A power distribution network mobile energy storage optimization scheduling method and system under extreme weather

Abstract

The application discloses a power distribution network mobile energy storage optimization scheduling method and system under extreme weather, and belongs to the field of power distribution network optimization operation. Firstly, a time-space movement characteristic and energy characteristic model of the mobile energy storage device is established to maximize support for the load, then a user power failure perception minimum and mobile energy storage device movement coefficient minimum target optimization scheduling model is established by taking into account the user power failure perception reliability, and the mobile energy storage optimization scheduling scheme considering the minimum user power failure perception under extreme weather accidents is determined according to the model. The application reduces the user side load reduction by the access of the mobile energy storage, minimizes the user power failure perception, reduces the user load loss, and improves the power distribution network resilience and power supply level.

Description

Technical Field

[0001] This invention relates to the field of distribution network optimization operation, and in particular to a method and system for optimizing the scheduling of mobile energy storage in distribution networks under extreme weather conditions. Background Technology

[0002] Existing studies on the optimal scheduling of mobile energy storage under extreme weather conditions aim to minimize the cost of power outages or the minimum load shedding, with little consideration for differences in energy demand among users at the time of a power outage, and neglecting the differentiated perceptions of the severity of the same power outage by different users. Simply considering the cost of power outages and the minimum load shedding cannot fully and accurately reflect the differentiated impact of power outage events on different users at different times. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for optimizing the scheduling of mobile energy storage in distribution networks under extreme weather conditions. By accessing mobile energy storage, the reduction of load on the user side is reduced, minimizing the user's perception of power outages and reducing the user's load loss, while improving the resilience and power supply level of the distribution network.

[0004] To achieve the above objectives, the present invention provides the following solution:

[0005] A method for optimizing the scheduling of mobile energy storage in distribution networks under extreme weather conditions includes:

[0006] Establish spatiotemporal mobility and energy characteristics models for mobile energy storage devices;

[0007] In the event of power outages in the target area caused by extreme weather, a mobile energy storage optimization scheduling model is established based on the spatiotemporal mobility characteristic model and the energy characteristic model, with the optimization objectives of minimizing the user's power outage perception and minimizing the mobility coefficient of the mobile energy storage device.

[0008] Solve the mobile energy storage optimization scheduling model to determine the optimal mobile energy storage scheduling scheme for the target area under extreme weather conditions; the mobile energy storage scheduling scheme includes the access location of mobile energy storage and the charging and discharging power.

[0009] A mobile energy storage optimization dispatch system for power distribution networks under extreme weather conditions includes:

[0010] The characteristic model building module is used to build spatiotemporal mobility characteristic models and energy characteristic models of mobile energy storage devices;

[0011] The optimization scheduling model establishment module is used to establish a mobile energy storage optimization scheduling model with the optimization objectives of minimizing the user's power outage perception and minimizing the mobility coefficient of mobile energy storage devices, based on the spatiotemporal mobility characteristic model and energy characteristic model, in the event of power outage in the target area caused by extreme weather.

[0012] The solution module is used to solve the mobile energy storage optimization scheduling model and determine the optimal mobile energy storage scheduling scheme for the target area under extreme weather conditions; the mobile energy storage scheduling scheme includes the access location of mobile energy storage and the charging and discharging power.

[0013] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the aforementioned method for optimizing the scheduling of mobile energy storage in a distribution network under extreme weather conditions.

[0014] A computer-readable storage medium having a computer program stored thereon, which, when executed, implements the aforementioned method for optimizing the scheduling of mobile energy storage in a distribution network under extreme weather conditions.

