Method, device and medium for determining charging price of optical storage charging station

Abstract

Embodiments of the present application provide a charging price determination method, device and equipment of a light storage charging station and a medium. The method comprises: obtaining basic parameters of the light storage charging station; the basic parameters comprise photovoltaic output data, charging load data, energy storage system parameters and grid interaction data of the light storage charging station; based on the basic parameters, an optimal decision variable set that meets a plurality of preset power station basic constraint conditions is solved with the goal of maximizing power station revenue; the optimal decision variable set comprises an optimal electric vehicle charging load curve; and the optimal electric vehicle charging price of the light storage charging station is determined according to the optimal electric vehicle charging load curve and a pre-constructed mapping relationship between the electric vehicle charging load and the charging price. The method is used to improve the pricing accuracy of the charging price, thereby meeting the dual requirements of stability and economy of the power system.

Description

Technical Field

[0001] This application relates to the field of power grids, and in particular to a method for determining the charging electricity price of a photovoltaic-storage charging station. Background Technology

[0002] As an important component of the new power system, integrated photovoltaic, energy storage and charging power stations play a key role in promoting the high-proportion consumption of renewable energy, improving grid flexibility and meeting the charging needs of the rapidly developing electric vehicles.

[0003] Currently, the charging load of electric vehicles in these power stations is influenced by user travel behavior, exhibiting randomness and spatiotemporal transferability. This makes the overall load curve of the power station difficult to predict and control, directly restricting the stability of power station operation and the improvement of overall efficiency. Against this backdrop, electric vehicle charging pricing, as a key means of regulating user charging behavior and optimizing the load curve, plays a decisive role in the operational effectiveness of the power station due to its rationality and scientific validity.

[0004] Existing technical solutions mostly adopt fixed electricity prices or simple time-of-use pricing models, which fail to effectively shape the load curve. Therefore, there is an urgent need for a new technology to improve the pricing accuracy of charging electricity prices, so as to meet the dual requirements of stability and economy for future new power systems. Summary of the Invention

[0005] This application provides a method for determining the charging price of a photovoltaic-storage charging station, which aims to improve the pricing accuracy of the charging price and thus meet the dual requirements of the power system for stability and economy.

[0006] In a first aspect, embodiments of this application provide a method for determining the charging electricity price of a photovoltaic-storage charging station, including:

[0007] Obtain the basic parameters of the photovoltaic-storage charging station; the basic parameters include the photovoltaic output data, charging load data, energy storage system parameters, and grid interaction data of the photovoltaic-storage charging station;

[0008] Based on the aforementioned basic parameters, with the goal of maximizing power plant revenue, the optimal set of decision variables is solved under multiple preset basic constraints of the power plant; the optimal set of decision variables includes the optimal electric vehicle charging load curve.

[0009] Based on the optimal electric vehicle charging load curve and the pre-constructed mapping relationship between electric vehicle charging load and charging price, the optimal charging price for electric vehicles at the photovoltaic-storage charging station is determined.

[0010] In one possible implementation, the step of solving for the optimal set of decision variables based on the fundamental parameters, with the objective of maximizing power plant revenue, while satisfying multiple preset fundamental constraints of the power plant, includes:

[0011] Construct an objective function that aims to maximize the power plant's revenue;

[0012] The MILP solver is invoked to solve for the set of optimal decision variables corresponding to the objective function under the multiple power plant basic constraints.

[0013] In one possible implementation, the objective function is determined based on electric vehicle charging revenue, photovoltaic power generation revenue, grid purchase and sale costs, and energy storage system depreciation costs.

[0014] In one possible implementation, the plurality of power plant basic constraints include at least one of the following:

[0015] Upper limit constraints on grid-side power transmission; mutual exclusion constraints on grid power purchase and sales; peak shaving capacity constraints on energy storage system discharge power; valley filling capacity constraints on energy storage system charging power; upper and lower limit constraints on photovoltaic power output; upper and lower limit constraints on energy storage system state of charge; upper and lower limit constraints on energy storage system charging power; upper and lower limit constraints on energy storage system discharge power; mutual exclusion constraints on energy storage system charging power and discharge power; power balance constraints of power plants; baseline load constraints on total power plant load.

[0016] In one possible implementation, the method further includes:

[0017] Obtain multiple actual photovoltaic output curves of the photovoltaic-storage charging station under different weather conditions;

[0018] Cluster analysis of the multiple actual photovoltaic power output curves is used to obtain multiple actual photovoltaic power output curves corresponding to each weather condition;

[0019] Multiple actual photovoltaic output curves corresponding to the current weather at the photovoltaic-storage charging station are subjected to prediction processing based on a neural network model to obtain the photovoltaic output prediction curve from the photovoltaic output data.

[0020] In one possible implementation, the cluster analysis of the multiple actual photovoltaic output curves to obtain multiple actual photovoltaic output curves corresponding to each weather condition includes:

[0021] For each actual photovoltaic power output curve, extract the maximum power and power variance from the actual photovoltaic power output curve;

[0022] Based on the maximum power and power variance corresponding to each actual photovoltaic power output curve, the K-means clustering algorithm is used to perform cluster analysis on the multiple actual photovoltaic power output curves to obtain multiple actual photovoltaic power output curves corresponding to each weather condition.

