A distributed energy storage three-level joint configuration and operation optimization method and system

Abstract

A three-level joint configuration and operation optimization method and system for distributed energy storage, the method adopts a three-level energy storage system of distributed pumped storage, distributed electrochemical energy storage, and electric vehicle V2G mode, and for the distribution-traffic coupling system including the three-level energy storage system, a three-level joint configuration and operation optimization model is constructed, and the three-level joint configuration and operation optimization model of distributed energy storage is solved to obtain the optimal joint configuration and operation planning scheme. The distributed photovoltaic of the present invention adopts a local consumption mode, and utilizes the characteristics of electric vehicles that can move flexibly in time and space to complement the distributed pumped storage and distributed electrochemical energy storage systems, thereby minimizing the comprehensive cost of the three-level joint system and improving the consumption rate of distributed photovoltaics connected to the distribution network.

Description

Technical Field

[0001] The present invention belongs to the field of configuration and operation of distributed energy storage, and specifically relates to a distributed energy storage three-level joint configuration and operation optimization method and system. Background Art

[0002] As people's requirements for energy health and safety continue to evolve, new energy technologies are developing and popularizing at a high speed. Distributed new energy, led by photovoltaics, is connected to the grid on a large scale. While bringing environmentally friendly energy, how to achieve efficient energy consumption has gradually become a key issue in the development of new energy. At the distribution network level, the access of distributed new energy has gradually transformed the traditional passive distribution network into an active distribution network; in terms of smoothing the volatility of new energy output, distributed new energy needs to be equipped with sufficient distributed energy storage.

[0003] Currently, the most widely used energy storage methods are electrochemical energy storage and large-scale pumped-storage power station energy storage. Among them, electrochemical energy storage technology is relatively mature and has higher working efficiency, but its unit capacity cost is expensive and is not suitable for large-scale use. Although large-scale pumped-storage power stations can store energy in a centralized manner, which is convenient for energy management and peak regulation of the power grid, their construction sites are highly dependent on geographical conditions. In recent years, the V2G mode of electric vehicles, which reversely supplies power to the power grid through on-board battery packs, is gaining more and more attention as an emerging energy storage method. Summary of the invention

[0004] The purpose of the present invention is to provide a distributed energy storage optimization method and system with three-level joint configuration and operation in order to solve the above problems existing in the prior art.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows:

[0006] In a first aspect, the present invention proposes a three-level joint configuration and operation optimization method for distributed energy storage, comprising:

[0007] S1. For the distribution-transportation coupling system including a three-level energy storage system, a three-level joint configuration and operation optimization model is constructed with the goal of minimizing its comprehensive cost. The three-level energy storage system includes a distributed pumped storage system, a distributed electrochemical energy storage system, and an electric vehicle smart charging pile energy storage system equipped with V2G function;

[0008] S2. Solve the three-level joint configuration and operation optimization model of distributed energy storage to obtain the optimal joint configuration and operation planning scheme.

[0009] In S1, the objective function of the three-level joint configuration and operation optimization model includes:

[0010] min(C P +C B +CV )[(1+α) γ -1] / αγ 2 +(C E +C S )d;

[0011]

[0012]

[0013]

[0014]

[0015]

[0016] In the above formula, C P is the configuration cost of distributed pumped storage, C B is the configuration cost of distributed electrochemical energy storage, C V is the configuration cost of the smart charging pile, α is the discount rate, γ is the investment period, C E is the daily electricity purchase cost, C S The cost of battery loss caused by reverse powering the electric vehicle, d is the number of typical days in a year; are the number of distributed pumped storage reversible units, distributed electrochemical energy storage units, and smart charging piles connected to the distribution network node e, respectively. P , PR B , PR V They are the unit configuration costs of distributed pumped storage, distributed electrochemical energy storage, and smart charging piles; is the outgoing line w of the upper power grid through the balancing node in time period t O The power output to the entire distribution network, is the electricity purchase price at time period t; is the number of smart charging piles put into use at node e in time period t, PR S Subsidize the battery loss cost of a single electric vehicle per unit time period.

[0017] In S1, the constraints of the three-level joint configuration and operation optimization model include the operation constraints of the three-level energy storage system, the configuration constraints of the three-level energy storage system, the charging demand allocation constraints of the electric vehicle smart charging piles, and the distribution network constraints;

[0018] The operating constraints of the three-level energy storage system include:

[0019]

[0020]

[0021]

[0022]

[0023]

[0024]

[0025]

[0026]

[0027]

[0028]

[0029] In the above formula, are the input power and output power of the distributed pumped storage power station connected to node e in time period t, ρ is the density of water, g is the acceleration of gravity, h is the height difference between the upper and lower reservoirs of the distributed pumped storage power station, ΔVI t，e is the amount of water discharged from the lower reservoir to the upper reservoir by the distributed pumped storage power station connected at node e during time period t, ΔVO t，e is the amount of water discharged from the upper reservoir to the lower reservoir by the distributed pumped storage power station connected to node e during time period t, γ PS is the energy transmission efficiency of the reversible unit in the distributed pumped storage power station, T U is the unit time; δ M is a large M constant, is the state binary variable of the distributed pumped storage power station connected at node e in time period t; ΔV t，e is the actual amount of water discharged or pumped by the distributed pumped storage power station connected to node e during time period t, is the water volume of the upper reservoir of the distributed pumped storage power station connected to node e in time period t; is the water volume of the lower reservoir of the distributed pumped storage power station connected at node e in time period t, and tn is the last time period in a typical day; P CH , P DI They are the maximum charging power and maximum discharging power of a single electrochemical energy storage unit, is the number of distributed electrochemical energy storage units connected to the distribution network node e, is the actual output or input power of the electrochemical energy storage connected at node e during time period t; η BL , η BH are the lower and upper limits of the electrochemical energy storage energy state, IC Bis the installed capacity of a single electrochemical energy storage unit, BC O is the initial capacity of a single electrochemical energy storage unit, is the actual output or input power of the electrochemical energy storage connected to node e in any period tx of a typical day; is the number of smart charging piles connected to the distribution network node e, IC V The installation capacity of a single smart charging pile. The simultaneous rate of electric vehicles connecting to smart charging piles, is the actual output or input power of the smart charging pile connected to node e during time period t; η VL , η VH are the lower and upper limits of the energy state of the battery on board the electric vehicle, VC O is the initial capacity of a single electric vehicle battery, is the actual output or input power of the electrochemical energy storage connected at node e in any period ty during the peak parking period of electric vehicles, ta is the starting period of the peak parking period of electric vehicles, and tb is the ending period of the peak parking period of electric vehicles.

