Power distribution network new energy carrying capacity improvement method and evaluation device considering mobile energy storage
Patent Information
- Application Number
- CN202610851237.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-06-12
- Publication Date
- 2026-09-15
Smart Images
Figure CN122763530A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of power system distribution network technology, specifically relating to a method and evaluation device for improving the new energy carrying capacity of distribution networks considering mobile energy storage. Background Technology
[0002] Currently, with the global energy structure transitioning to renewable energy, the scale of new energy sources such as photovoltaic (PV) and wind turbines (WT) being integrated into distribution systems continues to expand. Improving the hosting capacity (HC) of distribution systems has become a crucial objective in power system planning and operation. Existing technologies typically employ voltage control, active power curtailment, and energy storage systems (ESSs) to mitigate voltage exceedances, increased feeder losses, and transmission line and transformer overloads caused by high-penetration new energy sources. Based on this, the optimal power flow (OPF) method and the mixed-integer second-order cone programming (MISOCP) model are widely used to assess the maximum new energy capacity that existing distribution networks can accommodate without violating operational constraints. However, current research largely focuses on the capacity analysis of single voltage levels (such as low-voltage networks), while joint modeling and scheduling optimization of integrated medium- and low-voltage systems remain in the exploratory stage.
[0003] However, most existing studies only consider static energy storage systems (ESS), failing to fully utilize the flexible spatial and temporal scheduling capabilities of MESSs. Because static energy storage has a fixed location, its charging and discharging scheduling cannot dynamically respond to fluctuations in renewable energy output and changes in load demand, thus limiting further improvements in system carrying capacity. Existing research on MESSs often employs simplified models, failing to accurately characterize the cross-network movement and charging / discharging behavior of MESSs at different sites. Furthermore, most studies are limited to single-voltage distribution systems, neglecting the physical coupling characteristics between medium-voltage and low-voltage networks. This results in the system's inability to fully utilize the flexibility of MESSs to enhance renewable energy integration capabilities in actual operation, thereby affecting overall carrying capacity and renewable energy absorption efficiency. Therefore, establishing a scheduling model that accurately describes the spatiotemporal flexibility of mobile energy storage and organically embedding it into the optimal power flow framework of the distribution network to systematically improve the renewable energy carrying capacity of the integrated medium-voltage-low-voltage distribution network has become an urgent technical problem to be solved. Summary of the Invention
[0004] The technical problem addressed in this application is that existing static energy storage systems have fixed locations and limited adjustment ranges, and existing mobile energy storage scheduling models do not adequately characterize spatiotemporal behavior, resulting in the inability to fully utilize the spatiotemporal flexibility of mobile energy storage to systematically improve the renewable energy carrying capacity of the integrated medium-voltage and low-voltage distribution network.
[0005] To achieve the above objectives, this application adopts the following technical solution: In a first aspect, this application provides a method for enhancing the renewable energy carrying capacity of a distribution network by considering mobile energy storage, including: In the coupled medium- and low-voltage power distribution network and transportation network, multiple mobile energy storage systems and their corresponding sites are deployed. The sites are simultaneously connected to nodes in the low-voltage power distribution network and corresponding locations in the transportation network. A scheduling model for mobile energy storage systems based on a spatiotemporal network is constructed. The spatiotemporal network represents the travel path and parking status of mobile energy storage systems in different time periods through site spatiotemporal nodes, migration arcs, and parking arcs. Arc-related binary variables are used to describe the travel and parking behavior of each mobile energy storage system in different time periods, as well as its charging and discharging behavior at each site. Based on the spatiotemporal network-based mobile energy storage system scheduling model, the charging power and discharging power of each mobile energy storage system in each time period are determined. At the same time, the state of charge of each mobile energy storage system is managed to ensure that it meets the charging and discharging constraints and the allowable range of the state of charge, thus obtaining the mobile energy storage system scheduling decision. The scheduling decision of the mobile energy storage system is embedded into the optimal power flow calculation model of the medium-voltage-low-voltage distribution network. Under the premise of satisfying the bus voltage constraint, branch power flow constraint and medium-voltage-low-voltage transformer capacity constraint, the new energy carrying capacity of the medium-voltage-low-voltage distribution network is optimized to determine the maximum new energy installed capacity that can be connected.
[0006] As an optional implementation, the MESS scheduling model based on spatiotemporal networks is as follows:
[0007]
[0008]
[0009] in, Let MESSω be the initial position. and These are sets of arcs that start and end at spatiotemporal node n, respectively. : A collection of device numbers for Mobile Energy Storage Systems (MESS). Representing the Mobile energy storage platform; : Set of scheduling time periods Representing the One scheduling period; The set of nodes within the distribution network that can dock / access MESS. Physical nodes representing the distribution network; : The set of all directed edges in the spatiotemporal network, including energy storage driving edges, parking edges, and charging / discharging edges. 0 1. Binary decision variables; MESS is used to move / stop / operate along this spacetime edge; It means not passing through this edge; For the first The initial spatiotemporal position state of the MESS platform; The constraints are:
[0010]
[0011]
[0012]
[0013]
[0014]
[0015]
[0016] in, and These represent the charging and discharging power of MESSω at time period t at station m, respectively. and These are its maximum charging power and maximum discharging power, respectively. and This is a binary variable used to characterize whether the MESS performs charging or discharging during time period t; , and These represent the current, maximum, and minimum states of charge of the SoC, respectively. Scene Index A set of random scenes; Time index A set of scheduling time sequences; Energy storage unit / node index A collection of energy storage units; : No. Time, energy storage The corresponding distribution network / power link set; Scene Downlink Power transmission factor / available capacity factor; The previous moment SOC; Scene Lower charging efficiency Discharge efficiency; : Scheduling time step (e.g., 1h, 0.5h); Scene Lower rated energy storage capacity.
[0017] As an optional implementation, in the MESS scheduling model based on spatiotemporal networks, charging and discharging operations are mutually exclusive within the same time period, and each MESS is only on a migration arc or a stationary arc within any given time period. The state of charge (SoC) of the MESS is calculated cumulatively from the initial state of charge, charge / discharge power, and time period length, and is limited to between the minimum SoC and the maximum SoC.
[0018] As an optional implementation, the optimal power flow calculation model of the medium-voltage-low-voltage distribution network includes bus voltage constraints, branch power flow constraints, and MV-LV transformer capacity constraints, and is solved by mixed integer second-order cone programming.
