Energy storage power station power optimization method based on graph calculation

Abstract

The application belongs to the technical field of energy storage power stations, and particularly relates to an energy storage power station energy optimization method based on graph calculation, which comprises the following steps: S1, predicting future power generation capacity and load demand of the energy storage power station based on graph calculation: obtaining historical power generation data and load data of distributed power stations in the grid-connected area of the energy storage power station, and external influence data, constructing a space-time graph neural network model, constructing the space-time graph neural network model, and obtaining prediction results of power generation data and load data of each hour period in the future 24 hours; S2, calculating power and load values of each node in the power grid through power flow analysis; and calculating the energy that the energy storage power station needs to provide or consume according to the predicted future power generation capacity and load demand. The application realizes optimization of energy of the energy storage power station, and reduces the cost of the energy storage power station.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage power stations, and specifically relates to a method for optimizing the power consumption of an energy storage power station based on graph calculation. Background Art

[0002] Currently, wind power and photovoltaic power generation are widely integrated into the power system. However, the intermittent and fluctuating nature of both wind and photovoltaic power generation increases the peak load regulation pressure on the power system and leads to wind and solar power curtailment. Energy storage can effectively alleviate these issues, enabling the absorption of wind and photovoltaic power generation, reducing power system production costs, and reducing carbon emissions. However, energy storage power stations have the following problems:

[0003] High safety and reliability requirements: Power balance between power generation and load is an important foundation for the safe operation of the power system. The integration of energy storage systems, especially large-scale battery energy storage systems, must ensure their safety and reliability.

[0004] Low prediction accuracy: The power grid environment is complex. Traditional time series prediction methods are often based on statistical models such as ARIMA and VAR. However, for complex time series data, the prediction accuracy is often not high.

[0005] There is a waste of electricity in energy storage power stations, which also require large investments and long payback periods. Summary of the Invention

[0006] In order to solve the above technical problems, namely, the waste of electricity in energy storage power stations and high operating costs, the present invention provides the following technical solutions:

[0007] A method for optimizing the power consumption of an energy storage power station based on graph computing includes the following steps:

[0008] Step S1: Predicting the future power generation capacity and load demand of the energy storage power station based on graph calculation: Obtain historical power generation and load data of distributed power stations in the grid-connected area of ​​the energy storage power station, as well as external impact data, and construct a power grid topology structure. Based on the power grid topology structure, construct a spatiotemporal graph neural network model, and use the spatiotemporal graph neural network model to predict the power generation capacity and load demand for each hour in the next 24 hours;

[0009] Step S2: Obtain the technical parameters and real-time operating data of the distribution network in the power grid topology structure, calculate the power and load values ​​of each node in the power grid through power flow analysis, and output the power and voltage of each node in the power grid as the power flow analysis results; and calculate the amount of electricity that the energy storage power station needs to provide or consume based on the predicted future power generation capacity and load demand.

[0010] Furthermore, step S1 specifically includes the following steps:

[0011] Step S11, data collection and preprocessing: Obtain historical power generation data of distributed power stations in the energy storage station grid-connected area, including solar and wind power stations, grid load data, holiday data, weather data, and equipment operating status data in the energy storage station grid-connected area; preprocess the collected data, including cleaning the data, processing missing values ​​and outliers, and normalizing the data, to obtain relevant time series data of the energy storage station grid-connected area after preprocessing;

[0012] Step S12: Constructing a power grid topology graph structure: Converting the time series data processed in step S11 into a graph structure, mapping the timestamps and attribute information in the time series data to nodes and edges in the graph structure, defining nodes, edges, and their attributes, where nodes represent power stations, substations, and load points, and edges represent power flow paths, including transmission lines or distribution lines, and distribution line edges, to obtain a power grid topology graph structure;

[0013] Step S13: Using the power grid topology structure obtained in step S12 as an input to a graph neural network model to construct a spatiotemporal graph neural network model;

[0014] Step S14: Use the historical data from step S11 to train and verify the spatiotemporal graph neural network model, evaluate the performance of the spatiotemporal graph neural network model by comparing the difference between the prediction results of the spatiotemporal graph neural network model and the actual power grid status, and make adjustments and optimizations;

[0015] Step S15: Prediction and verification: Use the trained spatiotemporal graph neural network model to predict future power generation capacity and load demand, and evaluate the accuracy and generalization ability of the dynamic relationship graph model through cross-validation or holdout method.

