Configuration method, system, equipment and medium based on fixed and mobile energy storage

Abstract

The invention discloses a configuration method, system and equipment based on fixed and mobile energy storage, and a medium, and relates to the technical field of energy storage planning configuration optimization. The method comprises the following steps: determining a configuration scheme of access points and capacity of fixed energy storage and quantity of mobile energy storage by adopting an upper-layer model; solving the constructed lower-layer model based on a configuration scheme of access points and capacity of fixed energy storage and the number of mobile energy storage, and determining a multi-target optimal solution set so as to determine access points of mobile energy storage and a charging and discharging power time sequence and a charging and discharging power time sequence of fixed energy storage; the upper and lower layer models are subjected to joint iteration solution to obtain an optimal configuration scheme and an optimal operation scheduling time sequence scheme of fixed energy storage and mobile energy storage, so that the consumption level of distributed photovoltaic in the power distribution network is remarkably improved, and flexible resources such as a fixed-mobile energy storage system and the like are fully utilized to solve an electric power gap caused by photovoltaic load fluctuation in the area; and voltage fluctuation is smoothed.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage planning and configuration, and in particular to a configuration method, system, device and medium based on fixed and mobile energy storage. Background Art

[0002] With growing awareness of environmental protection and the continued rise in global energy demand, the utilization and development of renewable energy has become a key path for development in the energy sector. As a promising new energy source, photovoltaic power generation is gaining increasing attention and recognition. However, distributed photovoltaic power (such as photovoltaic and wind power) is intermittent and unstable, resulting in large fluctuations in output power. This can easily lead to a mismatch between power supply and load demand, posing new challenges to the stable operation of power systems. To address this issue, energy storage systems (ESS), as a key technical solution, can effectively smooth out fluctuations in distributed photovoltaic output and improve the stability and reliability of power systems.

[0003] Previous studies often used fixed energy storage to absorb distributed photovoltaics and then optimize the configuration of energy storage in the distribution network, but did not consider the limitations of fixed energy storage for subsequent participation in load scheduling; the fixed energy storage used in existing studies can only achieve flexible transfer of electricity in time, but cannot achieve flexible transfer of flexible resources in space, and cannot achieve resource sharing under different source and load scenarios in the same area, which greatly increases the investment cost of distributed energy storage devices; there are also studies that use mobile energy storage to achieve flexible transfer of electricity in time and space, but the configuration mode is single, and it seems powerless when encountering situations with concentrated loads and high demand. At this time, the only option is to increase the capacity of mobile energy storage, but the cost will also increase accordingly. Summary of the Invention

[0004] The purpose of the present invention is to provide a configuration method, system, equipment and medium based on fixed and mobile energy storage, which can improve the absorption level of distributed photovoltaics in the distribution network and make full use of flexible resources such as fixed-mobile energy storage systems to solve the power gap caused by fluctuations in photovoltaic loads in the area.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A configuration method based on fixed and mobile energy storage, including:

[0007] Obtain operating data of low-voltage substations in the distribution network;

[0008] Constructing an upper-level model; the upper-level model includes corresponding objective functions and constraints; the objective functions of the upper-level model include functions constructed with the goals of optimizing energy storage investment cost and operating costs, optimizing the overall voltage quality throughout the year, and optimizing the overall photovoltaic absorption rate throughout the year; the constraints of the upper-level model include energy storage investment cost constraints, energy storage configuration quantity constraints, upper and lower limit constraints on connected energy storage locations, energy storage state of charge constraints, and power balance constraints;

[0009] Constructing a lower-level model; the lower-level model includes corresponding objective functions and constraints; the objective function of the lower-level model includes a function constructed with the goals of achieving the best intraday photovoltaic absorption rate, the best intraday voltage quality, and the minimum intraday energy storage operation cost; the constraints of the lower-level model include node voltage constraints, power flow equation constraints, energy storage operation constraints, and distributed photovoltaic operation constraints;

[0010] Based on the operating data, the upper model and the lower model are jointly iteratively solved, and a final configuration plan is output when the iterative result meets the iterative conditions; the final configuration plan is a daily operation scheduling timing plan for fixed and mobile energy storage to solve the power gap caused by photovoltaic load fluctuations in the area and smooth voltage fluctuations; the final configuration plan includes the optimal configuration plan and the optimal daily operation scheduling timing plan for fixed and mobile energy storage in the low-voltage substation of the distribution network.

[0011] Optionally, based on the operating data, the upper layer model and the lower layer model are jointly iteratively solved, and a final configuration solution is output when the iteration result meets the iteration condition, specifically including:

[0012] The access points and scheduling sequence of mobile energy storage and the scheduling sequence of fixed energy storage output by the lower model in the previous iteration round are used as the input of the upper model in the current iteration round, and the access points and capacity of fixed energy storage and the number of mobile energy storage output by the upper model are used as the input of the lower model in the current iteration round. The upper and lower models are jointly iterated and solved. When the iteration conditions are met, the output results of the upper model and the lower model in this round are used as the final configuration plan.

[0013] Optionally, the construction process of the upper model includes:

[0014] First, the modeling results of investment cost and operating expenses are as follows:

[0015] g 11 =ε cap Q FESS +ε car K FESS +δ cap Q MESS +δ car K MESS

[0016] Where g 11 The investment cost for the configuration of fixed and mobile energy storage; cap、 ε car are the unit capacity cost and unit quantity cost of fixed energy storage respectively; δ cap , δ car are the unit capacity cost and unit quantity cost of mobile energy storage; Q FESS , K FESS are the configuration capacity and configuration quantity of fixed energy storage respectively; Q MESS , K MESS They are the configured capacity and quantity of mobile energy storage;

[0017] and:

[0018] Operating costs 12 = Electricity purchase cost + loss - discharge cost

[0019]

[0020] Discharge loss cost = C sell (t)·P dis (t)·(1-η d )·Δt

[0021] Discharge cost = C sell (t)·P dis (t)·η d ·Δt

[0022] So we get:

[0023]

[0024] g1=g 11 +g 12

[0025] Where, P load (t) is the load power in period t; P chg (t) is the charging power of the energy storage in time period t; P dis (t) is the discharge power of energy storage in time period t; η c ,η d is the charge and discharge efficiency; C buy (t) is the electricity purchase price in period t, C sell (t) is the electricity price in period t;

[0026] Secondly, the modeling results of the total voltage quality throughout the year are:

[0027]

[0028] Where g2 is the node voltage deviation value, which is used to represent the total voltage quality throughout the year; U i,t,d is the actual voltage amplitude of node i at time t on day d, is the specified voltage amplitude of node i at time t on day d, U i,max,d -U i,min,d is the maximum voltage deviation allowed for node i on day d, and N is the number of all nodes in the distribution network;

[0029] Finally, the modeling results of the total photovoltaic absorption rate for the whole year are as follows:

[0030]

[0031] Where, P load,t,d is the power directly supplied by distributed photovoltaics to the load during the t period on the dth day; ηFESS and ηMESS are the charging efficiencies of fixed and mobile energy storage, respectively; P FESS,t,d 、P MESS,t,d are the charging power of fixed and mobile energy storage in the t period on the dth day; Δt is the time interval; P max,t,d is the theoretical maximum power generation of distributed photovoltaics during the t period on the dth day;

[0032] The objective function G of the upper model is constructed based on the three modeling results:

[0033]

[0034] Where G represents the objective function; g1 is the energy storage investment cost and operating expenses; g2 is the total voltage quality throughout the year; g3 is the total photovoltaic absorption rate throughout the year;

[0035] The upper layer model is constructed according to the objective function of the upper layer model and the corresponding constraint conditions.

[0036] Optionally, the lower layer model is expressed as:

[0037] minF=-λ1f1+λ2f2+λ3f3

[0038] Where F is the multi-objective optimal solution set, f1 is the daily photovoltaic absorption rate, λ1 is the weight coefficient of the daily photovoltaic absorption rate, f2 is the daily voltage quality, λ2 is the weight coefficient of the daily voltage quality, f3 is the daily energy storage operation cost, and λ3 is the weight coefficient of the daily energy storage operation cost.

[0039] Optionally, the upper model is solved using the NSGA-II algorithm, and the lower model is solved using a decomposition-based multi-objective evolutionary algorithm. The specific process includes:

[0040] 6. For the multi-objective function constructed by the upper-level model, the NSGA-II algorithm is used to directly find all non-dominated solutions (i.e., Pareto fronts) without weighted summation. The access node locations and configuration capacity of the initialized fixed energy storage and the number of mobile energy storage are used as inputs. The Pareto optimal frontier is formed under the premise of minimizing the total objective function G as much as possible through iterative calculation. After the iterative calculation is completed, the Pareto optimal solution set is output, that is, the optimal configuration target and voltage quality and the optimal distributed photovoltaic absorption rate are obtained, thereby determining the optimal solution for the fixed and mobile energy storage configuration.

[0041] For the multi-objective function constructed by the lower-level model, a decomposed multi-objective evolutionary algorithm (MOEA / D) is called, which receives a series of different node locations of fixed and mobile energy storage systems connected to the grid as input parameters. After iterative calculation and optimization, the output Pareto optimal solution set aims to maximize the distributed photovoltaic absorption rate, maximize voltage quality, and minimize the cost of energy storage operation. In the obtained optimal solution set, the optimal operation and scheduling strategy of the fixed and mobile energy storage systems is determined according to different load scenarios. If the decision maker does not accept all the scheduling solutions obtained under the scenario, the fixed and mobile energy storage access location sequence obtained under the load scenario is used as the return value to modify and optimize the upper-level configuration plan iteratively, and finally further obtain the intraday operation scheduling optimization plan for fixed and mobile energy storage in units of days.

[0042] The present invention also provides a configuration system based on fixed and mobile energy storage, comprising:

[0043] Data acquisition unit, used to obtain operating data of the low-voltage section of the distribution network;

[0044] A first model construction unit is configured to construct an upper-level model; the upper-level model includes corresponding objective functions and constraints; the objective functions of the upper-level model include functions constructed with the goals of optimizing energy storage investment costs and operating costs, optimizing the overall voltage quality throughout the year, and optimizing the overall photovoltaic absorption rate throughout the year; the constraints of the upper-level model include energy storage investment cost constraints, energy storage configuration quantity constraints, upper and lower limit constraints on connected energy storage locations, energy storage state of charge constraints, and power balance constraints;

[0045] A second model construction unit is configured to construct a lower-level model; the lower-level model includes corresponding objective functions and constraints; the objective function of the lower-level model includes a function constructed with the goals of achieving the best intraday photovoltaic absorption rate, the best intraday voltage quality, and the minimum intraday energy storage operation cost; the constraints of the lower-level model include node voltage constraints, power flow equation constraints, energy storage operation constraints, and distributed photovoltaic operation constraints;

[0046] The optimization configuration unit is used to jointly iteratively solve the upper model and the lower model based on the operating data, and output a final configuration plan when the iterative result meets the iterative conditions; the final configuration plan is a daily operation scheduling timing plan for fixed and mobile energy storage to solve the power gap caused by photovoltaic load fluctuations in the area and smooth voltage fluctuations; the final configuration plan includes the optimal configuration plan and the optimal daily operation scheduling timing plan for fixed and mobile energy storage in the low-voltage substation of the distribution network.

[0047] The present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the above-mentioned configuration method based on fixed and mobile energy storage.

[0048] The present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the configuration method based on fixed and mobile energy storage as described above.

[0049] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0050] The present invention discloses a configuration method, system, device and medium based on fixed and mobile energy storage. The method includes obtaining operating data of the low-voltage substation of the distribution network; further constructing an upper-layer model and a lower-layer model, and based on the operating data, jointly iteratively solving the upper-layer model and the lower-layer model. When the iterative result meets the iterative conditions, the final configuration scheme is output. The scheme significantly improves the absorption level of distributed photovoltaics in the distribution network, and makes full use of flexible resources such as fixed-mobile energy storage systems to solve the power gap caused by photovoltaic load fluctuations in the area and smooth voltage fluctuations.