[0019] As an optional implementation, the objective function of the optimal power flow calculation model for the medium-voltage-low-voltage distribution network is to obtain the maximum renewable energy carrying capacity of the integrated MV-LV distribution system, taking into account the flexibility of MESS. Its expression is as follows:
[0020] in, and These represent the photovoltaic and wind power capacities to be installed at LV node i, respectively. The constraints include:
[0021]
[0022]
[0023]
[0024]
[0025]
[0026]
[0027]
[0028]
[0029]
[0030]
[0031]
[0032]
[0033] in, , and Let $T$ represent the active power flow, the generating power of node $i$, and the load power of branch $i$ in network $n$ during time period $t$, respectively; and let $Q$ represent the corresponding reactive power. This represents the active power injected into the MV network by the LV network n through the corresponding MV-LV transformer. If it is negative, it means that the LV network n is absorbing active power from the MV network. and These represent the existing renewable energy injection power and aggregated MESS injection power of node i in LV network n during time period t, respectively; the normalized photovoltaic and wind power curves are denoted as follows: and ; and These are the squares of the node voltage magnitude and the squares of the branch current magnitude, respectively; where The set of MESS stations connected to node i in LV network n; : No. The set of nodes in a distribution network zone; Node number within the distribution network; Low-voltage node Photovoltaic power generation capacity; Low-voltage node Wind power installed capacity; Distribution network node; Medium-voltage distribution network zone set; : Time Node Traditional power supply active / reactive power output; : with medium-voltage nodes A set of interconnected low- and medium-voltage connection points; : Active / reactive power flowing from the low-voltage side to the medium-voltage side at any given time; : Time Node Active / reactive load power; : Time Node Flow to Node Active / reactive power of the line; Low-voltage distribution network zone set; : Real-time photovoltaic / wind power output coefficient; : The reactive power flowing from the low-voltage side to the medium-voltage side at all times; : Low-voltage nodes at all times reactive load power; : Low-voltage nodes at all times Flow to Node Active / reactive power of the line; : No. Within each low-voltage zone, accessible nodes A collection of mobile energy storage devices; A collection of energy storage operation modes; : Time of the first Active power of energy storage discharge; : Time of the first Taiwan's energy storage charging active power; :line Resistance / reactance; : Timetable The current amplitude; : No. A set of lines for each distribution network zone; : Set of low-voltage side nodes for low-voltage distribution transformers; Minimum / maximum active power limits for low-voltage to medium-voltage tie lines; Minimum / maximum reactive power limits for low-voltage to medium-voltage tie lines; :node Minimum / maximum allowable voltage amplitude; :line Maximum permissible current.
[0034] As an alternative implementation, the MESS moves between different stations via a transportation network, selects the shortest path for the movement, and calculates the migration time based on average vehicle speed and time period length.
[0035] As an optional implementation, the MESS site configuration of the LV node in the medium-voltage-low-voltage distribution network is related to the node load type and the power output curve of the new energy source; The number, location, and capacity of the MESS can be adjusted according to the scale of the power distribution network and the distribution of new energy sources.
[0036] As an optional implementation, the MESS scheduling decision is embedded into the optimal power flow calculation model of the medium-voltage-low-voltage distribution network, and iteratively solved in each scheduling cycle to dynamically adjust the MESS movement and charging / discharging strategies to cope with the fluctuations in renewable energy power.
[0037] As an optional implementation, the method also includes taking into account the charging and discharging efficiency, energy loss, and traffic constraints of the MESS.
[0038] Secondly, this application provides a distribution network renewable energy carrying capacity enhancement device considering mobile energy storage, comprising: The deployment module is used to deploy multiple mobile energy storage systems and their corresponding sites in a coupled medium-voltage-low-voltage power distribution network and a transportation network. The sites are simultaneously connected to nodes in the low-voltage power distribution network and corresponding locations in the transportation network. The module is used to build a scheduling model for mobile energy storage systems based on a spatiotemporal network. The spatiotemporal network represents the travel path and parking status of mobile energy storage systems in different time periods through site spatiotemporal nodes, migration arcs and parking arcs. Arc-related binary variables are used to describe the travel and parking behavior of each mobile energy storage system in different time periods and its charging and discharging behavior at each site. The management module is used to determine the charging power and discharging power of each mobile energy storage system in each time period according to the spatiotemporal network-based mobile energy storage system scheduling model, and at the same time manage the state of charge of each mobile energy storage system to ensure that it meets the charging and discharging constraints and the allowable range of the state of charge, so as to obtain the mobile energy storage system scheduling decision. The optimization module is used to embed the scheduling decision of the mobile energy storage system into the optimal power flow calculation model of the medium-voltage-low-voltage distribution network. Under the premise of satisfying the bus voltage constraint, branch power flow constraint and medium-voltage-low-voltage transformer capacity constraint, the module optimizes the new energy carrying capacity of the medium-voltage-low-voltage distribution network to determine the maximum new energy installed capacity that can be connected.
[0039] In addition, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for enhancing the new energy carrying capacity of a distribution network considering mobile energy storage as described above.
[0040] In addition, this application also provides an electronic device, including: a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method for improving the carrying capacity of new energy in the distribution network considering mobile energy storage as described above.
[0041] Compared with the prior art, this application has at least the following beneficial effects: This application constructs a mobile energy storage scheduling model based on a spatiotemporal network, employing arc-dependent binary variables to meticulously characterize the travel path, parking state, charging and discharging behavior, and temporal connections of Mobile Energy Storage Systems (MESS) in a coupled environment of transportation and distribution networks. This overcomes the shortcomings of existing models in insufficiently characterizing the spatiotemporal behavior of MESS. Based on this, MESS scheduling decisions are organically embedded into the optimal power flow calculation framework of a comprehensive medium-voltage-low-voltage distribution network. Under the premise of satisfying operational constraints such as bus voltage, branch power flow, and transformer capacity, the application jointly optimizes the renewable energy installed capacity and the charging, discharging, and mobility strategies of MESS. Compared to static energy storage systems, this application fully utilizes the spatial mobility and temporal schedulability of MESS, enabling dynamic transfer of energy storage capacity to critical nodes where renewable energy output is limited or insufficient, thereby systematically improving the renewable energy carrying capacity of the comprehensive distribution network without the need for infrastructure expansion. Simulation results show that the total renewable energy carrying capacity of the system can be significantly improved after adopting the method of this application, verifying the effectiveness and technological advancement of the proposed solution. Attached Figure Description
[0042] Figure 1 This is a flowchart of a method for enhancing the renewable energy carrying capacity of a power distribution network, taking into account mobile energy storage, according to an embodiment of this application.
[0043] Figure 2 This application provides a schematic diagram of the integrated MV-LV-transportation system and MESS. Figure 3 This is a schematic diagram of the spatiotemporal network (TSN) model provided in this application; Figure 4 This is a schematic diagram of the improved 33-node MV test system containing 3 LV networks provided in this application; Figure 5 This is the normalized MV load, PV and wind power output curve provided in this application; Figure 6 This is a schematic diagram of the MESS scheduling results provided in this application; Figure 7 This is the bus voltage distribution diagram of LV#1 provided in this application. Detailed Implementation
[0044] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.
[0045] Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this application and in its specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
[0046] Terminology Explanation: 1. Mobile Energy Storage System (MESS): An energy storage device that integrates energy storage batteries onto a mobile carrier, enabling it to travel in transportation networks and connect to the power distribution network at different geographical locations for charging and discharging operations, with scheduling flexibility in both time and space.
