Energy storage optimization configuration method and system for network construction type optical storage charging station under network construction requirement

Abstract

According to the energy storage optimization configuration method and system for the network-building type optical storage charging station under the network-building requirement, network-building supporting capacity indexes of the optical storage charging station are built and comprise the active power adjustment margin index of an energy storage device, the active power variation index of an energy storage converter and the economic index of active power support provided by the optical storage charging station under different time scales; on the basis of a multi-time-scale double-layer model architecture, in an established energy storage configuration optimization model, an upper-layer model comprises optimization targets and constraints of economic indexes for providing active power support by an optical storage charging station on the basis of the service life scale of an energy storage device; the lower-layer model comprises an optimization target of an economic index based on active power support provided by the optical storage charging station under the daily total time period number scale, a basic constraint, a relaxation constraint and a network construction constraint; and by taking the power demand of the energy storage device in each time period as an initialized population, iteratively solving the energy storage configuration optimization model by adopting a genetic algorithm to obtain a configuration scheme, thereby ensuring the network construction supporting capability.

Description

Technical Field

[0001] This invention belongs to the field of grid-based energy storage technology, and specifically relates to a method and system for optimizing the energy storage configuration of a grid-based photovoltaic-energy storage charging station that takes into account grid construction requirements. Background Technology

[0002] With the transformation of the global energy structure and the increasing proportion of renewable energy, the stability and reliability of the power grid face new challenges. The intermittency and volatility of renewable energy make it difficult to maintain the supply-demand balance of the power grid, and energy storage technology has been widely used as a key means to solve this problem. However, due to the high cost of current energy storage systems, the capacity configuration is very limited. How to maximize the economical and efficient use of limited energy storage resources is a key issue that needs to be studied in practical applications such as integrated photovoltaic-storage-charging stations.

[0003] Existing technologies offer numerous methods for optimizing energy storage configurations, primarily addressing issues such as reducing investment costs, ensuring safe and stable operation of generating units, and addressing the inability to reuse energy across different application scenarios. Furthermore, current technologies focus on the rapid response of energy storage systems to charging loads and reduce curtailment of solar power, but rarely consider the ability of grid-based energy storage to actively regulate grid frequency and improve grid stability. With the emergence and development of grid-based energy storage technologies, integrated photovoltaic-storage-charging power plants are placing new demands on the optimal configuration of energy storage. A two-layer optimization configuration method for distributed energy storage systems is proposed. The upper layer optimizes the comprehensive annual cost and annual revenue of energy storage, while the lower layer uses the K-means clustering algorithm to solve classic operating scenarios. Its advantage lies in solving the voltage limit exceedance problem caused by distributed photovoltaics, achieving better voltage control, and improving the absorption capacity of distributed photovoltaics. However, a series of studies represented by this method are based on traditional grid-connected photovoltaic-energy storage charging stations, without considering the grid-connected role of photovoltaic-energy storage charging stations under the trend of new energy. A grid-connected energy storage capacity configuration method and device combined with robust optimization configures the capacity of energy storage power stations based on the scientific characterization of power uncertainty to accurately match the fluctuation of usage demand and improve the utilization rate of energy storage. However, this method focuses on considering the power uncertainty in the process of new energy power generation, and the demand for grid connection is not scientifically characterized, failing to highlight the characteristics of grid-connected energy storage that distinguish it from traditional grid-connected energy storage. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for optimizing the energy storage configuration of grid-connected photovoltaic-storage-charging stations under grid construction requirements. By defining and quantifying indicators reflecting grid construction capabilities, and based on a multi-timescale bi-level programming model for grid construction requirements, it fully considers the impact of grid construction requirements such as grid frequency regulation, inertia response, and damping control on the energy storage optimization configuration results. This ensures the grid support capability of the energy storage configuration scheme and has broader application prospects in weak grid scenarios where the proportion of new energy is gradually increasing.

[0005] The present invention adopts the following technical solution.

[0006] This invention proposes a method for optimizing the energy storage configuration of a grid-type photovoltaic-storage-charging station under grid construction requirements, characterized by comprising:

[0007] The goal is to obtain the energy demand of the energy storage device at each time period when the total power purchase cost of the power distribution network of the charging station operator is minimized.

[0008] Establish grid support capability indicators for photovoltaic-storage-charging stations, including: active power regulation margin indicators for energy storage devices, active power variation indicators for energy storage converters, and economic indicators for active power support provided by photovoltaic-storage-charging stations at different time scales; the time scales include: the service life of energy storage devices and the total number of time periods per day.

[0009] Based on a multi-timescale two-layer model architecture, the established energy storage configuration optimization model includes an upper-layer model that includes optimization objectives and constraints for economic indicators of active power support provided by photovoltaic-storage-charging stations based on the service life of energy storage devices, and a lower-layer model that includes optimization objectives, basic constraints, relaxation constraints, and network constraints for economic indicators of active power support provided by photovoltaic-storage-charging stations based on the total number of time periods per day.

[0010] Using the electricity demand of the energy storage device at different times as the initial population, a genetic algorithm is used to iteratively solve the energy storage configuration optimization model to obtain the configuration scheme.

[0011] The goal of minimizing the total power purchase cost of the photovoltaic-storage charging station operator's distribution network includes: the objective function of minimizing the charging cost of the energy storage devices of the photovoltaic-storage charging station within the total time period T, and the constraints of the objective function;

[0012] The objective function for minimizing the total power purchase cost of the power distribution network for photovoltaic-storage charging station operators satisfies the following relationship:

[0013]

[0014] In the formula, C represents the total cost for the photovoltaic-storage charging station operator to purchase electricity from the distribution network. c represents the charging power of the energy storage device in the photovoltaic-energy storage charging station during time period i. i This represents the real-time purchase cost per unit of electricity that the photovoltaic-storage charging station operator buys from the grid during time period i, where i = 1, 2, ..., T, T is the total number of time periods, and t is the monitoring and sampling control time interval of the photovoltaic-storage charging station.

[0015] The constraints of the objective function include:

[0016] 1) Energy storage capacity constraints;

[0017]

[0018] In the formula, S t x represents the real-time remaining energy (%) of the energy storage device in the photovoltaic-energy storage charging station. i x represents the charging and discharging power of the energy storage device in the photovoltaic-energy storage charging station during time period i. i >0 indicates that the energy storage device is charging during time period i, x i <0 indicates that the energy storage device discharges during time period i, and S0 is the initial charge (%) of the energy storage device in the photovoltaic-energy storage charging station. min S max These represent the lower and upper limits of the state of charge (SOC) of the energy storage devices in the photovoltaic-storage charging station, respectively. charge The actual stored electricity of the photovoltaic-storage charging station;

[0019] 2) Constraints on the charging and discharging power of the energy storage system;

[0020] P min ≤x i ≤P max

[0021] In the formula, P min P max These are the lower and upper limits of the charging and discharging power of the energy storage devices in the photovoltaic-energy storage charging station, respectively.

[0022] 3) Load fluctuation constraints

[0023] L t =Load t +P t L min ≤L t ≤L max

[0024] In the formula, L t For the real-time load of the photovoltaic-storage charging station, Load t For the real-time charging load of electric vehicles, P t L represents the real-time charging and discharging power of the energy storage device in the photovoltaic-energy storage charging station. min L max These represent the lower and upper limits of load fluctuations for photovoltaic-storage charging stations, respectively.

[0025] By utilizing the upper limit of the charging and discharging power of the energy storage device and the difference between the charging and discharging power in different time periods, and comparing it with the rated active power of the energy storage device, an active power regulation margin index for the energy storage device is established. This index includes the maximum active power regulation amount of the energy storage device in the photovoltaic-energy storage charging station participating in grid frequency regulation and its constraints, satisfying the following relationship:

[0026] P max -P c (i)-P d (i)>20%P N

[0027] In the formula, P max P is the upper limit of the charging and discharging power of the energy storage device in the photovoltaic-energy storage charging station. c (i), P d (i) represents the charging and discharging power of the energy storage device in the photovoltaic-energy storage charging station during time period i, respectively. N This refers to the rated active power of the energy storage device.

