A method and system for collaborative optimization scheduling between a gravity energy storage system and a conventional energy storage system

Abstract

The present application relates to the technical field of power system management, in particular to a collaborative optimization scheduling method and system between a gravity energy storage system and a conventional energy storage system. The method comprises: configuring the gravity energy storage system and the conventional energy storage system on the power supply side of a large power grid; collecting the operation data of the gravity energy storage system and the conventional energy storage system in a period of time respectively to form historical operation data, and predicting the power demand value of a specific future time period according to the historical operation data by using a demand prediction model; inputting the power demand value of the specific future time period into a system optimization model, and iteratively training the system optimization model to finally obtain the charging and discharging plan of the gravity energy storage system and the conventional energy storage system in each time period; the present application can formulate accurate charging and discharging strategies by collecting the operation data of the two systems in real time and applying a machine learning method to predict the power grid demand, so as to store energy at the lowest cost and release energy at the peak period to optimize the cost benefit.

Description

Technical Field

[0001] This invention relates to the field of power system management technology, and in particular to a collaborative optimization scheduling method and system that combines gravity energy storage systems and conventional energy storage systems, for improving the operating efficiency and energy utilization rate of power systems. Background Technology

[0002] With the rapid increase in renewable energy penetration, the power system faces the dual pressures of intermittent power generation and intensified load fluctuations. Energy storage technology, as a key means to smooth out fluctuations and improve grid flexibility, has formed a diverse range of technological routes, primarily based on electrochemical energy storage (such as lithium batteries) and physical energy storage (such as pumped hydro storage and gravity storage). Among these, gravity storage, with its long lifespan (30-50 years), low cost of electricity (0.3-0.6 yuan / kWh), and environmental friendliness, has become an emerging direction for large-scale energy storage; while electrochemical energy storage, with its fast response (millisecond-level) and high energy density, is suitable for short-term frequency regulation and peak load support.

[0003] However, a single energy storage technology struggles to balance economic viability, response speed, and sustainability. Existing technologies for the coordinated scheduling of gravity and electrochemical energy storage face three core challenges: technological integration barriers, limitations of optimization models, and insufficient prediction accuracy and robustness. Regarding technological integration barriers, gravity and electrochemical energy storage exhibit significant differences in charge-discharge characteristics, efficiency curves, and lifespan degradation mechanisms (e.g., gravity storage efficiency varies with altitude, while lithium battery efficiency fluctuates with SOC). Existing coordinated models often employ simple weighted or priority scheduling without establishing a dynamic coupling mechanism, resulting in insufficient release of energy storage potential. For example, the MILP model proposed by Zhang et al. (2022) only aims to minimize total cost, failing to quantify the impact of equipment aging on long-term economics; in actual operation, excessive use of lithium batteries may lead to a sharp reduction in lifespan. Regarding limitations of optimization models, existing coordinated optimization models often focus on static constraints (such as power balance and capacity limitations), neglecting dynamic factors such as renewable energy prediction errors, electricity price fluctuations, and flexible loads on the user side. Studies show that even with models that only consider static constraints, the wind and solar curtailment rate is still as high as 15%-20% in scenarios where wind and solar penetration is >30% (Wang et al., 2023).

[0004] Furthermore, traditional Lagrange relaxation methods use a fixed step size to update coordination parameters, which can easily lead to local optima during sudden demand changes, reducing convergence speed. Regarding insufficient prediction accuracy and robustness, existing demand forecasting techniques (such as ARIMA and LSTM) are less adaptable to extreme weather events and sudden load changes. According to IEEE PES statistics, the error of traditional forecasting models increases by 40%-60% during typhoon weather compared to normal days, directly affecting the reliability of scheduling strategies. At the same time, most studies do not quantify the uncertainty of forecast results (such as confidence intervals and probability distributions), resulting in insufficient robustness of the optimization models.

[0005] Although existing studies have attempted to improve system efficiency through coordinated scheduling, the following key issues remain unresolved: (1) The problem of a singular objective function. Most models take the minimization of total cost as the sole objective and do not incorporate multi-dimensional indicators such as carbon trading costs, equipment lifespan losses, and grid stability penalties, making it difficult to reflect the complexity of actual operation. For example, the model of Liu et al. (2021) suffers from significant economic deterioration when the carbon price is >200 yuan / ton because it ignores carbon emission constraints.

[0006] (2) The problem of rigid coordination mechanisms: Existing coordination strategies (such as fixed weight allocation and static priority) cannot dynamically respond to real-time electricity price fluctuations and changes in renewable energy output. Case studies show that in scenarios of sudden drops in photovoltaic output, the grid frequency deviation of fixed coordination strategies is 35% higher than that of dynamic strategies. The problem of lacking a full life cycle perspective: Existing technologies have not established a closed-loop feedback mechanism for equipment aging, economics, and dispatch strategies, resulting in an underestimation of long-term operating costs. Simulations show that the total cost of the model that ignores the cycle life of lithium batteries is 22%-28% lower than the actual value after 5 years. Summary of the Invention

[0007] Objective: To overcome the shortcomings of existing technologies, this invention proposes a collaborative optimization scheduling method between gravity energy storage and conventional energy storage systems. It focuses on introducing equipment aging cost functions and renewable energy fluctuation tolerance constraints, and employs an adaptive Lagrange multiplier update algorithm and a hierarchical game framework. The adaptive Lagrange multiplier update algorithm and a two-layer model work together; the upper layer provides initial multiplier values, and the lower layer adjusts the multipliers based on real-time deviation feedback, forming a dynamic closed-loop optimization. Furthermore, it integrates a Transformer prediction model and robust optimization theory. This not only overcomes the technical limitations of single energy storage technologies but also improves the operating efficiency and energy utilization of the entire power system, possessing significant theoretical value and practical application prospects. This invention also provides a collaborative optimization scheduling system between gravity energy storage and conventional energy storage systems.