[0030] The configuration constraints of the three-level energy storage system include:

[0031]

[0032]

[0033]

[0034]

[0035]

[0036] In the above formula, They are the number of distributed pumped storage reversible units, the number of distributed electrochemical energy storage units, and the number of smart charging piles connected to the distribution network node e. They are the planning node variables of distributed pumped storage, distributed electrochemical energy storage, and smart charging piles at node e respectively; are the alternative access node constants of distributed pumped storage and smart charging pile at node e respectively.

[0037] The charging demand allocation constraints of the electric vehicle intelligent charging pile include:

[0038]

[0039]

[0040]

[0041] In the above formula, is the traffic flow of electric vehicles with charging needs at the starting and ending points to od in time period t, is the traffic flow of electric vehicles that use path k for the origin-destination pair od and charge at the public charging pile on road l in time period t; is the active power output by the distribution network to the electric vehicle connected to node e during time period t, is the correlation matrix between road l and distribution network node e, ED U is the average charging energy requirement of an electric vehicle; f M The upper limit of the number of electric vehicles that can be served by a charging station on a road in a unit time period;

[0042] The distribution network constraints include:

[0043]

[0044]

[0045]

[0046]

[0047]

[0048] In the above formula, are respectively the active power and reactive power on line w in time period t, where w is all the lines connected to the distribution network node e. are the basic active load and reactive load connected to node e in time period t, respectively. is the actual output or input power of the distributed pumped storage power station connected to node e during time period t, is the photovoltaic output connected to node e in time period t; Active power and reactive power on line d in period t, LC d is the capacity of the distribution network line d, d is any distribution network line; IPV e is the photovoltaic installed capacity connected to the distribution network node e; ΔU t，d is the voltage drop on line d during time period t, are the resistance and reactance of the distribution network line d, U N is the rated voltage of the distribution network bus.

[0049] In a second aspect, the present invention proposes a three-level joint configuration and operation optimization system for distributed energy storage, including a model building module and a model solving module;

[0050] The model building module is used to construct a three-level joint configuration and operation optimization model for a distribution-transportation coupling system including a three-level energy storage system, with the goal of minimizing its comprehensive cost. The three-level energy storage system includes a distributed pumped storage system, a distributed electrochemical energy storage system, and an electric vehicle smart charging pile energy storage system equipped with a V2G function;

[0051] The model solving module is used to solve the three-level joint configuration and operation optimization model of distributed energy storage to obtain the optimal joint configuration and operation planning solution.

[0052] The model building module includes an objective function building unit;

[0053] The objective function construction unit is used to construct the following objective function:

[0054] min(C P +C B +C V )[(1+α) γ -1] / αγ 2 +(C E +C S )d;

[0055]

[0056]

[0057]

[0058]

[0059]

[0060] In the above formula, C P is the configuration cost of distributed pumped storage, C B is the configuration cost of distributed electrochemical energy storage, C V is the configuration cost of the smart charging pile, α is the discount rate, γ is the investment period, C E is the daily electricity purchase cost, C S The cost of battery loss caused by reverse powering the electric vehicle, d is the number of typical days in a year; are the number of distributed pumped storage reversible units, distributed electrochemical energy storage units, and smart charging piles connected to the distribution network node e, respectively. P , PR B , PR V They are the unit configuration costs of distributed pumped storage, distributed electrochemical energy storage, and smart charging piles; is the outgoing line w of the upper power grid through the balancing node in time period t OThe power output to the entire distribution network, is the electricity purchase price at time period t; is the number of smart charging piles put into use at node e in time period t, PR S Subsidize the battery loss cost of a single electric vehicle per unit time period.

[0061] The model building module also includes an operation constraint unit, a configuration constraint unit, a charging demand allocation constraint unit, and a distribution network constraint unit;

[0062] The operation constraint unit is used to construct the following operation constraint conditions of the three-level energy storage system:

[0063] In S1, the constraints of the three-level joint configuration and operation optimization model include the operation constraints of the three-level energy storage system, the configuration constraints of the three-level energy storage system, the charging demand allocation constraints of the electric vehicle smart charging piles, and the distribution network constraints;

[0064] The operating constraints of the three-level energy storage system include:

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073]

[0074]

[0075] In the above formula, are the input power and output power of the distributed pumped storage power station connected to node e in time period t, ρ is the density of water, g is the acceleration of gravity, h is the height difference between the upper and lower reservoirs of the distributed pumped storage power station, ΔVI t，e is the amount of water discharged from the lower reservoir to the upper reservoir by the distributed pumped storage power station connected at node e during time period t, ΔVO t，e is the amount of water discharged from the upper reservoir to the lower reservoir by the distributed pumped storage power station connected to node e during time period t, γ PSis the energy transmission efficiency of the reversible unit in the distributed pumped storage power station, T U is the unit time; δ M is the large M constant, is the state binary variable of the distributed pumped storage power station connected at node e in time period t; ΔV t，e is the actual amount of water discharged or pumped by the distributed pumped storage power station connected to node e during time period t, is the water volume of the upper reservoir of the distributed pumped storage power station connected to node e in time period t; is the water volume of the lower reservoir of the distributed pumped storage power station connected at node e in time period t, and tn is the last time period in a typical day; P CH , P DI They are the maximum charging power and maximum discharging power of a single electrochemical energy storage unit, is the number of distributed electrochemical energy storage units connected to the distribution network node e, is the actual output or input power of the electrochemical energy storage connected at node e during time period t; η BL , η BH are the lower and upper limits of the electrochemical energy storage energy state, IC B is the installed capacity of a single electrochemical energy storage unit, BC O is the initial capacity of a single electrochemical energy storage unit, is the actual output or input power of the electrochemical energy storage connected to node e in any period tx of a typical day; is the number of smart charging piles connected to the distribution network node e, IC V The installation capacity of a single smart charging pile. The simultaneous rate of electric vehicles connecting to smart charging piles, is the actual output or input power of the smart charging pile connected to node e during time period t; η VL , η VH are the lower and upper limits of the energy state of the battery on board the electric vehicle, VC O is the initial capacity of a single electric vehicle battery, is the actual output or input power of the electrochemical energy storage connected at node e in any period ty during the peak parking period of electric vehicles, ta is the starting period of the peak parking period of electric vehicles, and tb is the ending period of the peak parking period of electric vehicles.