[0028] An active power variation index for the energy storage converter is established using the maximum value of the active power variation and the rated active power of the energy storage device, including:

[0029] The maximum value of the change in active power of the energy storage converter under inertial response is not less than 10%P. N The maximum value of the active power variation of the energy storage converter under damping control is not less than 10%P. N .

[0030] The economic indicators for the active power support provided by photovoltaic-storage charging stations at different time scales satisfy the following relationship:

[0031]

[0032] In the formula, The economic indicators for providing active power support for photovoltaic-storage charging stations are as follows: when Γ = T, the time scale is the total number of time periods T per day; when Γ = Y, the time scale is the service life of the energy storage device. up v down These refer to the economic benefits that a photovoltaic-storage charging station can provide per unit of charging and discharging active power support. These are the active power frequency deficits for the photovoltaic-storage charging station during time period i, respectively;

[0033]

[0034] In the formula, F N (i) For the active power regulation requirements of the photovoltaic-storage charging station during time period i, F S (i) is the active power regulation capability of the photovoltaic-storage-charging station in time period i.

[0035] The energy storage configuration optimization model includes: an upper-level model with the service life of the energy storage device as the time scale, and a lower-level model with the total number of time periods per day as the time scale;

[0036] In the upper-level model, the annual comprehensive cost is constituted by economic indicators such as the annual equivalent investment cost of the energy storage device, the annual operation and maintenance cost, and the service life of the energy storage device as the time scale for providing active power support to the photovoltaic-storage charging station. The optimization objective is to minimize the annual comprehensive cost, and the energy storage device capacity is the decision variable.

[0037] In the lower-level model, the economic indicators of the active power support provided by the photovoltaic-storage charging station under the time scale of the total number of time periods per day and the maximization of the daily revenue of the photovoltaic-storage charging station constitute the optimization objective, and the power purchased and sold by the photovoltaic-storage charging station and the charging and discharging power of the energy storage device are the decision variables.

[0038] The optimization objective of the upper-level model satisfies the following relationship:

[0039]

[0040] In the formula, F up As the optimization objective of the upper-level model, C TCC C represents the equivalent annual investment cost of the energy storage device. OM The annual operation and maintenance cost of energy storage devices, Economic indicators for providing active power support for photovoltaic-storage charging stations based on the service life of energy storage devices.

[0041] The annualized investment cost of energy storage devices satisfies the following relationship:

[0042]

[0043] In the formula, μ CRF C represents the annual capital recovery rate. inv For the fixed construction cost of energy storage devices, N ba P represents the number of energy storage devices. ba,n E ba,n Let be the active power and capacity of the nth energy storage device, respectively; a and b are the unit power cost and unit capacity cost of the energy storage device under different charging rates, respectively; y is the service life of the energy storage device; and r is the discount rate.

[0044] The annual operation and maintenance cost of energy storage devices is estimated as a percentage of the initial investment in the energy storage device.

[0045] The constraints of the upper-level model include:

[0046] A1) Capacity constraint of energy storage device, 0≤E ba,n ≤E max,n E max,n This represents the upper limit of the capacity of the nth energy storage device;

[0047] A2) Maximum power constraint of energy storage device, 0≤P ba,n ≤P max,n P max,n This represents the upper limit of the power of the nth energy storage device.

[0048] The optimization objective of the lower-level model satisfies the following relationship:

[0049]

[0050] In the formula, F low For the optimization objective of the lower-level model, c ev The price per unit charging power for electric vehicles, P ev (i) represents the charging power of the electric vehicle during time period i, and c s The price of electricity sold to the grid by the photovoltaic power generation unit of the photovoltaic power generation and energy storage charging station, P s (i) represents the grid-connected power of the photovoltaic power generation of the photovoltaic-storage charging station during time period i, c e (i) represents the unit power electricity price for time period i, P p (i) represents the power purchased from the grid during time period i, and c b The cost per unit power loss during charging and discharging of the energy storage device in a photovoltaic-energy storage charging station, P c (i), P d (i) represents the charging and discharging power of the energy storage device in the photovoltaic-energy storage charging station during time period i. The economic indicator for providing active power support to photovoltaic-storage charging stations on a time scale based on the total number of time periods per day, where T is the total number of time periods.

[0051] The basic constraints of the lower-level model include:

[0052] B1) Power balance constraints of photovoltaic-storage charging stations;

[0053] B2) Energy balance constraints of energy storage devices;

[0054] B3) Constraints on the depth of charge and discharge of energy storage devices;

[0055] B4), SOC cycle balance constraints of energy storage devices;

[0056] B5) Charge and discharge power constraints of energy storage devices;

[0057] B6), Photovoltaic output constraints;

[0058] B7) Charging power constraints for electric vehicles.

[0059] The relaxation constraints of the lower-level model include: the direction of active power flow in each time period and the range of initial power values ​​for the energy storage device.

[0060] The network constraints of the lower-level model include: network support constraints and network stability constraints;

[0061] D1) The maximum value and constraints of the active power regulation of the energy storage device of the photovoltaic-storage charging station participating in the grid frequency regulation, the maximum value and constraints of the active power change of the energy storage converter under inertial response, and the maximum value and constraints of the active power change of the energy storage converter under damping control are used as the grid support constraints of the lower-level model.

[0062] D2) Network stability constraints, satisfying the following relationship:

[0063]

[0064] In the formula, P s (i) represents the grid-connected power output of the photovoltaic power generation at the photovoltaic-storage charging station during time period i, P. p (i) represents the amount of electricity purchased from the grid during time period i, P g,max This represents the maximum power exchanged between the photovoltaic-storage charging station and the power grid.

[0065] The solution process for the energy storage configuration optimization model includes:

[0066] Based on historical data of photovoltaic-storage charging stations, several typical scenarios were identified. The electricity demand of the energy storage device at each time period when the total power purchase cost of the distribution network for the charging station operator is minimized under the typical scenario was used as the initial population.

[0067] Based on the multi-level constraints in the lower-level model, the method for calculating the fitness function during the solution of the lower-level model is improved, including:

[0068] In the first to τ iterations, the basic fitness function of the population obtained in the current iteration that satisfies the basic constraints, the relaxed fitness function that satisfies the relaxed constraints, and the web-building fitness function that satisfies the web-building constraints are calculated respectively. The weighted sum of the basic fitness function, the relaxed fitness function, and the web-building fitness function is used as the fitness function of the population obtained in the current iteration.

[0069] In the τ+1 to N iterations, the basic fitness function of the population obtained in the current iteration satisfies the basic constraints and the network fitness function satisfies the network constraints, respectively; the weighted sum of the basic fitness function and the network fitness function is used as the fitness function of the population obtained in the current iteration.

[0070] Based on the initial population and the improved fitness function calculation method, the lower-level model is solved iteratively. The obtained power purchase and sale of the photovoltaic-storage charging station and the charging and discharging power of the energy storage device are fed back to the upper-level model. The upper-level model is solved iteratively, and the obtained energy storage device capacity is used as the input data of the lower-level model. Through iterative solution, the optimal solution of the energy storage device capacity is obtained as the energy storage device capacity stabilization scheme, and the optimal solutions of the power purchase and sale of the photovoltaic-storage charging station and the charging and discharging power of the energy storage device are obtained as the energy storage device operation scheme.