[0023] In one possible implementation, the mapping relationship between the electric vehicle charging load and the charging electricity price can be expressed as:

[0024]

[0025] in, This represents the electric vehicle charging load at time t. This represents the user's baseline load at time t; This represents the electricity price response coefficient for the preset charging load, used to reflect the user's sensitivity to changes in charging electricity prices; Let represent the charging electricity price at time t; This indicates the base electricity price for charging.

[0026] Secondly, embodiments of this application provide a device for determining the charging electricity price of a photovoltaic-storage charging station, comprising:

[0027] The acquisition module is used to acquire the basic parameters of the photovoltaic-storage charging station; the basic parameters include the photovoltaic output data, charging load data, energy storage system parameters, and grid interaction data of the photovoltaic-storage charging station.

[0028] The solution module is used to solve for the optimal set of decision variables based on the aforementioned basic parameters, with the goal of maximizing power plant revenue, under multiple preset basic constraints of the power plant; the optimal set of decision variables includes the optimal electric vehicle charging load curve.

[0029] The determination module is used to determine the optimal charging price for electric vehicles at the photovoltaic-storage charging station based on the optimal electric vehicle charging load curve and the pre-constructed mapping relationship between electric vehicle charging load and charging price.

[0030] In one possible implementation, the solving module is specifically used for:

[0031] Construct an objective function that aims to maximize the power plant's revenue;

[0032] The MILP solver is invoked to solve for the set of optimal decision variables corresponding to the objective function under the multiple power plant basic constraints.

[0033] In one possible implementation, the objective function in the solution module is determined based on electric vehicle charging revenue, photovoltaic power generation revenue, grid purchase and sale costs, and energy storage system depreciation costs.

[0034] In one possible implementation, the multiple power plant fundamental constraints in the solution module include at least one of the following:

[0035] Upper limit constraints on grid-side power transmission; mutual exclusion constraints on grid power purchase and sales; peak shaving capacity constraints on energy storage system discharge power; valley filling capacity constraints on energy storage system charging power; upper and lower limit constraints on photovoltaic power output; upper and lower limit constraints on energy storage system state of charge; upper and lower limit constraints on energy storage system charging power; upper and lower limit constraints on energy storage system discharge power; mutual exclusion constraints on energy storage system charging power and discharge power; power balance constraints of power plants; baseline load constraints on total power plant load.

[0036] In one possible implementation, the device further includes:

[0037] The processing module includes:

[0038] The first processing unit is used to acquire multiple actual photovoltaic output curves of the photovoltaic energy storage charging station under different weather conditions;

[0039] The second processing unit is used to perform cluster analysis on the multiple actual photovoltaic power output curves to obtain multiple actual photovoltaic power output curves corresponding to each weather condition.

[0040] The third processing unit is used to perform prediction processing based on a neural network model on multiple actual photovoltaic power output curves corresponding to the current weather of the photovoltaic power storage charging station, and to obtain the photovoltaic power output prediction curve in the photovoltaic power output data.

[0041] In one possible implementation, the second processing unit is specifically used for:

[0042] For each actual photovoltaic power output curve, extract the maximum power and power variance from the actual photovoltaic power output curve;

[0043] Based on the maximum power and power variance corresponding to each actual photovoltaic power output curve, the K-means clustering algorithm is used to perform cluster analysis on the multiple actual photovoltaic power output curves to obtain multiple actual photovoltaic power output curves corresponding to each weather condition.

[0044] In one possible implementation, the mapping relationship between the electric vehicle charging load and the charging electricity price in the determining module can be expressed as:

[0045]

[0046] in, This represents the electric vehicle charging load at time t. This represents the user's baseline load at time t; This represents the electricity price response coefficient for the preset charging load, used to reflect the user's sensitivity to changes in charging electricity prices; Let represent the charging electricity price at time t; This indicates the base electricity price for charging.

[0047] Thirdly, embodiments of this application provide a computer device, including: a memory and a processor;

[0048] The memory stores computer-executed instructions;

[0049] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0050] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0051] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0052] The method, apparatus, equipment, and medium for determining the charging price of photovoltaic-storage charging stations provided in this application improve the accuracy of pricing by solving the optimal set of decision variables under multiple preset basic constraints of the photovoltaic-storage charging station with the goal of maximizing the station's revenue based on the multi-dimensional basic parameters of the photovoltaic-storage charging station. Furthermore, it derives the optimal charging price based on the mapping relationship between charging load and electricity price. This allows the electricity price to effectively shape the load curve, thereby meeting the dual requirements of stability and economy for future new power systems. Attached Figure Description

[0053] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0054] Figure 1 A flowchart illustrating a method for determining the charging price of a photovoltaic-storage charging station, provided in Embodiment 1 of this application;

[0055] Figure 2 This is a schematic diagram of the structure of a charging price determination device for a photovoltaic energy storage charging station provided in Embodiment 3 of this application;

[0056] Figure 3 This is a schematic diagram of the structure of a charging price determination device for a photovoltaic energy storage charging station provided in Embodiment 4 of this application;

[0057] Figure 4 A schematic diagram of the structure of the computer device provided in this application.

[0058] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0059] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0060] To address the technical problems mentioned above, the inventors discovered during their research that the electric vehicle charging load curve can be used as an optimization variable. By introducing a charging price-load mapping relationship, a revenue-maximizing optimization framework can be constructed. This method innovatively achieves integrated modeling of "charging load shaping + revenue optimization," which not only dynamically shapes the load distribution but also improves the power station's revenue, meeting the dual requirements of flexibility and economy for future new power systems. Therefore, this invention has the effect of improving the accuracy of electric vehicle charging price setting for photovoltaic-storage charging stations.