[0008] Technical solution: In a first aspect, the present invention provides a method for coordinated optimization scheduling between a gravity energy storage system and a conventional energy storage system, the method comprising the following steps:

[0009] Gravity energy storage system and conventional energy storage system are configured on the power source side of the large power grid. The gravity energy storage system adopts a vertical frame type gravity energy storage system, and the conventional energy storage system is an electrochemical energy storage system.

[0010] Operational data of the gravity energy storage system and the conventional energy storage system are collected over a period of time to form historical operational data. The demand forecasting model is then used to predict the electricity demand for a specific future period based on the historical operational data.

[0011] The electricity demand values ​​for a specific future time period are input into the system optimization model, and the model is iteratively trained to obtain the charging and discharging plans for the gravity energy storage system and the conventional energy storage system in each time period. The system optimization model includes an optimization model and a coordination model. The optimization model applies a decomposition-coordination method to the collaborative optimization of the gravity energy storage system and the electrochemical energy storage system. This decomposition-coordination method decomposes the overall optimization problem into two sub-problems: one for the gravity energy storage system and the other for the conventional energy storage system. Each sub-problem independently optimizes its own operating strategy while satisfying balance constraints. These balance constraints are used to balance the operational decisions of the two subsystems, ensuring the overall optimization problem is achieved. These balance constraints are implemented through a two-layer coordination model, specifically:

[0012] The upper-layer coordination model is responsible for global optimization, while the lower-layer model is responsible for real-time control. The upper-layer model sets constraints and corresponding objective functions based on the global variables corresponding to the gravity energy storage system and the conventional energy storage system. The lower-layer model constructs constraints and corresponding objective functions based on the control variables corresponding to the gravity energy storage system and the conventional energy storage system.

[0013] Furthermore, including:

[0014] The demand forecasting model predicts the electricity demand for a specific future time period based on the historical operating data, including:

[0015] By inputting past electricity consumption data, weather conditions, calendar information, and relevant economic indicators into a neural network model, the model can obtain information on electricity demand, demand volatility and uncertainty at a specific future point in time, as well as the maximum demand forecast for the future period.

[0016] Furthermore, including:

[0017] The optimization model applies the decomposition and coordination method to the synergistic optimization of gravity energy storage systems and electrochemical energy storage systems, specifically including the following steps:

[0018] Data from different sources is integrated into a unified data framework. The data from different sources includes the electricity demand value for a specific future time period and the collected operating data of the gravity energy storage system and conventional energy storage system. Based on the above data, the overall optimization problem is defined as minimizing the total system cost, and an overall optimization objective is set.

[0019] The decomposition and coordination method includes:

[0020] The overall optimization problem is decomposed into two sub-problems: the optimization problem of gravity energy storage system and the optimization problem of conventional energy storage system, and the objective functions and constraints of the optimization problems of gravity energy storage system and conventional energy storage system are set respectively.

[0021] To balance the operational decisions of the two subsystems by setting shared variables or parameters, ensuring that the overall system objective is achieved; specifically: setting power balance constraints to ensure that the total output power of the two systems meets the power demand in each time period; and setting a common objective function to adjust their respective strategies, wherein the common objective function is to minimize the total cost or maximize the efficiency.

[0022] Furthermore, including:

[0023] Iterative training of the system optimization model includes:

[0024] The process iteratively solves each subproblem and updates the Lagrange multipliers, gradually adjusting the strategies for each subproblem to minimize the overall cost and satisfy power balance. Specifically, for each iteration, two subproblems are solved independently first. Then, the magnitude of the power imbalance is determined based on the solution results of the subproblems. If a significant power imbalance is found, the Lagrange multipliers are adaptively adjusted until the power balance constraint is met. Finally, the constraints of the two subproblems are adjusted through the common objective function.

[0025] Furthermore, including:

[0026] The upper-level model sets constraints and corresponding objective functions based on the global variables corresponding to the gravity energy storage system and the conventional energy storage system, including:

[0027] Set global variables, i.e., continuous variables: time in gravity energy storage systems and conventional energy storage systems. t Charge and discharge power Time in gravity energy storage systems and conventional energy storage systems t Energy storage capacity Integer variables: the on / off states of equipment in gravity energy storage systems and conventional energy storage systems;

[0028] Setting constraints based on the global variables includes:

[0029] Static constraints, denoted as Between the minimum and maximum values Between the minimum and maximum values;

[0030] Renewable energy consumption constraints are expressed as: ;

[0031] The long-term constraint on power balance is expressed as: ;

[0032] in, Indicates the output power of renewable energy. Indicates tolerance for fluctuations in renewable energy. This represents the electricity demand value for a specific time period. T Indicates a time range or the total number of items within a certain time period;

[0033] Construct the corresponding objective function, expressed as: ;

[0034] in, For time t Operating costs of gravity energy storage systems; For time t The operating cost of conventional energy storage systems, For time t Penalties for imbalances in electricity supply and demand; For time t Equipment aging costs.