[0071] This invention also proposes a grid-type photovoltaic-storage-charging station energy storage optimization configuration system under grid construction requirements, comprising:

[0072] The planning module is used to obtain the energy demand of the energy storage device at each time period when the total power purchase cost of the power distribution network of the charging station operator is minimized.

[0073] The indicator establishment module is used to establish grid support capability indicators for photovoltaic-storage-charging stations, including: active power regulation margin indicators of energy storage devices, active power change indicators of energy storage converters, and economic indicators of active power support provided by photovoltaic-storage-charging stations at different time scales; the time scales include: the service life of energy storage devices and the total number of time periods per day.

[0074] The model building module is used to build an energy storage configuration optimization model based on a multi-time-scale two-layer model architecture. The upper-layer model includes the optimization objectives and constraints of the economic indicators of the active power support provided by the photovoltaic-storage-charging station under the scale of the service life of the energy storage device. The lower-layer model includes the optimization objectives, basic constraints, relaxation constraints and network constraints of the economic indicators of the active power support provided by the photovoltaic-storage-charging station under the scale of the total number of time periods per day.

[0075] The model solving module is used to initialize the energy storage device's power demand at different times and use a genetic algorithm to iteratively solve the energy storage configuration optimization model to obtain the configuration scheme.

[0076] The present invention is also a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to perform operations according to the instructions to execute the steps of the method.

[0077] The present invention is also a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method.

[0078] The beneficial effects of this invention are as follows: Compared with the prior art, this invention, in the process of energy storage configuration, defines and quantifies the grid support capability index of photovoltaic-storage-charging piles based on the grid construction requirements of grid-type energy storage devices participating in grid frequency regulation, inertia response, and damping control. While ensuring that the energy storage configuration scheme has sufficient grid support capability, it also describes the grid support constraints of energy storage devices and photovoltaic-storage-charging stations, serving as important constraints in the lower-level planning model. Furthermore, the economic index of active power support provided by the photovoltaic-storage-charging station not only links the grid support capability of the photovoltaic-storage-charging station with economic benefits, but also, in the two-layer model, the economic index of active power support provided by the photovoltaic-storage-charging station at different time scales acts on the optimization objectives of the upper and lower layers respectively. While ensuring computational efficiency, it fully utilizes various resources to improve the reliability of the calculation results, thereby ensuring that the obtained energy storage configuration and operation scheme can meet the grid construction requirements of the photovoltaic-storage-charging station.

[0079] The method proposed in this invention estimates the energy storage demand based on linear programming, providing initial population information for the genetic algorithm to solve the problem. This is closer to the optimal solution than randomly generating a population. At the same time, the economic soft constraints in the lower-level planning model facilitate rapid convergence of the optimization search and accelerate the model solution speed. Attached Figure Description

[0080] Figure 1 This is a flowchart of an energy storage optimization configuration method for a grid-type photovoltaic-storage-charging station under grid construction requirements, as proposed in this invention.

[0081] Figure 2 This is a photovoltaic power output curve diagram in an embodiment of the present invention;

[0082] Figure 3 This is a time-of-use electricity price curve diagram in an embodiment of the present invention;

[0083] Figure 4 This is a system load curve diagram in an embodiment of the present invention;

[0084] Figure 5 This is a comprehensive cost curve of the energy storage system in an embodiment of the present invention;

[0085] Figure 6 This is a curve of the system's active power output configuration scheme obtained from an embodiment of the present invention;

[0086] Figure 7 This is a graph of the energy storage SOC obtained from an embodiment of the present invention. Detailed Implementation

[0087] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0088] This invention proposes an energy storage optimization configuration method for grid-type photovoltaic-storage-charging stations under grid construction requirements. The grid-type photovoltaic-storage-charging station is an integrated power station for photovoltaic, energy storage, and charging. The charging station includes a photovoltaic power generation device, a grid-type energy storage device, and charging piles.

[0089] like Figure 1 As shown, the energy storage optimization configuration method includes:

[0090] Step 1: Obtain the power demand of the energy storage device at each time period when the total power purchase cost of the power distribution network of the charging station operator is minimized.

[0091] Based on actual data such as charging load fluctuations, and under the objective of minimizing the total cost for charging station operators to purchase electricity from the distribution network, the charging and discharging power of the energy storage devices at the photovoltaic-storage charging station is obtained at different times, thereby estimating the energy demand of the energy storage devices at different times. The process of charging load fluctuation data → power operation plan → energy demand estimation is a simplified model. The preliminary data obtained is used to generate the initial population information in the subsequent genetic algorithm, preventing the randomly generated population from deviating significantly from the optimal solution, which would lead to slow convergence and high computational cost.

[0092] Specifically, based on actual data such as charging load fluctuations, an objective function and its constraints are established to minimize the total cost for charging station operators to purchase electricity from the distribution network. A linear programming method is used to determine the power operation scheme of the energy storage device in each time period using the objective function and its constraints, thereby estimating the energy demand of the energy storage device in each time period. This invention estimates the energy storage demand based on linear programming, providing initial population information for the genetic algorithm solution and accelerating the model solution speed. In the embodiments, using linear programming is a non-restrictive but preferred choice.

[0093] Specifically, within the total time period T, when the cost of charging the energy storage device at the photovoltaic-storage charging station corresponding to the monitoring sampling control time interval t is minimized, the total power purchase cost of the photovoltaic-storage charging station operator is minimized; the objective function for minimizing the total power purchase cost of the photovoltaic-storage charging station operator satisfies the following relationship:

[0094]

[0095] In the formula, C represents the total cost for the photovoltaic-storage charging station operator to purchase electricity from the distribution network. c represents the charging power of the energy storage device in the photovoltaic-energy storage charging station during time period i. i The real-time purchase cost of a unit of electricity purchased from the grid by the photovoltaic-storage charging station operator in time period i, where i = 1, 2, ..., T, T is the total number of time periods, and t is the monitoring sampling and control time interval of the photovoltaic-storage charging station.

[0096] The photovoltaic-storage charging station operator monitors and samples the photovoltaic-storage charging station according to the monitoring sampling control time interval. Based on the operation data of the photovoltaic-storage charging station with the total number of time periods per day as the time scale in typical scenarios, the start and end times of the time interval t are reasonably selected. The charging period of the energy storage device is given priority as the monitoring sampling control time interval of the photovoltaic-storage charging station, thereby improving the monitoring efficiency of the photovoltaic-storage charging station operator.

[0097] Specifically, the constraints of the objective function include:

[0098] 1) Energy storage capacity constraints;

[0099]

[0100] In the formula, S t S0 represents the real-time remaining energy (%) of the energy storage device in the photovoltaic-energy storage charging station, and S0 represents the initial energy (%) of the energy storage device in the photovoltaic-energy storage charging station. min S max These represent the lower and upper limits of the state of charge (SOC) of the energy storage devices in the photovoltaic-storage charging station, respectively. charge The actual stored electricity of the photovoltaic-storage charging station;

[0101] 2) Constraints on the charging and discharging power of the energy storage system;

[0102] P min ≤x i ≤P max

[0103] In the formula, P min P max These are the lower and upper limits of the charging and discharging power of the energy storage devices in the photovoltaic-energy storage charging station, respectively.

[0104] 3) Load fluctuation constraints

[0105] L t =Load t +P t L min ≤L t ≤L max

[0106] In the formula, L t For the real-time load of the photovoltaic-storage charging station, Load t For the real-time charging load of electric vehicles, P t L represents the real-time charging and discharging power of the energy storage device in the photovoltaic-energy storage charging station. min L max These represent the lower and upper limits of load fluctuations for photovoltaic-storage charging stations, respectively.

[0107] Step 2: Establish grid support capability indicators for photovoltaic-storage charging stations, including: active power regulation margin indicators for energy storage devices, active power variation indicators for energy storage converters, and economic indicators for active power support provided by photovoltaic-storage charging stations at different time scales; among which, the time scale includes: the service life of energy storage devices and the total number of time periods per day.

