Light storage and charging integrated charging station optimization method and system considering shared energy storage

Abstract

The invention discloses a method and a system for optimizing an optical storage and charging integrated charging station considering shared energy storage. The method comprises the following steps: acquiring related parameters and scene data of the optical storage and charging integrated charging station considering shared energy storage; taking the minimum annual investment cost and the minimum annual operation cost of the system as an objective function, considering energy storage capacity constraint and power constraint, establishing an upper-layer planning model, and solving to obtain the optimal energy storage capacity and power configuration of the optical storage and charging integrated charging station; taking the minimum typical daily operation cost as an objective function, considering power balance constraint, shared energy storage constraint and electric vehicle constraint, and establishing a lower-layer operation model; and establishing a double-layer optimization model of the optical storage and charging integrated charging station considering the shared energy storage, and solving the double-layer optimization model to obtain an optimal configuration and operation scheme of the optical storage and charging integrated charging station considering the shared energy storage. According to the method, renewable energy sources can be better integrated, the development of low-carbon economy is promoted, theoretical support is provided for flexible dispatching of a power system, and the stability and reliability of the power system can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power-transportation network coordinated optimization, and relates to a method and system for optimizing a photovoltaic storage and charging integrated charging station taking into account shared energy storage. Background Art

[0002] The integrated photovoltaic storage and charging station is a comprehensive energy charging station that integrates photovoltaic power generation, energy storage system and charging facilities. This charging station stores the electricity generated by the photovoltaic power generation system and powers electric vehicles or other equipment through charging facilities when needed, realizing green and efficient energy utilization. With the increase in the number of new energy vehicles, the impact of traditional charging stations on the power grid and energy consumption issues are becoming increasingly prominent. The integrated photovoltaic storage and charging station can use photovoltaic power generation and energy storage systems to optimize energy configuration and reduce electricity costs. At the same time, it can alleviate the impact of charging piles on the regional power grid when charging with high current, and improve energy conversion efficiency.

[0003] At the same time, how to effectively store and utilize intermittent energy such as photovoltaics has become an important issue. Shared energy storage power stations provide services to multiple charging stations through centralized large-scale independent energy storage power stations, including capacity leasing and peak load assistance, thereby improving the utilization efficiency and economic benefits of energy storage resources. The operating mechanism characteristics of shared energy storage power stations include more efficient scheduling and operation, more controllable safety and quality, and more prominent economic benefits. They are coordinated through the power grid to promote the full release of energy storage capacity at all ends of the source, grid and load.

[0004] Existing research has achieved certain results in the field of distributed energy storage, especially in the preliminary exploration of the application potential, functional positioning and economic efficiency of electric vehicle energy storage technology. However, these studies still need to be strengthened in comprehensively evaluating the potential of different energy storage methods and electric vehicle energy storage, improving system flexibility, and in-depth analysis of economic efficiency. In particular, more innovations and breakthroughs are urgently needed in the commercial application of electric vehicle energy storage and the exploration of new energy storage application scenarios. In addition, as a mobile energy storage device, electric vehicles have great regulatory potential in demand-side management. At present, how to make full use of the energy storage characteristics of electric vehicles and improve the power system's efficiency in absorbing renewable energy through vehicle-grid interaction technology is still an area that requires in-depth research and practice. Summary of the invention

[0005] In order to solve the deficiencies in the prior art, the present invention provides a method and system for optimizing a photovoltaic, storage and charging integrated charging station taking into account shared energy storage. The access of shared energy storage has a significant positive impact on reducing the planning and operating costs of electric vehicle charging stations. By solving a two-layer optimization scheduling model with the lowest annual investment cost and annual operating cost of the charging station as the upper objective function and the lowest typical daily operating cost as the lower objective function, the optimal configuration and operation plan of the photovoltaic, storage and charging integrated charging station taking into account shared energy storage is obtained, thereby achieving reasonable pricing of energy storage service fees, effectively controlling the discharge loss cost of electric vehicles, further reducing the operating costs of electric vehicle charging stations, and improving the operating economy of charging stations.

[0006] The present invention adopts the following technical solution.

[0007] A first aspect of the present invention provides a method for optimizing a photovoltaic storage and charging integrated charging station taking into account shared energy storage, comprising:

[0008] Obtain relevant parameters and scenario data of the integrated photovoltaic storage and charging station taking into account shared energy storage;

[0009] Taking the lowest annual investment cost and annual operating cost of the system as the objective function, and taking the energy storage capacity constraint and power constraint as the constraint conditions, an upper-level planning model of a photovoltaic storage and charging integrated charging station taking into account shared energy storage is established to obtain the upper limit of the shared energy storage capacity and the maximum charging and discharging power of the photovoltaic storage and charging integrated charging station;

[0010] Based on the capacity upper limit and maximum charging and discharging power of the shared energy storage, taking the minimum typical daily operating cost as the objective function, and taking the power balance constraint, shared energy storage constraint and electric vehicle constraint as the constraint conditions, a lower-level operation model of the photovoltaic storage and charging integrated charging station taking into account the shared energy storage is established to obtain the shared energy storage charging and discharging power and the main network purchased power and feed them back to the upper-level planning model;

[0011] Based on the upper-level planning model and the lower-level operation model, a two-level optimization model of a photovoltaic storage and charging integrated charging station taking into account shared energy storage is established;

[0012] Based on the acquired relevant parameters and scenario data, the two-layer optimization model is solved to obtain the optimal configuration and operation plan of the integrated photovoltaic storage and charging station taking into account shared energy storage.