[0035] Furthermore, including:

[0036] The lower-level model is constructed based on the control variables of the gravity energy storage system and the conventional energy storage system, establishing constraints and corresponding objective functions, specifically including:

[0037] Set control variables, namely the real-time charging and discharging power adjustment of the gravity energy storage system and the conventional energy storage system. , ;

[0038] Set constraints based on the global variables, including

[0039] Dynamic electricity price response, expressed as: ;

[0040] in, Indicates real-time electricity price. Indicates the benchmark electricity price. This represents the dynamic electricity price response coefficient, which determines the intensity of the impact of electricity price fluctuations on power adjustments.

[0041] Power rate limiting, expressed as: ;

[0042] in, This indicates the amount of charging power adjustment (kW). This indicates the electricity price threshold (yuan / kWh). Power adjustment is triggered when the real-time electricity price exceeds this value. This indicates the maximum permissible charge and discharge power (kW) of the energy storage system.

[0043] Power grid frequency regulation is represented as: ;

[0044] in, K Indicates the frequency adjustment coefficient. Indicates the real-time frequency of the power grid. Indicates the nominal frequency;

[0045] Construct the corresponding objective function, expressed as: ;

[0046] in,

[0047] H For future prediction time domain; C Grid ( k (This refers to the real-time electricity price.) This is the power balance weighting coefficient.

[0048] Furthermore, including:

[0049] The overall optimization problem includes: defining the overall optimization problem as minimizing the total system cost while satisfying power demand and technical constraints, wherein the total system cost includes operating costs, charging and discharging costs, and possible maintenance and depreciation costs;

[0050] Overall optimization goal: ;

[0051] in, and They are in time The operating costs of gravity energy storage systems and electrochemical energy storage systems, This represents a penalty term for power imbalance, used to characterize the penalty imposed when the deviation between actual output and demand exceeds a threshold, in order to improve grid stability. This represents the cost of equipment aging, used to quantify the impact of charge-discharge cycles on energy storage lifespan. The weighting coefficient for the penalty item of power supply and demand imbalance; This is the weighting coefficient for equipment aging costs, and the specific value is set according to the power grid stability requirements and equipment lifespan priorities.

[0052] Furthermore, it includes: the Lagrange multipliers Used for relaxing the power balance, it is represented as: ;

[0053] in, and They are in time The charging and discharging power of gravity energy storage systems and conventional energy storage systems, while It is time The electricity demand value is obtained through the demand forecasting model. T Indicates the total number of time ranges or time periods.

[0054] Furthermore, including:

[0055] If a significant power imbalance exists, the Lagrange multipliers are adaptively adjusted until the power balance constraint condition is met. This adaptive adjustment of the Lagrange multipliers... Represented as: ;

[0056] in, The step size parameter is equal to The step size parameter changes over time, which accelerates model convergence.

[0057] On the other hand, the present invention also provides a collaborative optimization scheduling system between a gravity energy storage system and a conventional energy storage system, the system comprising:

[0058] A configuration module is used to configure a gravity energy storage system and a conventional energy storage system on the power supply side of a large power grid. The gravity energy storage system adopts a vertical frame type gravity energy storage, and the conventional energy storage system is an electrochemical energy storage system.

[0059] The data acquisition and prediction module is used to collect the operating data of the gravity energy storage system and the conventional energy storage system over a period of time, respectively, to form historical operating data, and to use the demand prediction model to predict the electricity demand value for a specific future period based on the historical operating data.

[0060] An optimization module is used to input the electricity demand value for a specific future time period into the system optimization model and iteratively train the system optimization model to finally obtain the charging and discharging plans of the gravity energy storage system and the conventional energy storage system for each time period. The system optimization model includes an optimization model and a coordination model. The optimization model applies a decomposition and coordination method to the collaborative optimization of the gravity energy storage system and the electrochemical energy storage system. The decomposition and coordination method decomposes the overall optimization problem into two sub-problems: one for the gravity energy storage system and the other for the conventional energy storage system. Each sub-problem independently optimizes its own operating strategy and satisfies balance constraints. The balance constraints are used to balance the operational decisions of the two subsystems to ensure that the overall optimization problem is achieved. The balance constraints are implemented through a two-layer coordination model, specifically:

[0061] The upper-layer coordination model is responsible for global optimization, while the lower-layer model is responsible for real-time control. The upper-layer model sets constraints and corresponding objective functions based on the global variables corresponding to the gravity energy storage system and the conventional energy storage system. The lower-layer model constructs constraints and corresponding objective functions based on the control variables corresponding to the gravity energy storage system and the conventional energy storage system.

[0062] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0063] This invention significantly improves the overall efficiency of the power system by integrating gravity energy storage systems and conventional electrochemical energy storage systems through a collaborative optimization scheduling method. By collecting real-time operational data from both systems and applying machine learning methods to predict grid demand, this invention can formulate precise charging and discharging strategies, enabling energy storage at the lowest cost and release during peak periods to optimize cost-effectiveness.