[0108] This invention defines and quantifies the grid-support capability indicators of photovoltaic-storage-charging stations to reflect the grid-connection capability of energy storage devices and photovoltaic-storage-charging stations.

[0109] Specifically, step 2 includes:

[0110] Step 2.1: Using the difference between the upper limit of the charging and discharging power of the energy storage device and the charging and discharging power in each time period, establish the active power adjustment margin index of the energy storage device with the rated active power of the energy storage device.

[0111] Among them, the active power regulation margin index of energy storage devices includes: the maximum value of active power regulation of energy storage devices in photovoltaic-storage charging stations participating in grid frequency regulation and its constraints.

[0112] Specifically, the active power regulation capability of a photovoltaic-storage charging station is mainly determined by the energy storage device. To ensure the grid-connected photovoltaic-storage charging station's ability to regulate the grid frequency, the grid-connected energy storage device in the photovoltaic-storage charging station should have sufficient active power regulation margin. Therefore, the maximum value of the active power regulation amount of the energy storage device in the photovoltaic-storage charging station participating in grid frequency regulation and its constraints satisfy the following relationship:

[0113] P max -P c (i)-P d (i)>20%P N

[0114] In the formula, P max P is the upper limit of the charging and discharging power of the energy storage device in the photovoltaic-energy storage charging station. c (i), P d (i) represents the charging and discharging power of the energy storage device in the photovoltaic-energy storage charging station during time period i, respectively. N The rated active power of the energy storage device;

[0115] P max -P c (i)-P d (i) indicates the maximum active power regulation amount participating in grid frequency regulation, which should not be less than 20% of P. N This ensures that at any given time, the grid-type photovoltaic-storage-charging integrated power station guarantees a 20% active power regulation margin for charging and discharging.

[0116] Step 2.2: Establish an active power change index for the energy storage converter using the maximum value of the active power change of the energy storage converter and the rated active power of the energy storage device.

[0117] The active power change indicators of the energy storage converter include, but are not limited to: the maximum value and constraints of the active power change of the energy storage converter under inertial response, and the maximum value and constraints of the active power change of the energy storage converter under damping control.

[0118] Specifically, grid-connected energy storage converters should possess inertial response capabilities. Throughout the entire process of frequency disturbances in the power grid, the grid-connected energy storage converter should automatically adjust its active power to suppress rapid changes in the power grid frequency. Therefore, under inertial response, the maximum value of the active power change of the grid-connected energy storage converter should not be less than 10%P. N ;

[0119] Specifically, grid-connected energy storage converters should also possess damping control functionality. Throughout the entire process of grid frequency disturbances, when the grid frequency oscillation exceeds the set value of the system frequency oscillation amplitude, the grid-connected energy storage converter should automatically adjust its active power through damping control to suppress the grid frequency oscillation amplitude. Therefore, under damping control, the maximum value of the active power change of the grid-connected energy storage converter should not be less than 10%P. N .

[0120] Step 2.3: Establish economic indicators for the active power support provided by photovoltaic-storage charging stations at different time scales;

[0121] For grid-type photovoltaic-storage charging stations, in addition to cost, active power support capability should also be considered as one of the optimization objectives. Therefore, economic indicators are introduced to quantify the active power support capability of photovoltaic-storage charging stations.

[0122] Specifically, the active power regulation requirement of the photovoltaic-storage charging station satisfies the following relationship:

[0123] F N (i)=P j (i+1)-P j (i)

[0124] In the formula, F N (i) For the active power regulation requirements of the photovoltaic-storage charging station during time period i, P j (i) represents the net load value for time period i;

[0125] Specifically, when the grid-connected energy storage converter absorbs energy from the grid, the active power regulation capability of the photovoltaic-storage charging station satisfies the following relationship:

[0126] F S (i)=P max -P c (i)

[0127] When a grid-connected energy storage converter supplies energy to the grid, the active power regulation capability of the photovoltaic-energy storage charging station satisfies the following relationship:

[0128] F S (i)=P max -P d (i)

[0129] In the formula, F S(i) The active power regulation capability of the photovoltaic-storage charging station during time period i, P max P is the upper limit of the charging and discharging power of the energy storage device in the photovoltaic-energy storage charging station. c (i), P d (i) represents the charging and discharging power of the energy storage device of the photovoltaic-energy storage charging station during time period i;

[0130] Compare F N (i) and F S (i), when F N (i) <F S (i) When the photovoltaic-storage charging station meets the active power regulation requirements, it has good grid support capabilities and can actively maintain grid stability when the grid is disturbed; when F N (i)>F S (i) When the photovoltaic-storage charging station cannot fully meet the active power regulation requirements, it can only provide partial grid support or cannot provide grid support when the grid is disturbed.

[0131] Specifically, the active power deficit of a photovoltaic-storage charging station satisfies the following relationship:

[0132]

[0133] In the formula, These are the active power frequency deficits for the photovoltaic-storage charging station during time period i, respectively;

[0134] Specifically, the economic indicators for the active power support provided by photovoltaic-storage charging stations satisfy the following relationship:

[0135]

[0136] In the formula, The economic indicators for providing active power support for photovoltaic-storage charging stations are as follows: when Γ = T, the time scale is the total number of time periods T per day; when Γ = Y, the time scale is the service life of the energy storage device. up v down These refer to the economic benefits that a photovoltaic-storage charging station can provide per unit of charging and discharging active power support. These are the active power frequency deficits for the photovoltaic-storage charging station during time period i, respectively;

[0137] In the embodiment, v up v down It is the price per unit of power, an economic incentive mechanism designed to select schemes with grid support capabilities. It is the economic reward of v yuan obtained by a power station with active power support capability for each unit of power provided. When the active power deficit is 0, active power support cannot be provided, and the economic benefit is 0.

[0138] Based on the grid-building requirements of grid-based energy storage devices participating in grid frequency regulation, inertia response and damping control, this invention defines and quantifies the grid-building support capability indicators of photovoltaic-storage-charging piles. While ensuring that the energy storage configuration scheme has sufficient grid-building support capability, it is also used to describe the grid-building support constraints of energy storage devices and photovoltaic-storage-charging stations, serving as an important constraint condition in the lower-level planning model.

[0139] Step 3: Based on the multi-timescale dual-layer model architecture, the energy storage configuration optimization model is established. The upper-layer model includes the optimization objectives and constraints of the economic indicators of the active power support provided by the photovoltaic-storage-charging station under the scale of the service life of the energy storage device. The lower-layer model includes the optimization objectives, basic constraints, relaxation constraints and network constraints of the economic indicators of the active power support provided by the photovoltaic-storage-charging station under the scale of the total number of time periods per day.

[0140] This includes: an upper-level model based on the service life of energy storage devices and a lower-level model based on the total number of time periods per day;

[0141] This invention establishes a two-layer planning model that takes into account the grid construction demand across multiple time scales. This model includes optimization problems at two time scales: the upper layer uses the annual comprehensive cost as the optimization objective to determine the energy storage capacity configuration; the lower layer uses the comprehensive operating cost of the power system and the grid construction support capability as optimization objectives to obtain the optimal economic dispatch strategy.

[0142] Specifically, step 3 includes:

[0143] Step 3.1: The upper-level model includes optimization objectives and constraints. It uses the annual equivalent investment cost, annual operation and maintenance cost, and the lifespan of the energy storage device as economic indicators to provide active power support for the photovoltaic-storage charging station over a time scale, constituting the annual comprehensive cost. The optimization objective is to minimize the annual comprehensive cost, with the energy storage device capacity as the decision variable. The optimization objective of the upper-level model satisfies the following relationship:

[0144]

[0145] In the formula, F up As the optimization objective of the upper-level model, C TCC C represents the equivalent annual investment cost of the energy storage device. OM The annual operation and maintenance cost of energy storage devices, Economic indicators for providing active power support for photovoltaic-storage charging stations over a time scale of years;

[0146] In the optimization objective of the upper-level model, an economic indicator for the active power support provided by the photovoltaic-storage charging station is introduced, with the time scale being years. In the embodiment, the upper-level time scale is the service life of the energy storage device.