[0061] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0062] Figure 1 This is a flowchart illustrating a method for determining the charging price of a photovoltaic-storage charging station according to Embodiment 1 of this application. Figure 1 As shown, the method includes:

[0063] S101. Obtain the basic parameters of the photovoltaic-storage charging station.

[0064] The basic parameters include photovoltaic output data, charging load data, energy storage system parameters, and grid interaction data of the photovoltaic-storage charging station.

[0065] In this step, to successfully establish an optimization model that includes the objective function and constraints, it is necessary to obtain multi-dimensional basic parameters of the photovoltaic-storage-charging station in advance. These basic parameters refer to the core data that supports the operational characteristics and interaction rules of each link in the power station's "source-load-storage-grid" system.

[0066] As a specific example, photovoltaic output data includes, for instance, the photovoltaic output curve of the power plant, the photovoltaic grid connection price, and the upper and lower limits of photovoltaic output; charging load data includes, for instance, the electricity load of the charging station and the baseline load of the power plant; energy storage system parameters include, for instance, the energy storage system depreciation cost and the upper and lower limits of the energy storage system's discharge power and charging power; and grid interaction data includes, for instance, the grid purchase price of electricity and the upper and lower limits of the grid purchase power or sales power.

[0067] S102. Based on the basic parameters, with the goal of maximizing the power plant revenue, solve for the optimal set of decision variables that satisfy multiple preset basic constraints of the power plant.

[0068] The optimal set of decision variables includes the optimal electric vehicle charging load curve.

[0069] It should be understood that in practical applications, there are multiple controllable variables in a power plant, and the coordination and control of these multiple variables are required when solving the objective function. For example, the set of decision variables includes the electric vehicle charging load curve, as well as the energy storage system's state of charge change curve, energy storage system charging power, energy storage system discharging power, grid power purchase, and grid power sales, etc.

[0070] In this step, the basic parameters are input as known quantities into the objective function and multiple power station basic constraints to construct the optimization model. The objective function refers to a pre-constructed function that aims to maximize the revenue of a power station. After clarifying each parameter in the optimization model, the optimal set of decision variables that maximizes the revenue of the power station under multiple power station basic constraints will be solved to obtain the optimal electric vehicle charging load curve.

[0071] For example, the optimal decision variable set also includes the energy storage system state of charge change curve corresponding to the optimal electric vehicle charging load curve, the energy storage system charging power, the energy storage system discharging power, the grid power purchase power, and the grid power sales power.

[0072] Among them, power plant revenue refers to the comprehensive net revenue of the power plant, which can be determined based on various revenue items of the power grid (such as revenue from photovoltaic power sales and revenue from electric vehicle charging) and various cost items (such as power grid purchase costs and equipment operating costs).

[0073] The basic constraints of a power station are the constraints on the power station determined in advance based on the physical laws, equipment safety boundaries, and market assessment rules that must be followed during the operation of the photovoltaic-storage-charging integrated power station.

[0074] It should be understood that the objective function should be set to meet the power plant's economic requirements, and the constraints should be set to meet the power plant's requirements for operational stability, reliability, and safety.

[0075] S103. Based on the optimal electric vehicle charging load curve and the pre-constructed mapping relationship between electric vehicle charging load and charging price, determine the optimal charging price for electric vehicles at the photovoltaic-storage charging station.

[0076] In this step, after determining the optimal electric vehicle charging load curve that satisfies the basic constraints of the power station and maximizes its revenue, the electricity price corresponding to this load curve is derived based on the pre-constructed mapping relationship between electric vehicle charging load and charging price, thus obtaining the optimal electric vehicle charging price. The optimal electric vehicle charging price includes the charging price set by the power station for different time points.

[0077] The method for determining the charging price of a photovoltaic-storage charging station provided in this application improves the accuracy of pricing by solving the optimal set of decision variables under multiple preset basic constraints of the power station with the goal of maximizing the power station's revenue, based on the multi-dimensional basic parameters of the photovoltaic-storage charging station. It also derives the optimal charging price based on the mapping relationship between charging load and electricity price. This allows the electricity price to effectively shape the load curve, thereby meeting the dual requirements of stability and economy for future new power systems.

[0078] Furthermore, Embodiment 2 of this application provides a specific method for determining the charging price of a photovoltaic-storage charging station. Based on the above embodiments, this embodiment provides a detailed description of the specific implementation of each of the above steps, including:

[0079] Step 1: Obtain the basic parameters of the photovoltaic-storage charging station.

[0080] The basic parameters include photovoltaic output data, charging load data, energy storage system parameters, and grid interaction data of the photovoltaic-storage charging station.

[0081] The photovoltaic output data includes a photovoltaic output prediction curve. In one possible implementation, this photovoltaic output curve is obtained using the methods described in steps 1.1 to 1.3 below:

[0082] Step 1.1: Obtain multiple actual photovoltaic output curves of the photovoltaic-storage charging station under different weather conditions.

[0083] Among them, many actual photovoltaic power output curves can be obtained by collecting real-time output power of photovoltaic units under different weather conditions using photovoltaic power monitoring devices.

[0084] Step 1.2: Perform cluster analysis on multiple actual photovoltaic power output curves to obtain multiple actual photovoltaic power output curves corresponding to each weather condition.