[0016] Furthermore, in step S13, a spatiotemporal graph neural network model is constructed, including:

[0017] The changing trend and spatial distribution characteristics of the power grid topology structure in the time series are calculated to obtain the embedded representation of the graph and input it into the encoder. The encoder then performs feature extraction and combines it with the attention mechanism to output a dynamic graph of the power grid status at a preset time in the future in the decoder, ultimately obtaining a spatiotemporal graph neural network model.

[0018] Furthermore, step S13 more specifically includes:

[0019] First, time series analysis technology is used to capture the temporal trends of each node and its attributes in the power grid topology graph. The power grid topology and time series data are converted into a graph embedding representation to obtain the graph embedding representation.

[0020] Input the embedded representation of the graph into the encoder;

[0021] Feature extraction combined with the attention mechanism: An attention mechanism is introduced into the encoder to capture key changes in the power grid. After being processed by the encoder, the extracted features are input into the decoder. The decoder outputs a dynamic graph of the power grid status within a preset hourly period in the future to obtain a spatiotemporal graph neural network model.

[0022] Furthermore, specifically, step S2 includes: obtaining technical parameters and real-time operating data of the distribution network in the power grid topology structure, including the load and voltage of each node, inputting the power grid topology structure and real-time operating data into a power flow calculation tool, using the power flow calculation method to obtain the power flow analysis results of the distribution network, and outputting the power and voltage of each node in the power grid as the power flow analysis results.

[0023] Furthermore, specifically, step S2 includes:

[0024] Step S21: Obtain the power generation capacity and load demand for each hour of the next day predicted by the spatiotemporal graph neural network model, and obtain the power value of each intermediate node in the circuit through power flow calculation:

[0025] The spatiotemporal graph neural network model is used to predict the power generation capacity and load demand for each hour of the next day. The predicted power generation capacity and load demand are used as inputs to the power flow calculation tool. The power flow calculation is performed again, and the power and voltage of each node in the power grid are output as the power flow analysis results, thereby calculating the power value of each intermediate node in the circuit.

[0026] Step S22: Obtain the load forecast value of the intermediate node and the load value calculated based on the power flow analysis result, and calculate the amount of electricity that the energy storage power station needs to provide or consume:

[0027] Based on the results of the power flow analysis, the actual load value of the intermediate node is calculated. By comparing the load forecast value of the intermediate node with the load value of the power flow analysis, the amount of electricity that the energy storage power station needs to provide or consume is calculated.

[0028] Furthermore, it also includes:

[0029] Step S3: Based on the results of the power flow analysis, a profit maximization model is established to output the optimal profit model, including the way the power storage station participates in the power market, the charging power and discharging power in each hour period, and the charging and discharging time.

[0030] Step S3 specifically includes:

[0031] Calculate the full life cycle cost of energy storage, namely the levelized cost of storage (LCOS);

[0032] Using the life cycle cost of energy storage (LCOS), determine the objective function and constraints for maximizing the economic benefits of energy storage power plants;

[0033] By using optimization methods to solve the optimal combination of energy storage participation in battery leasing, electricity market trading, and power auxiliary services, as well as the optimal charging and discharging power, charging and discharging hour period, and charging and discharging duration, the economic income of the energy storage power station can be maximized.

[0034] Furthermore, the full life cycle cost of energy storage, namely the levelized cost of energy storage (LCOS), is calculated using the following formula:

[0035]

[0036] Among them, d is the discharge time at rated power, η is the cycle efficiency of the energy storage station, T is the system life, n(t) is the number of cycles per year, r is the discount rate, Q&M(t) is the operation and maintenance cost in the tth year, C E Installation cost that varies with capacity, C P Installation costs vary with power.

[0037] Furthermore, the objective function for maximizing the economic benefits of the energy storage power station is expressed as:

[0038]

[0039] P is the price, represents the price of electricity sold under the wth electricity market activity mode during the d1th hour period, represents the price of electricity purchased under the wth electricity market activity mode during the d1th hour period;

[0040] S is power, It represents the discharge power under the w-th electricity market activity mode during the d1-th hour period, and the negative sign indicates discharge; represents the charging power under the w-th electricity market activity mode during the d1-th hour period, and the positive sign indicates charging;

[0041] It represents the discharge duration under the w-th electricity market activity mode within the d1-th hour period, and the negative sign indicates discharge; represents the charging time under the wth electricity market activity mode during the d1th hour period, and a positive sign indicates charging;

[0042] Income rent is the rental income for the τ period.