[0051] The upper-level model includes corresponding objective functions and constraints. The objective functions of the upper-level model include functions constructed with the goals of optimizing energy storage investment costs and operating expenses, optimizing the overall voltage quality throughout the year, and optimizing the overall photovoltaic absorption rate throughout the year. The constraints of the upper-level model include energy storage investment cost constraints, energy storage configuration quantity constraints, upper and lower limit constraints on connected energy storage locations, energy storage state of charge constraints, and power balance constraints. The lower-level model includes corresponding objective functions and constraints. The objective functions of the lower-level model include functions constructed with the goals of optimizing the intraday photovoltaic absorption rate, optimizing the intraday voltage quality, and minimizing the intraday energy storage operating costs. The constraints of the lower-level model include node voltage constraints, power flow equation constraints, energy storage operation constraints, and distributed photovoltaic operation constraints. The final configuration plan is a daily operation scheduling scheme that uses fixed and mobile energy storage to resolve power shortages caused by photovoltaic load fluctuations within the region and smooth voltage fluctuations. The final configuration plan includes the optimal configuration plan and the optimal daily operation scheduling scheme for fixed and mobile energy storage in the low-voltage substation area of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 This is a basic block diagram of the fixed and mobile energy storage configuration strategy method in this embodiment;

[0054] Figure 2 This is a technical architecture diagram of this embodiment;

[0055] Figure 3 This is a specific implementation block diagram of the NSGA-II algorithm used in this embodiment;

[0056] Figure 4 This is a specific implementation block diagram of the decomposition-based multi-objective evolutionary algorithm (MOEA / D) used in this embodiment. DETAILED DESCRIPTION

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0058] The purpose of the present invention is to provide a configuration method, system, equipment and medium based on fixed and mobile energy storage, which can improve the absorption level of distributed photovoltaics in the distribution network and make full use of flexible resources such as fixed-mobile energy storage systems to solve the power gap caused by fluctuations in photovoltaic loads in the area.

[0059] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0060] like Figure 1 As shown, the present invention provides a configuration method based on fixed and mobile energy storage, comprising:

[0061] Step 100: Obtain operating data of the low-voltage substation of the distribution network.

[0062] Step 200: Construct an upper-level model; the upper-level model includes corresponding objective functions and constraints; the objective functions of the upper-level model include functions constructed with the goals of optimizing energy storage investment costs and operating costs, optimizing the total voltage quality throughout the year, and optimizing the total photovoltaic absorption rate throughout the year; the constraints of the upper-level model include energy storage investment cost constraints, energy storage configuration quantity constraints, upper and lower limit constraints on connected energy storage locations, energy storage charge state constraints, and power balance constraints.

[0063] Step 300: Construct a lower-level model; the lower-level model includes corresponding objective functions and constraints; the objective function of the lower-level model includes a function constructed with the goals of achieving the best intraday photovoltaic absorption rate, the best intraday voltage quality, and the minimum intraday energy storage operation cost; the constraints of the lower-level model include node voltage constraints, power flow equation constraints, energy storage operation constraints, and distributed photovoltaic operation constraints.

[0064] Step 400: Based on the operating data, the upper model and the lower model are jointly iteratively solved, and a final configuration plan is output when the iterative result meets the iterative conditions; the final configuration plan is a daily operation scheduling timing plan for fixed and mobile energy storage to solve the power gap caused by photovoltaic load fluctuations in the area and smooth voltage fluctuations; the final configuration plan includes the optimal configuration plan and the optimal daily operation scheduling timing plan for fixed and mobile energy storage in the low-voltage substation of the distribution network.

[0065] As a specific implementation method, Example 1 shown below is provided.

[0066] This embodiment uses the expectation of fixed and mobile energy storage for optimizing the distribution network within a low-voltage substation as an example. The expectation of fixed and mobile energy storage for optimizing the distribution network within a low-voltage substation can be raised from the following two aspects:

[0067] (1) For regional nodes with a high degree of distributed energy abandonment in the low-voltage substation of the distribution network, fixed energy storage or mobile energy storage should be used for absorption, and the basic optimization goal of configuring fixed and mobile energy storage is to ensure that the distributed photovoltaic absorption rate is as high as possible and the configuration cost is the lowest, thereby ensuring the economic efficiency of the distribution network.

[0068] (2) Taking days as a time unit and according to the daily load power consumption pattern of the nodes, fixed energy storage or mobile energy storage is preferentially dispatched for power support at nodes with a high probability of short-term load demand increase, and a basic daily operation scheduling plan for fixed and mobile energy storage is derived to alleviate the power gap caused by the fluctuation of photovoltaic load in the area, which leads to voltage deviation of distribution network nodes and other problems.

[0069] Based on the above description, the fixed and mobile energy storage configuration strategy provided in this embodiment has the following basic implementation process: Figure 1 Specifically, the implementation process of this method mainly includes:

[0070] Step 1: Divide the network into regions based on factors such as power load demand, regional economy, and transportation. Based on the divided regions, areas with concentrated and stable loads are selected as locations for configuring fixed energy storage, while locations with large load fluctuations and unstable demand are selected as locations for mobile energy storage. Construct an upper-level model with the goals of optimizing energy storage investment and operating costs, optimizing overall voltage quality throughout the year, and optimizing the overall photovoltaic absorption rate throughout the year. The upper-level model, constructed with the goals of optimizing energy storage investment and operating costs, optimizing overall voltage quality throughout the year, and optimizing the overall photovoltaic absorption rate throughout the year, refers to an energy storage configuration model that takes into account the charging and absorption of the surplus power generated by distributed photovoltaic power within the substation area by fixed and mobile energy storage systems, as well as the capacity investment cost, quantity investment cost, and energy storage operating cost required for configuring fixed and mobile energy storage, while minimizing the year-round voltage quality.

[0071] In actual application, the specific implementation process of step 1 can be:

[0072] (1) Modeling of fixed and mobile energy storage configuration costs and operating expenses. The modeling results are:

[0073] g 11 =ε cap Q FESS +ε car K FESS +δ cap Q MESS +δ car K MESS

[0074] Where g 11 The investment cost for the configuration of fixed and mobile energy storage; cap , ε carare the unit capacity cost and unit quantity cost of fixed energy storage respectively; δ cap , δ car are the unit capacity cost and unit quantity cost of mobile energy storage; Q FESS , K FESS are the configuration capacity and configuration quantity of fixed energy storage respectively; Q MESS , K MESS They are the configuration capacity and quantity of mobile energy storage respectively.

[0075] Operating costs 12 =Electricity purchase cost + loss - discharge cost.

[0076]

[0077] Where, P load (t) is the load power in period t; P chg (t) is the charging power of the energy storage in time period t; P dis (t) is the discharge power of energy storage in time period t; η c , η d is the charge and discharge efficiency; C buy (t) is the electricity purchase price in period t.