[0147] The equivalent annual investment cost of energy storage devices satisfies the following relationship:

[0148]

[0149] In the formula, μ CRF C represents the annual capital recovery rate. inv For the fixed construction cost of energy storage devices, N ba P represents the number of energy storage devices. ba,n E ba,n Let be the active power and capacity of the nth energy storage device, respectively; a and b are the unit power cost and unit capacity cost of the energy storage device under different charging rates, respectively; y is the service life of the energy storage device; and r is the discount rate.

[0150] The annual operation and maintenance cost of energy storage devices is approximately estimated as a certain percentage of the initial investment in the energy storage devices.

[0151] Step 3.2, the constraints of the upper-level model include:

[0152] A1) Capacity constraint of energy storage device, 0≤E ba,n ≤E max,n E max,n This represents the upper limit of the capacity of the nth energy storage device;

[0153] A2) Maximum power constraint of energy storage device, 0≤P ba,n ≤P max,n P max,n This represents the upper limit of the active power of the nth energy storage device.

[0154] Step 3.3, the lower-level model includes optimization objective, basic constraints, relaxation constraints and network constraints. The economic indicators of the active power support provided by the photovoltaic-storage charging station under the time scale of the total number of time periods per day and the maximization of the daily revenue of the photovoltaic-storage charging station constitute the optimization objective. The power purchased and sold by the photovoltaic-storage charging station and the charging and discharging power of the energy storage device are the decision variables.

[0155] The optimization objective of the lower-level model satisfies the following relationship:

[0156]

[0157] In the formula, F low For the optimization objective of the lower-level model, c ev The price per unit charging power for electric vehicles, P ev (i) represents the charging power of the electric vehicle during time period i, and c s The price of electricity sold to the grid by the photovoltaic power generation unit of the photovoltaic power generation and energy storage charging station, P s (i) represents the grid-connected power of the photovoltaic power generation of the photovoltaic-storage charging station during time period i, c e(i) represents the unit power electricity price for time period i, P p (i) represents the power purchased from the grid during time period i, and c b The cost per unit power loss during charging and discharging of the energy storage device in a photovoltaic-energy storage charging station, P c (i), P d (i) represents the charging and discharging power of the energy storage device in the photovoltaic-energy storage charging station during time period i. The economic indicator for providing active power support to photovoltaic-storage charging stations on a time scale based on the total number of time periods per day, where T is the total number of time periods;

[0158] The optimization objective of the lower-level model is to maximize the daily revenue of the photovoltaic-storage charging station, namely, the sum of charging revenue, photovoltaic power output grid connection revenue, and grid support economic incentives minus electricity purchase costs and energy storage battery loss costs. In the optimization objective of the lower-level model, an economic indicator of the active power support provided by the photovoltaic-storage charging station at the lower-level time scale is introduced. In the example, the lower-level time scale is the total number of time periods.

[0159] The optimization objectives of the upper-layer and lower-layer models of this invention use economic indicators of active power support provided by photovoltaic-storage-charging stations at different time scales to achieve coverage of photovoltaic-storage-charging station network support throughout the entire life cycle. By coordinating the economic indicators of active power support provided by photovoltaic-storage-charging stations at the upper-layer and lower-layer time scales, coarse-grained parallelism and fine-grained parallelism are achieved. Coarse-grained parallelism ensures computational efficiency, while fine-grained parallelism ensures full utilization of computational resources and reliability of computational results.

[0160] Step 3.4, the basic constraints of the lower-level model include:

[0161] B1) Power balance constraints of photovoltaic-storage charging stations:

[0162] The active power of energy storage devices and the exchange power between energy storage devices and the grid are constantly changing. Therefore, the output of photovoltaic power, electric vehicle load, electricity sold to the grid, purchased electricity, and the charging and discharging power of energy storage devices need to be kept in balance.

[0163] P pv (i)-P ev (i)-P s (i)+P p (i)-P c (i)+P d (i)=0

[0164] In the formula, P pv (i) represents the photovoltaic output of the photovoltaic power station during time period i, P ev (i) represents the charging power of the electric vehicle during time period i, P s(i) represents the grid-connected power output of the photovoltaic power generation at the photovoltaic-storage charging station during time period i, P. p (i) represents the amount of electricity purchased from the grid during time period i, P c (i), P d (i) represents the charging and discharging power of the energy storage device of the photovoltaic-energy storage charging station during time period i;

[0165] In the embodiment, P within any time period s (i) and P p At least one of (i) is zero, P c (i) and P d At least one of (i) is zero;

[0166] B2) The energy balance constraint of the energy storage device satisfies the following relationship:

[0167]

[0168] In the formula, E ba (t0), E ba (t1) represents the capacity of the energy storage device at times t0 and t1, respectively, and σ ba The self-discharge rate of the energy storage device;

[0169] The change in the power of an energy storage device is partly affected by the self-discharge energy of the energy storage battery, and partly determined by the charging and discharging power of the energy storage battery.

[0170] B3) The charge / discharge depth constraint of the energy storage device satisfies the following relationship:

[0171] (1-D)E ba,max ≤E ba (i)≤E ba,max

[0172] In the formula, D is the maximum depth of discharge of the energy storage device, and E ba (i) represents the capacity of the energy storage device during time period i, E ba,max This represents the upper limit of the capacity of energy storage devices;

[0173] At any given time interval, when E ba (i)>E ba,max Then the energy storage device stops charging, P c (i) is zero; when E ba (i)<(1-D)E ba,max Then the energy storage device stops discharging, P d (i) is zero;

[0174] B4) The SOC cycle balance constraint of the energy storage device satisfies the following relationship:

[0175]

[0176] In the formula, E ba (0) represents the initial capacity of the energy storage device, E ba (T) represents the capacity of the energy storage device during time period T, and S0 represents the initial energy capacity (%) of the energy storage device. T The amount of electricity stored in the energy storage device during time period T;

[0177] B5) The charging and discharging power constraints of the energy storage device satisfy the following relationship:

[0178]

[0179] In the formula, P max The upper limit of the charging and discharging power of the energy storage device in the photovoltaic-energy storage charging station;

[0180] B6) Photovoltaic output constraints satisfy the following relationship:

[0181] P pv,min ≤P pv (i)≤P pv,max

[0182] In the formula, P pv,min P pv,max These represent the minimum and maximum output of the photovoltaic power in the photovoltaic-storage charging station, respectively.

[0183] B7) The charging power constraint of electric vehicles satisfies the following relationship:

[0184] 0≤P ev (i)≤P ev,max

[0185] In the formula, P ev,max This refers to the upper limit of charging power for electric vehicles.

[0186] Step 3.5, the relaxation constraints of the lower-level model include: the direction of active power flow in each time period and the range of initial power values ​​of the energy storage device;

[0187] C1) Based on the energy flow analysis under time-of-use pricing, the power flow direction during peak and valley periods is determined. The power flow direction of each period is used as the relaxation constraint of the lower-level model. The soft constraints of the lower-level model are selectively relaxed when the model has no solution, which can speed up the iterative solution.

[0188] C2) To ensure that the energy storage device can participate normally in the energy dispatching of the photovoltaic-storage charging station within a day, the initial power value of the energy storage device is set to [40%, 60%] as a relaxation constraint for the lower-level model.

