A method and device for configuring capacity of mobile energy storage

Abstract

The present invention discloses a capacity configuration method and device for mobile energy storage, the method comprising: establishing a multi-objective optimization configuration model for mobile energy storage with the goal of maximizing load peak reduction, maximizing power supply reliability improvement, maximizing energy storage equipment utilization, and minimizing the number of mobile energy storage configurations after the mobile energy storage is configured in the distribution network; solving the multi-objective optimization configuration model for mobile energy storage by enumeration method to obtain the configuration quantity, rated power level, and rated discharge time of the mobile energy storage, which are used to realize the capacity configuration of the mobile energy storage. The present invention fully considers the various mobile energy storage application benefits such as short-term peak shaving, improving power supply reliability, and low storage and high generation utilization, establishes a multi-objective optimization configuration model for mobile energy storage, and proposes a model solving method based on the enumeration of rated power levels of mobile energy storage, which can effectively support the formulation of solutions for multiple application scenarios of mobile energy storage and greatly promote the application and promotion of mobile energy storage systems.

Description

Technical Field

[0001] The present invention belongs to the technical field of mobile energy storage, and in particular relates to a capacity configuration method and device for mobile energy storage. Background Art

[0002] The new power system has endowed energy storage with a more important strategic position, making it an indispensable element of the new power system with four-dimensional interaction of "source, grid, load and storage". Mobile energy storage (MESS), as an important branch of the energy storage system, can be moved between different load units by trucks. It has the advantages of modular design, flexibility, sharing and wide coverage, and has great application potential in many aspects (such as emergency power supply, seasonal peak shaving and local equipment overload, etc.). How to reasonably configure it based on its comprehensive value is of great significance to building a safe and economical distribution network.

[0003] The existing optimization configuration methods for mobile energy storage have the following main problems: first, few studies take into account the application efficiency of the distribution network under normal operation and fault conditions at the same time; second, most model solving methods use mathematical optimization methods and intelligent heuristic algorithms. These methods are difficult to intervene manually, the algorithms are immature, the calculations are time-consuming, or the algorithms may be unstable for different instances, making them difficult to promote and apply. Summary of the invention

[0004] The purpose of the present invention is to provide a capacity configuration method and device for mobile energy storage, so as to solve the problems that the existing capacity optimization configuration method of mobile energy storage fails to comprehensively analyze the comprehensive efficiency of mobile energy storage, and the model solving method is difficult to intervene manually, the algorithm is immature, the calculation is time-consuming, and the algorithm may be unstable for different instances, making it difficult to promote and apply.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] In a first aspect, a method for capacity configuration of mobile energy storage is provided, comprising: establishing a multi-objective optimization configuration model for mobile energy storage with the goals of maximizing load peak reduction, maximizing power supply reliability improvement, maximizing energy storage equipment utilization, and minimizing the number of mobile energy storage configurations after the mobile energy storage is configured in the distribution network; solving the multi-objective optimization configuration model for mobile energy storage by using an enumeration method to obtain the configuration number, rated power level, and rated discharge time of the mobile energy storage, which are used to realize the capacity configuration of the mobile energy storage.

[0007] Furthermore, the mobile energy storage multi-objective optimization configuration model includes:

[0008]

[0009] Among them, f is the objective function of multi-objective optimization configuration of mobile energy storage, which contains four sub-objective functions, namely, load peak reduction, power supply reliability improvement value, energy storage equipment utilization rate and mobile energy storage configuration quantity. max f means the maximum load peak reduction, the maximum power supply reliability improvement value, the maximum energy storage equipment utilization rate and the minimum number of mobile energy storage configurations; N MESS B is the number of mobile energy storage devices in the planning area; b1 (N MESS ), B b2 (N MESS ) and B b3 (N MESS ) are N MESS The total annual peak shaving amount, average power outage time reduction per household in the distribution network and annual energy storage equipment utilization rate corresponding to each mobile energy storage; reach The time required for the mobile energy storage to travel from its site to the emergency installation location; t limit The maximum allowable time for the mobile energy storage vehicle to reach the emergency installation position; P b is the rated power of a single mobile energy storage; Ω mess It is a set of discrete rated power levels for mobile energy storage;

[0010] The mobile energy storage multi-objective optimization configuration model of formula (1) is transformed into a linear weighted comprehensive utility model:

[0011]

[0012] Among them, f0 is the comprehensive benefit value of mobile energy storage; ω1, ω2, ω3 and ω4 are weight coefficients; and N MESS The normalized indicators include the total annual peak shaving amount corresponding to each mobile energy storage, the reduction in average power outage time per household in the distribution network, and the annual energy storage equipment utilization rate.