[0013] Preferably, the acquired relevant parameters and scenario data of the photovoltaic storage and charging integrated charging station taking into account shared energy storage include:

[0014] Obtain electricity prices at different time periods for integrated photovoltaic, storage and charging stations, daily investment recovery coefficient of shared energy storage, unit investment capacity and power cost, unit charging and discharging operation and maintenance cost, maximum capacity planning and maximum power planning, maximum charging and discharging power of electric vehicles, battery SOC of electric vehicles, charging and discharging efficiency of electric vehicles, fixed load of charging stations and photovoltaic output scenario data.

[0015] Preferably, the objective function of the upper-level planning model is:

[0016]

[0017]

[0018] In the formula, C y The annual investment cost and annual operating cost of the system;

[0019] D is the number of days in a year; I is the set of electric vehicle charging stations;

[0020] is the average daily grid electricity purchase cost of the i-th electric vehicle charging station;

[0021] C op , C cap They are the average daily operation and maintenance cost of shared energy storage and the average daily energy storage investment cost;

[0022] T is the time set; K t is the electricity selling price of the power grid at time t;

[0023] is the power purchased from the grid by the i-th electric vehicle charging station at time t;

[0024] α is the daily investment recovery factor of shared energy storage;

[0025] E max , P max They are the upper capacity limit and maximum charge and discharge power of shared energy storage respectively;

[0026] c cap 、c con are the unit investment capacity and power cost respectively;

[0027] λ is the dynamic discount rate; x is the normal operating life of shared energy storage;

[0028] c op Charging and discharging operation and maintenance costs for shared energy storage units;

[0029] They are respectively the charging power and discharging power of the shared energy storage at time t.

[0030] Preferably, the calculation formula of the dynamic discount rate is:

[0031] λ=λ0(1+σe -τt )

[0032] Among them, λ0 is the basic discount rate; σ is the risk coefficient; τ is the decay rate parameter.

[0033] Preferably, the energy storage capacity constraint and power constraint of the upper-level planning model are:

[0034] E max ≤E MAX (6)

[0035] P max ≤P MAX (7) In the formula, E max , P max They are the upper capacity limit and maximum charge and discharge power of shared energy storage respectively;

[0036] E MAX is the maximum capacity planning value, P MAX Plan for maximum power.

[0037] Preferably, the objective function of the lower layer operation model is:

[0038]

[0039] In the formula, C d is the typical daily operating cost;

[0040] is the average daily grid electricity purchase cost of the i-th electric vehicle charging station;

[0041] c op Charging and discharging operation and maintenance costs for shared energy storage units;

[0042] I is a collection of electric vehicle charging stations.

[0043] Preferably, the power balance constraint is:

[0044]

[0045] In the formula, is the photovoltaic power generation power of the i-th electric vehicle charging station at time t;

[0046] is the power purchased from the grid by the i-th electric vehicle charging station at time t;

[0047] They are respectively the charging power and discharging power of the shared energy storage at time t;

[0048] is the total electric energy transmission power output by the i-th electric vehicle charging station at time t;

[0049] The total electric power transmitted by other power stations to the i-th electric vehicle charging station at time t;

[0050] are the charging and discharging power of a single electric vehicle at time t respectively;

[0051] is the fixed load of the i-th electric vehicle charging station at time t;

[0052] Δt is the unit time length, and T is the time set.

[0053] Preferably, the shared energy storage constraint is:

[0054]

[0055] E0=E T (12)

[0056]

[0057]

[0058] In the formula, E t 、E t-1 The energy storage capacity is shared at time t and t-1;

[0059] They are respectively the charging power and discharging power of the shared energy storage at time t;

[0060] η ch , η dis They are the charging efficiency and discharging efficiency of shared energy storage respectively;

[0061] E max The capacity cap for shared energy storage;

[0062] γ min , γ max They are the upper and lower limit ratios of the shared energy storage power to the maximum capacity;

[0063] E0, E T They are the starting and final daily power consumption of shared energy storage respectively;

[0064] P max is the maximum charging and discharging power of the shared energy storage;

[0065] are the power of charging and discharging through shared energy storage at the i-th electric vehicle charging station at time t;

[0066] Δt is the unit time length, and T is the time set.