[0189] Step 3.6, the network constraints of the lower-level model include: network support constraints and network stability constraints;

[0190] D1) The maximum value and constraints of the active power regulation of the energy storage device of the photovoltaic-storage charging station participating in the grid frequency regulation, the maximum value and constraints of the active power change of the energy storage converter under inertial response, and the maximum value and constraints of the active power change of the energy storage converter under damping control are used as the grid support constraints of the lower-level model.

[0191] D2) Network stability constraints, satisfying the following relationship:

[0192]

[0193] In the formula, P g,max This represents the maximum power exchanged between the photovoltaic-storage charging station and the power grid.

[0194] If the power exchanged between the photovoltaic and energy storage charging station and the power grid exceeds the maximum value, it will threaten the stability of the grid.

[0195] The basic constraints are to satisfy fundamental physical laws such as power balance of the power station and conservation of energy storage capacity. Grid constraints are necessary conditions to ensure that the system has sufficient grid support capacity. Both are essential conditions that must be met for the operation of grid-type photovoltaic-storage-charging stations. Relaxation constraints are supplementary conditions based on general experience in design. In most cases, the optimal solution satisfies relaxation constraints, so they can be used to accelerate model solving and save computing power. In extreme cases, the optimal solution may not satisfy the constraint, so it is considered a non-essential supplementary constraint.

[0196] Step 3.7: The optimization objectives and constraints of the upper-level model, and the optimization objectives, basic constraints, relaxation constraints, and network constraints of the lower-level model constitute the energy storage configuration optimization model.

[0197] Step 4: Using the electricity demand of the energy storage device at different times as the initial population, the genetic algorithm is used to iteratively solve the energy storage configuration optimization model to obtain the configuration scheme.

[0198] In the embodiment, the optimal solution for the capacity of the energy storage device obtained through multiple iterations is used as the capacity stabilization scheme for the energy storage device, and the optimal solution for the power purchase and sale of the photovoltaic-energy storage charging station and the optimal solution for the charging and discharging power of the energy storage device are used as the operation scheme for the energy storage device.

[0199] Specifically, using the estimated electricity demand of energy storage devices at different times as the initial population, a genetic algorithm is employed to solve the energy storage configuration optimization model, including:

[0200] Step 4.1: Based on the historical data of the photovoltaic-storage charging station, determine several typical scenarios. Use the power demand of the energy storage device at each time period when the total power purchase cost of the distribution network of the charging station operator is minimized under the typical scenario as the initial population.

[0201] Since the initial population is based on typical scenarios corresponding to historical data, and is also based on the energy demand of the energy storage device estimated by linear programming in each time period, it is closer to the optimal solution than the randomly generated initial population, thus significantly improving the model solution speed. Moreover, the energy demand of the energy storage device estimated under typical scenarios has the same time scale as the economic indicators of the active power support provided by the photovoltaic-storage-charging station in the lower-level model, providing a more effective interface that meets the actual needs of the system for the starting point of the iterative calculation of the lower-level model, and significantly saving calculation time based on the accurate extraction of the corresponding time scale calculation resources.

[0202] Step 4.2, based on the multi-level constraints in the lower-level model, improve the calculation method of the fitness function when solving the lower-level model, including:

[0203] In the first to τ iterations, the basic fitness function of the population obtained in the current iteration that satisfies the basic constraints, the relaxed fitness function that satisfies the relaxed constraints, and the web-building fitness function that satisfies the web-building constraints are calculated respectively. The weighted sum of the basic fitness function, the relaxed fitness function, and the web-building fitness function is used as the fitness function of the population obtained in the current iteration.

[0204] In the τ+1 to N iterations, the basic fitness function of the population obtained in the current iteration satisfies the basic constraints and the network fitness function satisfies the network constraints, respectively; the weighted sum of the basic fitness function and the network fitness function is used as the fitness function of the population obtained in the current iteration.

[0205] In this embodiment, τ is 10 and N is an integer not less than 20.

[0206] The basic constraints are to satisfy fundamental physical laws such as power balance and energy conservation in power storage. Grid constraints are necessary to ensure the system has sufficient grid support capacity. Both are essential conditions for the operation of grid-type photovoltaic-storage-charging stations. Relaxation constraints are supplementary conditions based on general experience. In most cases, the optimal solution satisfies the relaxation constraints. Therefore, calculating the relaxation fitness function that satisfies the relaxation constraints in the first to τ iterations can accelerate model solving and save computational resources. However, in extreme cases, the optimal solution may not satisfy the constraint. Therefore, in the τ+1 to N iterations, the relaxation constraints are relaxed, and it is not necessary to calculate the relaxation fitness function that satisfies the relaxation constraints.

[0207] Step 4.3: Based on the initial population and the improved fitness function calculation method, iteratively solve the lower-level model, and feed back the obtained power purchase and sale of the photovoltaic-storage charging station and the charging and discharging power of the energy storage device to the upper-level model. Iteratively solve the upper-level model, and use the obtained energy storage device capacity as the input data of the lower-level model. Through iterative solution, the optimal solution of the energy storage device capacity is obtained as the energy storage device capacity stabilization scheme, and the optimal solutions of the power purchase and sale of the photovoltaic-storage charging station and the charging and discharging power of the energy storage device are obtained as the energy storage device operation scheme.

[0208] In this embodiment, a genetic algorithm is used to solve the lower-level model. After the power purchased and sold by the photovoltaic-storage charging station and the charging and discharging power of the energy storage device are fed back to the upper-level model, the upper-level model first calculates the operation and maintenance cost and economic reward of the photovoltaic-storage charging station. If the operation and maintenance cost of the photovoltaic-storage charging station calculated in the current iteration is less than that calculated in the previous iteration, and the economic reward calculated in the current iteration is more than that calculated in the previous iteration, then the power purchased and sold by the photovoltaic-storage charging station and the charging and discharging power of the energy storage device fed back by the lower-level model are used as input data, and the genetic algorithm is used to solve the optimization objective and constraints of the upper-level model to update the energy storage device capacity calculated in the previous iteration.

[0209] The energy storage device capacity setting scheme and the energy storage device operation scheme constitute the energy storage optimization configuration scheme of the photovoltaic-energy storage charging station.

[0210] Figures 2 to 4 These are the photovoltaic output, time-of-use electricity price, and system load curves in the embodiments of the present invention. Figure 5 This is a comprehensive cost curve of the energy storage system in the embodiments of the present invention; Figure 6 and Figure 7 These are the system operation active power output configuration scheme and energy storage SOC curve diagram in the embodiments of the present invention. Figures 2 to 4 The sampling interval was 1 hour. Photovoltaic output peaked at noon, approaching 90kW. Electricity prices were 0.75 yuan / kWh during peak hours in the morning and evening; 0.5 yuan / kWh during midday; and 0.25 yuan / kWh during off-peak hours in the early morning. The peak and off-peak load trends were similar to the electricity price trends, with the maximum load exceeding 350kW. The maximum charging / discharging power and rated power (PN) of the installed energy storage were both 150kW. Figure 5 It can be seen that the annual comprehensive cost of the distributed energy storage system changes with the number of iterations. It can be seen that the objective converges when the number of iterations reaches 12, at which point the minimum system cost is 664,000 yuan / year and the energy storage capacity is configured to be 300kWh. Figure 6To minimize system costs, the allocation of photovoltaic output, grid-connected power, energy storage discharge power, and energy storage charging power is determined based on the system load. It can be seen that energy storage primarily charges during periods of low electricity prices (00:00-08:00 and 13:00-17:00) and discharges during peak electricity prices (08:00-10:00 and 19:00-22:00). This energy storage operation strategy can leverage peak-valley price differences to reduce charging and discharging costs. Figure 7 It can be seen that the energy storage SOC reaches its upper or lower limit at times 9, 11, 18, 19, and 23. The difference between the active power and the maximum charge / discharge power is always greater than 30kW, meaning that the power adjustment margin meets the grid support capability requirement of more than 20% PN. According to this operating scheme, when the grid experiences disturbances at any time of day, the energy storage system can effectively support the stable operation of the grid and achieve optimal economic benefits.