[0076] The configuration constraint unit is used to construct the following configuration constraint conditions for the three-level energy storage system:

[0077]

[0078]

[0079]

[0080]

[0081]

[0082] In the above formula, They are the number of distributed pumped storage reversible units, the number of distributed electrochemical energy storage units, and the number of smart charging piles connected to the distribution network node e. They are the planning node variables of distributed pumped storage, distributed electrochemical energy storage, and smart charging piles at node e respectively; are the alternative access node constants of distributed pumped storage and smart charging pile at node e respectively.

[0083] The charging demand allocation constraint unit is used to construct the following charging demand allocation constraint conditions for the electric vehicle intelligent charging pile:

[0084]

[0085]

[0086]

[0087] In the above formula, is the traffic flow of electric vehicles with charging needs at the starting and ending points to od in time period t, is the traffic flow of electric vehicles that use path k for the origin-destination pair od and charge at the public charging pile on road l in time period t; is the active power output by the distribution network to the electric vehicle connected to node e during time period t, is the correlation matrix between road l and distribution network node e, ED U is the average charging energy requirement of an electric vehicle; f M The upper limit of the number of electric vehicles that can be served by a charging station on a road in a unit time period;

[0088] The distribution network constraint unit is used to construct the following distribution network constraint conditions:

[0089]

[0090]

[0091]

[0092]

[0093]

[0094] In the above formula, are respectively the active power and reactive power on line w in time period t, where w is all the lines connected to the distribution network node e. are the basic active load and reactive load connected to node e in time period t, respectively. is the actual output or input power of the distributed pumped storage power station connected to node e during time period t, is the photovoltaic output connected to node e in time period t; Active power and reactive power on line d in period t, LC d is the capacity of the distribution network line d, d is any distribution network line; IPV e is the photovoltaic installed capacity connected to the distribution network node e; ΔU t，d is the voltage drop on line d during time period t, are the resistance and reactance of the distribution network line d, U N is the rated voltage of the distribution network bus.

[0095] Compared with the prior art, the present invention has the following beneficial effects:

[0096] The present invention proposes a distributed energy storage three-level joint configuration and operation optimization method and system. The method is aimed at a distribution-traffic coupling system including a distributed pumped storage system, a distributed electrochemical energy storage system and an electric vehicle smart charging pile equipped with a V2G function, and takes minimizing its comprehensive cost as the goal. A three-level joint configuration and operation optimization model is constructed, and the three-level joint configuration and operation optimization model of the distributed energy storage is solved to obtain the optimal joint configuration and operation planning scheme. On the one hand, this method connects distributed pumped storage to a three-level combined energy storage system. Unlike large-scale pumped storage power stations, distributed pumped storage power stations are more flexible in construction location, smaller in capacity and connected to a low voltage level, which can effectively improve the local absorption capacity of new energy in the region; on the other hand, this method uses electric vehicles equipped with V2G functions to reversely supply power to the power grid through on-board battery packs, giving electric vehicles the attributes of distributed energy storage, and using electric vehicles as smart mobile charging piles to flexibly assist in the local absorption of new energy in time and space. Its characteristics and different application scenarios complement distributed pumped storage and distributed electrochemical energy storage systems, minimizing the overall cost of the three-level combined system while improving the absorption rate of distributed photovoltaics connected to the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 The figure is an overall flow chart of the method of the present invention.

[0098] Figure 2 This is a structural diagram of the power distribution-transportation coupling system described in the present invention.

[0099] Figure 3 This is a topological structure diagram of the example described in Example 1.

[0100] Figure 4 This is the time-of-use electricity price diagram of the example described in Example 1.

[0101] Figure 5 This is a photovoltaic output curve diagram of the example described in Example 1.

[0102] Figure 6 This is a location planning diagram for each distributed energy storage in the example described in Example 1.

[0103] Figure 7 The structure diagram of the system of the present invention is shown in FIG. DETAILED DESCRIPTION

[0104] The present invention is further described in detail below in conjunction with specific implementations and drawings.

[0105] The present invention proposes a three-level joint configuration and operation optimization method and system for distributed energy storage. The distribution-transportation coupling system considered includes a distribution network, a transportation network, distributed photovoltaics, electric vehicles, distributed electrochemical energy storage, and distributed pumped storage. Its structure is as follows: Figure 2 As shown. In terms of load, the basic load of the distribution network access and the charging load of electric vehicles are mainly considered. Distributed photovoltaics adopt the mode of local consumption to avoid additional pressure on the distribution network; in terms of distributed energy storage, a three-level energy storage system of distributed pumped storage, distributed electrochemical energy storage, and electric vehicle V2G mode is adopted, and its joint configuration and operation mode are optimized to improve the consumption rate of distributed photovoltaics connected to the distribution network and minimize the overall cost of the system.

[0106] In terms of electric vehicles, the present invention considers two user behavior modes: daily charging and discharging and route charging. Daily charging and discharging refers to the charging and discharging behavior of daily commuting electric vehicles. Such electric vehicles are charged at slow charging piles at home at night, and go to the parking lot of the workplace during the day to access charging piles with V2G functions. Without affecting the use of the owner, it can be used as a distributed energy storage with energy transfer in time and space. On the other hand, route charging refers to the charging behavior when passing by charging facilities during the journey. Considering the timeliness of the journey in this mode, the present invention only considers the charging behavior of route mode electric vehicles without considering their reverse power supply behavior.

[0107] Embodiment 1:

[0108] This embodiment takes a 27-node distribution network and a 24-node transportation network as the research object, and considers one distributed pumped storage power station, two distributed electrochemical energy storage nodes and two intelligent charging pile configuration nodes. The topological structure is as follows: Figure 3 As shown. Parameters: The configuration costs of distributed pumped storage reversible units, distributed electrochemical energy storage, and smart charging piles are 5 million yuan, 1 million yuan, and 100,000 yuan respectively; the installed capacity of a single distributed pumped storage reversible unit is 1 megawatt; the energy transmission efficiency of the reversible unit in the distributed pumped storage power station is 0.85; the installed capacity of a single electrochemical energy storage is 500 kWh; the lower and upper limits of the energy state of the electrochemical energy storage are 0.15 and 0.85 respectively; the maximum charging power and maximum discharge power of a single electrochemical energy storage are 250 kW; the initial power of a single electrochemical energy storage is 250 kWh; the installed capacity of a single smart charging pile is 80 kW; the lower and upper limits of the energy state of the battery on board an electric vehicle are 0.15 and 0.85 respectively; the energy state of a single electric vehicle is 100 kWh; the energy state of the battery on board an ... The initial capacity of the on-board battery is 60 kWh; the simultaneous rate of electric vehicles connected to smart charging piles is 0.2; the battery loss subsidy cost per vehicle per unit time period is 50 yuan; the upper limits of the number of distributed pumped storage reversible units, distributed electrochemical energy storage and smart charging piles connected to the same node are 10, 10 and 50 respectively; the unit time is 1 hour; the investment period is 10 years; the discount rate is 0.05; the number of typical days in a year is 365 days; the rated voltage of the distribution network is 10 kV, and the upper and lower limits of the distribution network bus voltage are 10.5 kV and 9.5 kV respectively; the average charging energy demand per electric vehicle is 40 kWh; the charging and discharging power of the electric vehicle smart charging pile is 80 kW; the lower and upper limits of the water volume in the upper reservoir of the distributed pumped storage power station are 3×10 4 Cubic meters and 1.5×10 5 cubic meters; the lower and upper limits of the water volume in the lower reservoir of the distributed pumped storage power station are 3×10 4 Cubic meters and 1.5×10 5 cubic meters; the upper limit of water volume change per unit time in distributed pumped storage power stations is 2×10 4 cubic meters; the height difference between the upper and lower reservoirs of the distributed pumped storage power station is 50 meters; the density of water is 1×10 3 kg / m3. The time-of-use electricity price and photovoltaic output characteristic curve used in the calculation example are as follows: Figure 4 and Figure 5 shown.

[0109] like Figure 1 As shown, a three-level joint configuration and operation optimization method for distributed energy storage is performed in the following steps:

[0110] 1. For the distribution-transportation coupling system including a three-level energy storage system, a three-level joint configuration and operation optimization model is constructed with the goal of minimizing its comprehensive cost;

[0111] The model takes into account the configuration cost of distributed pumped storage, distributed electrochemical energy storage, smart charging piles (with V2G function), electricity purchase cost, and the objective function of the battery loss subsidy cost caused by the reverse power supply of electric vehicles.

[0112] min(C P +C B +C V )[(1+α) γ -1] / αγ 2 +(C E +C S )d;

[0113]

[0114]

[0115]

[0116]

[0117]

[0118] In the above formula, C P is the configuration cost of distributed pumped storage, C B is the configuration cost of distributed electrochemical energy storage, C V is the configuration cost of the smart charging pile, α is the discount rate, γ is the investment period, C E is the daily electricity purchase cost, C S The cost of battery loss caused by reverse powering the electric vehicle, d is the number of typical days in a year; are the number of distributed pumped storage reversible units, distributed electrochemical energy storage units, and smart charging piles connected to the distribution network node e, respectively. P , PR B , PR V They are the unit configuration costs of distributed pumped storage, distributed electrochemical energy storage, and smart charging piles; is the outgoing line w of the upper power grid through the balancing node in time period t O The power output to the entire distribution network, is the electricity purchase price at time period t; is the number of smart charging piles put into use at node e in time period t, PR S Subsidize the cost of battery loss per unit time for a single electric vehicle;

[0119] The operating constraints of the three-level energy storage system of the model include the operating constraints of distributed pumped storage power stations, the operating constraints of distributed electrochemical energy storage, and the operating constraints of electric vehicle smart charging piles;

[0120] The operating constraints for distributed pumped storage power stations include:

[0121] Constraints on the relationship between water level changes and input and output power:

[0122]

[0123]

[0124] Output power constraints of distributed pumped storage:

[0125]

[0126]

[0127] Constraints on water volume changes in distributed pumped storage power stations:

[0128]

[0129]

[0130]

[0131] Capacity boundary constraints of the upper and lower reservoirs of a distributed pumped storage power station:

[0132]

[0133]

[0134] Real-time water storage constraints of the upper and lower reservoirs of a distributed pumped storage power station in each time period:

[0135]

[0136]

[0137] Constraints on the amount of water pumped or discharged per unit time in a distributed pumped storage power station:

[0138]

[0139] In the above formula, are the input power and output power of the distributed pumped storage power station connected to node e in time period t, ρ is the density of water, g is the acceleration of gravity, h is the height difference between the upper and lower reservoirs of the distributed pumped storage power station, ΔVI t，e is the amount of water discharged from the lower reservoir to the upper reservoir by the distributed pumped storage power station connected at node e during time period t, ΔVO t，eis the amount of water discharged from the upper reservoir to the lower reservoir by the distributed pumped storage power station connected to node e during time period t, γ PS is the energy transmission efficiency of the reversible unit in the distributed pumped storage power station, T U is the unit time; is the actual output or input power of the distributed pumped storage power station connected to node e during time period t. When , is the output power; is the number of distributed pumped storage reversible units connected to the distribution network node e, IC P is the installed capacity of a single distributed pumped storage reversible unit; δ M is the large M constant, is the state binary variable of the distributed pumped storage power station connected at node e in time period t. When the water is released, Pumping water; ΔV t，e is the actual discharge or pumping volume of the distributed pumped storage power station connected to node e during time period t. t，e >0, the distributed pumped storage power station discharges water to generate electricity; VU m , VU M They are the lower and upper water limits of the upper reservoir of the distributed pumped storage power station. VD is the water volume of the upper reservoir of the distributed pumped storage power station connected to node e in time period t; m 、VD M They are the lower and upper water limits of the lower reservoir of the distributed pumped storage power station. is the water volume of the lower reservoir of the distributed pumped storage power station connected at node e in time period t; tn is the last time period in a typical day; ΔV M The upper limit of water volume change per unit time in a distributed pumped storage power station;

[0140] The operating constraints for distributed electrochemical energy storage include:

[0141] Charging and discharging power constraints of distributed electrochemical energy storage:

[0142]

[0143] State constraints of distributed electrochemical energy storage:

[0144]

[0145] In the above formula, P CH , P DI They are the maximum charging power and maximum discharging power of a single electrochemical energy storage unit, is the number of distributed electrochemical energy storage units connected to the distribution network node e, is the actual output or input power of the electrochemical energy storage connected at node e during time period t. When is the output power; η BL , η BH are the lower and upper limits of the electrochemical energy storage energy state, IC B is the installed capacity of a single electrochemical energy storage unit, BC O is the initial capacity of a single electrochemical energy storage unit, is the actual output or input power of the electrochemical energy storage connected to node e in any period tx of a typical day;

[0146] Smart charging piles for electric vehicles are deployed in areas with dense electric vehicle distribution in the city center. During the peak parking period during the day, when users park their vehicles at work and connect their vehicles to smart charging piles, the V2G mode is used to discharge the on-board batteries of electric vehicles. Before the end of the peak parking period, the on-board batteries are charged to the original power level and the battery loss cost is paid.