[0085] In this step, considering that weather is an important factor affecting the photovoltaic power output curve, cluster analysis of multiple actual photovoltaic power output curves can be used to obtain multiple actual photovoltaic power output curves corresponding to each weather condition.

[0086] As a specific implementation method, this step can be implemented using steps 1.2.1 to 1.2.2 as follows:

[0087] Step 1.2.1: For each actual photovoltaic power output curve, extract the maximum power and power variance from the actual photovoltaic power output curve.

[0088] Specifically, for each collected actual photovoltaic power output curve, two core feature indicators are extracted: "maximum power" and "power variance". Maximum power refers to the maximum power value over the entire time period of the power output curve; power variance refers to the dispersion of power values ​​across all time periods relative to the average power, reflecting the degree of fluctuation in the curve.

[0089] Step 1.2.2: Based on the maximum power and power variance corresponding to each actual photovoltaic power output curve, the K-means clustering algorithm is used to perform cluster analysis on multiple actual photovoltaic power output curves to obtain multiple actual photovoltaic power output curves corresponding to each weather condition.

[0090] In this step, the maximum power and power variance of each curve are used as the samples corresponding to the curve; the K-means clustering algorithm is used to calculate the similarity between samples so that samples with high feature similarity are divided into the same cluster; for each cluster, the weather label of the multiple photovoltaic power output curves is determined according to the actual weather conditions corresponding to the multiple photovoltaic power output curves under the cluster, and multiple actual photovoltaic power output curves corresponding to each weather condition are obtained.

[0091] In detail, cluster analysis using the K-means clustering algorithm includes: first, randomly selecting K samples as initial cluster centers; then calculating the squared Euclidean distance from each sample to each cluster center. ,in, This represents the i-th sample. Let represent the k-th cluster center; based on the squared Euclidean distance of each sample, classify the sample into the cluster containing the nearest center; based on the characteristics of each sample in each cluster, redetermine new cluster centers, and repeat the distance-based classification step to obtain new clusters, and repeat this step until the change in cluster centers meets the following requirements:

[0092]

[0093] in, This represents the k-th new cluster; This indicates the preset threshold value for the variable.

[0094] The methods provided in steps 1.2.1 to 1.2.2 above extract the maximum power and power variance of the photovoltaic output curve as core features and use K-means clustering to classify the curves according to weather type. This allows the photovoltaic output curves under different weather conditions to be accurately divided into corresponding feature clusters. This not only quantifies the differences in the intensity and fluctuation characteristics of photovoltaic output under various weather scenarios, but also realizes the aggregation of output curves under the same weather conditions, thus achieving the technical effect of providing accurate scenario-specific data support for subsequent photovoltaic output prediction.

[0095] Step 1.3: Perform prediction processing based on a neural network model on multiple actual photovoltaic power output curves corresponding to the current weather at the photovoltaic power storage charging station to obtain the photovoltaic power output prediction curve in the photovoltaic power output data.

[0096] In this step, multiple actual photovoltaic (PV) output curves corresponding to the current weather will be obtained. These curves will then be used to train the initial neural network model, enabling the model to output a PV output prediction curve for the current weather. This PV output prediction curve can characterize the typical processing pattern under the current weather conditions.

[0097] Optionally, multiple actual photovoltaic (PV) output curves corresponding to sunny days can be pre-processed using a neural network model to obtain the PV output prediction curve for sunny days. For each weather condition other than sunny days, multiple actual PV output curves corresponding to that weather condition are used to calculate the output difference between that weather condition and sunny days at each time period. Therefore, the PV output prediction curve for the current weather at the PV-storage charging station can be represented as follows: ,in, This represents the difference in power output at time t compared to a sunny day. This represents the photovoltaic power output forecast curve for sunny days.

[0098] The method provided in steps 1.1 to 1.3 of this implementation involves collecting actual photovoltaic power output curves under different weather conditions, classifying the curves according to weather scenarios using a clustering algorithm, and then conducting neural network prediction based on multiple actual photovoltaic power output curves corresponding to the current weather. This approach ensures that the prediction process fully adapts to the intensity and fluctuation characteristics of photovoltaic power output under the current weather conditions, effectively avoiding prediction bias caused by the mixing of different weather curves. This significantly improves the accuracy and stability of photovoltaic power output prediction, providing reliable source-side data support for subsequent electricity price formulation.

[0099] Step 2: Construct the objective function, which aims to maximize the power plant's revenue.

[0100] In this step, an objective function will be established with the goal of maximizing the power plant's revenue.

[0101] In one possible implementation, the objective function in this step is determined based on electric vehicle charging revenue, photovoltaic power generation revenue, grid purchase and sale costs, and energy storage system depreciation costs.

[0102] Specifically, electric vehicle charging revenue refers to the direct operating income obtained by providing charging services to electric vehicle users and charging them for electricity; photovoltaic grid-connected electricity sales revenue refers to the income obtained by selling the surplus electricity generated by the photovoltaic units of the power station to the external power grid after meeting its own charging load, energy storage charging, and rigid load electricity demand; grid purchase and exchange costs refer to the expenditures incurred when the photovoltaic output of the power station is insufficient and the energy storage discharge cannot fill the gap between the charging load and rigid load; and energy storage system depreciation costs refer to the cost of value loss amortized due to equipment aging, performance degradation, and service life loss of energy storage equipment (such as battery packs, PCS, etc.) during use, which belongs to the fixed operation and maintenance costs of the power station.