[0043] Furthermore, the constraints of the objective function include energy storage capacity constraints, charging power and discharging power constraints, energy storage energy balance constraints, and initial and final energy level constraints of the energy storage system. The specific formula for the energy storage capacity constraint is as follows:

[0044] E min -Erent <E d1 <E max -E rent

[0045]

[0046] Among them, E min Indicates the minimum energy level of energy storage, E max Indicates the maximum energy level of energy storage, E rent Indicates the capacity of the leased energy storage battery, E d1 Represents the electric energy at the energy storage time d1, where d1 = 1, 2, 3, ..., τ.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] 1. The present invention constructs a power grid topology by acquiring historical power generation and load data from distributed power stations in the grid-connected area of ​​the energy storage power station, as well as external influencing data such as holidays and weather. This allows for predictions of power generation and load data for each hour of the next 24 hours. The system also obtains power flow analysis results from the distribution network and outputs the power and voltage of each node in the grid as power flow analysis results. This system allocates power from the energy storage power station on demand, saving power and reducing operating costs.

[0049] 2. Based on the results of the power flow analysis, a profit maximization model is established to output the optimal profit model, including participation methods, charging and discharging power in each hour period, and sufficient duration. This invention achieves energy optimization and reduces the cost of the storage station. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flow chart of the method for optimizing the power consumption of an energy storage power station according to the present invention. DETAILED DESCRIPTION

[0051] The technical solution of the present invention will be clearly described below in conjunction with the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0052] Peak-valley price differentials refer to the difference in electricity prices between peak and off-peak periods. Energy storage plants can store energy during low-price periods and release it during peak periods, profiting from the price differential.

[0053] Ancillary services refer to paid value-added services provided by power companies to users, such as peak shaving (peak-valley electricity price difference), frequency regulation, and reactive power regulation. Energy storage power stations can generate revenue by providing these ancillary services.

[0054] Power flow calculation is a term in electrical engineering that refers to the calculation of active power, reactive power, and voltage distribution within a power grid, given the power system's network topology, component parameters, and generation and load parameters. Power flow calculation determines the steady-state operating parameters of each component of the power system, based on the given grid structure and parameters and the operating conditions of components such as generators and loads. Typically, given operating conditions include the power of each source and load point in the system, the voltage at the hub, and the voltage and phase angle at the equilibrium point. The operating parameters to be determined include the voltage amplitude and phase angle at each busbar node, the power distribution of each branch, and the power loss of the network.

[0055] like Figure 1 As shown, the present invention provides a method for optimizing the power consumption of an energy storage power station based on graph calculation, comprising the following steps:

[0056] Step S1: Predict the future power generation capacity and load demand of the energy storage power station based on the graph calculation:

[0057] The historical power generation and load data of distributed power stations in the grid-connected area of ​​the energy storage power station, as well as external impact data, are obtained to construct a grid topology structure. Based on the grid topology structure, a spatiotemporal graph neural network model is constructed. The power generation demand and load demand forecast results for each hour in the next 24 hours are predicted through the spatiotemporal graph neural network model.

[0058] Power generation capacity forecast: Considers the impact of weather forecasts (especially for solar and wind power) and seasonal changes on renewable energy generation. Load demand forecast: Analyzes historical data and considers the impact of factors such as weekdays and holidays and temperature changes on load demand.

[0059] Step S1 specifically includes the following steps:

[0060] Step S11: Data collection and preprocessing:

[0061] Obtain historical power generation data for distributed power stations in the energy storage power station's grid-connected area, including solar and wind power stations, grid load data for the energy storage power station's grid-connected area, holiday data, weather data, and equipment operating status data. Preprocess the collected data, including cleaning, addressing missing values ​​and outliers, and normalizing the data.

[0062] Step S12: Constructing a power grid topology structure:

[0063] The time series data processed in step S11 is converted into a graph structure, and the timestamps and attribute information in the time series data are mapped to the nodes and edges in the graph structure.