[0067] Preferably, the electric vehicle constraints are:

[0068]

[0069] In the formula, are the charging and discharging power of the j-th electric vehicle at time t;

[0070] is the maximum value of the charging and discharging power of the jth electric vehicle at time t;

[0071] u Ev,j,t is the charge and discharge state of the jth electric car at time t, if u Ev,j,t is 1, indicating charging state, if u Ev,j,t 0, indicating the discharge state;

[0072] S soc,j,t , S soc,j,t-1 is the battery SOC of the j-th electric vehicle at time t and t-1;

[0073] are the power consumption and expected power consumption of the j-th electric vehicle at the time of off-grid disconnection;

[0074] S soc,j,td , are the power consumption and expected power consumption of the j-th electric vehicle at the time of off-grid disconnection;

[0075] are the charging and discharging efficiencies of electric vehicles, respectively;

[0076] T Ev The electric vehicle is connected to the grid;

[0077] T start , T depart They are the time when electric vehicles enter the grid and the time when they leave the grid;

[0078] Δt is the unit time length.

[0079] Preferably, the two-layer optimization model is solved based on the acquired relevant parameters and scenario data to obtain the optimal configuration and operation plan of the photovoltaic storage and charging integrated charging station taking into account shared energy storage, including:

[0080] The Gurobi solver is used to iteratively solve the two-layer optimization model using relevant parameters and scenario data to obtain the optimal shared energy storage capacity upper limit, shared energy storage maximum charging and discharging power, shared energy storage charging and discharging power, electric vehicle charging and discharging power, and main network purchased power of the integrated photovoltaic storage and charging station taking into account shared energy storage.

[0081] A second aspect of the present invention provides a photovoltaic storage and charging integrated charging station optimization system taking into account shared energy storage, the system comprising:

[0082] A data acquisition module, used to obtain relevant parameters and scenario data of the photovoltaic storage and charging integrated charging station taking into account shared energy storage;

[0083] An upper-level planning model building module is used to establish an upper-level planning model of a photovoltaic storage and charging integrated charging station taking into account shared energy storage, taking the lowest annual system investment cost and annual operating cost as the objective function, and taking energy storage capacity constraint and power constraint as constraints, so as to obtain the upper limit of the shared energy storage capacity and the maximum charging and discharging power of the photovoltaic storage and charging integrated charging station;

[0084] A lower-level planning model building module is used to establish a lower-level operation model of a photovoltaic storage and charging integrated charging station taking into account shared energy storage based on the capacity upper limit and maximum charging and discharging power of the shared energy storage, taking the minimum typical daily operating cost as the objective function, and taking the power balance constraint, shared energy storage constraint and electric vehicle constraint as the constraint conditions, so as to obtain the energy storage charging and discharging power and the main network purchased power and feed them back to the upper-level planning model;

[0085] A two-layer optimization model building module, used to establish a two-layer optimization model of a photovoltaic storage and charging integrated charging station taking into account shared energy storage based on the upper-layer planning model and the lower-layer operation model;

[0086] The model solving module is used to solve the double-layer optimization model based on the acquired relevant parameters and scenario data to obtain the optimal configuration and operation plan of the integrated photovoltaic storage and charging station taking into account shared energy storage.

[0087] A third aspect of the present invention provides a terminal, comprising a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0088] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method when executed by a processor.

[0089] Compared with the prior art, the beneficial effects of the present invention are at least:

[0090] The present invention studies the optimal operation strategy of an integrated photovoltaic storage and charging station under a shared energy storage mode, takes into account the energy storage capacity constraints and power constraints, and constructs an upper-level integrated photovoltaic storage and charging station planning model with the goal of minimizing the annual investment cost and annual operating cost of the charging station. Considering the power balance constraints, shared energy storage constraints and electric vehicle constraints, a lower-level charging station operation optimization model with the goal of minimizing the typical daily operating cost of the charging station is constructed. By constructing a two-layer optimization scheduling model, the upper layer takes the lowest annual investment cost and annual operating cost of the charging station as the goal, and the lower layer takes the lowest typical daily operating cost as the goal, and compares different operating modes of interconnected energy storage and shared energy storage. The charging station configuration and operation plan of the present invention under the shared energy storage mode can effectively reduce the investment and operating costs of the charging station, can better integrate renewable energy, promote the development of a low-carbon economy, provide theoretical support for the flexible scheduling of the power system, and help improve the stability and reliability of the power system.

[0091] The upper model of the present invention aims to minimize the annual investment cost and annual operating cost of the charging station, and combines the energy storage capacity constraint and power constraint to optimize the capacity and power configuration of the shared energy storage, which can reduce investment and operating costs from an overall perspective and improve resource utilization efficiency. The lower model of the present invention aims to minimize the typical daily operating cost, taking into account the power balance constraint, shared energy storage constraint and electric vehicle constraint, and realizes the dynamic operation optimization of the charging station. This two-layer optimization structure ensures comprehensive optimization from both long-term and short-term dimensions.

[0092] The upper-level planning model of the present invention combines the dynamic discount rate with the daily investment recovery coefficient to fully reflect the risk and return relationship within the energy storage life cycle. The dynamic discount rate is obtained by introducing the risk coefficient σ and the time decay factor e -τt , it is possible to consider a higher discount rate in the early stage to cope with the greater uncertainty and capital cost in the early stage, and provide reasonable risk compensation. As time goes by, the risk factors gradually decay, and the discount rate gradually returns to the basic level, making the return rate of energy storage more stable and in line with long-term investment expectations. This design fully considers the dynamic changes of risks over time, can adapt to the characteristics of different types of energy storage, and makes the investment recovery model more accurate and closer to reality by flexibly adjusting the impact and decay rate of initial risks, ultimately achieving a balance between risk management and maximizing returns, and providing investors with a more forward-looking and stable return calculation method.