[0047] 2. Hosting Capacity (HC): The maximum installed capacity of new energy sources that the system can accept, provided that the existing power distribution system can operate safely and stably and that no operational constraints are violated (such as voltage over-limit, branch overload, transformer capacity limit, etc.).
[0048] 3. Time-Space Network (TSN): A modeling method that unifies the time and space dimensions into a single graph structure. It uses "site time-space nodes" as the geographical location of a specific point in time and "migration arcs" and "dwelling arcs" as state transitions to characterize the behavior of mobile devices in the time-space dimension.
[0049] 4. Arc-related binary variables: 0-1 decision variables defined on each arc of the spatiotemporal network. A value of 1 indicates that the mobile energy storage system is in the state represented by that arc during that time period (driving or stationary). They are used to accurately describe the unique state of each MESS in each time period.
[0050] 5. Migration arc: An arc in a spatiotemporal network that connects different stations at different points in time, used to represent the movement of a mobile energy storage system between two stations.
[0051] 6. Dwelling Arc: An arc in a spatiotemporal network that connects the same site at two adjacent points in time, used to allow a mobile energy storage system to stay at a site for a complete scheduling period.
[0052] 7. Mixed-Integer Second-Order Cone Programming (MISOCP): A class of mathematical optimization problems in which the objective function and constraints include linear functions, integer variables and second-order cone constraints. The global optimum can be efficiently solved using commercial solvers.
[0053] 8. Optimal Power Flow (OPF): A power system optimization calculation method that, under the premise of satisfying the constraints of safe operation of the power grid, adjusts controllable variables to achieve an optimal goal (such as minimizing network losses or maximizing carrying capacity).
[0054] 9. Comprehensive MV-LV Distribution Network: A distribution system model that includes both medium voltage (MV) and low voltage (LV) levels. The two voltage levels are coupled together through MV / LV transformers to more accurately assess the operating characteristics of actual distribution networks.
[0055] 10. Second-order cone relaxation: A mathematical processing technique that transforms non-convex quadratic constraints into convex second-order cone constraints, used to convert non-convex power flow models of distribution networks into convex optimization models that can be solved efficiently.
[0056] 11. State of Charge (SoC): Describes the percentage of remaining electrical energy in an energy storage system relative to its total capacity. The value is usually between 0 and 1 and is a key indicator for managing the charging and discharging behavior of an energy storage system.
[0057] 12. Cut-set: In graph theory, it refers to the set of all edges connecting two subsets after the set of nodes in a graph is divided into two subsets; in the TSN model, it is used to help describe the positional and state relationships of MESS at different time points.
[0058] With the continuous integration of renewable energy sources (RESs) into the existing power system, enhancing the renewable energy hosting capacity (HC) of the existing distribution system has become a critical issue. Considering the significant spatiotemporal flexibility of mobile energy storage systems (MESSs), they possess the potential to enhance renewable energy hosting capacity. Therefore, this application proposes a MESS scheduling model based on a time-space network (TSN), using arc-related variables to finely characterize the spatiotemporal behavior and scheduling decisions of MESSs. Furthermore, to evaluate the improvement effect on system hosting capacity, this application constructs a renewable energy hosting capacity evaluation model based on optimal power flow (OPF) for integrated medium-voltage-low-voltage (MV-LV) distribution networks. The established problem is formulated as a mixed-integer second-order cone programming (MISOCP) model, seeking the maximum hosting capacity while satisfying system operating constraints. This application is validated using a comprehensive test system consisting of the Sioux Falls transportation network, a 33-node distribution system, and three 18-node distribution systems. The results show that using MESS as a means to enhance the carrying capacity of new energy sources is effective, demonstrating its technological potential under the policy-driven demand for grid connection of renewable energy.
[0059] This application provides a method and corresponding dispatching device for enhancing the renewable energy carrying capacity of distribution networks by incorporating mobile energy storage. This addresses the technical problem that existing static energy storage systems have fixed locations and limited adjustment ranges, and existing mobile energy storage dispatching models lack sufficient spatiotemporal behavior characterization, thus failing to fully utilize the spatiotemporal flexibility of mobile energy storage to systematically improve the renewable energy carrying capacity of integrated medium-voltage and low-voltage distribution networks. First, a dispatching model capable of precisely describing the spatiotemporal behavior of Mobile Energy Storage (MESS) is proposed. Then, this model is embedded into a renewable energy carrying capacity assessment model based on optimal power flow for integrated medium-voltage and low-voltage distribution networks to verify the effectiveness of MESS as a means of enhancing carrying capacity. Finally, the constructed optimization problem is modeled as a mixed-integer second-order cone programming problem, used to obtain the enhanced renewable energy carrying capacity of the system under the support of MESS flexibility. Detailed explanation follows.
[0060] Example 1 like Figure 1 As shown, a method for enhancing the renewable energy carrying capacity of a distribution network considering mobile energy storage is provided. This method coordinates the operation of a mobile energy storage system and the distribution network to improve the renewable energy carrying capacity. In this embodiment, the method includes the following steps: Step S101: Deploy multiple MESS and their corresponding sites in the medium-voltage-low-voltage power distribution network, and construct a MESS scheduling model based on the spatiotemporal network TSN.
[0061] Specifically, the first step is to deploy multiple Mobile Energy Storage Systems (MESS) and their corresponding sites within the coupled medium-to-low voltage (MV-LV) power distribution network and transportation network. These sites are key coupling elements, each simultaneously connected to a specific node in the low-voltage (LV) power distribution network and a corresponding geographical location in the transportation network. This provides a physical interface for the interaction of MESS between the power system and the transportation system.
[0062] Based on this, a MESS scheduling model based on a Time-Space Network (TSN) is constructed. The core of this model lies in using an abstract graph structure to accurately describe the movement and dwelling states of MESS devices across different geographical locations over time. Specifically, the TSN is constructed using three types of elements: station TSN nodes, representing a MESS station at a specific point in time; migration arcs, connecting different station TSN nodes at different points in time, representing the MESS's journey between two stations; and dwelling arcs, connecting TSN nodes at two adjacent points in time at the same station, representing the MESS's state of staying at a station for a period of time.
[0063] To mathematically represent this model, this application employs arc-related binary variables to characterize the driving and charging / discharging behavior of each MESS in different time periods. These binary variables correspond to each arc in the TSN; a value of 1 indicates that the corresponding MESS is located on that arc (driving or stationary) during that time period. In this way, the complex temporal and spatial scheduling problem can be transformed into a series of discrete, solvable decision variables.
[0064] By introducing the TSN model and arc-related binary variables, the mobility, stationary state, and connection relationship with the distribution network of MESS were systematically mathematically modeled, laying the model foundation for subsequent accurate and feasible scheduling decisions.
[0065] Step S102: Based on the MESS scheduling model based on the spatiotemporal network, determine the charging power and discharging power of each MESS in each time period, and manage the state of charge (SoC) of each MESS to ensure that the charging and discharging constraints meet the allowable range, and obtain the MESS scheduling decision.
[0066] Specifically, within the TSN model framework constructed in step S101, this step determines the specific operational behavior of MESS by solving a series of mathematical constraints.