[0064] This invention utilizes an adaptive Lagrange multiplier update algorithm in conjunction with a two-layer model. The upper layer provides initial values ​​for the multipliers, while the lower layer adjusts the multipliers based on real-time deviation feedback, forming a dynamic closed-loop optimization. Furthermore, by combining hierarchical iteration (the upper layer optimizes global parameters, while the lower layer adjusts them in real time) with the adaptive multiplier, local optima are avoided, thereby improving convergence speed and robustness.

[0065] This invention addresses changes in grid demand and fluctuations in renewable energy output through real-time strategy adjustments, further enhancing grid stability and reliability. Furthermore, through regular evaluation and feedback, the system continuously improves its dispatch model to adapt to environmental changes, thereby continuously improving the power system's operational efficiency and adaptability, optimizing energy management, reducing energy waste, and enhancing the system's resilience to renewable energy fluctuations. This results in significant economic and social benefits. Attached Figure Description

[0066] Figure 1 This is a schematic diagram of the collaborative optimization scheduling system between the gravity energy storage system and the conventional energy storage system described in this embodiment of the invention;

[0067] Figure 2 This is a schematic diagram of the system optimization model composition according to an embodiment of the present invention;

[0068] Figure 3 This is a flowchart illustrating the implementation of the collaborative optimization scheduling method described in this embodiment of the invention.

[0069] Figure 4 This is a flowchart of the collaborative optimization scheduling method described in an embodiment of the present invention. Detailed Implementation

[0070] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0071] Example 1: As Figure 4 As shown in the figure, this embodiment of the invention provides a method for coordinated optimization scheduling between a gravity energy storage system and a conventional energy storage system. The method includes the following steps:

[0072] S1 configures a gravity energy storage system and a conventional energy storage system on the power supply side of the large power grid. The gravity energy storage system adopts a vertical frame type gravity energy storage, and the conventional energy storage system is an electrochemical energy storage system.

[0073] S2 collects operational data from the gravity energy storage system and the conventional energy storage system over a period of time to form historical operational data, and uses the demand forecasting model to predict the electricity demand for a specific future period based on the historical operational data.

[0074] In a preferred embodiment of this invention, the demand forecasting model predicts the electricity demand for a specific future time period based on the historical operating data, including:

[0075] By inputting past electricity consumption data, weather conditions, calendar information, and relevant economic indicators into the demand forecasting model, the model obtains information on electricity demand, demand volatility and uncertainty at specific future points in time, as well as the maximum demand forecast for the future period.

[0076] S3 inputs the electricity demand value for a specific future time period into the system optimization model and iteratively trains the system optimization model to finally obtain the charging and discharging plans of the gravity energy storage system and the conventional energy storage system for each time period.

[0077] In this embodiment, the system optimization model includes an optimization model and a coordination model. The optimization model applies the decomposition and coordination method to the collaborative optimization of the gravity energy storage system and the electrochemical energy storage system. The decomposition and coordination method decomposes the overall optimization problem into two sub-problems: one for the gravity energy storage system and the other for the conventional energy storage system. Each sub-problem independently optimizes its own operating strategy and satisfies the balance constraint. The balance constraint is used to balance the operational decisions of the two subsystems to ensure that the overall optimization problem is achieved.

[0078] In this embodiment, the balance constraint is the core mechanism of the decomposition and coordination method in the optimization model, used to ensure that the decisions of the two subsystems satisfy the overall power balance. The coordination model (two-layer structure) is the specific framework for achieving this balance.

[0079] Upper-level model (global optimization layer): Sets global variables and long-term constraints (such as long-term power balance constraints) to generate charging and discharging plans.

[0080] Lower-level model (real-time control layer): Dynamically adjusts strategies (such as charging and discharging power adjustment) based on real-time data to ensure the executability of the upper-level plan.

[0081] The relationship between the two is as follows: the balance constraint is the coordination condition in the optimization model, which is relaxed through Lagrange multipliers. The two-layer coordination model is the architecture for executing the balance constraint. The upper layer outputs the global plan, and the lower layer feeds back real-time data, forming a closed-loop optimization.

[0082] Therefore, the coordination model comprises two layers: the upper layer is responsible for global optimization, and the lower layer is responsible for real-time control. The upper layer model sets constraints and corresponding objective functions based on the global variables corresponding to the gravity energy storage system and the conventional energy storage system. The lower layer model constructs constraints and corresponding objective functions based on the control variables corresponding to the gravity energy storage system and the conventional energy storage system.

[0083] In this embodiment, the optimization model applies the decomposition and coordination method to the collaborative optimization of the gravity energy storage system and the electrochemical energy storage system, specifically including the following steps:

[0084] Data from different sources is integrated into a unified data framework. The data from different sources includes the electricity demand value for a specific future time period and the collected operating data of the gravity energy storage system and conventional energy storage system. Based on the above data, the overall optimization problem is defined as minimizing the total system cost, and an overall optimization objective is set.

[0085] The decomposition and coordination method includes:

[0086] The overall optimization problem is decomposed into two sub-problems: the optimization problem of gravity energy storage system and the optimization problem of conventional energy storage system, and the objective functions and constraints of the optimization problems of gravity energy storage system and conventional energy storage system are set respectively.