[0103] As a concrete example, the objective function can be expressed by the following formula:

[0104]

[0105] in, p represents the electric vehicle charging load at time t; EV (t) represents the charging price of the electric vehicle at time t. Based on the mapping relationship between charging load and price, the charging price can be expressed as an expression with a charging load variable; P sell (t) represents the power sold by the grid at time t; p sell (t) represents the price at which the power station sells photovoltaic electricity to the grid at time t; P buy (t) represents the power purchased by the grid at time t; p buy (t) represents the price at which the power station purchases electricity from the grid at time t; This indicates a preset time period, such as 24 hours, 48 ​​hours, etc., which can be determined according to the overall scheduling cycle of the power station. This application does not impose specific restrictions on this; C indicates... Depreciation cost of energy storage system over time.

[0106] For example, the mapping relationship is as follows: ;in, This represents the user's baseline load at time t; This represents the electricity price response coefficient for the preset charging load, used to reflect the user's sensitivity to changes in charging electricity prices; This indicates the base electricity price for charging.

[0107] Alternatively, C can be obtained, for example, through a length-of-life averaging algorithm, which involves spreading the purchase cost of the energy storage system over its design lifespan and then adjusting it accordingly. Internally; or, C can be calculated based on the number of charge-discharge cycles of the energy storage system, i.e. ,in, Indicates the preset time period The total change in the state of charge of the internal energy storage; E represents the design cycle number of the energy storage device; c represents the unit capacity purchase price of the energy storage device.

[0108] It should be understood that This indicates the revenue generated from electric vehicle charging within a preset time period; This indicates the revenue from selling electricity to the grid during the preset time period. C represents the grid purchase and exchange cost within the preset time period; C represents the depreciation cost of the energy storage system within the preset time period.

[0109] In one possible implementation, the multiple power plant basic constraints include at least one of the following:

[0110] Upper limit constraints on grid-side power transmission; mutual exclusion constraints on grid power purchase and sales; peak shaving capacity constraints on energy storage system discharge power; valley filling capacity constraints on energy storage system charging power; upper and lower limit constraints on photovoltaic power output; upper and lower limit constraints on energy storage system state of charge; upper and lower limit constraints on energy storage system charging power; upper and lower limit constraints on energy storage system discharge power; mutual exclusion constraints on energy storage system charging power and discharge power; power balance constraints of power plants; baseline load constraints on total power plant load.

[0111] Specifically, the specific expressions for each parameter can be represented by the formulas in 1) to 11) below:

[0112] 1) The upper limit constraint on power transmission on the grid side can be expressed as:

[0113]

[0114]

[0115] in, This indicates the upper limit of power transmission on the grid side.

[0116] 2) The mutual exclusion constraint between the power purchased and the power sold by the power grid can be expressed as:

[0117]

[0118] 3) The peak-shaving capability constraint of the energy storage system's discharge power can be expressed by the following formula:

[0119]

[0120]

[0121]

[0122]

[0123] in, Indicates peak hours, such as 8:00-22:00; This represents the discharge power of the energy storage system at time t (kW). This represents the peak channel discharge power (kW) at time t. This represents the discharge power (kW) of the channel used for other purposes at time t. This indicates the upper limit of the discharge power (kW) of the energy storage device. This indicates the peak-shaving power capability (kW) of the energy storage device. Indicates the discharge efficiency (%) of the energy storage system; This indicates the available energy budget (kWh) for energy storage devices during peak hours. Indicates the most recent moment before the start of the peak. State of charge (kWh); This represents the lower limit of the state of charge (kWh) of the energy storage system. Indicates the charging efficiency of the energy storage system (%). This represents the energy (kWh) that the energy storage device can recharge during peak hours. This indicates the total duration of the peak period, for example, 4 hours.

[0124] It should be understood that during peak hours The goal is for energy storage to achieve peak shaving through a combination of "reserved power + reserved energy," and then to infer peak shaving capability from the reserved energy and power. To this end, discharge power is divided into "peak segment channels" and "other-purpose channels," and dual constraints of power and energy are applied to the peak segment channels. This yields the aforementioned peak shaving capability constraints for limiting the discharge power of the energy storage system.

[0125] 4) The valley-filling capacity constraint of the energy storage system's charging power can be expressed by the following formula:

[0126]

[0127]

[0128]

[0129]

[0130] in, Indicates the valley period, such as 0:00-6:00; Let t represent the charging power of the energy storage system at time t (kW); This represents the peak channel charging power (kW) at time t. This represents the charging power (kW) of other channels at time t. This indicates the upper limit of the charging power (kW) of the energy storage device. This indicates the valley-filling power capacity (kW) of the energy storage device. Indicates the charging efficiency of the energy storage system (%). This indicates the available energy budget (kWh) for the energy storage device during the valley period. This indicates the upper limit of the state of charge (kWh) of the energy storage system. Indicates the charging efficiency of the energy storage system (%). This indicates the amount of discharge (kWh) that the energy storage device may discharge during a valley period. This indicates the total duration of the valley period, for example, 6 hours.

[0131] It should be understood that, similar to peak shaving capabilities, during off-peak hours... Similarly, energy storage is required to calculate valley filling capacity in the form of "instantaneous power + cumulative energy".

[0132] 5) The upper and lower limits of photovoltaic power output can be expressed by the following formula:

[0133]

[0134] in, This represents the photovoltaic output of the photovoltaic unit at time t; Indicates the upper limit of photovoltaic power output; This indicates the lower limit of photovoltaic power output.