[0015] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0016] This invention discloses a method and system for optimizing the scheduling of mobile energy storage in distribution networks under extreme weather conditions. First, a model of the spatiotemporal mobility and energy characteristics of the mobile energy storage device is established to maximize load support. Then, considering the reliability of user power outage perception, a target optimization scheduling model is established that minimizes user power outage perception and the mobility coefficient of the mobile energy storage device. Based on this model, an optimized scheduling scheme for mobile energy storage that minimizes user power outage perception is determined under extreme weather events. This invention reduces load reduction on the user side by integrating mobile energy storage, minimizing user power outage perception, reducing load losses for users, and simultaneously improving the resilience and power supply level of the distribution network. Attached Figure Description

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

[0018] Figure 1 This is a flowchart of a method for optimizing the scheduling of mobile energy storage in a distribution network under extreme weather conditions, provided as an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] The purpose of this invention is to provide a method and system for optimizing the scheduling of mobile energy storage in distribution networks under extreme weather conditions. By accessing mobile energy storage, the reduction of load on the user side is reduced, minimizing the user's perception of power outages and reducing the user's load loss, while improving the resilience and power supply level of the distribution network.

[0021] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] like Figure 1 As shown, this embodiment of the invention provides a method for optimized scheduling of mobile energy storage in distribution networks under extreme weather conditions, including:

[0023] Step 1: Establish a spatiotemporal mobility characteristic model and an energy characteristic model for the mobile energy storage device.

[0024] Spatiotemporal mobility characteristic model of mobile energy storage devices

[0025]

[0026]

[0027]

[0028]

[0029] Where N is the set of access nodes. Let δ be the position flag variable for the mobile energy storage device m traveling from user i to user j at time t. A value of 1 indicates the energy storage device has moved within the chain, and a value of 0 indicates it has not moved. Assume δ route A set of routes, Let i be the set of routes starting from user i. Let M be the set of routes ending with user i. Let T be the set of mobile energy storage devices and T be the set of discrete time nodes. Formula (1) means that at any time, each mobile energy storage device can only be located in one travel chain. Formula (2) means that the arrival chain at any time is equal to the departure chain at the previous time. Formulas (3) and (4) mean the initial and final state requirements of the mobile energy storage device.

[0030] Spatiotemporal energy model of mobile energy storage devices

[0031]

[0032] Among them: E m η represents the energy state of the mobile energy storage device m, which is related to the power exchanged and efficiency at the battery swapping station in the previous moment. d η c Efficiency in the discharge and charge states, respectively; These represent the discharge and charging power of the mobile energy storage device m at user i, respectively.

[0033] The energy balance formula for mobile energy storage devices must also satisfy the following constraints.

[0034]

[0035]

[0036]

[0037]

[0038]

[0039]

[0040]

[0041] Among them, formulas (6) and (7) represent the upper and lower limits of charging and discharging power constraints, and formula (8) represents the mutual exclusion constraints of charging and discharging of mobile energy storage devices; represent the charging and discharging states of energy storage, respectively, and are 0-1 variables; Formula (10) represents the upper and lower limits of the energy of the mobile energy storage device m at time t, and Formula (11) represents that the energy state of the mobile energy storage device m is the same at the initial and final times.

[0042] Step 2: In the event of power outages in the target area caused by extreme weather, establish a mobile energy storage optimization scheduling model based on the spatiotemporal mobility characteristic model and the energy characteristic model, with the optimization objectives of minimizing the user's power outage perception and minimizing the mobility coefficient of the mobile energy storage device.

[0043] Assume that the quantity and capacity of mobile energy storage in the region are known.

[0044] Optimization Model: Establish an optimal scheduling model that minimizes user power outage perception and the mobility coefficient of mobile energy storage devices.

[0045] Decision variables: Mobile energy storage access power and location

[0046] Assume that the power outage time and duration can be obtained through system and dispatch personnel assessment and are therefore known quantities. Using user data from an accident under extreme weather conditions in a certain region as example data, the power data of users at the time of the power outage is obtained based on historical load curves.

[0047] Suppose that an accident x in the distribution network begins on the Tth day. x,st Hours, ending at T x,edIf the accident x causes a power outage for user i for hours, the degree of impact of the accident x on user i is defined as the user's power loss perception as shown in equation (12). The smaller the value, the smaller the user's power loss perception.

[0048]

[0049] For user i, the load data at time t during an accident x under extreme weather conditions; P N,i This represents the maximum power consumption of user i.