[0013] Furthermore, N MESS Normalized index of the total annual peak shaving amount corresponding to mobile energy storage It is expressed as:

[0014]

[0015] Among them, ΔP max To plan the regional load peak reduction after configuring a single mobile energy storage, It is the maximum value corresponding to the annual peak shaving total amount indicator; It is the minimum value corresponding to the annual total peak shaving indicator.

[0016] Furthermore, N MESSNormalized index of the reduction of average power outage time per household in the distribution network corresponding to a mobile energy storage It is expressed as:

[0017]

[0018] Among them, t int k is the average annual power outage time per household in the distribution network area before energy storage is applied; E The percentage of annual average power outage time reduced per household after energy storage application; The maximum value corresponding to the annual average household power outage time reduction indicator; It is the minimum value corresponding to the annual average household power outage time reduction indicator.

[0019] Furthermore, N MESS Normalized index of annual energy storage equipment utilization rate corresponding to mobile energy storage It is expressed as:

[0020]

[0021] Among them, N d N is the number of days of low storage and high generation operation during the whole life cycle of mobile energy storage; h is the total number of daily time segments; ΔP d,m and ΔP c,m are the discharge power and charging power at the mth moment, respectively, and ΔP d,m ΔP c,m =0; c1, c2 and c3 are the value weight coefficients corresponding to the peak, flat and valley periods of the load curve; Ω1, Ω2 and Ω3 are the number sets of peak, flat and valley periods of the typical daily load curve; is the maximum value corresponding to the annual energy storage equipment utilization index; is the minimum value corresponding to the annual energy storage equipment utilization rate;

[0022] As an approximate calculation, formula (6) is simplified to:

[0023]

[0024] Among them, E b is the rated capacity of a single mobile energy storage, that is, the rated power P of a single mobile energy storage b With rated discharge time t dis The product of; Δc is the difference in regional peak and valley value weight coefficients.

[0025] Furthermore, the enumeration method is used to solve the multi-objective optimization configuration model of mobile energy storage, including:

[0026] (1) Determine the minimum number of mobile energy storage configurations N MESS,min and the maximum value NMESS,max , let the number of mobile energy storage configurations N MESS =N MESS,min ;

[0027] (2) For the determined number of mobile energy storage configurations N MESS , calculate the calculation under different rated power levels and different discharge times and benefit value f, determine the number of configurations N MESS The corresponding maximum profit value f opt (N MESS )=max{f NMESS (P b ,t)} and record the corresponding rated power level and optimized rated discharge time;

[0028] (3) If N MESS <N MESS,max , then let N MESS =N MESS +1, return to step (2); otherwise, determine the optimal benefit of mobile energy storage in the region, that is, f max =max{f opt (N MESS )}, the corresponding number of mobile energy storage configurations, rated power levels and rated discharge time are the corresponding optimal solutions;

[0029] (4) Qualified sites are selected as candidate sites, and the final number, specific location, and service scope of mobile energy storage are determined by comprehensively considering the priority of restoring power to designated important loads and the maximum allowable time for restoring power.

[0030] Furthermore, the range of the number of mobile energy storage configurations is determined by the following method:

[0031] First, according to the actual maximum time limit t allowed in different power supply areas limit and the average vehicle speed v car , the maximum moving distance of the mobile energy storage vehicle is estimated to be v car ·t limit , and based on this, the equivalent area of ​​the maximum service range of a single mobile energy storage is estimated:

[0032] S one =π(v car ·t limit ) 2 (8)

[0033] Then, based on the planned regional power supply area S z Estimate the minimum number of mobile energy storage configurations N in the planning area MESS,min ,Right now:

[0034]

[0035] Finally, the upper limit value N of the number of mobile energy storage configurations is determined based on the installation site area, land cost, and number of management personnel of the mobile energy storage. MESS,max .