[0135] It should be understood that the above This is the aforementioned photovoltaic power output prediction curve; the upper limit of photovoltaic power output generally occurs during the period of maximum solar irradiance at noon.

[0136] 6) The upper and lower limits of the state of charge of an energy storage system can be expressed by the following formula:

[0137]

[0138]

[0139] in, This represents the state of charge of the energy storage system at time t.

[0140] 7) The upper and lower limits of the charging power of the energy storage system can be expressed as:

[0141]

[0142] 8) Upper and lower limits of discharge power constraints for energy storage systems

[0143]

[0144] 9) The mutual exclusion constraint between the charging power and discharging power of an energy storage system can be expressed as:

[0145]

[0146] 10) The power balance constraint of the power plant can be expressed as:

[0147]

[0148] in, This represents the rigid load of the charging station at time t (i.e., the fixed power consumption of the charging station, which is approximately unchanged over time).

[0149] It should be understood that power balance is required at all times in photovoltaic-storage charging stations. That is, the total power of photovoltaic power generation, grid purchase, and energy storage discharge should be equivalent to the total power station load (including rigid load and electric vehicle charging load), energy storage charging, and grid sales.

[0150] 11) The baseline load constraint of the total load of the power plant can be expressed as:

[0151]

[0152]

[0153] in, This represents the total load of the power station at time t; This represents the baseline load at time t; This represents the maximum allowable load deviation at time t, for example, 5%-10% of the baseline load.

[0154] It should be understood that baseline load refers to a pre-set reference value of load that the power plant should maintain under normal and stable operation when it is not invoked by external dispatch instructions. By constraining the deviation between the total load of the power plant and the baseline load to not exceed the allowable range of the operating agency, it can be ensured that the power plant can maintain a stable load curve when it is not invoked, and avoid the recovery of compensation costs due to excessive deviation.

[0155] Optionally, the basic constraints of the power plant may also include a call duration constraint, which can be expressed by the following formula:

[0156]

[0157] Where H represents the preset minimum call duration; This indicates the flag variable that triggers the call at time t; This is a binary variable used to indicate whether the power station is in a calling state during time period t.

[0158] It should be understood that once a power station is invoked, it must continuously provide regulation capability for a certain period of time. This invoke duration constraint is intended to ensure that the power station is invoked for no less than H hours.

[0159] The method provided in this implementation sets constraints from multiple perspectives, including the operating conditions of the photovoltaic units in the power plant, the operating conditions of the energy storage equipment, the market's peak shaving and valley filling capacity assessment requirements, and the power plant's load requirements. This ensures that in the optimal set of decision variables, the photovoltaic units always operate within the rated power operating parameter range, the energy storage equipment is maintained within the safe charge range and charge / discharge power threshold, and the formulated scheme meets the market's peak shaving and valley filling capacity requirements and is anchored to the stable fluctuation range of the power plant's load. This achieves the optimal electric vehicle load curve, which not only ensures the long-term reliable operation of the core equipment and avoids the risks of overcharging, over-discharging, and over-power operation, but also meets the market assessment standards of the grid side and avoids compliance penalties. This demonstrates that the pricing method provided by this scheme improves the economic benefits and operational reliability of the power plant.

[0160] Step 3: Call the Mixed Integer Linear Programming (MILP) solver to find the optimal set of decision variables corresponding to the objective function under the condition of satisfying multiple power plant basic constraints.

[0161] In this step, since this scheme is a problem of solving a single objective under multiple linear constraints, and the MILP solver is a mature tool for solving optimization problems with multiple variables and multiple constraints, the MILP solver can be used to solve this problem.

[0162] The MILP solver can be any commercially available solver, and this application does not impose any specific restrictions on the choice of the solver.

[0163] Step 4: Determine the optimal charging price for electric vehicles at the photovoltaic-storage charging station based on the optimal electric vehicle charging load curve and the pre-built mapping relationship between electric vehicle charging load and charging price.

[0164] In one possible implementation, the mapping relationship between electric vehicle charging load and charging electricity price can be expressed as:

[0165]

[0166] in, This represents the electric vehicle charging load at time t. This represents the user's baseline load at time t; This represents the electricity price response coefficient for the preset charging load, used to reflect the user's sensitivity to changes in charging electricity prices; Let represent the charging electricity price at time t; This indicates the base electricity price for charging.

[0167] Specifically, the baseline load This refers to the inherent load level of the power plant at time t when there is no intervention in electricity price response. This benchmark load can be obtained based on historical data statistical analysis of the shipping area, for example, by extracting the benchmark value after removing interference factors from historical load data based on similar operating conditions.

[0168] In addition, the above response coefficients Historical data regression analysis can be used to obtain this information. For example, it can be calculated using univariate linear regression based on historical electricity price-load operation data accumulated by the power plant. The core logic is to use a mathematical model to fit the correlation between electricity price and charging load.

[0169] It should be understood that this mapping relationship reflects that when electricity prices are higher than the reference value, users tend to reduce or postpone their charging needs, i.e. Accordingly, charging demand will decrease; conversely, when electricity prices are low, users will increase their charging needs or charge in advance, i.e. The corresponding increase.