[0067] First, the model determines whether each MESS should charge or discharge when connected to a certain station (i.e., located on a "stalling arc") based on system demand (such as fluctuations in renewable energy and load changes) and the MESS's own parameters, and calculates the specific charging and discharging power. The values of these two variables will determine the power support provided by the MESS to the power distribution system.
[0068] Secondly, the model must strictly manage the State of Charge (SoC) of each MESS. SoC is an indicator describing the percentage of remaining energy within the MESS. The model uses an energy balance equation to correlate the current SoC of the MESS with the SoC of the previous time period and the charging / discharging power of the current time period. Simultaneously, the model imposes charging and discharging constraints to ensure that the SoC is always within a safe and healthy range, i.e., between the minimum and maximum SoC. Furthermore, the model enforces charging and discharging mutual exclusion constraints to ensure that the same MESS cannot be charged and discharged simultaneously within the same time period.
[0069] Finally, by solving the above constraints, the model will output a complete set of scheduling instructions for each MESS in each time period, including: whether to move, which station to move to, whether to charge or discharge upon arrival, and the power at which to charge or discharge. This set of instructions constitutes the MESS scheduling decision.
[0070] The technical effect of this step is that it transforms the abstract TSN model into specific, feasible and safe MESS operation instructions, ensuring that the energy state of the MESS always meets physical and safety constraints when performing scheduling tasks, while realizing effective energy interaction with the power distribution system.
[0071] Step S103: Embed the MESS scheduling decision into the optimal power flow calculation model of the medium-voltage-low-voltage distribution network, and optimize the new energy capacity in the medium-voltage-low-voltage distribution network under the premise of satisfying the constraints of bus voltage, branch power flow and transformer capacity.
[0072] Specifically, this step leverages the flexibility of MESS (Mechanical Execution System) for coupled optimization of the distribution network's operating status. First, the MESS scheduling decision obtained in step S102 (i.e., the charging and discharging power of MESS at each site during each time period) is transformed into the equivalent injected power in the distribution network and incorporated into the power flow calculation of the distribution network. Specifically, the aggregated MESS injected power at an LV node is determined by the algebraic sum of the charging and discharging power of all MESS connected to that node.
[0073] Next, this MESS injected power is substituted into a computational model based on Optimal Power Flow (OPF). This model aims to maximize the system's renewable energy carrying capacity, i.e., to seek the maximum installable renewable energy capacity. During the optimization process, the model must satisfy a series of strict safety constraints: Bus voltage constraint: The voltage amplitude of all nodes in the system must be maintained within the allowable safe range (e.g., 0.9 pu to 1.1 pu).
[0074] Branch power flow constraints: The power (current) flowing through all lines and transformers must not exceed their thermal stability limits.
[0075] MV / LV transformer capacity constraints: The active and reactive power flowing through a medium-voltage to low-voltage transformer, especially the reverse power, must not exceed its rated capacity or a given upper limit.
[0076] By embedding the MESS scheduling decision into the OPF model for integrated solution, the maximum renewable energy capacity that the system can safely accommodate under the flexible support of MESS can be found. This solution process typically employs a mixed-integer second-order cone programming (MISOCP) method to ensure solution efficiency and global optimality. The final optimization result is the improved renewable energy carrying capacity of the distribution network sought in this application.
[0077] The technical effect of this step is that it unifies the spatiotemporal scheduling capability of MESS with the physical operation constraints of the distribution network within an optimization framework. This enables the quantitative assessment and maximization of the improvement effect of MESS on the carrying capacity of new energy sources while ensuring system safety, and solves the problem of the inability of existing technologies to coordinate the optimization of the spatiotemporal behavior of MESS and the power flow of the grid.
[0078] Example 2 This embodiment uses Figures 2 to 7 Using the comprehensive testing system shown as an example, the entire process from model building to result output is described in detail.
[0079] Considering that MESS primarily operates within existing transportation networks and provides services to facilities in the power distribution system at different geographical locations, a suitable transportation network model is needed in addition to the power distribution system model. Therefore, this application constructs an integrated MV-LV-transportation system to describe the collaborative relationship between the power distribution system and the MESS.
[0080] The general framework of the integrated MV-LV transportation system provided in this embodiment is as follows: Figure 2 As shown. The system consists of two subsystems: a transportation network and an integrated MV-LV power distribution network. The latter can be represented as a graph structure composed of multiple subgraphs, where... , , They are collections of medium-voltage and low-voltage power grids, and Let be the set of nodes and the set of paths in network n, respectively. Further, define the mapping... A set of MESS (Mean Energy Sets) is used to map each MV bus to a subset of its connected LV networks, where P(·) is the power set. The two networks are coupled to each other through a set of MESS stations M, and a set of MESS (denoted as Ω) can access these stations and perform charging and discharging operations at each station. For simplicity, this application only considers two new energy sources: photovoltaic and wind power.
[0081] To realize the detailed spatiotemporal behavior of MESS in the integrated system and support scheduling decisions, this embodiment employs spatiotemporal network (TSN) modeling technology. Meanwhile, it is assumed that when MESS... At both sites , When traveling between stations, always choose the shortest path in the transportation network connecting the two stations. The shortest path length can be obtained using existing methods such as Dijkstra's algorithm; the corresponding travel time is defined as... , for MESSω from Drive to The required number of time periods, of which v avg Let Δt be the average speed of the MESS, and Δt be the length of a single time period. This is a rounding operator. This application further assumes that all MESSs have the same average vehicle speed, and that Δt is consistent with the power system operating timescale. Let... To study the set of time periods within the time domain, index t is used; each time period has two endpoints, and all endpoints constitute a set of time points. .
[0082] To illustrate the construction of a TSN more intuitively, consider a simple integrated MV-LV-transport system containing four LV networks, and assume that each LV network is configured with one MESS station. A complete MESS-managed TSN is as follows: Figure 3 As shown, it mainly consists of three types of elements: 1) site spatiotemporal nodes 1) A station at a specific point in time; 2) Migration arc ,for Feasible start-end point pairs, such as arcs For MESS Leave at any time and Time arrives ;3) Stopping arc This is used to connect spatiotemporal nodes of the same site at two adjacent points in time, indicating that MESS stays at that site during a certain period of time, for example... For MESS in the time period t 0 stay .
[0083] Under the TSN model, the spatiotemporal decoupling decision of MESS can be considered as a combination of a series of spatiotemporal arcs. This application uses arc-related binary variables. Describe it as follows: When MESSω is located on arc When the time interval t is reached, the variable takes the value 1. To facilitate the characterization of MESS behavior using arcs and spatiotemporal nodes, this application also introduces the concept of a cut-set for each time interval t.
[0084] Based on the above definitions, the TSN-based MESS scheduling model can be written as: (1) (2) (3) The above constraints indicate that, within any given time period, each MESS can only lie on one arc; simultaneously, the model satisfies flow conservation: if a MESS ends its previous journey at a certain spatiotemporal node, it must continue its movement along a feasible arc originating from that node in the next time period. Let MESSω be the initial position. and These are sets of arcs that start and end at the spatiotemporal node n, respectively.