[0093] The power balance constraint model of the present invention not only takes into account photovoltaic power generation, but also integrates the dynamic characteristics of electric vehicle charging and discharging and power transmission between power stations to ensure the power balance of the system under different operating conditions. This constraint improves the stability and flexibility of system operation.

[0094] The shared energy storage constraint model of the present invention takes into account constraints such as the charging and discharging efficiency, maximum capacity ratio, and daily starting and ending power of energy storage, and can reasonably allocate and utilize shared energy storage, avoid resource waste, and improve economic benefits. The shared energy storage model improves the utilization rate of energy storage facilities by sharing energy storage resources among multiple charging stations, reduces the redundancy and cost of separately configured energy storage systems, enables charging stations to respond more flexibly to load fluctuations and peak electricity demand, reduces the impact on the power grid, and improves the stability and reliability of the entire system.

[0095] The present invention performs refined electric vehicle constraint modeling on the SOC, charge and discharge status, maximum power limit, etc. of the electric vehicle, can simulate the complex interaction between the electric vehicle and the power grid, and support the implementation and optimization of the vehicle-grid interaction technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] Figure 1 is a flow chart of the method of the present invention;

[0097] Figure 2 This is a framework diagram of an integrated photovoltaic, storage and charging station that takes into account shared energy storage;

[0098] Figure 3 It is the service fee unit price impact curve. DETAILED DESCRIPTION

[0099] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0100] Embodiment 1 of the present invention provides a method for optimizing a photovoltaic storage and charging integrated charging station taking into account shared energy storage, such as Figure 1 As shown, the following steps are included:

[0101] Step 1: Obtain relevant parameters and scenario data of a photovoltaic storage and charging integrated charging station taking into account shared energy storage;

[0102] Further preferably, the electricity prices of the photovoltaic storage charging station at different time periods, the initial parameters related to the shared energy storage, the parameters related to the charging and discharging of electric vehicles, the power load demand of the charging station and the photovoltaic output scenario data are obtained;

[0103] Specifically, the electricity prices of integrated photovoltaic storage and charging stations at different time periods are obtained, as well as the daily investment recovery coefficient of shared energy storage, unit investment capacity and power cost, unit charging and discharging operation and maintenance cost, maximum capacity planning and maximum power planning, maximum charging and discharging power of electric vehicles, battery SOC of electric vehicles, charging and discharging efficiency of electric vehicles, fixed load of charging stations and photovoltaic output scenario data.

[0104] Step 2: Taking the lowest annual investment cost and annual operating cost of the system as the objective function, and taking the energy storage capacity constraint and power constraint as the constraint conditions, an upper-level planning model of the photovoltaic storage and charging integrated charging station taking into account the shared energy storage is established to obtain the upper limit of the shared energy storage capacity and the maximum charging and discharging power of the photovoltaic storage and charging integrated charging station;

[0105] Further preferably, for the obtained relevant parameters and scenario data of the photovoltaic storage and charging integrated charging station model taking into account shared energy storage, taking the lowest annual system investment cost and annual operating cost as the objective function, and taking the energy storage capacity constraint and power constraint as the constraint conditions, an upper-level planning model of the photovoltaic storage and charging integrated charging station taking into account shared energy storage is established, and the optimal energy storage capacity and power configuration of the photovoltaic storage and charging integrated charging station taking into account shared energy storage is solved;

[0106] The objective function to minimize the annual investment cost and annual operating cost of the system is:

[0107]

[0108] C cap =α(c cap E max +c con P max ) (3)

[0109]

[0110] In the formula, C y is the annual investment cost and annual operating cost; D is the number of days in a year; is the average daily grid electricity purchase cost of the i-th electric vehicle charging station; I is the charging station set; C op , C cap are the average daily operation and maintenance cost of shared energy storage and the average daily energy storage investment cost respectively; T is the time set; K t is the electricity selling price of the power grid at time t; is the power purchased from the power grid by the i-th electric vehicle charging station at time t; α is the daily investment recovery coefficient of energy storage; E max , P max are the upper capacity limit and maximum charge and discharge power of shared energy storage respectively; c cap 、c conare the unit investment capacity and power cost respectively; λ is the dynamic discount rate; x is the normal operation life of energy storage; c op The unit charging and discharging operation and maintenance cost of energy storage; is the charging power of energy storage at time t; is the discharge power of the energy storage at time t.

[0111] The calculation formula of the dynamic discount rate is:

[0112] λ=λ0(1+σe -τt )

[0113] Among them, λ0 is the basic discount rate; σ is the risk coefficient; τ is the decay rate parameter.