[0170] The mapping relationship provided by this implementation method, by coupling core parameters such as benchmark load, electricity price response coefficient, real-time charging electricity price and benchmark electricity price, enables the constructed mapping relationship to accurately quantify the characteristics of electricity load changes fluctuating with electricity price, providing accurate and effective data support for determining electricity price based on the mapping relationship, thereby further improving the accuracy of electricity price setting.

[0171] Optionally, while determining the optimal electric vehicle charging load curve, the optimal energy storage system state of charge change curve can also be obtained to formulate peak shaving and valley filling strategies, thereby evaluating the benefits of power plants participating in peak shaving and valley filling, such as government subsidies.

[0172] Optionally, after determining the optimal charging price for electric vehicles at the solar-electric-storage charging station, the revenue generated by the station's charging business can also be calculated. .

[0173] The method for determining the charging price of a photovoltaic-storage charging station provided in this embodiment constructs an objective function aimed at maximizing the station's revenue and uses a MILP solver to find the optimal set of decision variables corresponding to the objective function under multiple basic constraints of the station. This allows the charging price decision to be precisely coordinated and adapted to the fluctuations in photovoltaic output, the charging and discharging status of energy storage, the charging load demand, and the grid dispatch requirements. At the same time, it ensures that the decision results strictly conform to core constraints such as safe equipment operation, stable load fluctuations, and peak shaving and valley filling assessments. The result is a charging price scheme that balances optimal revenue and compliant operation, effectively improving the operating revenue of the station, reducing the risk of equipment failure and dispatch violations, and enhancing the reliability of the coordinated operation between the station and the grid.

[0174] Figure 2 This is a schematic diagram of the structure of a charging price determination device for a photovoltaic-storage charging station provided in Embodiment 3 of this application, as shown below. Figure 2 As shown, the charging price determination device 20 for the photovoltaic-storage charging station provided in this embodiment includes:

[0175] The acquisition module 201 is used to acquire the basic parameters of the photovoltaic-storage charging station. The basic parameters include the photovoltaic output data, charging load data, energy storage system parameters, and grid interaction data of the photovoltaic-storage charging station.

[0176] The solver module 202 is used to solve for the optimal set of decision variables based on the basic parameters and with the goal of maximizing the power plant revenue, under the condition of satisfying multiple preset basic constraints of the power plant; the optimal set of decision variables includes the optimal electric vehicle charging load curve;

[0177] The determination module 203 is used to determine the optimal charging price for electric vehicles at the photovoltaic-storage charging station based on the optimal electric vehicle charging load curve and the pre-built mapping relationship between electric vehicle charging load and charging price.

[0178] The charging price determination device 20 for the photovoltaic energy storage charging station provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0179] Figure 3This is a schematic diagram of the structure of a charging price determination device for a photovoltaic-storage charging station provided in Embodiment 4 of this application, as shown below. Figure 3 As shown, based on the above embodiments, the charging price determination device 20 for the photovoltaic-storage charging station provided in this embodiment further includes:

[0180] Processing module 204 includes:

[0181] The first processing unit is used to acquire multiple actual photovoltaic output curves of the photovoltaic energy storage charging station under different weather conditions;

[0182] The second processing unit is used for cluster analysis of multiple actual photovoltaic power output curves to obtain multiple actual photovoltaic power output curves corresponding to each weather condition;

[0183] The third processing unit is used to perform prediction processing based on a neural network model on multiple actual photovoltaic power output curves corresponding to the current weather of the photovoltaic power storage charging station, and to obtain the photovoltaic power output prediction curve in the photovoltaic power output data.

[0184] In one possible implementation, the solver module 202 is specifically used for:

[0185] Construct an objective function that aims to maximize the power plant's revenue;

[0186] The MILP solver is invoked to solve for the optimal set of decision variables corresponding to the objective function under multiple power plant basic constraints.

[0187] In one possible implementation, the objective function in the solution module is determined based on electric vehicle charging revenue, photovoltaic power generation revenue, grid purchase and sale costs, and energy storage system depreciation costs.

[0188] In one possible implementation, the multiple power plant basic constraints in the solution module 202 include at least one of the following:

[0189] Upper limit constraints on grid-side power transmission; mutual exclusion constraints on grid power purchase and sales; peak shaving capacity constraints on energy storage system discharge power; valley filling capacity constraints on energy storage system charging power; upper and lower limit constraints on photovoltaic power output; upper and lower limit constraints on energy storage system state of charge; upper and lower limit constraints on energy storage system charging power; upper and lower limit constraints on energy storage system discharge power; mutual exclusion constraints on energy storage system charging power and discharge power; power balance constraints of power plants; baseline load constraints on total power plant load.

[0190] In one possible implementation, the second processing unit is specifically used for:

[0191] For each actual photovoltaic power output curve, extract the maximum power and power variance from the actual photovoltaic power output curve;

[0192] Based on the maximum power and power variance corresponding to each actual photovoltaic power output curve, the K-means clustering algorithm is used to perform cluster analysis on multiple actual photovoltaic power output curves to obtain multiple actual photovoltaic power output curves corresponding to each weather condition.

[0193] In one possible implementation, the mapping relationship between the electric vehicle charging load and the charging electricity price in the determination module 203 can be expressed as:

[0194]

[0195] in, This represents the electric vehicle charging load at time t. This represents the user's baseline load at time t; This represents the electricity price response coefficient for the preset charging load, used to reflect the user's sensitivity to changes in charging electricity prices; Let represent the charging electricity price at time t; This indicates the base electricity price for charging.