[0064] Define nodes: In a graph, nodes represent power plants, substations, and load points. Define edges: Edges represent power flow paths, which can be transmission lines or distribution lines. Transmission line edges connect power plants and substations, or between substations; distribution line edges distribute power from substations to load points. Assign node and edge attributes: Node attributes include power generation capacity, load levels, holiday data, weather data, and weather conditions. Edge attributes include line capacity, etc.

[0065] Graph Neural Networks (GNNs), as a new neural network model, can process data with non-Euclidean structures, such as graphs and networks. By converting time series data into graph structures and using GNNs for feature extraction and prediction, it can handle nonlinear relationships and complex time series data, thereby improving the accuracy of time series prediction.

[0066] Step S13: Use the power grid topology structure of step S12 as the input of the graph neural network model to construct a spatiotemporal graph neural network model:

[0067] The changing trend and spatial distribution characteristics of the power grid topology structure in time series are calculated to obtain the embedded representation of the graph and input it into the encoder. The encoder then extracts features and combines them with the attention mechanism to output a dynamic graph of the power grid state at a preset time in the future in the decoder, ultimately obtaining a spatiotemporal graph neural network model. Specifically:

[0068] First, time series analysis techniques (such as differencing, sliding averages, and autoregressive models) are used to capture the temporal trends of each node (such as power stations, substations, and load points) in the power grid topology and its attributes (such as power generation capacity, load levels, and weather conditions). Furthermore, spatial relationships between nodes are analyzed, such as the similarity of adjacent nodes and the flow of power between nodes, to capture the spatial distribution characteristics of the power grid. The power grid topology and time series data are then converted into a graph embedding representation, resulting in a graph embedding.

[0069] Input the graph embedding representation into the encoder: The resulting graph embedding representation is used as input to the encoder. The encoder is typically a graph neural network model that extracts graph feature information. In the encoder, graph neural network models such as graph convolutional networks (GCNs) and graph attention networks (GATs) can be used to process graph-structured data and extract its high-level features.

[0070] Integrating an attention mechanism for feature extraction: An attention mechanism is introduced into the encoder to dynamically focus on important nodes and edges in the graph during feature extraction. The attention mechanism assigns different weights based on the attributes of nodes and edges and the relationships between them, thereby capturing key changes in the power grid. The decoder outputs a dynamic graph of the power grid state for a preset hourly period in the future. After processing by the encoder, the extracted features are input into the decoder. The decoder is typically also a neural network model that predicts future power grid states based on the extracted features.

[0071] The decoder can generate dynamic graphs for a period of time in the future, which reflect the changes in the properties of nodes and edges in the power grid over time. Ultimately, a spatiotemporal graph neural network model is obtained.

[0072] Step S14, Model Training: Divide the historical data preprocessed in step S11 into a training set, a validation set, and a test set. Use the historical data to train and validate the spatiotemporal graph neural network model. By comparing the spatiotemporal graph neural network model's predictions with the actual grid status, the model's performance can be evaluated and necessary adjustments and optimizations can be made.

[0073] Choose a loss function and optimizer: Define a loss function (such as mean squared error) and an optimizer (such as Adam).

[0074] In power forecasting, the mean squared error (MSE) is a commonly used loss function. MSE calculates the average of the squared differences between the predicted and true values ​​and reflects the accuracy of the model's predictions. For each time step or hourly period, the MSE is calculated between the predicted load or generation and the actual load or generation. During training, the goal is to minimize the MSE to improve the model's forecast accuracy.

[0075] Commonly used optimizers include stochastic gradient descent (SGD), Adam, etc.

[0076] The model parameters are continuously updated through iterative training. In each iteration, forward propagation is first performed to calculate the model's predicted value, then the value of the loss function is calculated, followed by backpropagation to calculate the gradient and use the optimizer to update the parameters.

[0077] During training, monitor the value of the loss function and the performance of the model on the validation set.

[0078] When the performance on the validation set no longer improves, training can be stopped to avoid overfitting.

[0079] Step S15, prediction and verification: Use the trained spatiotemporal graph neural network model to predict future power generation capacity and load demand, and evaluate the accuracy and generalization ability of the model through cross-validation or holdout method.

[0080] Use the trained spatiotemporal graph neural network model to predict power generation capacity and load demand in the future.