[0147] The operating constraints for electric vehicle smart charging piles include:

[0148] The charging and discharging power constraints of electric vehicle onboard batteries:

[0149]

[0150] State constraints of electric vehicle onboard batteries:

[0151]

[0152] The lower limit of the battery capacity of electric vehicles at the end of the peak parking period is set, that is, the charging capacity during the peak parking period is not less than the discharge capacity:

[0153]

[0154] The number of smart charging piles put into use is limited:

[0155]

[0156] In the above formula, is the number of smart charging piles connected to the distribution network node e, IC V The installation capacity of a single smart charging pile. The simultaneous rate of electric vehicles connecting to smart charging piles, is the actual output or input power of the smart charging pile connected to node e during time period t. When is the output power; η VL , η VH are the lower and upper limits of the energy state of the battery on board the electric vehicle, VCO is the initial capacity of a single electric vehicle battery, is the actual output or input power of the electrochemical energy storage connected at node e in any period ty during the peak period of electric vehicle parking, ta is the starting period of the peak period of electric vehicle parking, and tb is the ending period of the peak period of electric vehicle parking; is the number of smart charging piles put into use at node e during time period t;

[0157] At the planning level, it is necessary to optimize the site selection and capacity of distributed pumped storage, distributed electrochemical energy storage, and smart charging piles. Affected by natural resource endowments, distributed pumped storage power stations can only be built at nodes with abundant water resources. In order to maximize the reverse power supply potential of electric vehicles, smart charging piles should be configured in areas with dense distribution of electric vehicles in the city center. In contrast, distributed electrochemical energy storage is less constrained by geographical conditions.

[0158] The configuration constraints of the three-level energy storage system of this model include:

[0159] The site selection constraints of distributed energy storage limit the configuration of various distributed energy storage facilities to selected nodes:

[0160]

[0161]

[0162]

[0163] Configuration node constraints for distributed pumped storage and electric vehicle smart charging piles:

[0164]

[0165]

[0166] Installation limits for various types of distributed energy storage:

[0167]

[0168]

[0169]

[0170] In the above formula, They are the number of distributed pumped storage reversible units, the number of distributed electrochemical energy storage units, and the number of smart charging piles connected to the distribution network node e. are the planning node variables of distributed pumped storage, distributed electrochemical energy storage, and smart charging pile at node e, respectively. When it is 1, the corresponding type of distributed energy storage is connected to the distribution network node e; are the candidate access node constants of distributed pumped storage and smart charging pile at node e, respectively. When it is 1, the corresponding type of distributed energy storage can be connected at the distribution network node e; n PM 、n BM 、n VM They are the upper limit of the number of distributed pumped storage reversible units, the upper limit of the number of distributed electrochemical energy storage units, and the upper limit of the number of smart charging piles;

[0171] In terms of electric vehicle charging demand, the daily charging load of household slow charging piles is highly regular and difficult to change, so the daily slow charging load is included in the basic load. The fast charging load of public charging piles has strong temporal and spatial flexibility. Optimizing the charging demand of public charging piles can improve the photovoltaic absorption capacity and the utilization efficiency of distributed energy storage facilities from the load level.

[0172] The charging demand allocation constraints of the electric vehicle smart charging piles in this model include:

[0173]

[0174]

[0175]

[0176] In the above formula, is the traffic flow of electric vehicles with charging needs at the starting and ending points to od in time period t, is the traffic flow of electric vehicles that use path k for the origin-destination pair od and charge at the public charging pile on road l in time period t; is the active power output by the distribution network to the electric vehicle connected to node e during time period t, is the correlation matrix between road l and distribution network node e. When road l is coupled with distribution network node e, ED U is the average charging energy requirement of an electric vehicle; f M The upper limit of the number of electric vehicles that can be served by a charging station on a road in a unit time period;

[0177] In order to reduce the operating pressure of the distribution network and reduce the risk of bottlenecks, photovoltaic power adopts the operation strategy of local consumption, without considering the situation of power return;

[0178] The distribution network constraints of the model include:

[0179] Power balance constraints of distribution network:

[0180]

[0181]

[0182]

[0183] Photovoltaic output constraints:

[0184]

[0185] Distribution network node voltage constraints:

[0186]

[0187]

[0188]

[0189] In the above formula, are the active power and reactive power on line w in time period t, respectively, where w is all the lines connected to the distribution network node e. are the basic active load and reactive load connected to node e in time period t, respectively. is the photovoltaic output connected to node e in time period t; Active power and reactive power on line d in period t, LC d is the capacity of the distribution network line d, d is any distribution network line; IPV e is the photovoltaic installed capacity connected to the distribution network node e; ΔU t，d is the voltage drop on line d during time period t, are the resistance and reactance of the distribution network line d, U N is the rated voltage of the distribution network bus; U t，a , U t，b are the bus voltage of the distribution network node a and the bus voltage of the distribution network node b in the time period t, respectively. Node a and node b are the two endpoints of the distribution network line d respectively; U m , U M They are the upper and lower limits of the bus voltage of the distribution network, U t，e is the voltage at node e during time period t.

[0190] 2. Solve the three-level joint configuration and operation optimization model of distributed energy storage and obtain the planning location of each distributed energy storage as follows: Figure 6 As shown, the capacity configuration scheme is shown in Table 1;

[0191] Table 1 Capacity configuration scheme of distributed energy storage

[0192]

[0193] The three-level joint optimization configuration and operation strategy of distributed energy storage proposed in the present invention (strategy 1) is compared with the independent configuration and operation strategy of distributed energy storage (strategy 2) applied to the 27-node distribution network-24-node transportation network example. The comparison results are shown in Table 2.

[0194] Table 2 Comparison results

[0195]

[0196] Strategy 2 does not consider the joint configuration and operation of the three energy storages, but performs independent configuration and operation respectively. By comparing Strategy 1 with Strategy 2, it can be seen that when Strategy 1 is adopted, the annual configuration cost, annual electricity purchase cost and annual battery loss subsidy cost are reduced by 13.40%, 6.99% and 2.01% respectively compared with Strategy 2. From the perspective of annual system comprehensive cost, Strategy 1 is reduced by 11.89% compared with Strategy 2. From the perspective of photovoltaic absorption rate, Strategy 1 is increased by 5.7% compared with Strategy 2. The above results show that the three-level joint optimization configuration and operation strategy of distributed energy storage proposed in the present invention achieves the effect of minimizing the comprehensive cost of the system.