[0196] The charging price determination device 20 for the photovoltaic energy storage charging station provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0197] Figure 4 A schematic diagram of the structure of the computer device provided in this application. Figure 4 As shown, the computer device 30 provided in this embodiment includes at least one processor 301 and a memory 302. Optionally, the device 30 further includes a communication component 303. The processor 301, memory 302, and communication component 303 are connected via a bus 304.

[0198] In a specific implementation, at least one processor 301 executes computer execution instructions stored in memory 302, causing at least one processor 301 to perform the above-described method.

[0199] The specific implementation process of processor 301 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0200] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0201] The memory may include read-only memory and random access memory. The memory may be volatile or non-volatile, or may include both. Non-volatile memory may include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may include random access memory (RAM), which serves as an external cache. Many forms of RAM are available by way of example, but not limitation. Examples include Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Sync Link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0202] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0203] This application also provides a computer program product, including a computer program that, when executed, implements the above-described method.

[0204] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0205] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as SRAM, EEPROM, EPROM, PROM, ROM, magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0206] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside within an ASIC. Alternatively, the processor and the readable storage medium can exist as discrete components in a device.

[0207] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0208] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0209] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0210] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0211] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0212] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for determining the charging electricity price of a photovoltaic-storage charging station, characterized in that, include: Obtain the basic parameters of the photovoltaic-storage charging station; the basic parameters include the photovoltaic output data, charging load data, energy storage system parameters, and grid interaction data of the photovoltaic-storage charging station; Based on the aforementioned basic parameters, with the goal of maximizing power plant revenue, the optimal set of decision variables is solved under multiple preset basic constraints of the power plant; the optimal set of decision variables includes the optimal electric vehicle charging load curve. Based on the optimal electric vehicle charging load curve and the pre-constructed mapping relationship between electric vehicle charging load and charging price, the optimal charging price for electric vehicles at the photovoltaic-storage charging station is determined.

2. The method according to claim 1, characterized in that, The process of finding the optimal set of decision variables based on the aforementioned fundamental parameters, with the objective of maximizing power plant revenue, and satisfying multiple preset fundamental constraints of the power plant, includes: Construct an objective function that aims to maximize the power plant's revenue; The MILP solver is invoked to solve for the set of optimal decision variables corresponding to the objective function under the multiple power plant basic constraints.

3. The method according to claim 2, characterized in that, The objective function is determined based on the revenue from electric vehicle charging, the revenue from selling electricity to the grid, the cost of grid-connected electricity purchases, and the depreciation cost of the energy storage system.

4. The method according to any one of claims 1 to 3, characterized in that, The basic constraints of the multiple power plants include at least one of the following: Upper limit constraints on grid-side power transmission; mutual exclusion constraints on grid power purchase and sales; peak shaving capacity constraints on energy storage system discharge power; valley filling capacity constraints on energy storage system charging power; upper and lower limit constraints on photovoltaic power output; upper and lower limit constraints on energy storage system state of charge; upper and lower limit constraints on energy storage system charging power; upper and lower limit constraints on energy storage system discharge power; mutual exclusion constraints on energy storage system charging power and discharge power; power balance constraints of power plants; baseline load constraints on total power plant load.

5. The method according to any one of claims 1 to 3, characterized in that, The method further includes: Obtain multiple actual photovoltaic output curves of the photovoltaic-storage charging station under different weather conditions; Cluster analysis of the multiple actual photovoltaic power output curves is used to obtain multiple actual photovoltaic power output curves corresponding to each weather condition; Multiple actual photovoltaic output curves corresponding to the current weather at the photovoltaic-storage charging station are subjected to prediction processing based on a neural network model to obtain the photovoltaic output prediction curve from the photovoltaic output data.

6. The method according to claim 5, characterized in that, The cluster analysis of the multiple actual photovoltaic output curves, obtaining multiple actual photovoltaic output curves corresponding to each weather condition, includes: For each actual photovoltaic power output curve, extract the maximum power and power variance from the actual photovoltaic power output curve; Based on the maximum power and power variance corresponding to each actual photovoltaic power output curve, the K-means clustering algorithm is used to perform cluster analysis on the multiple actual photovoltaic power output curves to obtain multiple actual photovoltaic power output curves corresponding to each weather condition.

7. The method according to any one of claims 1 to 3, characterized in that, The mapping relationship between the electric vehicle charging load and the charging electricity price can be expressed as: in, This represents the electric vehicle charging load at time t. This represents the user's baseline load at time t; This represents the electricity price response coefficient for the preset charging load, used to reflect the user's sensitivity to changes in charging electricity prices; Let represent the charging electricity price at time t; This indicates the base electricity price for charging.

8. A device for determining the charging electricity price of a photovoltaic-storage charging station, characterized in that, The device includes: The acquisition module is used to acquire the basic parameters of the photovoltaic-storage charging station; the basic parameters include the photovoltaic output data, charging load data, energy storage system parameters, and grid interaction data of the photovoltaic-storage charging station. The solution module is used to solve for the optimal set of decision variables based on the aforementioned basic parameters, with the goal of maximizing power plant revenue, under multiple preset basic constraints of the power plant; the optimal set of decision variables includes the optimal electric vehicle charging load curve. The determination module is used to determine the optimal charging price for electric vehicles at the photovoltaic-storage charging station based on the optimal electric vehicle charging load curve and the mapping relationship between electric vehicle charging load and charging price.

9. A computer device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

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