[0078]

[0079] Discharge loss cost = C sell (t)·P dis (t)·(1-η d )·Δt

[0080] Where C sell (t) is the electricity selling price in period t.

[0081] Discharge cost = C sell (t)·P dis (t)·η d ·Δt

[0082]

[0083] g1=g 11 +g 12

[0084] (2) In power system planning, node voltage deviation is the core indicator of voltage quality. This embodiment uses node voltage deviation to characterize voltage quality. The smaller the node voltage deviation, the better the voltage quality. The modeling results of the total voltage quality throughout the year are:

[0085]

[0086] Where g2 is the node voltage deviation value, which is used to represent the total voltage quality throughout the year; U i,t,d is the actual voltage amplitude of node i at time t on day d, is the specified voltage amplitude of node i at time t on day d, U i,max,d -U i,min,d is the maximum voltage deviation allowed for node i on day d, and N is the number of all nodes in the distribution network.

[0087] (3) The modeling results of the total photovoltaic absorption rate for the whole year are:

[0088] Obtain information about node i in the distribution network and determine the number of fixed or mobile energy storage devices that node i is allowed to access;

[0089] Obtain the maximum charging power data allowed for all fixed or mobile energy storage in node i during period t each day;

[0090] Obtain the distributed photovoltaic output power of node i in period t every day and the load demand power of node i in period t, and judge whether the distributed photovoltaic output power is greater than the load demand power.

[0091] When the distributed photovoltaic power generation at a node exceeds the load demand, the node is connected to fixed energy storage or mobile energy storage to absorb the distributed photovoltaic power generation. The absorption rate of energy storage for distributed photovoltaic power generation is:

[0092]

[0093] Where g1 is the annual average absorption rate of distributed photovoltaic power in the area; P load,t,d is the power directly supplied by distributed photovoltaics to the load during the t period on the dth day; ηFESS and ηMESS are the charging efficiencies of fixed and mobile energy storage, respectively; P FESS,t,d 、P MESS,t,d are the charging power of fixed and mobile energy storage in the t period on the dth day; Δt is the time interval; P max,t,d is the theoretical maximum power generation of distributed photovoltaics during the t period on the dth day.

[0094] The objective function G of the upper model is:

[0095]

[0096] Where G is an objective function; g1 is the energy storage investment cost and operating expenses; g2 is the total voltage quality throughout the year; and g3 is the total photovoltaic absorption rate throughout the year.

[0097] The constraints that need to be considered in the upper-level model include: energy storage investment cost constraints, energy storage configuration quantity constraints, upper and lower limit constraints on connected energy storage locations, energy storage charge state constraints, and power balance constraints. The specific mathematical expressions of each constraint are:

[0098] (1) Energy storage investment cost constraints:

[0099] C FESS +C MESS ≤B in

[0100] Where C FESS C is the fixed energy storage configuration fee; MESS Cost of configuring mobile energy storage; B in The total investment budget.

[0101] (2) Energy storage configuration quantity constraints:

[0102] Constraints on the number of connected fixed energy storage:

[0103]

[0104] Constraints on the number of mobile energy storage devices that can be connected:

[0105]

[0106] Where H FESS,i,t is the number of fixed energy storage connected to the distribution network node i during period t. K is the upper limit of the total number of fixed energy storage allowed to be connected within the substation area. i The upper limit of the fixed energy storage quantity allowed to be connected to a single node; H MESS,j,t is the number of mobile energy storage connected to the distribution network node j during period t. K' is the upper limit of the total number of mobile energy storage allowed to be connected within the substation area. K' i The upper limit of the number of mobile energy storage devices allowed to be connected to a single node.

[0107] (3) Upper and lower limit constraints for access to energy storage locations:

[0108]

[0109] Where K i Q is the maximum amount of fixed energy storage that node i can accommodate. FESS,k Q is the capacity of the kth fixed energy storage unit. FESS,i,max K is the maximum capacity of fixed energy storage allowed to be connected to node i. j is the maximum number of mobile energy storage that node j can accommodate; Q MESS,l Q is the capacity of the first mobile energy storage unit. MESS,j,max is the maximum capacity of mobile energy storage allowed to be connected to node j.

[0110] (4) Energy storage state of charge constraints:

[0111] S soc,min ≤S soc,f,k,t ≤S soc,max

[0112] S soc,min ≤S soc,m,l,t ≤S soc,max

[0113] Where S SOC,f,k,t S is the state of charge of the kth fixed energy storage battery at time t. SOC,max 、S SOC,min are the upper and lower limits of the state of charge of the fixed energy storage battery respectively; S SOC,m,l,t S is the state of charge of the lth mobile energy storage battery at time t. SOC,max 、S SOC,min They are the upper and lower limits of the state of charge of the mobile energy storage battery respectively.

[0114] (5) Power balance constraints:

[0115] P DG +P FESS +P MESS =P load

[0116] Where, P DG Expressed as distributed photovoltaic power generation power; P FESS 、P MESS Represent the charging and discharging power of fixed energy storage and mobile energy storage respectively; P load The power consumed by the load.

[0117] The energy storage system's absorption rate of distributed photovoltaic power output in the upper-level model refers to the value of the actual amount of electricity used by distributed photovoltaic power generation as a percentage of its total power generation within the same time scale. The energy storage system's absorption rate of distributed photovoltaic power output is an important indicator for measuring the operating efficiency of the power system. The higher the absorption rate, the higher the power utilization rate of distributed photovoltaic power generation, and the better the reliability and economic benefits of the distribution network. The configuration cost of fixed and mobile energy storage in the upper-level model is obtained by considering the cost of unit configuration capacity and unit quantity cost of fixed and mobile energy storage, multiplied by the rated capacity of a single fixed and mobile energy storage configuration and the total number of fixed and mobile energy storage configurations. The configuration cost of energy storage is an economic constraint on the upper-level model, which can avoid the problem of over-allocation of fixed and mobile energy storage.

[0118] Step 2: Use the upper-level model to determine the basic configuration plan for the access nodes and capacity of fixed energy storage and the number of mobile energy storage.

[0119] In the specific application process, the upper-level model can solve the access nodes and capacity of fixed energy storage and the number of mobile energy storage based on the historical distributed photovoltaic daily output curve, with the goals of optimizing energy storage investment cost and operating expenses, optimizing the total voltage quality throughout the year, and optimizing the total photovoltaic absorption rate throughout the year.