[0050] The mobility coefficient of the mobile energy storage device is given by equation (13).

[0051]

[0052] The objective function is F = F1 + F2.

[0053] C move C represents the mobile operation and maintenance coefficient for mobile energy storage devices. base This is the converted inherent device coefficient. C emove This represents the mobile energy loss coefficient for mobile energy storage devices.

[0054] Constraints:

[0055] (1) Distribution network power flow constraints

[0056]

[0057]

[0058]

[0059] In the formula, formulas (14) and (15) are the active power balance formula and the reactive power balance formula, respectively; and Let be the active power and reactive power of the power supply for user i at time t, respectively. and These represent the active and reactive load demands of user i under normal conditions at time t, respectively. Let be the reactive power reduction at user i at time t. and Let be the active and reactive power at time t, respectively, in the direction from user i to user k branch. and Let be the active and reactive power at time t, respectively, in the direction from user j to user i branch. and R represents the active and reactive power at time t in the direction from user i to user j branch. ji Let be the resistance of the branch between user i and user j. Let be the current flowing from user j to user i branch at time t. Let ψ be the current flowing from user i to user j branch at time t. i Let φ be the set of end nodes of the branch with user i as the first end node. i U is the set of the starting and ending nodes of a branch with user i as the ending node; i,t and U j,t Let r be the voltage of user i and user j at time t. ij x ij These are the resistance and reactance of the branch between user i and user j, respectively.

[0060] For the nonlinear constraint between voltage, current, and power, it can be transformed into the following second-order cone constraint using the second-order cone relaxation method:

[0061]

[0062] Let represent the square of the current in the branch between user i and user j at time t. This represents the square of the voltage of user i at time t.

[0063] (2) Distribution network structure constraints

[0064]

[0065]

[0066]

[0067]

[0068] In the formula, u ij This indicates the connection status between user i and user j; a connection is considered open when a fault occurs. L represents the number of nodes in the distribution network, and G represents the number of connected power sources. ij This represents the parent-child relationship between user j and user i. When user j is a child node of user i, s ij It is 1 if it is not 1, otherwise it is 0; s ij This represents the parent-child relationship between user i and user j. When user i is a child node of user j, s ij If the expression is 1, then it is 0; F(i) is the set of all users connected to user i.

[0069] The Big M method combines network topology constraints and power flow constraints.

[0070]

[0071] In the formula: M is a relatively large positive integer.

[0072] (3) Branch power constraints

[0073]

[0074] In the formula: P ijmax This represents the upper limit of active power transmission in the branch between user i and user j.

[0075] (4) Unit operation constraints

[0076]

[0077] S represents the maximum active and reactive power allowed to be injected by user i, respectively. i max This represents the maximum capacity at user i.

[0078] (5) Distribution network security constraints:

[0079]

[0080] In the formula: These are the upper and lower limits of the node voltage, respectively. pf is the upper limit of the branch current between user i and user j. i This is the lower limit of the node power factor.

[0081] Step 3: Solve the mobile energy storage optimization scheduling model to determine the optimal mobile energy storage scheduling scheme for the target area under extreme weather conditions; the mobile energy storage scheduling scheme includes the access location of mobile energy storage and the charging and discharging power.

[0082] Based on the objective function and constraints, determine the mobile energy storage connection location (i.e., which user i the mobile energy storage is connected to) and charging / discharging power that minimizes the user's perceived power outage under extreme weather conditions. and The size is determined using a solver.

[0083] This invention relates to an optimized scheduling strategy for mobile energy storage devices in distribution networks under extreme weather conditions, taking into account user perception. The method first establishes a model of the spatiotemporal mobility and energy characteristics of the mobile energy storage devices. Then, it establishes a multi-objective optimized scheduling model for mobile energy storage, considering user reliability. With the objectives of minimizing user power outage perception and minimizing economic losses during mobile energy storage device operation under fault conditions, it incorporates distribution network power flow constraints, generator operation constraints, and distribution network safety constraints. Finally, it proposes an optimized scheduling strategy for mobile energy storage devices in distribution networks under extreme weather conditions, minimizing user power outage perception. This strategy, through optimized scheduling of mobile energy storage, reduces load reduction on the user side during accidents by connecting mobile energy storage, minimizing user power outage perception, reducing load losses for users, and simultaneously improving distribution network resilience and power supply levels.