[0036] Furthermore, the discrete rated power level set of mobile energy storage Ω mess , that is, the rated power P of a single mobile energy storage b The enumeration object includes 30, 50, 100, 200, 250, 500, 630, 750, 1000, 1500 and 2000, for a total of 11 power levels.

[0037] In a second aspect, a capacity configuration device for mobile energy storage is provided, including: a model building module, which is used to establish a multi-objective optimization configuration model for mobile energy storage with the goals of maximizing load peak reduction, maximizing power supply reliability improvement, maximizing energy storage equipment utilization and minimizing the number of mobile energy storage configurations after the distribution network is configured with mobile energy storage; a model solving module, which is used to solve the multi-objective optimization configuration model for mobile energy storage by using an enumeration method to obtain the configuration number, rated power level and rated discharge time of the mobile energy storage, so as to realize the capacity configuration of the mobile energy storage.

[0038] In a third aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the capacity configuration method of the mobile energy storage as described in the first aspect is implemented.

[0039] In a fourth aspect, a computer device is provided, comprising: a memory for storing instructions; and a processor for executing the instructions, so that the device performs operations to implement the capacity configuration method of mobile energy storage as described in the first aspect.

[0040] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention establishes a multi-objective optimization configuration model for mobile energy storage with the goal of maximizing load peak reduction, maximizing power supply reliability improvement, maximizing energy storage equipment utilization and minimizing the number of energy storage configurations after the distribution network is configured with mobile energy storage; the multi-objective optimization configuration model for mobile energy storage is solved by an enumeration method to achieve the capacity configuration of mobile energy storage; the present invention fully considers various mobile energy storage application benefits such as short-term peak shaving, distribution network power supply reliability improvement and low-storage and high-generation utilization, fully considers the application efficiency under normal operation and fault conditions of the distribution network, establishes a multi-objective optimization configuration model for mobile energy storage and proposes a computationally stable model solving method based on enumeration of mobile energy storage rated power levels, effectively improves the efficiency of mobile energy storage capacity configuration, and at the same time improves the comprehensive benefits of mobile energy storage over the entire life cycle, the model solving method is simple and the algorithm is stable, which can effectively support the formulation of solutions for multiple application scenarios of mobile energy storage and greatly promote the application and promotion of mobile energy storage systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a main flow diagram of a capacity configuration method of a mobile energy storage provided by an embodiment of the present invention;

[0042] Figure 2 It is a schematic diagram of the solution steps of the multi-objective optimization configuration model of mobile energy storage in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0044] Embodiment 1:

[0045] like Figure 1 As shown, a capacity configuration method for mobile energy storage includes: establishing a multi-objective optimization configuration model for mobile energy storage with the goals of maximizing load peak reduction, maximizing power supply reliability improvement, maximizing energy storage equipment utilization and minimizing the number of energy storage configurations after the mobile energy storage is configured in the distribution network; solving the multi-objective optimization configuration model for mobile energy storage by using an enumeration method to obtain the configuration number, rated power level and rated discharge time of the mobile energy storage, which are used to realize the capacity configuration of the mobile energy storage.

[0046] The present invention takes into full account the benefits of various mobile energy storage applications, such as short-term peak shaving, improved reliability of distribution network power supply, and low storage and high generation utilization, and establishes a multi-objective optimization configuration model for mobile energy storage. In view of the problems existing in mathematical optimization algorithms and intelligent optimization algorithms, the present invention uses a computationally stable enumeration method to solve the model for practical purposes.

[0047] 1. After the distribution network is equipped with mobile energy storage, a multi-objective optimization configuration model for mobile energy storage is established with the goal of maximizing the load peak reduction, maximizing the improvement in power supply reliability, maximizing the utilization rate of energy storage equipment, and minimizing the number of energy storage configurations.

[0048] It is assumed that all mobile energy storage devices configured in the planning area are of the same specification (same rated capacity and rated power), and are deployed to corresponding locations for use (prioritized for short-term peak load reduction) in emergency power supply scenarios (referring to short-term load peaks and failures or planned power outages throughout the year), and are parked at their sites (i.e., long-term parking locations) in other normal circumstances.