[0087] To balance the operational decisions of the two subsystems by setting shared variables or parameters, ensuring that the overall system objective is achieved; specifically: setting power balance constraints to ensure that the total output power of the two systems meets the power demand in each time period; and setting a common objective function to adjust their respective strategies, wherein the common objective function is to minimize the total cost or maximize the efficiency.

[0088] Iterative training of the system optimization model includes:

[0089] The process iteratively solves each subproblem and updates the Lagrange multipliers, gradually adjusting the strategies for each subproblem to minimize the overall cost while satisfying power balance. In each iteration, two subproblems are solved independently first. Then, the magnitude of the power imbalance is determined based on the subproblem solutions. If a significant power imbalance exists, the Lagrange multipliers are adaptively adjusted until the power balance constraint is met. The constraints of the two subproblems are then adjusted using the common objective function. In this embodiment, the power imbalance is determined by calculating whether the deviation between the total output power and the demand value exceeds a threshold, which is set according to grid stability requirements. The Lagrange multipliers... Used for relaxing the power balance, it is represented as: ;

[0090] in, and They are in time The charging and discharging power of gravity energy storage systems and conventional energy storage systems, while It is time The electricity demand value is obtained through the demand forecasting model. T It indicates the total number of time ranges or time periods.

[0091] Among them, adaptive adjustment of Lagrange multipliers Represented as: ;

[0092] in, The step size parameter is equal to The step size parameter changes over time, which accelerates model convergence.

[0093] In this embodiment, the aforementioned upper-level model sets constraints and corresponding objective functions based on the global variables corresponding to the gravity energy storage system and the conventional energy storage system, including:

[0094] Set global variables, i.e., continuous variables: time in gravity energy storage systems and conventional energy storage systems.

[0095] t

[0096] Charge and discharge power Time in gravity energy storage systems and conventional energy storage systems t Energy storage capacity Integer variables: the on / off states of equipment in gravity energy storage systems and conventional energy storage systems;

[0097] Setting constraints based on the global variables includes:

[0098] Static constraints, denoted as Between the minimum and maximum values Between the minimum and maximum values;

[0099] Renewable energy consumption constraints are expressed as: ;

[0100] The long-term constraint on power balance is expressed as: ;

[0101] in, Indicates the output power of renewable energy. Indicates tolerance for fluctuations in renewable energy. This represents the electricity demand value for a specific time period. T Indicates a time range or the total number of items within a certain time period;

[0102] Construct the corresponding objective function, expressed as: ;

[0103] in, For time t Operating costs of gravity energy storage systems; For time t The operating cost of conventional energy storage systems, For time t Penalties for imbalances in electricity supply and demand; For time t Equipment aging costs.

[0104] In this embodiment, the lower-level model is constructed based on the control variables corresponding to the gravity energy storage system and the conventional energy storage system, including constraints and corresponding objective functions, specifically:

[0105] Set control variables, namely the real-time charging and discharging power adjustment of the gravity energy storage system and the conventional energy storage system. , ;

[0106] Set constraints based on the global variables, including

[0107] Dynamic electricity price response, expressed as: ;

[0108] in, Indicates real-time electricity price. Indicates the benchmark electricity price. This represents the dynamic electricity price response coefficient, which determines the intensity of the impact of electricity price fluctuations on power adjustments.

[0109] Power rate limiting, expressed as: ;

[0110] in, This indicates the amount of charging power adjustment (kW). This indicates the electricity price threshold (yuan / kWh). Power adjustment is triggered when the real-time electricity price exceeds this value. This indicates the maximum permissible charge and discharge power (kW) of the energy storage system.

[0111] Power grid frequency regulation is represented as: ;

[0112] in,

[0113] K

[0114] Indicates the frequency adjustment coefficient. Indicates the real-time frequency of the power grid. Indicates the nominal frequency;

[0115] Construct the corresponding objective function, expressed as: ;

[0116] in,

[0117] H For future prediction time domain; C Grid ( k (This refers to the real-time electricity price.) This is the power balance weighting coefficient.

[0118] The overall optimization problem mentioned in this embodiment includes: defining the overall optimization problem as minimizing the total system cost while satisfying power demand and technical constraints, wherein the total system cost includes operating cost, charging and discharging cost, and possible maintenance and depreciation cost;

[0119] Overall optimization goal: ;

[0120] in, and They are in time The operating costs of gravity energy storage systems and electrochemical energy storage systems, This represents a penalty term for power imbalance, used to characterize the penalty imposed when the deviation between actual output and demand exceeds a threshold, in order to improve grid stability. This represents the cost of equipment aging, used to quantify the impact of charge-discharge cycles on energy storage lifespan. The weighting coefficient for the penalty item of power supply and demand imbalance; This is the weighting coefficient for equipment aging costs, and the specific value is set according to the power grid stability requirements and equipment lifespan priorities.

[0121] To facilitate a better understanding of the above embodiments, this application provides a detailed description of the above solutions, such as... Figure 3As shown, it includes the following steps:

[0122] (1) Data collection: Collect operational data of gravity energy storage system and conventional energy storage system, including but not limited to the system's charging status, discharging status, remaining capacity, charging rate and discharging rate. The gravity energy storage system adopts vertical frame gravity energy storage, while the conventional energy storage system is electrochemical energy storage.

[0123] (2) Demand forecasting: Using statistical or machine learning methods to predict the total power grid demand for a specific future period based on historical data.