[0120] For the multi-objective function constructed by the above model, the absorption rate maximization problem is converted into a minimization problem. For the model solving the multi-objective function, the dimension and order of magnitude differences of different objectives will significantly affect the optimization direction. This embodiment chooses to use the NSGA-II algorithm to directly find all non-dominated solutions (i.e., Pareto frontiers) without weighted summation. The access node position and configuration capacity of the initialized fixed energy storage and the number of mobile energy storage are used as inputs. The Pareto optimal frontier is formed under the premise of minimizing the total objective function G as much as possible through iterative calculation. After the iterative calculation is completed, the Pareto optimal solution set is output, that is, the best configuration target and voltage quality and the optimal distributed photovoltaic absorption rate are obtained, and then the optimal solution for the configuration of fixed and mobile energy storage is determined. Among them, the implementation process of the NSGA-II algorithm is as follows: Figure 3 shown.

[0121] Step 3: Construct a lower-level model with the objectives of achieving the best daily PV absorption rate, the best daily voltage quality, and the lowest daily energy storage operating costs. This lower-level model is essentially a multi-objective optimization model for the participation of fixed and mobile energy storage in grid dispatch. This model primarily considers the following three objectives: achieving the best daily PV absorption rate, the best daily voltage quality, and the lowest daily energy storage operating costs after the fixed and mobile energy storage systems are connected to the distribution network in scenarios where there is a power gap caused by regional PV load fluctuations.

[0122] In actual application, the lower layer model can be expressed as:

[0123] minF=-λ1f1+λ2f2+λ3f3

[0124] Where F is the multi-objective optimal solution set, f1 is the daily photovoltaic absorption rate, λ1 is the weight coefficient of the daily photovoltaic absorption rate, f2 is the daily voltage quality, λ2 is the weight coefficient of the daily voltage quality, f3 is the daily energy storage operation cost, and λ3 is the weight coefficient of the daily energy storage operation cost.

[0125] In a multi-objective optimization model, directly using weighted summation may cause small numerical objectives to be "swallowed" by large numerical objectives. This is because the differences in the dimensions and magnitudes of different objectives can significantly affect the optimization direction. Optionally, normalization can be used to scale each objective function to the same magnitude to prevent numerical differences from masking the influence of small objectives. Specific methods include:

[0126]

[0127] Where, f i,min 、f i,max is the target f i Estimated minimum and maximum values of .

[0128] The weighted sum becomes:

[0129] F=-λ1f′1+λ2f′2+λ3f′3

[0130] The contribution of each goal is consistent in magnitude, and the weights reflect their actual importance.

[0131] The construction process of the lower model is:

[0132] (1) The modeling results of the daily photovoltaic absorption rate are as follows:

[0133]

[0134] Where f1 is the annual average absorption rate of distributed photovoltaic power in the area; P load,t,d is the power directly supplied to the load by distributed photovoltaics during period t in a day; ηFESS and ηMESS are the charging efficiencies of fixed and mobile energy storage, respectively; P FESS,t,d 、P MESS,t,d are the charging power of fixed and mobile energy storage in time period t within a day; Δt is the time interval; P max,t,d It is the theoretical maximum power generation of distributed photovoltaic in the period t in one day.

[0135] (2) In power system planning, node voltage deviation is the core indicator of voltage quality. This embodiment uses node voltage deviation to characterize voltage quality. The smaller the node voltage deviation, the smaller the voltage fluctuation, indicating better voltage quality. The modeling results of intraday voltage quality are:

[0136]

[0137] Where f2 is the node voltage deviation value; U i,t is the actual voltage amplitude of node i at time t, is the specified voltage amplitude of node i at time t, U i,max -U i,min is the maximum voltage deviation allowed at node i, and N is the number of all nodes in the distribution network.

[0138] (3) The modeling results of energy storage operation costs are as follows:

[0139] Operating cost f3 = electricity purchase cost + loss - discharge cost.

[0140]

[0141] Where, P load (t) is the load power in period t; P chg (t) is the charging power of the energy storage in time period t; P dis (t) is the discharge power of energy storage in time period t; η c , η dis the charge and discharge efficiency; C buy (t) is the electricity purchase price in period t.

[0142]

[0143] Discharge loss cost = C sell (t)·P dis (t)·(1-η d )·Δt

[0144] Where C sell (t) is the electricity selling price in period t.

[0145] Discharge cost = C sell (t)·P dis (t)·η d ·Δt

[0146]

[0147] The constraints that need to be considered in the lower-level model are: node voltage constraints, power flow equation constraints, energy storage operation constraints, and distributed photovoltaic operation constraints. The specific mathematical expressions of each constraint are:

[0148] (1) Node voltage constraint:

[0149] U i,min ≤U i,t ≤U i,max

[0150] Where U i,t is the voltage amplitude of the i-th node at time t. i,min is the lower limit of the node voltage at the i-th node. i,max is the upper limit of the node voltage at the i-th node.

[0151] (2) Power flow equation constraints:

[0152]

[0153] Where, P i,t is the active power injected into node i at time t; P DG,i,t 、 P L,i,t They are the active power of distributed photovoltaic at node i at time t, the active power charging and discharging power of the energy storage device, and the active power consumed by the load.

[0154] (3) Energy storage operation constraints:

[0155] 1. Discharge power constraints:

[0156]

[0157] Where, represents the discharge power of node j at a certain moment; Indicates the defined power rating.

[0158] 2. Power balance constraints:

[0159]

[0160] Where, It is represented by the remaining power of the energy storage device at node j at time t+1; η d Expressed as the discharge efficiency of energy storage.

[0161] (4) Distributed photovoltaic operation constraints:

[0162]

[0163] Where, P DG,i,t is the active output power of distributed photovoltaic at node i at time t; is the maximum active output power of distributed photovoltaic at node i at time t.

[0164] The node voltage deviation in the lower-level model is defined as the time-series average of the square sum of the voltage deviations of all nodes in the system during the optimization period. The smaller the voltage deviation value, the smaller the voltage fluctuation, which is an important indicator of system safety and power quality.

[0165] Step 4: Perform a global power flow calculation to screen out areas with voltage-exceeding-limit nodes and distinguish the area types. Set the time period as a load scheduling cycle, and combine the basic configuration scheme of fixed and mobile energy storage capacity and quantity to solve the lower-level model and obtain the multi-objective optimal solution set.