[0049] Considering that the frequency of emergency power supply is low, the benefits and calculations under different usage scenarios are relatively independent. This paper takes the maximum load peak reduction, the maximum power supply reliability improvement, the maximum energy storage equipment utilization and the minimum energy storage configuration as the goals after the distribution network is configured with energy storage, and establishes a multi-objective optimization configuration model for mobile energy storage, namely:

[0050]

[0051] Among them, f is the objective function of multi-objective optimization configuration of mobile energy storage, which contains four sub-objective functions, namely, load peak reduction, power supply reliability improvement value, energy storage equipment utilization rate and mobile energy storage configuration quantity. max f means the maximum load peak reduction, the maximum power supply reliability improvement value, the maximum energy storage equipment utilization rate and the minimum number of mobile energy storage configurations; N MESS B is the number of mobile energy storage devices in the planning area; b1 (N MESS ), B b2 (N MESS ) and B b3 (N MESS ) are N MESS The total amount of mobile energy storage annual peak shaving, the average reduction in power outage time per household in the distribution network, and the annual energy storage equipment utilization rate; t reach The time required for the mobile energy storage to travel from its site to the emergency installation location; t limit The maximum allowable time for the mobile energy storage vehicle to reach the emergency installation location, which needs to be determined based on the actual situation of the planning area; P b is the rated power of a single mobile energy storage; Ω mess It is a set of discrete rated power levels for mobile energy storage.

[0052] The mobile energy storage multi-objective optimization configuration model of formula (1) is transformed into a linear weighted comprehensive utility model:

[0053]

[0054] Among them, f0 is the comprehensive benefit value of mobile energy storage; ω1, ω2, ω3 and ω4 are weight coefficients; and N MESS The normalized index of the total annual peak shaving amount of mobile energy storage, the average power outage time reduction value per household in the distribution network and the annual energy storage equipment utilization rate is processed according to formula (3):

[0055]

[0056] in, is the normalized index, i∈{b1,b2,b3}; B i (N MESS ) is the index value; is the maximum value of the indicator; is the minimum value of the indicator.

[0057] 1.1. Calculation of the total annual peak shaving capacity of energy storage

[0058] N MESS Normalized index of the total annual peak shaving amount corresponding to mobile energy storage It is expressed as:

[0059]

[0060] Among them, ΔP max Plan the regional load peak reduction after configuring a single mobile energy storage; It is the maximum value corresponding to the annual peak shaving total amount indicator; It is the minimum value corresponding to the annual total peak shaving indicator.

[0061] 1.2. Calculation of reduction in average power outage time per household in the distribution network

[0062] At present, mobile energy storage is mainly used in voltage levels of 10kV and below. Considering that power outages are usually caused by medium-voltage line failures or maintenance (according to statistics, more than 80% of power system failures are caused by distribution network failures, especially medium-voltage distribution network failures), the present invention is mainly aimed at medium-voltage line power outages, N MESS After the mobile energy storage is deployed, the normalized index of the reduction in the average power outage time per household in the distribution network (the reduction in the average power outage time per household in the distribution network reflects the improvement in the annual power supply reliability rate of the distribution network) It is expressed as:

[0063]

[0064] Among them, t int is the average annual power outage time per household before energy storage is applied in the distribution network planning area; k E Indicates the proportion of reduction in original power outage losses due to energy storage application, The maximum value corresponding to the annual average household power outage time reduction indicator; It is the minimum value corresponding to the annual average household power outage time reduction indicator.

[0065] 1.3. Calculation of annual equipment utilization rate of mobile energy storage

[0066] N MESS Normalized index of annual equipment utilization of mobile energy storage It is expressed as:

[0067]

[0068] Among them, N d N is the number of days of low storage and high generation operation during the whole life cycle of mobile energy storage; h is the total number of daily time segments; ΔP d,m and ΔP c,m are the discharge power and charging power at the mth moment, respectively, and ΔP d,m ΔP c,m =0; c1, c2 and c3 are the value weight coefficients of the peak, flat and valley periods of the corresponding load curve, which are generally 1, and can also be taken according to the time-of-use electricity price; Ω1, Ω2 and Ω3 are the sets of the number of peak, flat and valley periods of the typical daily load curve; is the maximum value corresponding to the annual energy storage equipment utilization index; It is the minimum value corresponding to the annual energy storage equipment utilization rate.