[0124] Input data:

[0125] 1) Historical electricity demand data: Past electricity consumption data, usually recorded hourly or daily.

[0126] 2) Weather conditions: factors such as temperature, humidity, and wind speed that may affect electricity demand.

[0127] 3) Calendar information: weekdays and non-weekdays, holidays, etc., as this information is usually related to electricity demand patterns.

[0128] 4) Economic indicators: The level of economic activity, such as industrial output, may also affect electricity demand.

[0129] Output data:

[0130] Forecasted electricity demand: Forecasted electricity demand over a future period, such as hourly demand over the next 24 hours or daily demand over the next week.

[0131] There are mainly three types of data: time series demand forecasts, which include electricity demand at a specific future point in time (usually calculated in 24-hour intervals).

[0132] The forecast data is based on historical load data, weather conditions, seasonal factors, and potential socioeconomic activities.

[0133] Information on demand volatility and uncertainty: Estimating the uncertainty and possible range of fluctuations in demand forecasts can provide confidence intervals or probability distributions for demand.

[0134] Peak demand information: The forecasting model focuses on the maximum demand forecast within a future time period to ensure that the power system can still operate stably during peak demand periods.

[0135] Application process:

[0136] 1) Data cleaning: handling missing values ​​and outliers, and formatting time series data.

[0137] 2) Feature engineering: Extract useful features from the raw input data, such as time series decomposition (seasonal, trend), rolling average of weather conditions, etc.

[0138] 3) Model selection: Select a model suitable for time series forecasting, such as ARIMA, seasonal ARIMA, or Long Short-Term Memory Network (LSTM).

[0139] 4) Model training: Train the model using historical data.

[0140] 5) Validation and testing: Use a portion of historical data for cross-validation to evaluate the model's predictive accuracy.

[0141] 6) Implement forecasting: Use models to forecast future electricity demand.

[0142] (3) Optimize model construction:

[0143] Input data:

[0144] 1) Forecasted electricity demand: obtained from the demand forecasting module.

[0145] 2) Energy storage system status data: including current energy level, historical charge and discharge records, etc.

[0146] 3) Electricity market data: Electricity price information, which may include real-time electricity prices and forecasted electricity prices.

[0147] 4) Operation and maintenance cost data: fixed and variable costs of system operation.

[0148] 5) Environmental and technical parameters: such as charge and discharge efficiency, equipment performance degradation, etc.

[0149] Output data:

[0150] Optimized charge and discharge strategy: charging and discharging schedules for the gravity energy storage system and the electrochemical energy storage system within each time period.

[0151] Application process:

[0152] 1) Data integration: Integrating data from different sources into a unified data framework.

[0153] 2) Definition of constraints and objective function: The optimization problem is defined based on the technical and economic constraints of the system.

[0154] 3) Optimization algorithm selection: Select a suitable optimization algorithm, such as linear programming, dynamic programming or a custom heuristic algorithm.

[0155] 4) Algorithm implementation: Write code to implement the optimization algorithm, including setting parameters and handling constraints.

[0156] 5) Run optimization: Input data into the optimization model and run the algorithm to generate charging and discharging strategies.

[0157] 6) Evaluation and Adjustment: Evaluate the actual performance of the optimization results and adjust the model or strategy as necessary.

[0158] Modeling steps:

[0159] Applying the decomposition coordination algorithm to the synergistic optimization of gravity energy storage systems and electrochemical energy storage systems can effectively handle the differences in operating characteristics and optimization objectives between these two systems, while simultaneously achieving the overall optimization goal. The specific implementation steps and key formulas are as follows:

[0160] Operational status data, especially data related to electricity consumption (such as historical load data), is primarily used to build demand forecasting models. The collected operational status data can also be directly used to set constraints for optimization models, including energy storage state of charge and equipment health status. The demand forecasting model is used to apply statistical or machine learning methods to predict the total grid demand for a specific future time period based on historical data.

[0161] 1) System Model and Problem Definition

[0162] First, the overall optimization problem is defined as minimizing the total system cost while satisfying power demand and technical constraints.

[0163] Total cost includes operating costs, charging and discharging costs, and possible maintenance and depreciation costs.

[0164] Overall optimization goal: ;

[0165] in, and They are in time The operating costs of gravity energy storage systems and electrochemical energy storage systems, This represents a penalty term for power imbalance, used to characterize the penalty imposed when the deviation between actual output and demand exceeds a threshold, in order to improve grid stability. This represents the cost of equipment aging, used to quantify the impact of charge-discharge cycles on energy storage lifespan. This is the weighting coefficient for the penalty item of power supply and demand imbalance, with a value range of 0.1-1.0; This is the weighting coefficient for equipment aging costs, with a value ranging from 0.05 to 0.5. The specific value is set according to the power grid stability requirements and equipment lifespan priority.

[0166] 2) Problem Breakdown

[0167] like Figure 2As shown, the overall problem is decomposed into two sub-problems: one for gravity-based energy storage systems and the other for electrochemical energy storage systems. Each sub-problem independently optimizes its own operating strategy, but must satisfy certain coordination constraints.

[0168] Subproblem definition:

[0169] Gravitational energy storage subproblem:

[0170] Objective function: ;constraint:

[0171] i. Power constraints:

[0172] Maximum and minimum output power limits ensure that system operation is within safe and technical capabilities.