[0085] When a MESS remains on a stationary arc, it connects to the corresponding site and can provide energy services to the corresponding LV network. The relationship between its power / energy behavior and spatiotemporal dynamics can be characterized by the following constraints: (4) (5) (6) (7) (8) (9) (10) in, and These represent the charging and discharging power of MESSω at time period t at station m, respectively. and These are its maximum charging power and maximum discharging power, respectively. and This is a binary variable used to characterize whether the MESS performs charging or discharging during time period t; , and These represent its current, maximum, and minimum states of charge (SoC).
[0086] The above constraints collectively determine the feasible power exchange behavior between MESS and the site, among which Let m be the set of stationary arcs during time period t. Furthermore, the model guarantees that each MESS performs at most one energy exchange behavior (charging or discharging) within the same time period, and describes its SoC evolution process through an energy balance equation, while ensuring that the SoC always remains within acceptable limits.
[0087] Furthermore, considering the flexibility of MESS (Medium-terminal Utilization System), the renewable energy carrying capacity assessment adopts an OPF (Optical Power Factor Optimization) framework to evaluate the maximum renewable energy carrying capacity of the integrated MV-LV (Multi-level Distribution System). Under this framework, the following operational constraints must be met simultaneously: 1) Bus voltage must be kept within the allowable range; 2) The power flow in the line must not exceed the thermal stability limit; 3) The reverse current on each LV transformer must not exceed the given upper limit.
[0088] Specifically, the objective function is to determine the maximum renewable energy carrying capacity of the integrated MV-LV distribution system while considering the flexibility of MESS. Its expression is as follows: (11) in, and These represent the photovoltaic and wind power capacities to be installed at LV node i, respectively.
[0089] Specifically, the constraints of the above objective function include: 1) Integrate MV-LV network constraints; This application employs a widely used branch power flow model and its cone relaxation form in power flow analysis, with the following specific constraints: (12) (13) (14) (15) (16) (17) (18)
[0090] (19) (20) (twenty one) (twenty two) (twenty three) in, , and These represent the active power flow, generation power of node i, and load power of branch (i,j) in network n during time period t, respectively. Unless otherwise specified, the corresponding Q variable in the text refers to the corresponding reactive power. This represents the active power injected into the MV network by the LV network n through the corresponding MV / LV transformer. If it is negative, it means that the LV network n is absorbing active power from the MV network. and These represent the existing renewable energy injection power and aggregated MESS injection power of node i in LV network n during time period t, respectively; the normalized photovoltaic and wind power curves are denoted as follows: and .in addition, and These represent the square of the node voltage amplitude and the square of the branch current amplitude, respectively.
[0091] The goal is to maximize the maximum renewable energy carrying capacity of the distribution network. : No. The set of nodes in a distribution network zone; Node number within the distribution network; Low-voltage node Photovoltaic power generation capacity; Low-voltage node Wind power installed capacity; Distribution network node; Medium-voltage distribution network zone set; : Time Node Traditional power supply active / reactive power output; : with medium-voltage nodes A set of interconnected low- and medium-voltage connection points; : Active / reactive power flowing from the low-voltage side to the medium-voltage side at any given time; : Time Node Active / reactive load power; : Time Node Flow to Node Active / reactive power of the line; Low-voltage distribution network zone set; : Time Node The existing distributed renewable energy sources have contributed their energy. : Photovoltaic / wind power output coefficient at any time (characterizing the volatility of new energy sources); : Time Node The active power of mobile energy storage (MESS) is positive when discharging and negative when charging; : The reactive power flowing from the low-voltage side to the medium-voltage side at any given time (with the same meaning as the medium-voltage part, corresponding to the low-voltage zoning scenario). : Low-voltage nodes at all times reactive load power; : Low-voltage nodes at all times Flow to Node Active / reactive power of the line; : No. Within each low-voltage zone, accessible nodes A collection of mobile energy storage devices; A collection of energy storage operation modes; : Time of the first Active power of energy storage discharge; : Time of the first Taiwan's energy storage charging active power; : Time Node The voltage amplitude; :line Resistance / reactance; : Timetable The current amplitude; : No. A set of lines for each distribution network zone; : Set of low-voltage side nodes for low-voltage distribution transformers; Minimum / maximum active power limits for low-voltage to medium-voltage tie lines; Minimum / maximum reactive power limits for low-voltage to medium-voltage tie lines; :node Minimum / maximum voltage values allowed (to meet power quality standards and ensure the normal operation of electrical equipment); :line Maximum permissible current (current carrying capacity).
[0092] The above constraints describe the active / reactive power balance relationship of nodes in the MV and LV networks, respectively, and provide a method for calculating the aggregated MESS output. This is the set of MESS stations connected to node i in the LV network n. Simultaneously, the model uses cone relaxation form as KVL constraints to characterize the voltage coupling relationship between MV and LV nodes, where s is the substation bus, and this application assumes that the MV substation bus voltage is constant at 1 p.u. Finally, MV / LV transformer capacity constraints, bus voltage constraints, and branch current constraints are further applied.
[0093] 2) In addition to the above-mentioned comprehensive MV-LV network constraints, this application also incorporates the established TSN-based MESS scheduling constraints into the optimization model.
[0094] Example 3 Based on Examples 1 and 2, Example 3 provides the simulation parameters of the test system as shown in Table 1.
[0095] Table 1 Simulation Parameters
[0096] This embodiment constructs a comprehensive MV-LV traffic test system for simulation. The system consists of a modified Sioux Falls traffic network with doubled road lengths, and a comprehensive MV-LV power distribution network, as follows: Figure 4 As shown. The MV section uses the IEEE 33-node test system with a voltage level of 12.66kV and a peak load of 3.72MW / 2.3MVar; the LV section uses three modified 18-node test systems with a voltage level reduced to 400V and peak loads set at 47.02kW and 47.96kVar. Each LV network is configured with one MESS site connected to LV node #2, corresponding to sites m1, m2, and m3; in the traffic network, they are located at nodes #3, #19, and #24, respectively.
[0097] This embodiment assumes that LV network #1 only installs new wind turbines, while LV networks #2 and #3 only install new photovoltaic (PV) power. LV nodes #2, #5, #8, #12, and #17 are designated as candidate renewable energy installation locations. Furthermore, LV networks #2 and #3 have already installed 30kWp PV arrays at nodes #10, #11, and #18, respectively. The local load types in the three LV networks are industrial, commercial, and residential loads, respectively, and the corresponding load curves use existing data. The normalized MV load, PV, and wind power curves are shown below. Figure 5The figure shows the normalized MV load, PV, and wind power output curves. The upper limits of reverse power flow for the MV / LV transformer are set to 100kW and 100kVar, respectively, and the upper and lower limits of the bus voltage are 1.1pu and 0.9pu, respectively. The branch thermal stability constraint is set according to the maximum current of 300A. The example considers a total of 6 MESS units, whose parameters are shown in Table 1, and the time step Δt is taken as 30min.
