch and branch roads Transmitted reactive power; , For nodes The upper limits of active power and reactive power of the connected load L; for Distributed generation (DG) at nodes The active power injected at the point; for Time Node Square of the voltage; and Representing branch roads Resistance and reactance; and For nodes Upper and lower limits of the squared value of the voltage; branch road The upper limit of the square of the current transmitted.

7. The method for optimizing and scheduling a high-proportion renewable energy distribution network based on mobile energy storage and flexible DC interconnection as described in claim 6, characterized in that, Step 6 includes: Step 6.1: Construct the line loss cost of the distribution network using equation (41). : (40) In equation (35), The unit cost of line loss. A collection of branches in a power distribution network; Step 6.2: Construct the voltage over-limit cost of the distribution network using equations (42) and (43). : (41) (42) In equations (40)-(43), This refers to the energy loss caused by voltage exceeding the limit; For the set of nodes in the distribution network; for Time Node Energy loss due to voltage exceeding limits; for Time Node The power of the load L; for Time Node Injection power at the location; for Time Node Voltage at the point; For voltage regulation dead zone, , These are the lower and upper limits of voltage regulation, respectively; This is the minimum allowable voltage value for the distribution network; This represents the maximum allowable voltage for the distribution network. Step 6.3: Construct the objective function using equation (40) : (43)。 8. The method for optimizing and scheduling a high-proportion renewable energy distribution network based on mobile energy storage and flexible DC interconnection as described in claim 7, is characterized in that, In step 7, equation (44) is used to transform the nonlinear constraints between voltage, current, and power in the operation constraints of the distribution network into second-order cone constraints: (44)。 9. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store a program that supports the processor in executing the high-proportion renewable energy distribution network optimization scheduling method according to any one of claims 1-8, and the processor is configured to execute the program stored in the memory.

10. A computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is run by the processor, it executes the steps of the high-proportion renewable energy distribution network optimization scheduling method as described in any one of claims 1-8.

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