[0069] As an approximate calculation, formula (6) is simplified to:

[0070]

[0071] Among them, E b is the rated capacity of a single mobile energy storage, that is, the rated power P of a single mobile energy storage b With rated discharge time t dis The product of; Δc is the difference in regional peak-valley weight coefficients.

[0072] 2. The enumeration method is used to solve the multi-objective optimization configuration model of mobile energy storage.

[0073] The present invention adopts a computationally stable enumeration method to approximate the model for practical purposes. Considering that the optimization model mainly involves three variables (i.e., the number of mobile energy storage configurations N MESS , Rated power P b and rated discharge time t dis ), the three variables can be enumerated one by one to determine their optimized values.

[0074] 2.1. Determination of enumeration range

[0075] 2.1.1 Minimum configuration quantity range of mobile energy storage

[0076] The range of the number of mobile energy storage configurations is determined by the following method: Taking into account the maximum allowable time limit for the mobile energy storage to reach the destination (such as the maximum allowable power outage time required by the user), the service range of the mobile energy storage vehicle is limited, and the mobile energy storage cannot meet the allowed time requirements beyond this range. Therefore, the number of mobile energy storage configurations within the regional scope can be roughly estimated based on the average driving speed of the mobile energy storage and the shortest allowable arrival time limit:

[0077] First, according to the actual maximum time limit t allowed in different power supply areas limit and the average vehicle speed v car , the maximum moving distance of the mobile energy storage vehicle is estimated to be v car ·t limit , and based on this, the equivalent area of ​​the maximum service range of a single mobile energy storage is estimated:

[0078] S one =π(v car ·t limit ) 2 (8)

[0079] Then, based on the planned regional power supply area S z Estimate the minimum number of mobile energy storage configurations N in the planning area MESS,min ,Right now:

[0080]

[0081] Finally, considering the limited installation area of ​​mobile energy storage, high land costs, and limited number of management personnel, there is an upper limit N on the number of mobile energy storage configurations. MESS,max , which can be determined based on the actual situation of the planning area (such as land area, available installation locations, number of management personnel, etc.).

[0082] 2.1.2 Rated power

[0083] Based on the relevant mobile electrochemical energy storage system standards, the rated power of a single mobile energy storage system P b The enumeration objects are 30, 50, 100, 200, 250, 500, 630, 750, 1000, 1500 and 2000 (kVA), that is, the discrete rated power level set of mobile energy storage Ω mess These 11 power levels are included.

[0084] 2.1.3 Rated discharge time

[0085] Continuous energy storage battery rated discharge time t disThe range is 0 to t maxdis =min(t L ,t bat ), where t bat In order to consider the maximum discharge time of mobile energy storage under the battery technology conditions. To facilitate the model solution, the step size of Δt (such as 0.01h) can be set in [0, min (t L ,t bat )]] to perform an approximate solution to the model.

[0086] 2.2. Enumeration variable optimization solution steps

[0087] Assuming that the mobile energy storages configured in the planning area are of the same specifications, considering that when the total energy storage capacity and charging / discharging power are the same, the more mobile energy storages are configured, the higher the vehicle cost or total cost tends to be.

[0088] like Figure 2 As shown, the specific model solution enumeration strategy is as follows:

[0089] (1) Determine the minimum number of mobile energy storage configurations N MESS,min and the maximum value N MESS,max , let the number of mobile energy storage configurations N MESS =N MESS,min .

[0090] (2) For the determined number of mobile energy storage configurations N MESS , calculate the calculation under different rated power levels and different discharge times and benefit value f, determine the number of configurations N MESS The corresponding maximum profit value f opt (N MESS )=max{f NMESS (P b ,t)} and record the corresponding rated power level and optimized rated discharge time.

[0091] (3) If N MESS <N MESS,max , then let N MESS =N MESS +1, return to step (2); otherwise, determine the optimal benefit of mobile energy storage in the region, that is, f max =max{f opt (N MESS )}, the corresponding number of mobile energy storage configurations, rated power levels and rated discharge time are the corresponding optimal solutions.

[0092] (4) Qualified sites (such as substations, operation and maintenance team offices, power supply business offices and surrounding plots) are selected as candidate sites. The final number of mobile energy storage configurations, specific locations, and service scope are determined by comprehensively considering the priority of restoring power to important loads and the maximum allowable time for restoring power.