[0098] To verify the effectiveness of MESS as a HC enhancement method, this application sets up two simulation scenarios: in scenario 1, the MESS remains fixed at its initial position, equivalent to a static ESS; in scenario 2, the MESS moves between networks according to the optimization results to fully utilize its spatiotemporal flexibility. The model is implemented using Python 3.9 and solved using the commercial solver CPLEX 12.8.0. The simulation results are shown in Table 2.
[0099] Table 2 Numerical Results
[0100] Table 2 presents the numerical results. Compared to Scenario 1, the total renewable energy carrying capacity in Scenario 2 increased from 380.9 kW to 474.4 kW, an increase of 24.5%. This indicates that the spatiotemporal mobility of MESS can provide additional operational flexibility to the system, thereby significantly improving the renewable energy carrying capacity. The results also show that the increase in HC mainly occurs in LV#1. This is because the new additions in LV#1 are wind power, while the new additions in LV#2 and LV#3 are photovoltaic power. Due to the difference in output curves between wind and photovoltaic power, when photovoltaic output is low in the first few hours, MESS in LV#2 and LV#3 will first be moved to LV#1 to support wind power access; subsequently, these MESS will return to LV#2 and LV#3 to support the improvement of local photovoltaic carrying capacity. In contrast, due to the lack of spatial mobility, static ESS can only provide limited support for system HC under its limited capacity.
[0101] Figure 6The charging and discharging scheduling results and corresponding paths of MESS#1, #2, and #3 are presented. It can be seen that the spatiotemporal flexibility of MESS is fully utilized to maximize the enhancement of renewable energy carrying capacity. For example, in the initial stage, MESS#2 departs from station m2 in LV#2, travels to m1 in LV#1 from 0:00 to 1:00, and stays there from 1:00 to 2:00 to support wind power integration; subsequently, it leaves m1 at 2:00 and arrives at m3 in LV#3 at 2:30, discharging locally to support the load. Similar behavior occurs again between 4:30 and 8:00. As the photovoltaic output in LV#2 and LV#3 gradually increases, MESS#2 then mainly moves back and forth between these two LV networks to support the enhancement of photovoltaic carrying capacity. MESS#1 and #3 also exhibit similar scheduling patterns. Notably, unlike MESS#2, after sunset at approximately 20:00, these two MESS units again begin moving between m1 and m3 to continue supporting the enhancement of wind power carrying capacity.
[0102] Figure 7 The bus voltage distribution for LV#1 is presented. It can be seen that no voltage exceedances occurred in the system during any time period, and all bus voltages remained within safe limits. This is because, in this example, the primary factor limiting system HC is not voltage constraint, but rather the power flow capacity constraint of the MV / LV transformer. The reverse power flow on the transformer had already reached its upper limit before any significant voltage rise occurred.
[0103] In summary, this application adopts MESS as a means to enhance the renewable energy carrying capacity in the integrated MV-LV system. First, a TSN-based MESS scheduling model is used to finely describe its spatiotemporal flexibility. Second, considering the coupling relationships between MESS, MV network, LV network, and transportation network, a maximum renewable energy carrying capacity evaluation model based on OPF is constructed. Through two scenario examples, this application demonstrates that MESS and its mobility can effectively improve the overall renewable energy carrying capacity of the system through cross-network transfers and charging / discharging behavior at corresponding stations. Future research could further consider local transportation networks oriented towards the LV distribution system and introduce a multi-layer TSN model to more finely characterize MESS; simultaneously, the uncertainties of renewable energy output and the transportation system could be further incorporated into the proposed model.
[0104] Example 4: This embodiment provides a distribution network renewable energy carrying capacity enhancement device that considers mobile energy storage. Through modular architecture design, the device realizes the hardware implementation of each step in the aforementioned method embodiment, and can efficiently and accurately evaluate the renewable energy carrying capacity of the distribution network.
[0105] Specifically, the system comprises a deployment module, a construction module, a management module, and an optimization module. The following section provides a detailed explanation of the functions, connections, and collaboration methods of each module, based on the device's operation in practical applications.
[0106] The deployment module is used to deploy multiple mobile energy storage systems and their corresponding sites in a coupled medium-voltage-low-voltage power distribution network and a transportation network. The sites are simultaneously connected to nodes in the low-voltage power distribution network and corresponding locations in the transportation network. The module is used to build a scheduling model for mobile energy storage systems based on a spatiotemporal network. The spatiotemporal network represents the travel path and parking status of mobile energy storage systems in different time periods through site spatiotemporal nodes, migration arcs and parking arcs. Arc-related binary variables are used to describe the travel and parking behavior of each mobile energy storage system in different time periods and its charging and discharging behavior at each site. The management module is used to determine the charging power and discharging power of each mobile energy storage system in each time period according to the spatiotemporal network-based mobile energy storage system scheduling model, and at the same time manage the state of charge of each mobile energy storage system to ensure that it meets the charging and discharging constraints and the allowable range of the state of charge, so as to obtain the mobile energy storage system scheduling decision. The optimization module is used to embed the scheduling decision of the mobile energy storage system into the optimal power flow calculation model of the medium-voltage-low-voltage distribution network. Under the premise of satisfying the bus voltage constraint, branch power flow constraint and medium-voltage-low-voltage transformer capacity constraint, the module optimizes the new energy carrying capacity of the medium-voltage-low-voltage distribution network to determine the maximum new energy installed capacity that can be connected.
[0107] In summary, the workflow of the power distribution network renewable energy carrying capacity enhancement device considering mobile energy storage provided in this embodiment is as follows: First, the deployment module acquires basic data of the power distribution network and transportation network, determines the deployment scheme of MESS and sites, and sends the configuration information to the construction module.
[0108] Secondly, the construction module establishes a MESS scheduling model based on the received configuration information, including constructing spatiotemporal nodes, migration arcs, and parking arcs, as well as defining arc-related binary variables and various constraints.
[0109] Then, the management module solves for the charging and discharging power and state of charge of MESS based on the TSN model, and generates detailed MESS scheduling decisions.
[0110] Finally, the optimization module embeds the MESS scheduling decision into the optimal power flow model, and optimizes and outputs the maximum renewable energy installed capacity that the system can access, while satisfying various power grid security constraints.
[0111] The four modules work together to form a complete automated processing flow from data input, model building, scheduling decisions to carrying capacity assessment. This device can be deployed in the dispatch center of a power distribution system operator as an auxiliary decision-making tool for planning renewable energy integration schemes or guiding the daily operation and scheduling of MESS (Mechanical Energy Service).
[0112] This embodiment, through the organic coordination of deployment, construction, management, and optimization modules, functionally modularizes each step in the aforementioned method claims. This enables a quantitative description and utilization of the spatiotemporal flexibility of mobile energy storage systems, and, while meeting grid security constraints, automatically assesses and optimizes the renewable energy carrying capacity of the distribution network. Compared to existing technologies, this device can fully utilize the spatial mobility of MESS (Mobile Energy Storage System) to dynamically transfer energy storage capacity to the nodes most in need of support, thereby significantly improving renewable energy carrying capacity without requiring infrastructure expansion.