[0166] The access nodes and capacity of fixed energy storage and the configuration quantity of mobile energy storage are obtained through calculation of the upper-level model, and the obtained access nodes and capacity of fixed energy storage and the configuration quantity of mobile energy storage are passed to the lower-level optimization model.

[0167] Step 5: Determine the access node locations of fixed and mobile energy storage based on the multi-objective optimal solution set.

[0168] Step 6: Using the access node locations of fixed and mobile energy storage as input, perform a joint iterative solution on the upper and lower models to obtain the optimal configuration plan for the fixed energy storage capacity and the number of mobile energy storage.

[0169] Step 7: Based on the optimized configuration of fixed and mobile energy storage capacity and quantity, an optimized operation and scheduling solution for fixed and mobile energy storage is obtained. After the iteration conditions are met, the optimal configuration solution for fixed and mobile energy storage in the distribution network and the optimal daily load operation and scheduling timing solution are obtained. The optimized operation and scheduling solution for fixed and mobile energy storage refers to the optimized operation and scheduling timing solution for fixed and mobile energy storage during the day.

[0170] The decomposed multi-objective evolutionary algorithm (MOEA / D) is called, which receives a series of different node locations of fixed and mobile energy storage systems connected to the power grid as input parameters. After iterative calculation and optimization, the output Pareto optimal solution set aims to maximize the distributed photovoltaic absorption rate, maximize the voltage quality, and reduce the cost of energy storage operation as much as possible. In the obtained optimal solution set, the optimal operation scheduling strategy of the fixed and mobile energy storage systems is determined according to different load scenarios. If the decision maker does not accept all the scheduling solutions obtained under the scenario, the fixed and mobile energy storage access location sequence obtained under the load scenario is used as the return value to modify and optimize the upper configuration plan iteratively, and finally further obtain the intraday operation scheduling optimization plan of fixed and mobile energy storage in units of days. Among them, the implementation process of the decomposition-based multi-objective evolutionary algorithm (MOEA / D) is as follows Figure 4 shown.

[0171] Based on the above description, by implementing steps 1 and 3, a two-layer optimization model can be obtained for the configuration and operation scheduling of fixed and mobile energy storage in the distributed photovoltaic consumption scenario, taking into account the power gap caused by the fluctuation of photovoltaic load in the area.

[0172] Among them, (1) the upper configuration model (i.e., the upper model) that considers the optimal energy storage investment cost and operating cost, the optimal total voltage quality throughout the year, and the optimal total photovoltaic absorption rate throughout the year includes:

[0173] If distributed PV generation does not exceed load demand, then distributed PV does not need to be absorbed, and the absorption rate is 100%. When distributed PV generation exceeds load demand, a mathematical model is used to directly use the ratio of the actual utilization rate of each distributed PV generation to the distributed PV generation as the optimization target of the distributed PV absorption rate, the upper function. The higher the absorption rate, the higher the distributed PV power utilization rate, and the better the stability and economic benefits of the power system.

[0174] While ensuring that the absorption rate of distributed photovoltaic power generation is as high as possible, the product of the unit configuration cost of fixed and mobile energy storage capacity and the total configuration capacity, plus the product of the unit quantity cost of fixed and mobile energy storage and the total configuration quantity, is considered as an economic constraint to achieve more accurate capacity and quantity determination of fixed and mobile energy storage. Therefore, the minimization of energy storage configuration cost and operating expenses is used as another objective function of the upper-level model, which can not only meet the absorption requirements of fixed and mobile energy storage for distributed photovoltaic power generation, but also ensure the economy of fixed and mobile energy storage configuration.

[0175] (2) The lower-level multi-objective optimization model (i.e., the lower-level model) for fixed and mobile energy storage participating in distribution network scheduling under the condition of power shortage caused by PV load fluctuation in the region includes:

[0176] Modeling the daily PV absorption rate reveals that distributed PV within a substation is affected by a variety of factors throughout the day. Large fluctuations can create temporary power gaps, potentially causing voltages to drop below or above normal for users within the distribution network. The energy storage system's absorption rate of distributed PV output refers to the actual amount of electricity utilized by distributed PV as a percentage of its total power generation over the same timeframe. This absorption rate is a key indicator of power system efficiency. A higher absorption rate increases the utilization rate of distributed PV power, leading to improved reliability and economic efficiency of the distribution network.

[0177] When modeling intraday voltage quality, node voltage deviation is a core indicator of voltage quality. This embodiment uses node voltage deviation to characterize voltage quality; smaller node voltage deviations indicate better voltage quality. A node voltage deviation of no more than 10% of the feeder's rated voltage is used as the voltage compliance indicator. A mathematical model is then used to define the node voltage deviation as the time-series average sum of the squared voltage deviations of all system nodes during the optimization period. Smaller node voltage deviations indicate better power quality, ensuring stable power system operation.

[0178] When modeling the daily energy storage operating costs, many influencing factors need to be considered during the energy storage operation process, such as electricity price fluctuations, charging and discharging efficiency, etc., when calculating the total operating cost. The smaller the result, the better the operating efficiency and economic benefits of the distribution network, thereby formulating the optimal charging and discharging strategy to avoid losses due to losses or misjudgment of timing.

[0179] The constructed distribution network fixed and mobile energy storage configuration strategy model is solved. The upper layer solves for the configuration of fixed energy storage access nodes and capacity, as well as the number of mobile energy storage units, taking into account the optimal energy storage investment and operating costs, the optimal year-round voltage quality, and the optimal year-round total photovoltaic absorption rate. The lower layer model performs a multi-objective optimization solution to determine the mobile energy storage access nodes and the fixed and mobile energy storage operation scheduling sequence.

[0180] Based on the above description, the specific implementation steps of the present invention are as follows: an upper-level model is constructed based on the goals of optimizing energy storage investment costs and operating costs, optimizing the overall voltage quality throughout the year, and optimizing the overall photovoltaic absorption rate throughout the year, and determining the basic configuration scheme for the access nodes and capacity of fixed energy storage and the number of mobile energy storage. A lower-level model is established by considering the power quality and economy of fixed and mobile energy storage in the process of load scheduling in distribution stations. In this strategy, each day is regarded as a load scheduling cycle. The basic configuration scheme of fixed and mobile energy storage obtained by the upper-level model is combined to solve the lower-level model constructed with the goals of achieving the best intraday photovoltaic absorption rate, the best intraday voltage quality, and the minimum intraday energy storage operating cost, determine the multi-objective optimal solution set, and then determine the operation scheduling sequence of fixed and mobile energy storage. The upper and lower models are then jointly solved to further obtain the optimized configuration scheme of fixed and mobile energy storage, and the optimized scheme for the operation scheduling sequence of fixed and mobile energy storage. After the iteration is completed, the optimal configuration scheme and the optimal intraday operation scheduling scheme of fixed and mobile energy storage in the distribution network are obtained.