[0081] The accuracy and generalization ability of the model are evaluated through cross-validation or holdout method. Common evaluation indicators include precision, recall rate, F1 score, MSE, MAE, etc.

[0082] Step S2: Obtain the technical parameters and real-time operating data of the distribution network in the power grid topology structure, calculate the power and load values ​​of each node in the power grid through power flow analysis, and output the power and voltage of each node in the power grid as the power flow analysis results; and calculate the amount of electricity that the energy storage power station needs to provide or consume based on the future power generation capacity and load demand predicted by the spatiotemporal graph neural network model. Specifically:

[0083] Obtain the technical parameters and real-time operating data of the distribution network from the grid topology diagram. Input the grid topology and real-time operating data into a power flow calculation tool. Using power flow calculation methods, obtain power flow analysis results for the distribution network. Output the power and voltage at each node in the grid as the power flow analysis results. Real-time operating data includes power injection (or load) and voltage at each node.

[0084] Power flow analysis is a crucial task in power systems. It aims to calculate the power and voltage distribution at each node in the power grid. By obtaining a grid topology diagram, along with technical parameters and real-time operating data of the distribution network, we can use power flow calculation tools to generate power flow analysis results. These results are crucial for stable power system operation, optimized dispatch, and fault analysis.

[0085] More specifically, step S2 includes:

[0086] 1) Obtain the power generation capacity and load demand for each hour of the next day predicted by graph calculation, and obtain the power value of each intermediate node in the circuit through power flow calculation.

[0087] Specifically, graph computing techniques (such as spatiotemporal graph neural network models) are used to predict power generation capacity and load demand for each hour of the next day. The predicted power generation capacity and load demand are input into a power flow calculation tool, which then performs another power flow analysis. The power and voltage at each node in the power grid are output as power flow analysis results, resulting in the power values ​​of each intermediate node in the circuit.

[0088] 2) Obtain the load forecast value of the intermediate node and the load value calculated based on the power flow analysis results, and calculate the amount of electricity that the energy storage power station needs to provide or consume.

[0089] Based on the results of the power flow analysis, the actual load values ​​of the intermediate nodes are calculated. By comparing the load forecast values ​​of the intermediate nodes with the load values ​​of the power flow analysis, the amount of electricity that the energy storage station needs to provide or absorb is calculated. This is achieved by calculating the difference between the two. A positive value indicates the amount of electricity the energy storage station needs to provide, while a negative value indicates the amount of electricity it needs to absorb.

[0090] Through the above steps, we can use power flow analysis technology to calculate the power and voltage at each node in the power grid. Based on the predicted future power generation capacity and load demand, we can calculate the amount of electricity that the energy storage power station needs to provide or consume. This will provide strong support for the stable operation and optimized scheduling of the power system.

[0091] Step S3: Establish a profit maximization model and output the optimal profit model: Based on the results of the power flow analysis, establish a profit maximization model and output the optimal profit model, including the way the power storage station participates in the power market, the charging power and discharging power in each hour, and the charging and discharging time. Step S3 specifically includes:

[0092] 1) Calculate the full life cycle cost of energy storage, i.e. the levelized cost of energy storage:

[0093] The full life cycle cost of energy storage is the levelized cost of storage (LCOS). LCOS can be summarized as the full life cycle cost of a storage technology divided by the cumulative amount of energy or power it transmits. It reflects the internal average electricity price when the net present value is zero, which is the profit point of the investment. The levelized cost of storage (LCOS) quantifies the discounted cost per unit discharge under a specific energy storage technology and application scenario, taking into account all technical and economic parameters that affect the discharge life cost. The specific calculation formula and the indicators involved are as follows:

[0094]

[0095] The definitions of each parameter are shown in Table 1:

[0096] Table 1

[0097] parameter unit definition d Hour Discharge time at rated power η ％ Energy storage station cycle efficiency T Year System life n(t) times / year Annual cycles r ％ Discount rate Q&M(t) ％ Operation and maintenance costs in year t (installation ratio) <![CDATA[C E ]]> Yuan / kWh Installation costs that vary with capacity <![CDATA[C P ]]> Yuan / kW Installation costs as a function of power

[0098] The levelized cost of storage (LCOS) is a key metric for evaluating the economic viability of energy storage technology. Its calculation formula takes into account the full lifecycle cost of an energy storage system, including installation costs, operation and maintenance expenses, and system lifespan.