[0093] Mobile energy storage systems meet the development needs of new power systems and are an important form of energy storage development in the future. It is urgent to propose a mobile energy storage capacity optimization configuration model and solution method to promote the large-scale application of mobile energy storage. However, few existing mobile energy storage capacity optimization configuration methods take into account the application value of distribution network normal operation and fault conditions at the same time, and most model solution methods use mathematical optimization methods and intelligent heuristic algorithms. These methods have various problems in practical application, such as difficulty in manual intervention, immature algorithms, time-consuming calculations, and possible algorithm instability for different instances, making them difficult to promote and apply.

[0094] The capacity configuration heuristic method for mobile energy storage proposed in the present invention fully considers various mobile energy storage application benefits such as short-term peak shaving, improved distribution network power supply reliability, and low-storage and high-generation utilization, establishes a multi-objective optimization configuration model for mobile energy storage, and proposes a model solving method based on the mobile energy storage rated power level enumeration method, which can effectively support the formulation of solutions for multiple application scenarios of mobile energy storage and greatly promote the application and promotion of mobile energy storage systems.

[0095] 3. To verify the effectiveness of the present invention, a simulation study was conducted on a typical system. The present invention developed a calculation program of the model method based on MATLAB 2016a and applied it to the calculation and analysis of a typical example.

[0096] 3.1 Basic Data

[0097] Taking a city’s power distribution network as an example, we conducted an analysis of the optimal configuration of mobile energy storage. The city can be divided into urban, suburban and rural areas according to the power supply area type, with a power supply planning area of ​​946.01km 2 、4349.4km 2 、3193.7km 2 The corresponding medium voltage line types are interconnected cable lines, interconnected overhead lines and radial overhead lines. The relevant basic data are shown in Tables 1 and 2.

[0098] Table 1 Calculation parameters

[0099]

[0100] Note: n ave The values ​​of 4, 5, and 10 represent urban, suburban, and rural areas, respectively; t intThe values ​​of 1.2264, 3.942, and 3.942 represent urban, suburban, and rural areas, respectively; t limit The values ​​of 15, 30 and 45 represent urban, suburban and rural areas respectively; t L The values ​​of 4.62, 4.78 and 4.78 represent urban, suburban and rural areas respectively.

[0101] Table 2 Reliability parameters of medium voltage lines

[0102]

[0103] 3.2 Calculation of optimal configuration of mobile energy storage

[0104] Referring to the calculation method of the minimum configuration quantity of mobile energy storage proposed in the present invention, it can be calculated that the minimum configuration quantity of mobile energy storage in urban areas, suburban areas and rural areas is 6, 7 and 3 respectively. The optimal configuration schemes for urban areas, suburban areas and rural areas can be obtained by enumeration calculation, as shown in Table 3.

[0105] Table 3 Optimal benefit planning schemes for MESS in different regions

[0106]

[0107] This application example verifies the practicability and effectiveness of the method proposed in the present invention, which can fully tap the potential value of mobile energy storage and greatly improve the overall economy of mobile energy storage under multiple service goals, which is of great significance to the application and promotion of mobile energy storage.

[0108] Embodiment 2:

[0109] Based on a capacity configuration method for mobile energy storage described in Example 1, this embodiment provides a capacity configuration device for mobile energy storage, including: a model building module, used to establish a mobile energy storage multi-objective optimization configuration model with the goals of maximizing load peak reduction, maximizing power supply reliability improvement, maximizing energy storage equipment utilization and minimizing the number of energy storage configurations after the distribution network is configured with energy storage; a model solving module, used to solve the mobile energy storage multi-objective optimization configuration model by using an enumeration method to obtain the configuration number, rated power level and rated discharge time of the mobile energy storage, so as to realize the capacity configuration of the mobile energy storage.

[0110] Embodiment three:

[0111] Based on the capacity configuration method of a mobile energy storage described in Example 1, this embodiment provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the capacity configuration method of the mobile energy storage described in Example 1 is implemented.

[0112] Embodiment 4:

[0113] Based on the capacity configuration method of mobile energy storage described in Example 1, this embodiment provides a computer device, including: a memory for storing instructions; a processor for executing the instructions, so that the device performs operations to implement the capacity configuration method of mobile energy storage as described in Example 1.