[0113] Example 5: This embodiment provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the method for enhancing the renewable energy carrying capacity of a distribution network considering mobile energy storage, as described in any of the above embodiments.
[0114] Specifically, the computer-readable storage medium referred to in this embodiment can be any tangible medium that can contain or store a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device. It should be understood that although the foregoing embodiments focus on describing the logical flow of the method and the modular architecture of the apparatus, the implementation of these logical steps and module functions ultimately depends on the execution of the computer program code. This embodiment stores the computer program implementing the aforementioned spatiotemporal network model construction, multi-level distribution network model construction, coupling mapping, and carrying capacity solution algorithms in a medium, enabling the technical solution to exist independently and circulate as a software product.
[0115] For example, the computer-readable storage medium can be a portable computer disk, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. Since the aforementioned method involves solving complex mixed-integer second-order cone programming (MISOCP) problems and calculating large-scale spatiotemporal network nodes, the computational resources are frequently accessed. Therefore, the program code stored in this storage medium is optimized to be efficiently invoked by the processor, thus ensuring computational accuracy while meeting the timeliness requirements of engineering applications. When this medium is loaded into computing devices such as servers, industrial control computers, or cloud platforms, the processor reads and executes the program code, automatically completing the entire process from model construction to load-bearing capacity result output, realizing the software-based implementation of the technical solution.
[0116] Example 6: This embodiment provides a distribution network renewable energy carrying capacity enhancement system that considers mobile energy storage. As a hardware carrier, this system can efficiently run the evaluation method described in the previous embodiment to realize the automated calculation and output of the renewable energy carrying capacity of the distribution network.
[0117] Specifically, the system includes a processor, a memory, and a computer program stored in the memory. When the processor executes the program, it implements the method for enhancing the renewable energy carrying capacity of the distribution network considering mobile energy storage, as described in any of the above embodiments. The processor is the core of the system's computation and is responsible for executing the computer program instructions stored in the memory. During the carrying capacity assessment process, the processor is responsible for parsing the input mobile energy storage parameters, traffic network topology data, and distribution network topology data, and generating model elements such as spatiotemporal nodes, migration arcs, and stationary arcs according to the preset spatiotemporal network construction logic. Simultaneously, the processor is also responsible for executing the Mixed Integer Second-Order Cone Programming (MISOCP) algorithm to find the optimal solution that satisfies all constraints through iterative calculation, i.e., the maximum renewable energy carrying capacity value. It should be understood that the processor can be a general-purpose central processing unit (CPU), digital signal processor (DSP), application-specific integrated circuit (ASIC), or field-programmable gate array (FPGA) or other hardware unit with data processing capabilities.
[0118] Memory is used to store computer programs and various data required during the evaluation process. Memory can include random access memory (RAM) and read-only memory (ROM). ROM is used to store the basic program code embedded in the system, such as the operating system kernel and low-level drivers; RAM is used to store temporary data generated during runtime, such as intermediate variables of the spatiotemporal network model, iterative data for power flow calculation, and slack variables in the optimization solution process. In addition, memory can also include external storage devices, such as hard disks, solid-state drives, or cloud storage interfaces, for long-term storage of historical evaluation results, mobile energy storage dispatch scheme libraries, and historical operating data of the distribution network for subsequent querying and analysis.
[0119] In terms of hardware connectivity, the processor interacts with the memory via the system bus. The system may also include input / output interfaces for connecting external input devices (such as keyboards, mice, and data import interfaces) and output devices (such as monitors, printers, and data export interfaces). For example, users can import Sioux Falls traffic network parameters and IEEE 33-node distribution network parameters through input devices. The processor then calls the program in memory to execute the evaluation logic, and finally displays the carrying capacity values and the corresponding mobile energy storage dispatch path diagram on the monitor.
[0120] Through the aforementioned hardware architecture, this embodiment transforms the complex spatiotemporal network modeling and multi-level distribution network coupled calculations into a concrete hardware execution process. The processor's high-speed computing power ensures the efficiency of solving mixed-integer programming problems, while the large-capacity memory guarantees the storage requirements for large-scale spatiotemporal network data. This system not only physically implements the evaluation method but also possesses good scalability and stability, meeting the practical needs of power grid planning and operation departments for real-time and accurate evaluation of renewable energy carrying capacity.
[0121] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0122] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0123] This application may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application may take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, readable storage media, optical storage, etc.) containing computer-usable program code.
[0124] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0125] Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort should fall within the scope of protection of this application.
[0126] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application, such as adjusting the time granularity of the spatiotemporal network model, using other linearization or convex relaxation methods to handle power flow constraints, or applying the method of this application to distribution network systems containing higher voltage levels, should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
Claims
1. A method for enhancing the renewable energy carrying capacity of a distribution network considering mobile energy storage, characterized in that, include: In the coupled medium- and low-voltage power distribution network and transportation network, multiple mobile energy storage systems and their corresponding sites are deployed. The sites are simultaneously connected to nodes in the low-voltage power distribution network and corresponding locations in the transportation network. A scheduling model for mobile energy storage systems based on a spatiotemporal network is constructed. The spatiotemporal network represents the travel path and parking status of mobile energy storage systems in different time periods through site spatiotemporal nodes, migration arcs, and parking arcs. Arc-related binary variables are used to describe the travel and parking behavior of each mobile energy storage system in different time periods, as well as its charging and discharging behavior at each site. Based on the spatiotemporal network-based mobile energy storage system scheduling model, the charging power and discharging power of each mobile energy storage system in each time period are determined. At the same time, the state of charge of each mobile energy storage system is managed to ensure that it meets the charging and discharging constraints and the allowable range of the state of charge, thus obtaining the mobile energy storage system scheduling decision. The scheduling decision of the mobile energy storage system is embedded into the optimal power flow calculation model of the medium-voltage-low-voltage distribution network. Under the premise of satisfying the bus voltage constraint, branch power flow constraint and medium-voltage-low-voltage transformer capacity constraint, the new energy carrying capacity of the medium-voltage-low-voltage distribution network is optimized to determine the maximum new energy installed capacity that can be connected.