[0211] This invention also proposes a grid-type photovoltaic-storage-charging station energy storage optimization configuration system under grid construction requirements, comprising:

[0212] The planning module is used to obtain the energy demand of the energy storage device at each time period when the total power purchase cost of the power distribution network of the charging station operator is minimized.

[0213] The indicator establishment module is used to establish grid support capability indicators for photovoltaic-storage-charging stations, including: active power regulation margin indicators of energy storage devices, active power change indicators of energy storage converters, and economic indicators of active power support provided by photovoltaic-storage-charging stations at different time scales; the time scales include: the service life of energy storage devices and the total number of time periods per day.

[0214] The model building module is used to build an energy storage configuration optimization model based on a multi-time-scale two-layer model architecture. The upper-layer model includes the optimization objectives and constraints of the economic indicators of the active power support provided by the photovoltaic-storage-charging station under the scale of the service life of the energy storage device. The lower-layer model includes the optimization objectives, basic constraints, relaxation constraints and network constraints of the economic indicators of the active power support provided by the photovoltaic-storage-charging station under the scale of the total number of time periods per day.

[0215] The model solving module is used to initialize the energy storage device's power demand at different times and use a genetic algorithm to iteratively solve the energy storage configuration optimization model to obtain the configuration scheme.

[0216] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0217] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0218] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0219] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status 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 the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving 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., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0220] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for optimizing the energy storage configuration of a grid-connected photovoltaic-storage-charging station under grid construction requirements, characterized in that, include: The goal is to obtain the energy demand of the energy storage device at each time period when the total power purchase cost of the power distribution network of the charging station operator is minimized. The grid support capability indicators for photovoltaic-storage-charging stations are established, including: the active power regulation margin indicator of energy storage devices, the active power variation indicator of energy storage converters, and the economic indicators of active power support provided by photovoltaic-storage-charging stations under different time scales. The time scales include: the service life of energy storage devices and the total number of time periods per day. Based on a multi-time-scale two-layer model architecture, the energy storage configuration optimization model is established. The upper-layer model includes the optimization objective and constraints of the economic indicators of active power support provided by photovoltaic-storage-charging stations based on the service life of energy storage devices. The lower-layer model includes the optimization objective, basic constraints, relaxation constraints, and grid constraints of the economic indicators of active power support provided by photovoltaic-storage-charging stations based on the total number of time periods per day. Using the electricity demand of energy storage devices in each time period as the initial population, a genetic algorithm is used to iteratively solve the energy storage configuration optimization model to obtain the configuration scheme.

2. The method for optimizing the energy storage configuration of a grid-type photovoltaic-storage-charging station under the grid construction requirements as described in claim 1, characterized in that, The goal of minimizing the total power purchase cost of the photovoltaic-storage charging station operator's distribution network includes: the objective function that minimizes the charging cost of the energy storage devices at the photovoltaic-storage charging station within the total time period T, and the constraints on the objective function.

3. The method for optimizing the energy storage configuration of a grid-type photovoltaic-storage-charging station under grid construction requirements as described in claim 2, characterized in that, The constraints of the objective function include: 1) Energy storage capacity constraints are as follows: In the formula, S t x represents the real-time remaining energy (%) of the energy storage device in the photovoltaic-energy storage charging station. i x represents the charging and discharging power of the energy storage device in the photovoltaic-energy storage charging station during time period i. i >0 indicates that the energy storage device is charging during time period i, x i <0 indicates that the energy storage device discharges during time period i, t is the monitoring and sampling control time interval of the photovoltaic-energy storage charging station, S0 is the initial energy capacity (%) of the energy storage device in the photovoltaic-energy storage charging station, i = 1, 2, ..., T, T is the total number of time periods, S min S max These represent the lower and upper limits of the state of charge (SOC) of the energy storage devices in the photovoltaic-storage charging station, respectively. charge The actual stored electricity of the photovoltaic-storage charging station; 2) The charging and discharging power constraints of the energy storage system are as follows: P min ≤x i ≤P max In the formula, P min P max These are the lower and upper limits of the charging and discharging power of the energy storage devices in the photovoltaic-energy storage charging station, respectively. 3) Load fluctuation constraints, as follows: L t =Load t +P t ,L min ≤L t ≤L max In the formula, L t For the real-time load of the photovoltaic-storage charging station, Load t For the real-time charging load of electric vehicles, P t L represents the real-time charging and discharging power of the energy storage device in the photovoltaic-energy storage charging station. min L max These represent the lower and upper limits of load fluctuations for photovoltaic-storage charging stations, respectively.

4. The method for optimizing the energy storage configuration of a grid-type photovoltaic-storage-charging station under the grid construction requirements as described in claim 1, characterized in that, By utilizing the upper limit of the charging and discharging power of the energy storage device and the difference between the charging and discharging power in different time periods, and comparing it with the rated active power of the energy storage device, an active power regulation margin index for the energy storage device is established. This index includes the maximum active power regulation amount of the energy storage device in the photovoltaic-energy storage charging station participating in grid frequency regulation and its constraints, satisfying the following relationship: P max -P c (i)-P d (i)>20%P N In the formula, P max P is the upper limit of the charging and discharging power of the energy storage device in the photovoltaic-energy storage charging station. c (i), P d (i) represents the charging and discharging power of the energy storage device in the photovoltaic-energy storage charging station during time period i, respectively. N This refers to the rated active power of the energy storage device.

5. The method for optimizing the energy storage configuration of a grid-type photovoltaic-storage-charging station under the grid construction requirements as described in claim 4, characterized in that, An active power variation index for the energy storage converter is established using the maximum value of the active power variation and the rated active power of the energy storage device, including: The maximum value of the change in active power of the energy storage converter under inertial response is not less than 10%P. N The maximum value of the active power variation of the energy storage converter under damping control is not less than 10%P. N .

6. The method for optimizing the energy storage configuration of a grid-type photovoltaic-storage-charging station under grid construction requirements as described in claim 5, characterized in that, The economic indicators for the active power support provided by photovoltaic-storage charging stations at different time scales satisfy the following relationship: In the formula, The economic indicators for providing active power support for photovoltaic-storage charging stations are as follows: when Γ = T, the time scale is the total number of time periods T per day; when Γ = Y, the time scale is the service life of the energy storage device. vup and vdown are the economic benefits corresponding to the unit charging and discharging active power support provided by the photovoltaic-storage charging station, respectively. These are the active power frequency deficits for the photovoltaic-storage charging station during time period i, respectively; In the formula, F N (i) For the active power regulation requirements of the photovoltaic-storage charging station during time period i, F S (i) is the active power regulation capability of the photovoltaic-storage-charging station in time period i.

7. The method for optimizing the energy storage configuration of a grid-type photovoltaic-storage-charging station under grid construction requirements as described in claim 6, characterized in that, The energy storage configuration optimization model includes: an upper-level model with the service life of the energy storage device as the time scale, and a lower-level model with the total number of time periods per day as the time scale; In the upper-level model, the annual comprehensive cost is constituted by economic indicators such as the annual equivalent investment cost of the energy storage device, the annual operation and maintenance cost, and the service life of the energy storage device as the time scale for providing active power support to the photovoltaic-storage charging station. The optimization objective is to minimize the annual comprehensive cost, and the energy storage device capacity is the decision variable. In the lower-level model, the economic indicators of the active power support provided by the photovoltaic-storage charging station under the time scale of the total number of time periods per day and the maximization of the daily revenue of the photovoltaic-storage charging station constitute the optimization objective, and the power purchased and sold by the photovoltaic-storage charging station and the charging and discharging power of the energy storage device are the decision variables.