[0114] The above objective function combines the dynamic discount rate with the daily investment recovery coefficient to fully reflect the risk and return relationship within the energy storage life cycle. The dynamic discount rate is calculated by introducing the risk coefficient σ and the time decay factor e -τt , it is possible to consider a higher discount rate in the early stage to cope with the greater uncertainty and capital cost in the early stage, and provide reasonable risk compensation. As time goes by, the risk factors gradually decay, and the discount rate gradually returns to the basic level, making the return rate of energy storage more stable and in line with long-term investment expectations. This design fully considers the dynamic changes of risks over time, can adapt to the characteristics of different types of energy storage, and makes the investment recovery model more accurate and closer to reality by flexibly adjusting the impact and decay rate of initial risks, ultimately achieving a balance between risk management and maximizing returns, and providing investors with a more forward-looking and stable return calculation method.

[0115] The energy storage capacity constraint and power constraint are:

[0116] E max ≤E MAX (6)

[0117] P max ≤P MAX (7)

[0118] In the formula, E MAX is the maximum capacity planning value, P MAX Plan for maximum power.

[0119] Step 3: Based on the capacity upper limit and maximum charging and discharging power of the shared energy storage, taking the minimum typical daily operating cost as the objective function, and taking the power balance constraint, shared energy storage constraint and electric vehicle constraint as the constraint conditions, establish the lower-level operation model of the photovoltaic storage and charging integrated charging station taking into account the shared energy storage, so as to obtain the shared energy storage charging and discharging power and the main network purchased power and feed them back to the upper-level planning model;

[0120] Further preferably, based on the optimal energy storage capacity and power configuration obtained by the upper-level planning model of the photovoltaic storage and charging integrated charging station taking into account shared energy storage in step 2, the minimum typical daily operating cost is taken as the objective function, and the power balance constraint, shared energy storage constraint, and electric vehicle constraint are taken as constraints to establish a lower-level operation model of the photovoltaic storage and charging integrated charging station taking into account shared energy storage;

[0121] The objective function to minimize the typical daily operating cost is:

[0122]

[0123] In the formula, C d Typical daily operating costs.

[0124] The power balance constraints, shared energy storage constraints, and electric vehicle constraints are:

[0125] (1) Power balance constraints:

[0126]

[0127] In the formula, is the photovoltaic power generation power of the i-th electric vehicle charging station at time t; is the total electric energy transmission power output by the i-th electric vehicle charging station at time t; The total electric power transmitted by other power stations to the i-th electric vehicle charging station at time t; are the charging and discharging power of a single electric vehicle at time t respectively; is the fixed load of the i-th electric vehicle charging station at time t.

[0128] (2) Shared energy storage constraints:

[0129]

[0130] E0=E T (12)

[0131]

[0132] In the formula, E t is the shared energy storage capacity at time t; η ch , η dis are the charging and discharging efficiency of energy storage respectively; min , γ max are the upper and lower limits of the proportion of energy storage capacity to the maximum capacity; E0, E T They are the starting and final power of energy storage per day respectively; are the charging and discharging powers of the i-th electric vehicle charging station through energy storage at time t; Δt is the unit time length.

[0133] (3) Electric vehicle restrictions:

[0134]

[0135] In the formula, are the charging and discharging power of the j-th electric vehicle at time t; is the maximum value of the charge and discharge power of the jth electric vehicle at time t; u Ev,j,t is the charge and discharge state of the j-th electric vehicle at time t, 1 indicates the charging state, and 0 indicates the discharging state; S soc,j,t is the battery SOC of the j-th electric vehicle at time t; are the off-grid power and expected power of the j-th electric vehicle respectively; S soc,j,td , are the power consumption and expected power consumption of the j-th electric vehicle at the time of off-grid disconnection; are the charging and discharging efficiency of electric vehicles respectively; T Ev The electric vehicle is connected to the grid; T start , T depart They are the time when electric vehicles enter the grid and the time when they leave the grid.

[0136] Step 4: Based on the upper-level planning model and lower-level operation model of the photovoltaic storage and charging integrated charging station taking into account shared energy storage in steps 2 and 3, a double-layer optimization model of the photovoltaic storage and charging integrated charging station taking into account shared energy storage is established;

[0137] Step 5: Based on the acquired relevant parameters and scenario data, solve the two-layer optimization model to obtain the optimal configuration and operation plan of the integrated photovoltaic storage and charging station taking into account shared energy storage.

[0138] Further preferably, based on the two-layer optimization model of the integrated photovoltaic storage and charging station taking into account the shared energy storage in step 4, the Gurobi solver is used to solve the model, and the two-layer optimization model of the integrated photovoltaic storage and charging station taking into account the shared energy storage is solved to obtain the optimal configuration and operation plan of the integrated photovoltaic storage and charging station taking into account the shared energy storage.

[0139] The following example illustrates the superiority of the photovoltaic storage and charging integrated charging station optimization method taking into account shared energy storage. Figure 2 The photovoltaic storage and charging integrated charging station system taking into account shared energy storage is shown. In order to compare the superiority of the method proposed in the present invention, the photovoltaic storage and charging integrated charging station optimization scheduling is performed for the charging station equipped with interconnected energy storage and the charging station connected to the shared energy storage method proposed in the present invention. The present invention is implemented through the Matlab optimization platform and the Gurob solver is used to solve the double-layer optimization problem.