[0173] ii. Energy storage capacity constraints:

[0174] Energy storage capacity limits for energy storage devices ensure that the maximum storable or releaseable energy is not exceeded. ;in, It can be updated using the following dynamic equation: ; The charging efficiency of a gravity energy storage system (G) is the ratio of the energy actually stored in the system to the input energy during the charging process. The charging power of a gravity energy storage system (G) refers to the amount of electrical power used to charge the gravity energy storage system per unit time, usually measured in watts (W), kilowatts (kW), megawatts (MW), etc. This refers to the discharge power of a gravity energy storage system (G). It is the amount of electrical power discharged by the gravity energy storage system per unit time, also measured in watts (W), kilowatts (kW), megawatts (MW), etc. Refers to a time interval. In calculations involving energy and power, it is typically used to represent a discrete period of time. For example, when calculating energy, the change in energy within that time interval is obtained by multiplying the power by the time interval. The unit is usually a second (s) or an hour (h). Discharge efficiency refers to the discharge efficiency of a gravity energy storage system (G). It represents the ratio of the energy actually released from the storage system to the stored energy during discharge. For example, a discharge efficiency of 0.8 means that 100 units of stored energy can only release 80 units of energy during discharge.

[0175] Electrochemical energy storage problem:

[0176] Objective function: constraint:

[0177] Power constraints:

[0178] Similar to gravity-based energy storage systems, electrochemical systems also have maximum and minimum output power limitations. ;

[0179] Energy storage capacity constraints:

[0180] The energy capacity of electrochemical energy storage systems is also limited. ;

[0181] in, The update equation is: ; The charging efficiency refers to the energy storage efficiency (G) of an electrochemical energy storage system. It represents the ratio of the energy actually stored in the storage system to the input energy during the charging process.

[0182] This refers to the charging power of an electrochemical energy storage system (G). It is the electrical power required to charge a gravity energy storage system per unit time, typically measured in watts (W), kilowatts (kW), megawatts (MW), etc. This refers to the discharge power of an electrochemical energy storage system (G). Specifically, it refers to the electrical power discharged by the gravity energy storage system per unit time, also measured in watts (W), kilowatts (kW), megawatts (MW), etc. Refers to a time interval. In calculations involving energy and power, it is typically used to represent a discrete period of time. For example, when calculating energy, the change in energy within that time interval is obtained by multiplying the power by the time interval. The unit is usually a second (s) or an hour (h). The discharge efficiency refers to the efficiency of an electrochemical energy storage system (G). It represents the ratio of the energy actually released from the storage system to the stored energy during discharge. For example, a discharge efficiency of 0.8 means that 100 units of stored energy can only release 80 units of energy during discharge.

[0183] 3) Coordination Mechanism Design

[0184] A two-layer optimization model is established, with the upper layer responsible for global optimization and the lower layer responsible for real-time control.

[0185] The upper-level model (global optimization layer) belongs to the mixed-integer linear programming (MILP) model. Its core objective is to formulate a globally optimal scheduling strategy, minimize long-term operating costs, and balance economy and stability.

[0186] Variable type:

[0187] Continuous variable: charging and discharging power at different time periods Energy storage capacity .

[0188] Integer variables: Equipment on / off status, such as the start / stop flag of a gravity energy storage hoist. .

[0189] Objective function: ;

[0190] in, Operating costs (including electricity price and maintenance costs) of gravity energy storage systems; Operating costs of conventional energy storage systems; Penalties for imbalance between power supply and demand; Equipment aging cost (calculated based on the number of cycles and single-cycle loss).

[0191] Constraints:

[0192] Static constraints: ;

[0193] Renewable energy consumption constraints: ;

[0194] Long-term constraints on power balance: ;

[0195] in, This represents the output power of renewable energy, which is a predicted value based on meteorological forecasts and historical power output data, and is dynamically updated through a real-time monitoring system. This indicates the tolerance for fluctuations in renewable energy.

[0196] The lower-level model (real-time control layer) is a distributed model predictive control, with the control objective being to adjust the charging and discharging power in real time in response to electricity price fluctuations and grid frequency changes.

[0197] Controlled variable: Real-time charging and discharging power adjustment and ;

[0198] Objective function: ;

[0199] H : Predict time domain (future 1 hour, or 15 minutes), C Grid ( k Real-time electricity price Power balance weighting coefficient.

[0200] Setting constraints based on the global variables includes:

[0201] Dynamic electricity price response, expressed as: ;

[0202] in, Indicates real-time electricity price. Indicates the benchmark electricity price. This represents the dynamic electricity price response coefficient, which determines the intensity of the impact of electricity price fluctuations on power adjustments.

[0203] Power rate limiting, expressed as: ;

[0204] in, This indicates the amount of charging power adjustment (kW). This indicates the electricity price threshold; power adjustments are triggered when the real-time electricity price exceeds this value. This indicates the maximum allowable charging and discharging power of the energy storage system;

[0205] Power grid frequency regulation is represented as: ;

[0206] in, K Indicates the frequency adjustment coefficient. Indicates the real-time frequency of the power grid. Indicates the nominal frequency.