[0099] The total cost includes the capacity-variable installation cost, the power-variable installation cost, and the annual operation and maintenance costs (installation ratio), and also takes into account the discount rate. The cumulative transmitted energy is the total amount of energy transmitted by the energy storage system over its lifetime. The specific definitions and calculation methods of each parameter are given in the problem description.

[0100] 2) Using the life cycle cost (LCOS) of energy storage, determine the objective function and constraints for maximizing the economic benefits of the energy storage power station. This objective function maximizes the economic benefits of the energy storage power station while ensuring that the grid operation and the safety of the energy storage power station are not affected. The economic benefits of the energy storage power station mainly come from two aspects: first, the rental income from leasing the energy storage capacity to other users, and second, the income from participating in the electricity market (such as wind power, solar power, frequency regulation, peak regulation, etc.). Therefore, the objective function for maximizing the economic benefits of the energy storage power station is expressed as:

[0101]

[0102] Income in the formula rent Represents rental income,

[0103] It represents the income obtained from participating in the electricity market (such as wind power, solar power, frequency regulation, peak regulation, etc.).

[0104] Where τ is the total duration of the cycle, d1 is the hour segment. For example, if the cycle of τ is 24 hours, d1 is each hour in the cycle. n is the total number of ways in which the energy storage station participates in the electricity market, and w represents the way in which the energy storage station participates in the electricity market. For example, w = 1 means that the energy storage station participates in the electricity market through wind power, w = 2 means that the energy storage station participates in the electricity market through solar power, w = 3 means that the energy storage station participates in the electricity market through frequency modulation, and w = 4 means that the energy storage station participates in the electricity market through peak load regulation, etc.

[0105] P is the price, represents the price of electricity sold under the wth electricity market activity mode during the d1th hour period, represents the price of electricity purchased under the wth electricity market activity mode during the d1th hour period;

[0106] S is power, It represents the discharge power under the w-th electricity market activity mode during the d1-th hour period, and the negative sign indicates discharge; represents the charging power under the w-th electricity market activity mode during the d1-th hour period, and the positive sign indicates charging;

[0107] It represents the discharge duration under the w-th electricity market activity mode within the d1-th hour period, and the negative sign indicates discharge; It represents the charging time under the w-th electricity market activity mode during the d1-th hour period, and the positive sign indicates charging.

[0108] The income in the objective function maxProfirent It reflects capacity leasing, which refers to leasing the capacity of the energy storage power station to other users for use, thereby obtaining leasing income.

[0109] Income rent is the rental income of τ period, and its formula is:

[0110]

[0111] in represents the rental price of the energy storage battery within the τ period, E rent Indicates the capacity of the leased energy storage battery.

[0112] The constraints of the objective function include energy storage capacity constraints, charging power and discharging power constraints, energy storage energy balance constraints, and initial and final energy level constraints of the energy storage system.

[0113] (1) Energy storage capacity constraint: Since energy storage has a maximum energy storage capacity, it cannot store electricity indefinitely. The leased capacity cannot exceed the maximum energy storage capacity.

[0114] The specific formula of the constraint is as follows:

[0115] E min -E rent <E d1 <E max -E rent

[0116]

[0117] Among them E min Indicates the minimum energy level of energy storage, E max Indicates the maximum energy level of energy storage, E rent Indicates the capacity of the leased energy storage battery, E d1 Represents the electric energy at the energy storage time d1, where d1 = 1, 2, 3, ..., τ.

[0118] The minimum energy level of energy storage means that when the remaining capacity of the energy storage after discharge drops to 5% of the rated capacity, further discharge will stop; the maximum energy level of energy storage means that when the capacity of the energy storage after charging rises to 95% of the rated capacity, further charging will stop.

[0119] (2) Charging power and discharging power constraints: Charging and discharging efficiency constraints require that the charging power and discharging power of energy storage must be within the allowed range. The specific formula is as follows:

[0120]

[0121]

[0122] Indicates the maximum discharge power of the energy storage station, Indicates the maximum charging power of the energy storage station.

[0123] (3) Energy storage energy balance constraints:

[0124]

[0125] E d1,power Indicates the load value obtained by the flow calculation during the d1 hour period.