2. The method for enhancing the renewable energy carrying capacity of a distribution network considering mobile energy storage according to claim 1, characterized in that, The MESS scheduling model based on spatiotemporal networks is as follows: in, Let MESSω be the initial position. and These are sets of arcs that start and end at spatiotemporal node n, respectively. This is a set of device numbers for Mobile Energy Storage Systems (MESS). Representing the Mobile energy storage platform; For the set of scheduling time periods, Representing the One scheduling period; This refers to the set of nodes within the distribution network that can dock / access MESS. Physical nodes representing the distribution network; for The set of all directed edges in the spatiotemporal network, including energy storage driving edges, parking edges, and charging / discharging edges. 0 1. Binary decision variables; MESS is used to move / stop / operate along this spacetime edge; It means not passing through this edge; For the first The initial spatiotemporal position state of the MESS platform; The constraints of the MESS scheduling model based on spatiotemporal networks are as follows: in, and These represent the charging and discharging power of MESSω at time period t at station m, respectively. and These are its maximum charging power and maximum discharging power, respectively. and This is a binary variable used to characterize whether the MESS performs charging or discharging during time period t; , and These represent the current, maximum, and minimum states of charge of the SoC, respectively. For scene indexing, A set of random scenes; For time index, A set of scheduling time sequences; For energy storage unit / node index, A collection of energy storage units; For the first Time, energy storage The corresponding distribution network / power link set; For the scene Downlink Power transmission factor / available capacity factor; For the previous moment SOC; For the scene Lower charging efficiency For discharge efficiency; The scheduling time step (e.g., 1h, 0.5h); For the scene Lower rated energy storage capacity.
3. The method for enhancing the renewable energy carrying capacity of a distribution network considering mobile energy storage according to claim 1, characterized in that, In the MESS scheduling model based on spatiotemporal networks, charging and discharging operations are mutually exclusive within the same time period, and each MESS is only on a migration arc or a stationary arc within any given time period. The state of charge (SoC) of the MESS is calculated cumulatively from the initial state of charge, charge / discharge power, and time period length, and is limited to between the minimum SoC and the maximum SoC.
4. The method for enhancing the renewable energy carrying capacity of a distribution network considering mobile energy storage according to claim 1, characterized in that, The optimal power flow calculation model for the medium-voltage-low-voltage distribution network includes bus voltage constraints, branch power flow constraints, and MV-LV transformer capacity constraints, and is solved by mixed integer second-order cone programming.
5. The method for enhancing the renewable energy carrying capacity of a distribution network considering mobile energy storage according to claim 1, characterized in that, The objective function of the optimal power flow calculation model for the medium-voltage-low-voltage distribution network is to obtain the maximum renewable energy carrying capacity of the integrated MV-LV distribution system, taking into account the flexibility of MESS. Its expression is as follows: in, and These represent the photovoltaic and wind power capacities to be installed at LV node i, respectively. The constraints include: in, , and Let $T$ represent the active power flow, the generating power of node $i$, and the load power of branch $i$ in network $n$ during time period $t$; and let $Q$ represent the corresponding reactive power. This represents the active power injected into the MV network by the LV network n through the corresponding MV-LV transformer. If it is negative, it means that the LV network n is absorbing active power from the MV network. and These represent the existing renewable energy injection power and aggregated MESS injection power of node i in LV network n during time period t, respectively; the normalized photovoltaic and wind power curves are denoted as follows: and ; and These are the squares of the node voltage magnitude and the squares of the branch current magnitude, respectively; where The set of MESS stations connected to node i in LV network n; For the first The set of nodes in a distribution network zone; Node numbering within the distribution network; Low-voltage node Photovoltaic power generation capacity; Low-voltage node Wind power installed capacity; For distribution network nodes; This is a set of medium-voltage distribution network zones; for Time Node Traditional power supply active / reactive power output; To connect with medium-voltage nodes A set of interconnected low- and medium-voltage connection points; for Active / reactive power flowing from the low-voltage side to the medium-voltage side at any given time; for Time Node Active / reactive load power; for Time Node Flow to Node Active / reactive power of the line; This is a set of low-voltage distribution network zones. for Real-time photovoltaic / wind power output coefficient; for The reactive power flowing from the low-voltage side to the medium-voltage side at all times; for Low-voltage nodes at all times reactive load power; for Low-voltage nodes at all times Flow to Node Active / reactive power of the line; For the first Within each low-voltage zone, accessible nodes A collection of mobile energy storage devices; A set of energy storage operation modes; for Time of the first Active power of energy storage discharge; for Time of the first Taiwan's energy storage charging active power; For the line Resistance / reactance; for Timetable The current amplitude; For the first A set of lines for each distribution network zone; This refers to the set of low-voltage side nodes of the low-voltage side distribution transformer. Minimum / maximum active power limits for low-voltage to medium-voltage tie lines; Minimum / maximum reactive power limits for low-voltage to medium-voltage tie lines; For nodes Minimum / maximum allowable voltage amplitude; For the line Maximum permissible current.
6. The method for enhancing the renewable energy carrying capacity of a distribution network considering mobile energy storage according to claim 1, characterized in that, The MESS moves between different stations through the transportation network, selects the shortest path, and calculates the migration time based on average vehicle speed and time period length.
7. The method for enhancing the renewable energy carrying capacity of a distribution network considering mobile energy storage according to claim 1, characterized in that, The MESS site configuration of the LV node in the medium-voltage-low-voltage power distribution network is related to the node load type and the power output curve of the new energy source; The number, location, and capacity of the MESS can be adjusted according to the scale of the power distribution network and the distribution of new energy sources.
8. The method for enhancing the renewable energy carrying capacity of a distribution network considering mobile energy storage according to claim 1, characterized in that, The MESS scheduling decision is embedded into the optimal power flow calculation model of the medium-voltage-low-voltage distribution network. The solution is iteratively obtained in each scheduling cycle, and the MESS movement and charging / discharging strategies are dynamically adjusted to cope with the fluctuation of new energy power.
9. The method for enhancing the renewable energy carrying capacity of a distribution network considering mobile energy storage according to any one of claims 1 to 8, characterized in that, The method also includes comprehensive consideration of the charging and discharging efficiency, energy loss, and traffic constraints of MESS.
10. A power distribution network renewable energy carrying capacity enhancement device considering mobile energy storage, characterized in that, include: The deployment module is used to deploy multiple mobile energy storage systems and their corresponding sites in a coupled medium-voltage-low-voltage power distribution network and a transportation network. The sites are simultaneously connected to nodes in the low-voltage power distribution network and corresponding locations in the transportation network. The module is used to build a scheduling model for mobile energy storage systems based on a spatiotemporal network. The spatiotemporal network represents the travel path and parking status of mobile energy storage systems in different time periods through site spatiotemporal nodes, migration arcs and parking arcs. Arc-related binary variables are used to describe the travel and parking behavior of each mobile energy storage system in different time periods and its charging and discharging behavior at each site. The management module is used to determine the charging power and discharging power of each mobile energy storage system in each time period according to the spatiotemporal network-based mobile energy storage system scheduling model, and at the same time manage the state of charge of each mobile energy storage system to ensure that it meets the charging and discharging constraints and the allowable range of the state of charge, so as to obtain the mobile energy storage system scheduling decision. The optimization module is used to embed the scheduling decision of the mobile energy storage system into the optimal power flow calculation model of the medium-voltage-low-voltage distribution network. Under the premise of satisfying the bus voltage constraint, branch power flow constraint and medium-voltage-low-voltage transformer capacity constraint, the module optimizes the new energy carrying capacity of the medium-voltage-low-voltage distribution network to determine the maximum new energy installed capacity that can be connected.