[0197] Embodiment 2:

[0198] like Figure 7 As shown, a distributed energy storage three-level joint configuration and operation optimization system includes a model building module and a model solving module;

[0199] The model building module is used to construct a three-level joint configuration and operation optimization model for a distribution-transportation coupling system including a three-level energy storage system, with the goal of minimizing its comprehensive cost. The three-level energy storage system includes a distributed pumped storage system, a distributed electrochemical energy storage system, and an electric vehicle smart charging pile energy storage system equipped with a V2G function;

[0200] The model solving module is used to solve the three-level joint configuration and operation optimization model of distributed energy storage to obtain the optimal joint configuration and operation planning solution.

[0201] The model building module includes an objective function building unit;

[0202] The objective function construction unit is used to construct the following objective function:

[0203] min(C P +C B +C V )[(1+α) γ -1] / αγ 2 +(C E +CS )d;

[0204]

[0205]

[0206]

[0207]

[0208]

[0209] In the above formula, C P is the configuration cost of distributed pumped storage, C B is the configuration cost of distributed electrochemical energy storage, C V is the configuration cost of the smart charging pile, α is the discount rate, γ is the investment period, C E is the daily electricity purchase cost, C S The cost of battery loss caused by reverse powering the electric vehicle, d is the number of typical days in a year; are the number of distributed pumped storage reversible units, distributed electrochemical energy storage units, and smart charging piles connected to the distribution network node e, respectively. P , PR B , PR V They are the unit configuration costs of distributed pumped storage, distributed electrochemical energy storage, and smart charging piles; is the outgoing line w of the upper power grid through the balancing node in time period t O The power output to the entire distribution network, is the electricity purchase price at time period t; is the number of smart charging piles put into use at node e in time period t, PR S Subsidize the battery loss cost of a single electric vehicle per unit time period.

[0210] The model building module also includes an operation constraint unit, a configuration constraint unit, a charging demand allocation constraint unit, and a distribution network constraint unit;

[0211] The operation constraint unit is used to construct the following operation constraint conditions of the three-level energy storage system:

[0212] The operating constraints for distributed pumped storage power stations include:

[0213]

[0214]

[0215]

[0216]

[0217]

[0218]

[0219]

[0220]

[0221]

[0222]

[0223]

[0224]

[0225] In the above formula, are the input power and output power of the distributed pumped storage power station connected to node e in time period t, ρ is the density of water, g is the acceleration of gravity, h is the height difference between the upper and lower reservoirs of the distributed pumped storage power station, ΔVI t，e is the amount of water discharged from the lower reservoir to the upper reservoir by the distributed pumped storage power station connected at node e during time period t, ΔVO t，e is the amount of water discharged from the upper reservoir to the lower reservoir by the distributed pumped storage power station connected to node e during time period t, γ PS is the energy transmission efficiency of the reversible unit in the distributed pumped storage power station, T U is the unit time; is the actual output or input power of the distributed pumped storage power station connected to node e during time period t. When , is the output power; is the number of distributed pumped storage reversible units connected to the distribution network node e, IC P is the installed capacity of a single distributed pumped storage reversible unit; δ M is the large M constant, is the state binary variable of the distributed pumped storage power station connected at node e in time period t. When the water is released, Pumping water; ΔV t，e is the actual discharge or pumping volume of the distributed pumped storage power station connected to node e during time period t. t，e >0, the distributed pumped storage power station discharges water to generate electricity; VU m , VU M They are the lower and upper water limits of the upper reservoir of the distributed pumped storage power station. VD is the water volume of the upper reservoir of the distributed pumped storage power station connected to node e in time period t; m 、VD M They are the lower and upper water limits of the lower reservoir of the distributed pumped storage power station. is the water volume of the lower reservoir of the distributed pumped storage power station connected at node e in time period t; tn is the last time period in a typical day; ΔV M The upper limit of water volume change per unit time in a distributed pumped storage power station;

[0226] The operating constraints for distributed electrochemical energy storage include:

[0227]

[0228]

[0229] In the above formula, P CH , P DI They are the maximum charging power and maximum discharging power of a single electrochemical energy storage unit, is the number of distributed electrochemical energy storage units connected to the distribution network node e, is the actual output or input power of the electrochemical energy storage connected at node e during time period t. When is the output power; η BL , η BH are the lower and upper limits of the electrochemical energy storage energy state, IC B is the installed capacity of a single electrochemical energy storage unit, BC O is the initial capacity of a single electrochemical energy storage unit, is the actual output or input power of the electrochemical energy storage connected to node e in any period tx of a typical day;

[0230] The operating constraints for electric vehicle smart charging piles include:

[0231]

[0232]

[0233]

[0234]

[0235] In the above formula, is the number of smart charging piles connected to the distribution network node e, IC V The installation capacity of a single smart charging pile. The simultaneous rate of electric vehicles connecting to smart charging piles, is the actual output or input power of the smart charging pile connected to node e during time period t. When is the output power; η VL , η VH are the lower and upper limits of the energy state of the battery on board the electric vehicle, VC O is the initial capacity of a single electric vehicle battery, is the actual output or input power of the electrochemical energy storage connected at node e in any period ty during the peak period of electric vehicle parking, ta is the starting period of the peak period of electric vehicle parking, and tb is the ending period of the peak period of electric vehicle parking; is the number of smart charging piles put into use at node e during time period t.

[0236] The configuration constraint unit is used to construct the following configuration constraint conditions for the three-level energy storage system:

[0237]

[0238]

[0239]

[0240]

[0241]

[0242]

[0243]

[0244]

[0245] In the above formula, They are the number of distributed pumped storage reversible units, the number of distributed electrochemical energy storage units, and the number of smart charging piles connected to the distribution network node e. are the planning node variables of distributed pumped storage, distributed electrochemical energy storage, and smart charging pile at node e, respectively. When it is 1, the corresponding type of distributed energy storage is connected to the distribution network node e; are the candidate access node constants of distributed pumped storage and smart charging pile at node e, respectively. When it is 1, the corresponding type of distributed energy storage can be connected at the distribution network node e; n PM 、n BM 、n VM They are the upper limit of the number of distributed pumped storage reversible units, the upper limit of the number of distributed electrochemical energy storage units, and the upper limit of the number of smart charging piles.