[0126] E d1,predict Indicates the load forecast value of the node in hour d1.

[0127] (4) Initial and final energy level constraints of the energy storage system:

[0128]

[0129] It means that after a period of time, the energy state of the energy storage battery pack should remain balanced, that is, the total amount of charge and discharge of the energy storage battery pack in a period of time should be equal.

[0130] 3) Solving the optimal profit model: Input the known parameters of the objective function, such as installed capacity, participation method, electricity price, predicted power load at each node, and actual load calculated by power flow. By using optimization methods, the optimal combination of energy storage participation in battery leasing, power market trading, power ancillary services, etc., as well as the optimal charging and discharging power, charging and discharging hour period, and charging and discharging duration, is solved to maximize the economic income of the energy storage power station.

[0131] Optimization methods are used to find the optimal solution to the objective function and constraints mentioned above, using linear programming, nonlinear programming, or heuristic algorithms (such as genetic algorithms and particle swarm optimization). The results will include the optimal combination of various profit-making methods for the energy storage station, the optimal charging and discharging power, charging and discharging time periods, and the charging and discharging duration.

[0132] Output of the optimal profit model: Based on the solution, the optimal profit model for the energy storage power station is output, including information such as participation in electricity market activities, charging and discharging power and duration in each hour, etc. This will provide energy storage power station operators with a clear operating strategy and a plan to maximize economic benefits.

[0133] Through the above steps, we can establish a profit maximization model for the energy storage power station and output the optimal profit model to maximize the economic benefits of the energy storage power station. The above technical features constitute the best embodiment of the present invention, which has strong adaptability and optimal implementation effects. Non-essential technical features can be added or removed according to actual needs to meet the needs of different situations.

[0134] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions of the technical solution of the present invention by ordinary technicians in this field do not deviate from the essence and scope of the technical solution of the present invention.

Claims

1. A method for optimizing the power consumption of an energy storage power station based on graph computing, characterized in that: The steps include: Step S1: Predicting the future power generation capacity and load demand of the energy storage power station based on graph calculation: Obtain historical power generation and load data of distributed power stations in the grid-connected area of ​​the energy storage power station, as well as external impact data, and construct a power grid topology structure. Based on the power grid topology structure, construct a spatiotemporal graph neural network model, and use the spatiotemporal graph neural network model to predict the power generation capacity and load demand for each hour in the next 24 hours; The specific steps include: Step S11, data collection and preprocessing: Obtain historical power generation data of distributed power stations in the energy storage station grid-connected area, including solar and wind power stations, grid load data, holiday data, weather data, and equipment operating status data in the energy storage station grid-connected area; preprocess the collected data, including cleaning the data, processing missing values ​​and outliers, and normalizing the data, to obtain relevant time series data of the energy storage station grid-connected area after preprocessing; Step S12: Constructing a power grid topology graph structure: Converting the time series data processed in step S11 into a graph structure, mapping the timestamps and attribute information in the time series data to nodes and edges in the graph structure, defining nodes, edges, and their attributes, where nodes represent power stations, substations, and load points, and edges represent power flow paths, including transmission lines or distribution lines, and distribution line edges, to obtain a power grid topology graph structure; Step S13: Using the power grid topology structure obtained in step S12 as an input to a graph neural network model to construct a spatiotemporal graph neural network model; Specifically, they include: First, time series analysis technology is used to capture the temporal trends of each node and its attributes in the power grid topology graph. The power grid topology and time series data are converted into a graph embedding representation to obtain the graph embedding representation. Input the embedded representation of the graph into the encoder; Feature extraction using an attention mechanism: An attention mechanism is introduced into the encoder to capture key changes in the power grid. After processing by the encoder, the extracted features are input into the decoder, which then outputs a dynamic graph of the power grid status within a preset hourly period in the future, thereby obtaining a spatiotemporal graph neural network model. Step S14: Use the historical data from step S11 to train and verify the spatiotemporal graph neural network model, evaluate the performance of the spatiotemporal graph neural network model by comparing the difference between the prediction results of the spatiotemporal graph neural network model and the actual power grid status, and make adjustments and optimizations; Step S15, prediction and verification: using the trained spatiotemporal graph neural network model to predict future power generation capacity and load demand, and evaluating the accuracy and generalization ability of the dynamic relationship graph model through cross-validation or holdout method; Step S2: Obtain the technical parameters and real-time operating data of the distribution network in the power grid topology structure, calculate the power and load values ​​of each node in the power grid through power flow analysis, and output the power and voltage of each node in the power grid as the power flow analysis results; and calculate the amount of electricity that the energy storage power station needs to provide or consume based on the predicted future power generation capacity and load demand.