[0140] Based on this example, the optimization results of the charging station equipped with interconnected energy storage (see Table 1 for results), the optimization results of the charging station connected to shared energy storage (see Table 2 for results), and the service fee unit price impact curve (see Table 3 for results) are shown. Figure 3 ). This shows that after access to shared energy storage, the amount of electricity purchased from the power grid at charging station No. 1 has increased, while the amount of electricity purchased from the power grid at charging stations No. 2 and No. 3 has decreased significantly; after the charging stations are connected to shared energy storage, the operating costs of the three charging stations have all decreased compared to when the charging stations are equipped with interconnected energy storage. The increase in the unit price of service fees has led to a reduction in energy storage configuration, and the reduced discharge volume is compensated by the discharge of electric vehicles. The amount of electric vehicle charging has increased, and the power grid purchase fee has remained stable at a high level and has also increased slightly. This shows that shared energy storage has reduced the operating costs of integrated photovoltaic storage and charging stations and improved flexibility.

[0141] Table 1 Optimization results of interconnected energy storage equipped inside charging stations

[0142]

[0143] Table 2 Optimization results of charging station access to shared energy storage

[0144]

[0145] Embodiment 2 of the present invention provides a photovoltaic storage and charging integrated charging station optimization system taking into account shared energy storage, including:

[0146] A data acquisition module, used to obtain relevant parameters and scenario data of the photovoltaic storage and charging integrated charging station taking into account shared energy storage;

[0147] An upper-level planning model building module is used to establish an upper-level planning model of a photovoltaic storage and charging integrated charging station taking into account shared energy storage, taking the lowest annual system investment cost and annual operating cost as the objective function, and taking energy storage capacity constraint and power constraint as constraints, so as to obtain the upper limit of the shared energy storage capacity and the maximum charging and discharging power of the photovoltaic storage and charging integrated charging station;

[0148] A lower-level planning model building module is used to establish a lower-level operation model of a photovoltaic storage and charging integrated charging station taking into account shared energy storage based on the capacity upper limit and maximum charging and discharging power of the shared energy storage, taking the minimum typical daily operating cost as the objective function, and taking the power balance constraint, shared energy storage constraint and electric vehicle constraint as the constraint conditions, so as to obtain the energy storage charging and discharging power and the main network purchased power and feed them back to the upper-level planning model;

[0149] A two-layer optimization model building module, used to establish a two-layer optimization model of a photovoltaic storage and charging integrated charging station taking into account shared energy storage based on the upper-layer planning model and the lower-layer operation model;

[0150] The model solving module is used to solve the double-layer optimization model based on the acquired relevant parameters and scenario data to obtain the optimal configuration and operation plan of the integrated photovoltaic storage and charging station taking into account shared energy storage.

[0151] Embodiment 3 of the present invention provides a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0152] Embodiment 4 of the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method when executed by a processor.

[0153] Compared with the prior art, the beneficial effects of the present invention are at least:

[0154] The present invention studies the optimal operation strategy of an integrated photovoltaic storage and charging station under a shared energy storage mode, takes into account the energy storage capacity constraints and power constraints, and constructs an upper-level integrated photovoltaic storage and charging station planning model with the goal of minimizing the annual investment cost and annual operating cost of the charging station. Considering the power balance constraints, shared energy storage constraints and electric vehicle constraints, a lower-level charging station operation optimization model with the goal of minimizing the typical daily operating cost of the charging station is constructed. By constructing a two-layer optimization scheduling model, the upper layer takes the lowest annual investment cost and annual operating cost of the charging station as the goal, and the lower layer takes the lowest typical daily operating cost as the goal, and compares different operating modes of interconnected energy storage and shared energy storage. The charging station configuration and operation plan of the present invention under the shared energy storage mode can effectively reduce the investment and operating costs of the charging station, can better integrate renewable energy, promote the development of a low-carbon economy, provide theoretical support for the flexible scheduling of the power system, and help improve the stability and reliability of the power system.

[0155] The upper model of the present invention aims to minimize the annual investment cost and annual operating cost of the charging station, and combines the energy storage capacity constraint and power constraint to optimize the capacity and power configuration of the shared energy storage, which can reduce investment and operating costs from an overall perspective and improve resource utilization efficiency. The lower model of the present invention aims to minimize the typical daily operating cost, taking into account the power balance constraint, shared energy storage constraint and electric vehicle constraint, and realizes the dynamic operation optimization of the charging station. This two-layer optimization structure ensures comprehensive optimization from both long-term and short-term dimensions.

[0156] The upper-level planning model of the present invention combines the dynamic discount rate with the daily investment recovery coefficient to fully reflect the risk and return relationship within the life cycle of energy storage. By introducing the risk coefficient and the time decay factor, a higher discount rate can be considered in the initial stage to cope with the greater uncertainty and capital cost in the initial stage, and provide reasonable risk compensation. As time goes by, the risk factors gradually decay, and the discount rate gradually returns to the basic level, making the rate of return on energy storage more stable and in line with long-term investment expectations. This design fully considers the dynamic changes of risks over time, can adapt to the characteristics of different types of energy storage, and makes the investment recovery model more accurate and closer to reality by flexibly adjusting the impact and decay rate of initial risks, ultimately achieving a balance between risk management and maximum return, and providing investors with a more forward-looking and stable return calculation method.