[0246] The charging demand allocation constraint unit is used to construct the following charging demand allocation constraint conditions for the electric vehicle intelligent charging pile:

[0247]

[0248]

[0249]

[0250] In the above formula, is the traffic flow of electric vehicles with charging needs at the starting and ending points to od in time period t, is the traffic flow of electric vehicles that use path k for the origin-destination pair od and charge at the public charging pile on road l in time period t; is the active power output by the distribution network to the electric vehicle connected to node e during time period t, is the correlation matrix between road l and distribution network node e. When road l is coupled with distribution network node e, ED U is the average charging energy requirement of an electric vehicle; f M The upper limit of the number of electric vehicles that a charging station on a road can serve in a unit time period.

[0251] The distribution network constraint unit is used to construct the following distribution network constraint conditions:

[0252]

[0253]

[0254]

[0255]

[0256]

[0257] In the above formula, are the active power and reactive power on line w in time period t, respectively, where w is all the lines connected to the distribution network node e. are the basic active load and reactive load connected to node e in time period t, respectively. is the photovoltaic output connected to node e in time period t; Active power and reactive power on line d in period t, LC d is the capacity of the distribution network line d, d is any distribution network line; IPV e is the photovoltaic installed capacity connected to the distribution network node e; ΔU t，d is the voltage drop on line d during time period t, are the resistance and reactance of the distribution network line d, U N is the rated voltage of the distribution network bus; U t，a , U t，b are the bus voltage of the distribution network node a and the bus voltage of the distribution network node b in the time period t, respectively. Node a and node b are the two endpoints of the distribution network line d respectively; U m , U M They are the upper and lower limits of the bus voltage of the distribution network, U t，e is the voltage at node e during time period t.

Claims

1. A three-level joint configuration and operation optimization method for distributed energy storage, characterized in that: The method comprises: S1. For the distribution-transportation coupling system including a three-level energy storage system, a three-level joint configuration and operation optimization model is constructed with the goal of minimizing its comprehensive cost. The three-level energy storage system includes a distributed pumped storage system, a distributed electrochemical energy storage system, and an electric vehicle smart charging pile energy storage system equipped with V2G function; The objective function of the three-level joint configuration and operation optimization model includes: min(C P +C B +C V )[(1+a) γ -1] / ag 2 +(C E +C S )d; In the above formula, C P is the configuration cost of distributed pumped storage, C B is the configuration cost of distributed electrochemical energy storage, C V is the configuration cost of the smart charging pile, α is the discount rate, γ is the investment period, C E is the daily electricity purchase cost, C S The cost of battery loss caused by reverse powering the electric vehicle, d is the number of typical days in a year; are the number of distributed pumped storage reversible units, distributed electrochemical energy storage units, and smart charging piles connected to the distribution network node e, respectively. P , PR B , PR V They are the unit configuration costs of distributed pumped storage, distributed electrochemical energy storage, and smart charging piles. is the outgoing line w of the upper power grid through the balancing node in time period t O The power output to the entire distribution network, is the electricity purchase price at time period t, is the number of smart charging piles put into use at node e in time period t, PR S Subsidize the cost of battery loss per unit time for a single electric vehicle; The constraints of the three-level joint configuration and operation optimization model include the operation constraints of the three-level energy storage system, the configuration constraints of the three-level energy storage system, the charging demand allocation constraints of the electric vehicle smart charging piles, and the distribution network constraints; The operating constraints of the three-level energy storage system include: In the above formula, are the input power and output power of the distributed pumped storage power station connected to node e in time period t, ρ is the density of water, g is the acceleration of gravity, h is the height difference between the upper and lower reservoirs of the distributed pumped storage power station, ΔVI t，e is the amount of water discharged from the lower reservoir to the upper reservoir by the distributed pumped storage power station connected at node e during time period t, ΔVO t，e is the amount of water discharged from the upper reservoir to the lower reservoir by the distributed pumped storage power station connected to node e during time period t, γ PS is the energy transmission efficiency of the reversible unit in the distributed pumped storage power station, T U is the unit time, δ M is a large M constant, is the state binary variable of the distributed pumped storage power station connected at node e in time period t, ΔV t，e is the actual amount of water discharged or pumped by the distributed pumped storage power station connected to node e during time period t, is the water volume of the upper reservoir of the distributed pumped storage power station connected to node e in time period t, is the water volume of the distributed pumped storage power station reservoir connected at node e in time period t, tn is the last time period in a typical day, P CH , P DI They are the maximum charging power and maximum discharging power of a single electrochemical energy storage unit, is the number of distributed electrochemical energy storage units connected to the distribution network node e, is the actual output or input power of the electrochemical energy storage connected at node e during time period t, η BL , η BH are the lower and upper limits of the electrochemical energy storage energy state, IC B is the installed capacity of a single electrochemical energy storage unit, BC O is the initial capacity of a single electrochemical energy storage unit, is the actual output or input power of the electrochemical energy storage connected to node e in any period tx of a typical day, is the number of smart charging piles connected to the distribution network node e, IC V The installation capacity of a single smart charging pile. The simultaneous rate of electric vehicles connecting to smart charging piles, is the actual output or input power of the smart charging pile connected to node e during time period t, η VL , η VH are the lower and upper limits of the energy state of the battery on board the electric vehicle, VC O is the initial capacity of a single electric vehicle battery, is the actual output or input power of the electrochemical energy storage connected at node e in any period ty during the peak period of electric vehicle parking, ta is the starting period of the peak period of electric vehicle parking, and tb is the ending period of the peak period of electric vehicle parking; The configuration constraints of the three-level energy storage system include: In the above formula, They are the number of distributed pumped storage reversible units, the number of distributed electrochemical energy storage units, and the number of smart charging piles connected to the distribution network node e. are the planning node variables of distributed pumped storage, distributed electrochemical energy storage, and smart charging piles at node e, are the candidate access node constants of distributed pumped storage and smart charging pile at node e respectively; The charging demand allocation constraints of the electric vehicle intelligent charging pile include: In the above formula, is the traffic flow of electric vehicles with charging needs at the starting and ending points to od in time period t, is the traffic flow of electric vehicles that use path k for the origin-destination pair od and charge at the public charging pile on road l in time period t, is the active power output by the distribution network to the electric vehicle connected to node e during time period t, is the correlation matrix between road l and distribution network node e, ED U is the average charging energy requirement of an electric vehicle, f M The upper limit of the number of electric vehicles that can be served by a charging station on a road in a unit time period; The distribution network constraints include: In the above formula, are respectively the active power and reactive power on line w in time period t, where w is all the lines connected to the distribution network node e. are the basic active load and reactive load connected to node e in time period t, respectively. is the actual output or input power of the distributed pumped storage power station connected to node e during time period t, is the photovoltaic output connected to node e in time period t, Active power and reactive power on line d in period t, LC d is the capacity of the distribution network line d, d is any distribution network line, IPV e is the photovoltaic installed capacity connected to the distribution network node e, ΔU t，d is the voltage drop on line d during time period t, are the resistance and reactance of the distribution network line d, U N is the rated voltage of the distribution network bus; S2. Solve the three-level joint configuration and operation optimization model of distributed energy storage to obtain the optimal joint configuration and operation planning scheme.