[0084] This invention also provides a mobile energy storage optimized dispatching system for distribution networks under extreme weather conditions, comprising:

[0085] The characteristic model building module is used to build spatiotemporal mobility characteristic models and energy characteristic models of mobile energy storage devices;

[0086] The optimization scheduling model establishment module is used to establish a mobile energy storage optimization scheduling model with the optimization objectives of minimizing the user's power outage perception and minimizing the mobility coefficient of mobile energy storage devices, based on the spatiotemporal mobility characteristic model and energy characteristic model, in the event of power outage in the target area caused by extreme weather.

[0087] The solution module is used to solve the mobile energy storage optimization scheduling model and determine the optimal mobile energy storage scheduling scheme for the target area under extreme weather conditions; the mobile energy storage scheduling scheme includes the access location of mobile energy storage and the charging and discharging power.

[0088] The mobile energy storage optimization scheduling system for distribution networks under extreme weather conditions provided in this embodiment of the invention has a similar working principle and beneficial effects to the mobile energy storage optimization scheduling method for distribution networks under extreme weather conditions described in the above embodiments, so it will not be described in detail here. For details, please refer to the introduction of the above method embodiments.

[0089] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the aforementioned method for optimized scheduling of mobile energy storage in distribution networks under extreme weather conditions.

[0090] Furthermore, when the computer program in the aforementioned memory is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0091] Furthermore, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the aforementioned method for optimizing the scheduling of mobile energy storage in distribution networks under extreme weather conditions.

[0092] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0093] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for optimized scheduling of mobile energy storage in distribution networks under extreme weather conditions, characterized in that, include: Establish spatiotemporal mobility and energy characteristics models for mobile energy storage devices; In the event of power outages in the target area caused by extreme weather, a mobile energy storage optimization scheduling model is established based on the spatiotemporal mobility characteristic model and the energy characteristic model, with the optimization objectives of minimizing the user's power outage perception and minimizing the mobility coefficient of the mobile energy storage device. Solve the mobile energy storage optimization scheduling model to determine the optimal mobile energy storage scheduling scheme for the target area under extreme weather conditions; the mobile energy storage scheduling scheme includes the access location of the mobile energy storage and the charging and discharging power; The spatiotemporal movement characteristic model is as follows: In the formula, Indicates mobile energy storage device m exist t Always from the user i Go to user j The location flag variable, 1 indicates that the energy storage device moves on a travel chain; δ route A set representing routes; M It represents a collection of mobile energy storage devices; T A set representing discrete time points; Indicates from user i The set of starting routes, This indicates the end of the user's account. i The set of routes; Represents the set of access nodes; , , These represent mobile energy storage devices. m In 0, 1, T Always from the user i Go to user j Position flag variable; The energy characteristic model is as follows: The constraints are In the formula, , Mobile energy storage devices m exist t , t Energy state at time +1; , Mobile energy storage devices m In users i The charging and discharging power at the point; , Mobile energy storage devices m Efficiency in charging and discharging states; For time intervals; , Mobile energy storage devices m exist t The charging and discharging status at all times; , These are the maximum charging and discharging power, respectively. , These are the minimum charging and discharging power, respectively. For mobile energy storage devices m exist t Always from the user i Go to user i Position flag variable; , These represent the minimum and maximum energy states of a mobile energy storage device, respectively. , Mobile energy storage devices m In 0, T Energy state at any given moment; The objective function of the mobile energy storage optimization scheduling model is: F = F1 + F2 In the formula, F represents the objective function, F1 represents the user's power loss perception, and F2 represents the mobility coefficient; T x,st , T x,ed Indicates accidents under extreme weather conditions x The start and end times, For users i exist t The amount of load reduction at any given time. For users i Accidents under extreme weather conditions x middle t Load data at any given time. Indicates user i Maximum power consumption; C move This indicates the mobile operation and maintenance coefficient of the mobile energy storage device. C base This represents the converted inherent device coefficient. C emove This represents the mobile energy loss coefficient of the mobile energy storage device.