[0157] The power balance constraint model of the present invention not only takes into account photovoltaic power generation, but also integrates the dynamic characteristics of electric vehicle charging and discharging and power transmission between power stations to ensure the power balance of the system under different operating conditions. This constraint improves the stability and flexibility of system operation.

[0158] The shared energy storage constraint model of the present invention takes into account constraints such as the charging and discharging efficiency, maximum capacity ratio, and daily starting and ending power of energy storage, and can reasonably allocate and utilize shared energy storage, avoid resource waste, and improve economic benefits. The shared energy storage model improves the utilization rate of energy storage facilities by sharing energy storage resources among multiple charging stations, reduces the redundancy and cost of separately configured energy storage systems, enables charging stations to respond more flexibly to load fluctuations and peak electricity demand, reduces the impact on the power grid, and improves the stability and reliability of the entire system.

[0159] The present invention performs refined electric vehicle constraint modeling on the SOC, charge and discharge status, maximum power limit, etc. of the electric vehicle, can simulate the complex interaction between the electric vehicle and the power grid, and support the implementation and optimization of the vehicle-grid interaction technology.

[0160] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0161] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0162] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0163] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0164] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing a photovoltaic storage and charging station taking into account shared energy storage, characterized in that: include: Obtain relevant parameters and scenario data of the integrated photovoltaic storage and charging station taking into account shared energy storage; Taking the lowest annual investment cost and annual operating cost of the system as the objective function, and taking the energy storage capacity constraint and power constraint as the constraint conditions, an upper-level planning model of a photovoltaic storage and charging integrated charging station taking into account shared energy storage is established to obtain the upper limit of the shared energy storage capacity and the maximum charging and discharging power of the photovoltaic storage and charging integrated charging station; Based on the capacity upper limit and maximum charging and discharging power of the shared energy storage, taking the minimum typical daily operating cost as the objective function, and taking the power balance constraint, shared energy storage constraint and electric vehicle constraint as the constraint conditions, a lower-level operation model of the photovoltaic storage and charging integrated charging station taking into account the shared energy storage is established to obtain the shared energy storage charging and discharging power and the main network purchased power and feed them back to the upper-level planning model; Based on the upper-level planning model and the lower-level operation model, a two-level optimization model of a photovoltaic storage and charging integrated charging station taking into account shared energy storage is established; Based on the acquired relevant parameters and scenario data, the two-layer optimization model is solved to obtain the optimal configuration and operation plan of the integrated photovoltaic storage and charging station taking into account shared energy storage.

2. The method for optimizing a photovoltaic storage and charging station taking into account shared energy storage according to claim 1, characterized in that: The obtained relevant parameters and scenario data of the photovoltaic storage and charging integrated charging station taking into account shared energy storage include: Obtain electricity prices at different time periods for integrated photovoltaic, storage and charging stations, daily investment recovery coefficient of shared energy storage, unit investment capacity and power cost, unit charging and discharging operation and maintenance cost, maximum capacity planning and maximum power planning, maximum charging and discharging power of electric vehicles, battery SOC of electric vehicles, charging and discharging efficiency of electric vehicles, fixed load of charging stations and photovoltaic output scenario data.

3. The method for optimizing a photovoltaic storage and charging station taking into account shared energy storage according to claim 1, characterized in that: The objective function of the upper-level planning model is: C cap =α(c cap From max +c con P max ) (3) In the formula, C y The annual investment cost and annual operating cost of the system; D is the number of days in a year; I is the set of electric vehicle charging stations; is the average daily grid electricity purchase cost of the i-th electric vehicle charging station; C op , C cap They are the average daily operation and maintenance cost of shared energy storage and the average daily energy storage investment cost; T is the time set; K t is the electricity selling price of the power grid at time t; is the power purchased from the grid by the i-th electric vehicle charging station at time t; α is the daily investment recovery factor of shared energy storage; E max , P max They are the upper capacity limit and maximum charge and discharge power of shared energy storage respectively; c cap 、c con are the unit investment capacity and power cost respectively; λ is the dynamic discount rate; x is the normal operating life of shared energy storage; c op Charging and discharging operation and maintenance costs for shared energy storage units; They are respectively the charging power and discharging power of the shared energy storage at time t.

4. The method for optimizing a photovoltaic storage and charging station taking into account shared energy storage according to claim 1, characterized in that: The calculation formula of the dynamic discount rate is: λ=λ0(1+σe -τt ) Among them, λ0 is the basic discount rate; σ is the risk coefficient; τ is the decay rate parameter.

5. The method for optimizing a photovoltaic storage and charging station taking into account shared energy storage according to claim 1, characterized in that: The energy storage capacity constraint and power constraint of the upper-level planning model are: E max ≤E MAX (6)P max ≤P MAX (7) In the formula, E max , P max They are the upper capacity limit and maximum charge and discharge power of shared energy storage respectively; E MAX is the maximum capacity planning value, P MAX Plan for maximum power.