[0114] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention 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, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0115] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0116] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0117] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0118] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. A capacity configuration method for mobile energy storage, characterized in that: include: After the mobile energy storage is configured in the distribution network, a multi-objective optimization configuration model for mobile energy storage is established with the goals of maximizing the load peak reduction, maximizing the power supply reliability improvement, maximizing the utilization rate of energy storage equipment, and minimizing the number of mobile energy storage configurations. The enumeration method is used to solve the multi-objective optimization configuration model of mobile energy storage, and the configuration quantity, rated power level and rated discharge time of mobile energy storage are obtained to realize the capacity configuration of mobile energy storage. The mobile energy storage multi-objective optimization configuration model includes: （1） in, It is the objective function for multi-objective optimization configuration of mobile energy storage, which contains four sub-objective functions, namely, load peak reduction, power supply reliability improvement value, energy storage equipment utilization rate and mobile energy storage configuration quantity. It indicates the largest load peak reduction, the largest power supply reliability improvement, the largest energy storage equipment utilization rate and the least number of mobile energy storage configurations; The number of mobile energy storage devices to be configured in the planning area; , and They are The total annual peak shaving amount corresponding to each mobile energy storage, the average reduction in power outage time per household in the distribution network, and the annual energy storage equipment utilization rate; The time required for the mobile energy storage to travel from its site to the emergency installation location; The maximum allowable time for the mobile energy storage vehicle to reach the emergency installation location; is the rated power of a single mobile energy storage; It is a set of discrete rated power levels for mobile energy storage; The mobile energy storage multi-objective optimization configuration model of formula (1) is transformed into a linear weighted comprehensive utility model: （2） in, is the comprehensive benefit value of mobile energy storage; , , and is the weight coefficient; , and They are Normalized indicators of the total annual peak shaving amount, average power outage time reduction per household in the distribution network and annual energy storage equipment utilization rate corresponding to each mobile energy storage; The enumeration method is used to solve the multi-objective optimization configuration model of mobile energy storage, including: (1) Determine the minimum number of mobile energy storage configurations and maximum value , let the number of mobile energy storage configurations ; (2) For the number of mobile energy storage configurations determined , calculate the different rated power levels and different discharge times , , and benefit value , determine the number of configurations The corresponding maximum profit value And record the corresponding rated power level and optimized rated discharge time; (3) If , then let , return to step (2); otherwise, determine the optimal benefit of mobile energy storage in the region: , the corresponding number of mobile energy storage configurations, rated power levels and rated discharge time are the corresponding optimal solutions; (4) Qualified sites are selected as candidate sites, and the final number, specific location, and service scope of mobile energy storage are determined by comprehensively considering the priority of restoring power to designated important loads and the maximum allowable time for restoring power.

2. The capacity configuration method of mobile energy storage according to claim 1, characterized in that: Normalized index of the total annual peak shaving amount corresponding to mobile energy storage , expressed as: （4） in, To plan the regional load peak reduction after configuring a single mobile energy storage, It is the maximum value corresponding to the annual peak shaving total amount indicator; It is the minimum value corresponding to the annual total peak shaving indicator.

3. The capacity configuration method of mobile energy storage according to claim 1, characterized in that: Normalized index of the reduction of average power outage time per household in the distribution network corresponding to a mobile energy storage , expressed as: （5） in, The annual average power outage time per household in the distribution network area before energy storage is applied; The percentage of annual average power outage time reduced per household after energy storage application; The maximum value corresponding to the annual average household power outage time reduction indicator; It is the minimum value corresponding to the annual average household power outage time reduction indicator.

4. The capacity configuration method of mobile energy storage according to claim 1, characterized in that: Normalized index of annual energy storage equipment utilization rate corresponding to mobile energy storage , expressed as: （6） in, The number of days of low-storage and high-generation operation during the entire life cycle of mobile energy storage; The total number of daily time segments; and Respectively m The discharge power and charging power at each moment, and ; , and are the value weight coefficients corresponding to the peak, flat and valley periods of the load curve respectively; , and are the sets of time periods of peak, flat and valley of typical daily load curve; is the maximum value corresponding to the annual energy storage equipment utilization index; is the minimum value corresponding to the annual energy storage equipment utilization rate; As an approximate calculation, formula (6) is simplified to: （7） in, is the rated capacity of a single mobile energy storage, that is, the rated power of a single mobile energy storage Rated discharge time The product of It is the difference in regional peak and valley value weight coefficients.