[0181] In summary, the fixed and mobile energy storage configuration strategy method provided by the present invention aims to make full use of the electricity generated by distributed photovoltaics in the distribution network by using fixed and mobile energy storage systems, and to take into account the power gap caused by the fluctuation of photovoltaic load in the distribution station area, and to use the spatiotemporal flexibility of energy possessed by fixed and mobile energy storage to flexibly dispatch power to each substation in the distribution network. This can reduce or even eliminate the fluctuations and interferences brought to the distribution network by the uncertainty of distributed photovoltaic output, the problem of poor voltage quality, and at the same time meet the daily load requirements of users. This strategy can significantly improve the absorption level of distributed photovoltaics in the distribution network, and make full use of the spatiotemporal flexibility of electric energy of fixed and mobile energy storage systems to solve the power gap problem caused by the fluctuation of photovoltaic load in the area. It is of great significance to improving the distribution network's acceptance capacity for distributed photovoltaics and ensuring the economy and power quality of the distribution network.

[0182] Based on this, the fixed and mobile energy storage configuration strategy method provided by the present invention has the following advantages over the existing technology:

[0183] 1. Collaborative optimization of fixed and mobile energy storage to enhance spatiotemporal flexibility. Traditional research has focused on a single type of energy storage (e.g., fixed energy storage), which can only achieve flexible temporal transfer of electricity. The fixed and mobile energy storage collaboration adopted by this invention not only enables flexible temporal transfer of electricity, but also flexible spatial transfer of flexible resources. This allows for resource sharing in different source-load scenarios within the same region, solving the long-standing absorption problem of distributed photovoltaics and the problem of load increase and decrease in distribution networks at a lower cost. At the same time, this invention considers the discharge benefits of energy storage participating in the electricity market, providing investors with better investment decisions.

[0184] 2. The optimization of the configuration and scheduling of fixed and mobile energy storage in this invention aims to determine the optimal configuration capacity, quantity and optimal access location of fixed and mobile energy storage systems, and to determine the operation scheduling timing plan of fixed and mobile energy storage using days as the time measurement unit to maximize its ability to adjust to fluctuations in the output of distributed energy resources and to cope with power shortages caused by fluctuations in photovoltaic power in the region.

[0185] 3. Improve the absorption rate of renewable energy. With the large-scale integration of renewable energy into distribution networks as distributed photovoltaic power, the mismatch between user load and distributed photovoltaic power generation is becoming increasingly serious. The use of energy storage systems to mitigate the volatility of distributed photovoltaic power generation is becoming increasingly urgent. Fixed and mobile energy storage has the spatiotemporal flexibility of energy. Compared with general fixed energy storage systems, it is more capable of optimizing energy resource allocation and establishing a highly flexible and adaptable distributed energy architecture. This will help improve the absorption rate of distributed photovoltaic power generation within the distribution network and ensure user load requirements.

[0186] As another specific embodiment, Examples 2-4 shown below are also provided.

[0187] Example 2

[0188] A configuration system based on fixed and mobile energy storage, including:

[0189] Data acquisition unit, used to obtain operating data of the low-voltage section of the distribution network;

[0190] A first model construction unit is configured to construct an upper-level model; the upper-level model includes corresponding objective functions and constraints; the objective functions of the upper-level model include functions constructed with the goals of optimizing energy storage investment costs and operating costs, optimizing the overall voltage quality throughout the year, and optimizing the overall photovoltaic absorption rate throughout the year; the constraints of the upper-level model include energy storage investment cost constraints, energy storage configuration quantity constraints, upper and lower limit constraints on connected energy storage locations, energy storage state of charge constraints, and power balance constraints;

[0191] A second model construction unit is configured to construct a lower-level model; the lower-level model includes corresponding objective functions and constraints; the objective function of the lower-level model includes a function constructed with the goals of achieving the best intraday photovoltaic absorption rate, the best intraday voltage quality, and the minimum intraday energy storage operation cost; the constraints of the lower-level model include node voltage constraints, power flow equation constraints, energy storage operation constraints, and distributed photovoltaic operation constraints;

[0192] The optimization configuration unit is used to jointly iteratively solve the upper model and the lower model based on the operating data, and output a final configuration plan when the iterative result meets the iterative conditions; the final configuration plan is a daily operation scheduling timing plan for fixed and mobile energy storage to solve the power gap caused by photovoltaic load fluctuations in the area and smooth voltage fluctuations; the final configuration plan includes the optimal configuration plan and the optimal daily operation scheduling timing plan for fixed and mobile energy storage in the low-voltage substation of the distribution network.

[0193] Example 3

[0194] The present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the above-mentioned configuration method based on fixed and mobile energy storage.

[0195] Example 4

[0196] The present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the configuration method based on fixed and mobile energy storage as described above.

[0197] For the above-mentioned computer device, the computer device can be a database. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store pending transactions. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the fixed and mobile energy storage configuration strategies in Example 1 are implemented.

[0198] It should be noted that the object information (including but not limited to object device information, object personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the present invention are all information and data authorized by the object or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions.

[0199] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided by the present invention may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in each embodiment provided by the present invention may be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, etc., but are not limited to these.

[0200] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0201] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A configuration method based on fixed and mobile energy storage, characterized in that: include: Obtain operating data of low-voltage substations in the distribution network; Build the upper model; The upper model includes corresponding objective functions and constraints; The objective function of the upper model includes a function constructed with the goals of optimizing energy storage investment cost and operating expenses, optimizing the total voltage quality throughout the year, and optimizing the total photovoltaic absorption rate throughout the year; the constraints of the upper model include energy storage investment cost constraints, energy storage configuration quantity constraints, upper and lower limit constraints on connected energy storage locations, energy storage charge state constraints, and power balance constraints; Constructing a lower-level model; the lower-level model includes corresponding objective functions and constraints; the objective function of the lower-level model includes a function constructed with the goals of achieving the best intraday photovoltaic absorption rate, the best intraday voltage quality, and the minimum intraday energy storage operation cost; the constraints of the lower-level model include node voltage constraints, power flow equation constraints, energy storage operation constraints, and distributed photovoltaic operation constraints; Based on the operating data, jointly iteratively solving the upper model and the lower model, and outputting a final configuration solution when the iterative result meets the iterative condition; The final configuration plan is a daily operation scheduling timing plan that uses fixed and mobile energy storage to solve the power gap caused by photovoltaic load fluctuations in the area and smooth voltage fluctuations; the final configuration plan includes the optimal configuration plan and optimal daily operation scheduling timing plan for fixed and mobile energy storage in the low-voltage substation area of the distribution network.