6. The method for optimizing a photovoltaic storage and charging station taking into account shared energy storage according to claim 1, characterized in that: The objective function of the lower layer operation model is: In the formula, C d is the typical daily operating cost; is the average daily grid electricity purchase cost of the i-th electric vehicle charging station; c op Charging and discharging operation and maintenance costs for shared energy storage units; I is a collection of electric vehicle charging stations.

7. The method for optimizing a photovoltaic storage and charging station taking into account shared energy storage according to claim 1, characterized in that: The power balance constraint is: In the formula, is the photovoltaic power generation power of the i-th electric vehicle charging station at time t; is the power purchased from the grid by the i-th electric vehicle charging station at time t; They are respectively the charging power and discharging power of the shared energy storage at time t; is the total electric energy transmission power output by the i-th electric vehicle charging station at time t; The total electric power transmitted by other power stations to the i-th electric vehicle charging station at time t; are the charging and discharging power of a single electric vehicle at time t respectively; is the fixed load of the i-th electric vehicle charging station at time t; Δt is the unit time length, and T is the time set.

8. The method for optimizing a photovoltaic storage and charging station taking into account shared energy storage according to claim 1, characterized in that: The shared energy storage constraints are: E0=E T (12) In the formula, E t 、E t-1 The energy storage capacity is shared at time t and t-1; They are respectively the charging power and discharging power of the shared energy storage at time t; η ch , η dis They are the charging efficiency and discharging efficiency of shared energy storage respectively; E max The capacity cap for shared energy storage; γ min , γ max They are the upper and lower limit ratios of the shared energy storage power to the maximum capacity; E0, E T They are the starting and final daily power consumption of shared energy storage respectively; P max is the maximum charging and discharging power of the shared energy storage; are the power of charging and discharging through shared energy storage at the i-th electric vehicle charging station at time t; Δt is the unit time length, and T is the time set.

9. The method for optimizing a photovoltaic storage and charging station taking into account shared energy storage according to claim 1, characterized in that: The electric vehicle constraints are: In the formula, are the charging and discharging power of the j-th electric vehicle at time t; is the maximum value of the charging and discharging power of the jth electric vehicle at time t; u Ev,j,t is the charge and discharge state of the jth electric car at time t, if u Ev,j,t is 1, indicating charging state, if u Ev,j,t 0, indicating the discharge state; S soc,j,t , S soc,j,t-1 is the battery SOC of the j-th electric vehicle at time t and t-1; are the power consumption and expected power consumption of the j-th electric vehicle at the time of off-grid disconnection; S soc,j,td , are the power consumption and expected power consumption of the j-th electric vehicle at the time of off-grid disconnection; are the charging and discharging efficiencies of electric vehicles, respectively; T Ev The electric vehicle is connected to the grid; T start 、T depart They are the time when electric vehicles enter the grid and the time when they leave the grid; Δt is the unit time length.

10. The method for optimizing a photovoltaic storage and charging station taking into account shared energy storage according to claim 1, characterized in that: Based on the acquired relevant parameters and scenario data, the two-layer optimization model is solved to obtain the optimal configuration and operation plan of the photovoltaic storage and charging integrated charging station taking into account the shared energy storage, including: The Gurobi solver is used to iteratively solve the two-layer optimization model using relevant parameters and scenario data to obtain the optimal shared energy storage capacity upper limit, shared energy storage maximum charging and discharging power, shared energy storage charging and discharging power, electric vehicle charging and discharging power, and main network purchased power of the integrated photovoltaic storage and charging station taking into account shared energy storage.

11. A photovoltaic storage and charging integrated charging station optimization system taking into account shared energy storage, using the method described in any one of claims 1 to 10, characterized in that: The system comprises: A data acquisition module, used to obtain relevant parameters and scenario data of the photovoltaic storage and charging integrated charging station taking into account shared energy storage; An upper-level planning model building module is used to establish an upper-level planning model of a photovoltaic storage and charging integrated charging station taking into account shared energy storage, taking the lowest annual system investment cost and annual operating cost as the objective function, and taking energy storage capacity constraint and power constraint as constraints, so as to obtain the upper limit of the shared energy storage capacity and the maximum charging and discharging power of the photovoltaic storage and charging integrated charging station; A lower-level planning model building module is used to establish a lower-level operation model of a photovoltaic storage and charging integrated charging station taking into account shared energy storage based on the capacity upper limit and maximum charging and discharging power of the shared energy storage, taking the minimum typical daily operating cost as the objective function, and taking the power balance constraint, shared energy storage constraint and electric vehicle constraint as the constraint conditions, so as to obtain the energy storage charging and discharging power and the main network purchased power and feed them back to the upper-level planning model; A two-layer optimization model building module, used to establish a two-layer optimization model of a photovoltaic storage and charging integrated charging station taking into account shared energy storage based on the upper-layer planning model and the lower-layer operation model; The model solving module is used to solve the double-layer optimization model based on the acquired relevant parameters and scenario data to obtain the optimal configuration and operation plan of the integrated photovoltaic storage and charging station taking into account shared energy storage.

12. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-10.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

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