8. The method for optimizing the energy storage configuration of a grid-type photovoltaic-storage-charging station under grid construction requirements as described in claim 7, characterized in that, The optimization objective of the upper-level model satisfies the following relationship: In the formula, F up As the optimization objective of the upper-level model, C TCC C represents the equivalent annual investment cost of the energy storage device. OM The annual operation and maintenance cost of energy storage devices, Economic indicators for providing active power support for photovoltaic-storage charging stations based on the service life of energy storage devices.

9. The method for optimizing the energy storage configuration of a grid-type photovoltaic-storage-charging station under grid construction requirements as described in claim 8, characterized in that, The annualized investment cost of energy storage devices satisfies the following relationship: In the formula, μ CRF For the annual capital recovery rate, C inv For the fixed construction cost of energy storage devices, N ba P represents the number of energy storage devices. ba,n E ba,n Let be the active power and capacity of the nth energy storage device, respectively; a and b are the unit power cost and unit capacity cost of the energy storage device under different charging rates, respectively; y is the service life of the energy storage device; and r is the discount rate. The annual operation and maintenance cost of energy storage devices is estimated as a percentage of the initial investment in the energy storage device.

10. The method for optimizing the energy storage configuration of a grid-type photovoltaic-storage-charging station under the grid construction requirements according to claim 1, characterized in that, The constraints of the upper-level model include: A1) Capacity constraint of energy storage device, 0≤E ba,n ≤E max,n E max,n This represents the upper limit of the capacity of the nth energy storage device; A2) Maximum power constraint of energy storage device, 0≤P ba,n ≤P max,n P max,n This represents the upper limit of the active power of the nth energy storage device.

11. The method for optimizing the energy storage configuration of a grid-type photovoltaic-storage-charging station under grid construction requirements as described in claim 7, characterized in that, The optimization objective of the lower-level model satisfies the following relationship: In the formula, F low For the optimization objective of the lower-level model, c ev The price per unit charging power for electric vehicles, P ev (i) represents the charging power of the electric vehicle during time period i, and c s The price of electricity sold to the grid by the photovoltaic power generation unit of the photovoltaic power generation and energy storage charging station, P s (i) represents the grid-connected power of the photovoltaic power generation of the photovoltaic-storage charging station during time period i, c e (i) represents the unit power electricity price for time period i, P p (i) represents the power purchased from the grid during time period i, and c b The cost per unit power loss during charging and discharging of the energy storage device in a photovoltaic-energy storage charging station, P c (i), P d (i) represents the charging and discharging power of the energy storage device in the photovoltaic-energy storage charging station during time period i. The economic indicator for providing active power support to photovoltaic-storage charging stations on a time scale based on the total number of time periods per day, where T is the total number of time periods.

12. The method for optimizing the energy storage configuration of a grid-type photovoltaic-storage-charging station under grid construction requirements as described in claim 1, characterized in that, The basic constraints of the lower-level model include: B1) Power balance constraints of photovoltaic-storage charging stations; B2) Energy balance constraints of energy storage devices; B3) Constraints on the depth of charge and discharge of energy storage devices; B4), SOC cycle balance constraints of energy storage devices; B5) Charge and discharge power constraints of energy storage devices; B6), Photovoltaic output constraints; B7) Charging power constraints for electric vehicles.

13. The method for optimizing the energy storage configuration of a grid-type photovoltaic-storage-charging station under grid construction requirements as described in claim 1, characterized in that, The relaxation constraints of the lower-level model include: the direction of active power flow in each time period and the range of initial power values ​​for the energy storage device.

14. The method for optimizing the energy storage configuration of a grid-type photovoltaic-storage-charging station under grid construction requirements as described in claim 5, characterized in that, The network constraints of the lower-level model include: network support constraints and network stability constraints; D1) The maximum value and constraints of the active power regulation of the energy storage device of the photovoltaic-storage charging station participating in the grid frequency regulation, the maximum value and constraints of the active power change of the energy storage converter under inertial response, and the maximum value and constraints of the active power change of the energy storage converter under damping control are used as the grid support constraints of the lower-level model. D2) Network stability constraints, satisfying the following relationship: In the formula, P s (i) represents the grid-connected power output of the photovoltaic power generation at the photovoltaic-storage charging station during time period i, P. p (i) represents the amount of electricity purchased from the grid during time period i, P g,max This represents the maximum power exchanged between the photovoltaic-storage charging station and the power grid.

15. The method for optimizing the energy storage configuration of a grid-type photovoltaic-storage-charging station under grid construction requirements as described in claim 1, characterized in that, The solution process for the energy storage configuration optimization model includes: Based on historical data of photovoltaic-storage charging stations, several typical scenarios were identified. The electricity demand of the energy storage device at each time period when the total power purchase cost of the distribution network for the charging station operator is minimized under the typical scenario was used as the initial population. Based on the multi-level constraints in the lower-level model, the method for calculating the fitness function during the solution of the lower-level model is improved, including: In the first to τ iterations, the basic fitness function of the population obtained in the current iteration that satisfies the basic constraints, the relaxed fitness function that satisfies the relaxed constraints, and the web-building fitness function that satisfies the web-building constraints are calculated respectively. The weighted sum of the basic fitness function, the relaxed fitness function, and the web-building fitness function is used as the fitness function of the population obtained in the current iteration. In the τ+1 to N iterations, the basic fitness function of the population obtained in the current iteration satisfies the basic constraints and the network fitness function satisfies the network constraints, respectively; the weighted sum of the basic fitness function and the network fitness function is used as the fitness function of the population obtained in the current iteration. Based on the initial population and the improved fitness function calculation method, the lower-level model is solved iteratively. The obtained power purchase and sale of the photovoltaic-storage charging station and the charging and discharging power of the energy storage device are fed back to the upper-level model. The upper-level model is solved iteratively, and the obtained energy storage device capacity is used as the input data of the lower-level model. Through iterative solution, the optimal solution of the energy storage device capacity is obtained as the energy storage device capacity stabilization scheme, and the optimal solutions of the power purchase and sale of the photovoltaic-storage charging station and the charging and discharging power of the energy storage device are obtained as the energy storage device operation scheme.

16. A grid-connected photovoltaic-storage-charging station energy storage optimization configuration system under grid-connection requirements, used to implement the grid-connection-requirement-optimized energy storage configuration method for a grid-connected photovoltaic-storage-charging station under grid-connection requirements as described in any one of claims 1 to 15, characterized in that, include: The planning module is used to obtain the energy demand of the energy storage device at each time period when the total power purchase cost of the power distribution network of the charging station operator is minimized. The indicator establishment module is used to establish grid support capability indicators for photovoltaic-storage-charging stations, including: active power regulation margin indicators of energy storage devices, active power change indicators of energy storage converters, and economic indicators of active power support provided by photovoltaic-storage-charging stations at different time scales; the time scales include: the service life of energy storage devices and the total number of time periods per day. The model building module is used to build an energy storage configuration optimization model based on a multi-time-scale two-layer model architecture. The upper-layer model includes the optimization objectives and constraints of the economic indicators of the active power support provided by the photovoltaic-storage-charging station under the scale of the service life of the energy storage device. The lower-layer model includes the optimization objectives, basic constraints, relaxation constraints and network constraints of the economic indicators of the active power support provided by the photovoltaic-storage-charging station under the scale of the total number of time periods per day. The model solving module is used to initialize the energy storage device's power demand at different times and use a genetic algorithm to iteratively solve the energy storage configuration optimization model to obtain the configuration scheme.

17. 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 perform the steps of the method according to any one of claims 1-15.

18. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-15.

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