2. The configuration method based on fixed and mobile energy storage according to claim 1, characterized in that: Based on the operating data, the upper layer model and the lower layer model are jointly iteratively solved, and a final configuration solution is output when the iteration result meets the iteration condition, specifically including: The access points and scheduling sequence of mobile energy storage and the scheduling sequence of fixed energy storage output by the lower model in the previous iteration round are used as the input of the upper model in the current iteration round, and the access points and capacity of fixed energy storage and the number of mobile energy storage output by the upper model are used as the input of the lower model in the current iteration round. The upper and lower models are jointly iterated and solved. When the iteration conditions are met, the output results of the upper model and the lower model in this round are used as the final configuration plan.

3. The configuration method based on fixed and mobile energy storage according to claim 1, characterized in that: The construction process of the upper model includes: First, the modeling results of investment cost and operating expenses are as follows: g 11 =e cap Q FESS +e car K FESS +d cap Q MESS +d car K MESS Where g 11 The investment cost for the configuration of fixed and mobile energy storage; cap、 ε car are the unit capacity cost and unit quantity cost of fixed energy storage respectively; δ cap , δ car are the unit capacity cost and unit quantity cost of mobile energy storage; Q FESS , K FESS are the configuration capacity and configuration quantity of fixed energy storage respectively; Q MESS , K MESS They are the configured capacity and quantity of mobile energy storage; and: Operating costs 12 = Electricity purchase cost + loss - discharge cost Discharge loss cost = C sell (t)·P dis (t)·(1-η d )·Δt Discharge cost = C sell (t)·P dis (t)·η d ·Δt So we get: g1=g 11 +g 12 Where, P load (t) is the load power in period t; P chg (t) is the charging power of the energy storage in time period t; P dis (t) is the discharge power of energy storage in time period t; η c ,η d is the charge and discharge efficiency; C buy (t) is the electricity purchase price in period t, C sell (t) is the electricity price in period t; Secondly, the modeling results of the total voltage quality throughout the year are: Where g2 is the node voltage deviation value, which is used to represent the total voltage quality throughout the year; U i,t,d is the actual voltage amplitude of node i at time t on day d, is the specified voltage amplitude of node i at time t on day d, U i,max,d -U i,min,d is the maximum voltage deviation allowed for node i on day d, and N is the number of all nodes in the distribution network; Finally, the modeling results of the total photovoltaic absorption rate for the whole year are as follows: Where, P load,t,d is the power directly supplied by distributed photovoltaics to the load during the t period on the dth day; ηFESS and ηMESS are the charging efficiencies of fixed and mobile energy storage, respectively; P FESS,t,d 、P MESS,t,d are the charging power of fixed and mobile energy storage in the t period on the dth day; Δt is the time interval; P max,t,d is the theoretical maximum power generation of distributed photovoltaics during the t period on the dth day; The objective function G of the upper model is constructed based on the three modeling results: Where G represents the objective function; g1 is the energy storage investment cost and operating expenses; g2 is the total voltage quality throughout the year; g3 is the total photovoltaic absorption rate throughout the year; The upper layer model is constructed according to the objective function of the upper layer model and the corresponding constraint conditions.

4. The configuration method based on fixed and mobile energy storage according to claim 1, characterized in that: The lower layer model is expressed as: minF=-λ1f1+λ2f2+λ3f3 Where F is the multi-objective optimal solution set, f1 is the daily photovoltaic absorption rate, λ1 is the weight coefficient of the daily photovoltaic absorption rate, f2 is the daily voltage quality, λ2 is the weight coefficient of the daily voltage quality, f3 is the daily energy storage operation cost, and λ3 is the weight coefficient of the daily energy storage operation cost.

5. The configuration method based on fixed and mobile energy storage according to claim 1, characterized in that: The upper layer model is solved by using the NSGA-II algorithm, and the lower layer model is solved by using a multi-objective evolutionary algorithm based on decomposition.

6. A configuration system based on fixed and mobile energy storage, characterized in that: include: Data acquisition unit, used to obtain operating data of the low-voltage section of the distribution network; A first model building unit, used for building an upper-layer model; The upper model includes corresponding objective functions and constraints; the objective function of the upper model includes a function constructed with the goal of optimizing energy storage investment cost and operating costs, optimizing the total voltage quality throughout the year, and optimizing the total photovoltaic absorption rate throughout the year; the constraints of the upper model include energy storage investment cost constraints, energy storage configuration quantity constraints, upper and lower limit constraints on connected energy storage locations, energy storage charge state constraints, and power balance constraints; A second model construction unit is configured to construct a lower-level model; the lower-level model includes corresponding objective functions and constraints; the objective function of the lower-level model includes a function constructed with the goals of achieving the best intraday photovoltaic absorption rate, the best intraday voltage quality, and the minimum intraday energy storage operation cost; the constraints of the lower-level model include node voltage constraints, power flow equation constraints, energy storage operation constraints, and distributed photovoltaic operation constraints; An optimization configuration unit, configured to perform a joint iterative solution on the upper layer model and the lower layer model based on the operating data, and output a final configuration solution when an iterative result satisfies an iterative condition; The final configuration plan is a daily operation scheduling timing plan that uses fixed and mobile energy storage to solve the power gap caused by photovoltaic load fluctuations in the area and smooth voltage fluctuations; the final configuration plan includes the optimal configuration plan and optimal daily operation scheduling timing plan for fixed and mobile energy storage in the low-voltage substation area of the distribution network.

7. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the configuration method based on fixed and mobile energy storage according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that It stores a computer program, which, when executed by a processor, implements the configuration method based on fixed and mobile energy storage as described in any one of claims 1 to 5.

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