2. A three-level joint configuration and operation optimization system for distributed energy storage, characterized in that: The system includes a model building module and a model solving module; The model building module is used to construct a three-level joint configuration and operation optimization model for a distribution-transportation coupling system including a three-level energy storage system, with the goal of minimizing its comprehensive cost. The three-level energy storage system includes a distributed pumped storage system, a distributed electrochemical energy storage system, and an electric vehicle smart charging pile energy storage system equipped with a V2G function; The model construction module includes an objective function construction unit, an operation constraint unit, a configuration constraint unit, a charging demand allocation constraint unit and a distribution network constraint unit; The objective function construction unit is used to construct the following objective function: min(C P +C B +C V )[(1+a) γ -1] / ag 2 +(C E +C s )d; In the above formula, C P is the configuration cost of distributed pumped storage, C B is the configuration cost of distributed electrochemical energy storage, C V is the configuration cost of the smart charging pile, α is the discount rate, γ is the investment period, C E is the daily electricity purchase cost, C S The cost of battery loss caused by reverse powering the electric vehicle, d is the number of typical days in a year; are the number of distributed pumped storage reversible units, distributed electrochemical energy storage units, and smart charging piles connected to the distribution network node e, respectively. P , PR B , PR V They are the unit configuration costs of distributed pumped storage, distributed electrochemical energy storage, and smart charging piles. is the outgoing line w of the upper power grid through the balancing node in time period t O The power output to the entire distribution network, is the electricity purchase price at time period t, is the number of smart charging piles put into use at node e in time period t, PR S Subsidize the cost of battery loss per unit time for a single electric vehicle; The operation constraint unit is used to construct the following operation constraint conditions of the three-level energy storage system: In the above formula, are the input power and output power of the distributed pumped storage power station connected to node e in time period t, ρ is the density of water, g is the acceleration of gravity, h is the height difference between the upper and lower reservoirs of the distributed pumped storage power station, ΔVI t，e is the amount of water discharged from the lower reservoir to the upper reservoir by the distributed pumped storage power station connected at node e during time period t, ΔVO t，e is the amount of water discharged from the upper reservoir to the lower reservoir by the distributed pumped storage power station connected to node e during time period t, γ PS is the energy transmission efficiency of the reversible unit in the distributed pumped storage power station, T U is the unit time, δ M is a large M constant, is the state binary variable of the distributed pumped storage power station connected at node e in time period t, ΔV t，e is the actual amount of water discharged or pumped by the distributed pumped storage power station connected to node e during time period t, is the water volume of the upper reservoir of the distributed pumped storage power station connected to node e in time period t, is the water volume of the distributed pumped storage power station reservoir connected at node e in time period t, tn is the last time period in a typical day, P CH , P DI They are the maximum charging power and maximum discharging power of a single electrochemical energy storage unit, is the number of distributed electrochemical energy storage units connected to the distribution network node e, is the actual output or input power of the electrochemical energy storage connected at node e during time period t, η BL , η BH are the lower and upper limits of the electrochemical energy storage energy state, IC B is the installed capacity of a single electrochemical energy storage unit, BC O is the initial capacity of a single electrochemical energy storage unit, is the actual output or input power of the electrochemical energy storage connected to node e in any period tx of a typical day, is the number of smart charging piles connected to the distribution network node e, IC V The installation capacity of a single smart charging pile. The simultaneous rate of electric vehicles connecting to smart charging piles, is the actual output or input power of the smart charging pile connected to node e during time period t, η VL , η VH are the lower and upper limits of the energy state of the battery on board the electric vehicle, VC O is the initial capacity of a single electric vehicle battery, is the actual output or input power of the electrochemical energy storage connected at node e in any period ty during the peak period of electric vehicle parking, ta is the starting period of the peak period of electric vehicle parking, and tb is the ending period of the peak period of electric vehicle parking; The configuration constraint unit is used to construct the following configuration constraint conditions for the three-level energy storage system: In the above formula, They are the number of distributed pumped storage reversible units, the number of distributed electrochemical energy storage units, and the number of smart charging piles connected to the distribution network node e. are the planning node variables of distributed pumped storage, distributed electrochemical energy storage, and smart charging piles at node e, are the candidate access node constants of distributed pumped storage and smart charging pile at node e respectively; The charging demand allocation constraint unit is used to construct the following charging demand allocation constraint conditions for the electric vehicle intelligent charging pile: In the above formula, is the traffic flow of electric vehicles with charging needs at the starting and ending points to od in time period t, is the traffic flow of electric vehicles that use path k for the origin-destination pair od and charge at the public charging pile on road l in time period t, is the active power output by the distribution network to the electric vehicle connected to node e during time period t, is the correlation matrix between road l and distribution network node e, ED U is the average charging energy requirement of an electric vehicle, f M The upper limit of the number of electric vehicles that can be served by a charging station on a road in a unit time period; The distribution network constraint unit is used to construct the following distribution network constraint conditions: In the above formula, are respectively the active power and reactive power on line w in time period t, where w is all the lines connected to the distribution network node e. are the basic active load and reactive load connected to node e in time period t, respectively. is the actual output or input power of the distributed pumped storage power station connected to node e during time period t, is the photovoltaic output connected to node e in time period t, Active power and reactive power on line d in period t, LC d is the capacity of the distribution network line d, d is any distribution network line, IPV e is the photovoltaic installed capacity connected to the distribution network node e, ΔU t，d is the voltage drop on line d during time period t, are the resistance and reactance of the distribution network line d, U N is the rated voltage of the distribution network bus; The model solving module is used to solve the three-level joint configuration and operation optimization model of distributed energy storage to obtain the optimal joint configuration and operation planning solution.

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