2. The energy storage power station power optimization method according to claim 1, characterized in that: Specifically, step S2 includes: obtaining technical parameters and real-time operating data of the distribution network in the power grid topology structure, including the load and voltage of each node, inputting the power grid topology structure and real-time operating data into a power flow calculation tool, using the power flow calculation method to obtain the power flow analysis results of the distribution network, and outputting the power and voltage of each node in the power grid as the power flow analysis results.

3. The energy storage power station power optimization method according to claim 2, characterized in that: Specifically, step S2 includes: Step S21: Obtain the power generation capacity and load demand for each hour of the next day predicted by the spatiotemporal graph neural network model, and obtain the power value of each intermediate node in the circuit through power flow calculation: The spatiotemporal graph neural network model is used to predict the power generation capacity and load demand for each hour of the next day. The predicted power generation capacity and load demand are used as inputs to the power flow calculation tool. The power flow calculation is performed again, and the power and voltage of each node in the power grid are output as the power flow analysis results, thereby calculating the power value of each intermediate node in the circuit. Step S22: Obtain the load forecast value of the intermediate node and the load value calculated based on the power flow analysis result, and calculate the amount of electricity that the energy storage power station needs to provide or consume: Based on the results of the power flow analysis, the actual load value of the intermediate node is calculated. By comparing the load forecast value of the intermediate node with the load value of the power flow analysis, the amount of electricity that the energy storage power station needs to provide or consume is calculated.

4. The energy storage power station power optimization method according to claim 1, characterized in that: Also includes: Step S3: Based on the results of the power flow analysis, a profit maximization model is established to output the optimal profit model, including the way the power storage station participates in the power market, the charging power and discharging power in each hour period, and the charging and discharging time. Step S3 specifically includes: Calculate the full life cycle cost of energy storage, namely the levelized cost of storage (LCOS); Using the life cycle cost of energy storage (LCOS), determine the objective function and constraints for maximizing the economic benefits of energy storage power plants; By using optimization methods to solve the optimal combination of energy storage participation in battery leasing, electricity market trading, and power auxiliary services, as well as the optimal charging and discharging power, charging and discharging hour period, and charging and discharging duration, the economic income of the energy storage power station can be maximized.

5. The energy storage power station power optimization method according to claim 4, characterized in that: Calculate the full life cycle cost of energy storage, namely the levelized cost of energy storage , the specific calculation formula is as follows: ， Among them, d is the discharge time at rated power, Energy storage station cycle efficiency, T system life, n(t) annual cycle times, r discount rate, &M(t) operation and maintenance expenses in year t, Installation costs that vary with capacity, Installation costs vary with power.

6. The energy storage power station power optimization method according to claim 5, characterized in that: The objective function for maximizing the economic benefits of an energy storage power station is expressed as: P is the price, represents the price of electricity sold under the wth electricity market activity mode during the d1th hour period, represents the price of electricity purchased under the wth electricity market activity mode during the d1th hour period; S is power, It represents the discharge power under the w-th electricity market activity mode during the d1-th hour period, and the negative sign indicates discharge; represents the charging power under the w-th electricity market activity mode during the d1-th hour period, and the positive sign indicates charging; It represents the discharge duration under the w-th electricity market activity mode within the d1-th hour period, and the negative sign indicates discharge; represents the charging time under the wth electricity market activity mode during the d1th hour period, and a positive sign indicates charging; for Periodic rental income.

7. The energy storage power station power optimization method according to claim 6, characterized in that: The constraints of the objective function include energy storage capacity constraints, charging power and discharging power constraints, energy storage energy balance constraints, and initial and final energy level constraints of the energy storage system. The specific formula for energy storage capacity constraints is as follows: ， Among them, E min Indicates the minimum energy level of energy storage, E max Indicates the maximum energy level of energy storage, E rent Indicates the capacity of the leased energy storage battery. Represents the electric energy at the time of energy storage d1, and d1=1,2,3,..., .

Images