5. The capacity configuration method of mobile energy storage according to claim 1, characterized in that: The range of mobile energy storage configuration quantity is determined by the following method: First, according to the actual maximum time limit allowed in different power supply areas and the average vehicle speed , the maximum moving distance of the mobile energy storage vehicle is estimated to be , and based on this, the equivalent area of ​​the maximum service range of a single mobile energy storage is estimated: （8） Then, based on the planned power supply area Estimate the minimum number of mobile energy storage configurations in the planning area ,Right now: （9） Finally, the upper limit of the number of mobile energy storage configurations is determined based on the installation site area, land cost, and number of management personnel of the mobile energy storage. .

6. The capacity configuration method of mobile energy storage according to claim 1, characterized in that: Mobile energy storage discrete power rating set , that is, the rated power of a single mobile energy storage The enumeration object includes 30, 50, 100, 200, 250, 500, 630, 750, 1000, 1500 and 2000, for a total of 11 power levels.

7. A capacity configuration device for mobile energy storage, characterized in that: include: The model building module is used to establish a multi-objective optimization configuration model for mobile energy storage with the goals of maximizing load peak reduction, maximizing power supply reliability improvement, maximizing energy storage equipment utilization, and minimizing the number of mobile energy storage configurations after the mobile energy storage is configured in the distribution network; The model solving module is used to solve the multi-objective optimization configuration model of mobile energy storage by using the enumeration method to obtain the configuration quantity, rated power level and rated discharge time of mobile energy storage, so as to realize the capacity configuration of mobile energy storage; The mobile energy storage multi-objective optimization configuration model includes: （1） in, It is the objective function for multi-objective optimization configuration of mobile energy storage, which contains four sub-objective functions, namely, load peak reduction, power supply reliability improvement value, energy storage equipment utilization rate and mobile energy storage configuration quantity. It indicates the largest load peak reduction, the largest power supply reliability improvement, the largest energy storage equipment utilization rate and the least number of mobile energy storage configurations; The number of mobile energy storage devices to be configured in the planning area; , and They are The total annual peak shaving amount corresponding to each mobile energy storage, the average reduction in power outage time per household in the distribution network, and the annual energy storage equipment utilization rate; The time required for the mobile energy storage to travel from its site to the emergency installation location; The maximum allowable time for the mobile energy storage vehicle to reach the emergency installation location; is the rated power of a single mobile energy storage; It is a set of discrete rated power levels for mobile energy storage; The mobile energy storage multi-objective optimization configuration model of formula (1) is transformed into a linear weighted comprehensive utility model: （2） in, is the comprehensive benefit value of mobile energy storage; , , and is the weight coefficient; , and They are Normalized indicators of the total annual peak shaving amount, average power outage time reduction per household in the distribution network and annual energy storage equipment utilization rate corresponding to each mobile energy storage; The enumeration method is used to solve the multi-objective optimization configuration model of mobile energy storage, including: (1) Determine the minimum number of mobile energy storage configurations and maximum value , let the number of mobile energy storage configurations ; (2) For the number of mobile energy storage configurations determined , calculate the different rated power levels and different discharge times , , and benefit value , determine the number of configurations The corresponding maximum profit value And record the corresponding rated power level and optimized rated discharge time; (3) If , then let , return to step (2); otherwise, determine the optimal benefit of mobile energy storage in the region: , the corresponding number of mobile energy storage configurations, rated power levels and rated discharge time are the corresponding optimal solutions; (4) Qualified sites are selected as candidate sites, and the final number, specific location, and service scope of mobile energy storage are determined by comprehensively considering the priority of restoring power to designated important loads and the maximum allowable time for restoring power.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the capacity configuration method of mobile energy storage as described in any one of claims 1 to 6 is implemented.

9. A computer device, characterized in that: include: A memory for storing instructions; A processor is used to execute the instruction so that the device performs operations to implement the capacity configuration method of mobile energy storage as described in any one of claims 1 to 6.

Images