2. The method for optimized scheduling of mobile energy storage in distribution networks under extreme weather conditions according to claim 1, characterized in that, The constraints of the mobile energy storage optimization scheduling model include distribution network power flow constraints, distribution network structure constraints, branch power constraints, unit operation constraints, distribution network security constraints, spatiotemporal mobility characteristic model, and energy characteristic model.

3. The method for optimized scheduling of mobile energy storage in distribution networks under extreme weather conditions according to claim 2, characterized in that, The power flow constraints of the distribution network are: In the formula, and users respectively i exist t The active and reactive power of the power supply at any given moment. and users respectively i exist t Active and reactive load demands under normal operating conditions at all times. for t Always with the user i The amount of reactive power reduction at the location, and users respectively i To users k The direction of the branch road is t Active and reactive power at any given time and users respectively j To users i The direction of the branch road is t Active and reactive power at any given time and users respectively i To users j The direction of the branch road is t Active and reactive power at any given time For users i With users j The resistance of the branch between, For users j To users i The direction of the branch road is t The current flowing through at all times, For users i To users j The direction of the branch road is t The current flowing through at all times, For users i Let be the set of branch end nodes of the first node. For users i The set of the starting nodes of the branches of the terminal nodes; and users respectively i and users j exist t Voltage at time, , users respectively i With users j The resistance and reactance of the branches between them; The constraints of the power distribution network structure are: In the formula, Indicates user i With users j The connection between the two lines is open or closed. L For the number of nodes in the distribution network, G The number of power sources connected; s ij Indicates user j With users i The parent-child relationship, when the user j For users i When the child node, s ij If it is 1, then it is 0; s ij Indicates user i With users j The parent-child relationship, when the user i For users j When the child node, s ij It is 1 if it is not 1, and 0 otherwise; In order to connect with users i The set of all connected users; The branch power constraint is In the formula, For users i With users j The upper limit of active power transmission between branches; The unit's operating constraints are: In the formula, , users respectively i The maximum allowable active and reactive power to be injected. For users i The maximum capacity at that location; The distribution network security constraints are as follows: In the formula, , These are the upper and lower limits of the node voltage, respectively. For users i With users j The upper limit of the branch current between them. This is the lower limit of the node power factor.

4. The method for optimized scheduling of mobile energy storage in distribution networks under extreme weather conditions according to claim 3, characterized in that, The Big M method is used to combine power flow constraints and structural constraints of the distribution network. The combined constraints are as follows: In the formula, M is a positive integer. and users respectively i and users j exist t Voltage at a given moment.

5. A mobile energy storage optimized dispatch system for power distribution networks under extreme weather conditions, characterized in that, The system is used to implement the optimized scheduling method for mobile energy storage in distribution networks under extreme weather conditions as described in any one of claims 1-4. The system includes: The characteristic model building module is used to build spatiotemporal mobility characteristic models and energy characteristic models of mobile energy storage devices; The optimization scheduling model establishment module is used to establish a mobile energy storage optimization scheduling model with the optimization objectives of minimizing the user's power outage perception and minimizing the mobility coefficient of mobile energy storage devices, based on the spatiotemporal mobility characteristic model and energy characteristic model, in the event of power outage in the target area caused by extreme weather. The solution module is used to solve the mobile energy storage optimization scheduling model and determine the optimal mobile energy storage scheduling scheme for the target area under extreme weather conditions; the mobile energy storage scheduling scheme includes the access location of mobile energy storage and the charging and discharging power.

6. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the mobile energy storage optimization scheduling method for distribution networks under extreme weather conditions as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed, implements the method for optimizing the scheduling of mobile energy storage in distribution networks under extreme weather conditions as described in any one of claims 1 to 4.

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