[0207] The hierarchical collaboration mechanism is divided into data transmission and dynamic coordination. In terms of data transmission, the upper layer outputs the global charging and discharging plan to the lower layer, and the lower layer feeds back real-time operating data to the upper layer. In terms of dynamic coordination, the lower layer adjusts the charging and discharging power according to the real-time electricity price based on the upper layer's plan, and the upper layer periodically updates the model parameters to adapt to equipment aging and market changes.

[0208] Design a coordination mechanism to balance the operational decisions of two subsystems and ensure that the overall system objectives are achieved. This typically involves setting shared variables or parameters, such as power output demand or cost allocation.

[0209] Introducing Lagrange multipliers The power balance is relaxed: ;

[0210] in, and They are in time The output power of gravity energy storage and electrochemical energy storage, while It is the total system demand, which is obtained based on the demand forecasting model. T This indicates the total number of time ranges or time periods. For example, in this embodiment, the time period is counted within one day. T =24.

[0211] Coordination and Constraints

[0212] i. Power balance constraints:

[0213] Ensure that the total output power of the two systems meets the power demand in every time period.

[0214] ;

[0215] ii. Optimize goal coordination:

[0216] Each strategy is adjusted by a common objective function (such as minimizing total cost or maximizing efficiency).

[0217] .

[0218] 4) Iterative solution and optimization

[0219] The subproblems are solved iteratively and the Lagrange multipliers are updated accordingly. The strategies of each subsystem are gradually adjusted to minimize overall cost and meet power balance requirements.

[0220] Iterative updates:

[0221] For each iteration, the two subproblems are solved independently first, and then the Lagrange multipliers are adaptively adjusted according to the magnitude of the power imbalance. :

[0222] ;

[0223] in, The step size parameter is equal to The step size parameter changes over time, which accelerates model convergence.

[0224] (4) Scheduling strategy generation: Based on the results of the optimization model, specific charging and discharging strategies are generated to achieve the best cost-effectiveness and system stability.

[0225] (5) System Implementation and Monitoring: Implement the optimization results into actual operation and monitor system performance and actual output to ensure consistency with the optimization objectives. Adjust model parameters or re-optimize according to actual conditions to adapt to changes.

[0226] In this embodiment, the demand forecasting step uses machine learning techniques, employing the Transformer model to capture long-sequence dependencies, which can effectively reduce prediction errors, output the probability distribution of demand forecasts, and construct a robust optimization model. ,in, This represents the set of uncertainties in demand fluctuations. TotalCost This refers to the total cost under the worst-case scenario.

[0227] In this embodiment, the method further includes solving the optimization model using optimization software, such as commercial optimization software like CPLEX or Gurobi. These software programs can effectively handle large-scale optimization problems and provide globally optimal or near-optimal solutions.

[0228] In this embodiment, a scheduling strategy is further deployed in a real-time system. The scheduling strategy is automatically implemented through an advanced control system connected to the data exchange interface of the power grid operation center, which can receive instructions in real time and adjust the operating status of the energy storage device.

[0229] To verify the effectiveness of the method of the present invention, a specific embodiment is provided below:

[0230] Scenario Description: Assume a power system includes one gravity storage facility and two conventional energy storage facilities (one electrochemical storage and one pumped hydro storage). This power system needs to cope with daily load fluctuations and unstable output from renewable energy sources. Specifically, it involves five steps:

[0231] 1. Analyze the output of the optimization model

[0232] 1) Data collection and demand forecasting

[0233] First, the optimization model is built upon the collection of historical data and forecasts of future demand. This includes electricity demand, market electricity prices, and renewable energy output. This data is crucial for determining when to charge or discharge, as it directly impacts the economics and efficiency of the energy storage system.

[0234] 2) Model input parameters

[0235] Output power: The power that each energy storage system can provide or needs at different times.

[0236] Operating costs: The operating costs of each system, which may include charging costs (depending on electricity prices and efficiency) and system maintenance or depreciation costs.

[0237] Efficiency factor: Energy loss during charging and discharging, which is usually determined by the system's charging and discharging efficiency.

[0238] 3) The role and decision-making process of cost

[0239] In the optimization model, costs include not only electricity costs but also operating costs and potential maintenance costs. The optimization objective is typically to minimize total costs or maximize economic benefits. The specific steps are as follows:

[0240] Cost Calculation: Calculate the total cost of charging and discharging for each time period. This includes the cost of purchasing electricity (if it is a charging period and the electricity price is low) and the revenue from selling electricity (if it is a discharging period and the electricity price is high).

[0241] Efficiency Adjustment: Considering charge and discharge efficiency, calculate the actual cost and benefit after energy conversion. Low efficiency means more energy loss, thus affecting cost-effectiveness.

[0242] Optimization Solution: Using linear programming, dynamic programming, or other suitable optimization algorithms, the optimal charging and discharging strategy for each time period is determined based on cost and efficiency.

[0243] 4) Output decision

[0244] The model's output includes:

[0245] Charging or discharging decisions for each time period: Based on the principle of minimizing costs, determine which time periods should be charged and which time periods should be discharged.

[0246] Operating instructions: Specific charging and discharging power levels, and possible operating adjustment instructions.

[0247] 5) Practical application and adjustment

[0248] In practical applications, these decisions need to be integrated with the field operating system to adjust strategies in real time to respond to changes in the market and demand. Furthermore, the model's parameters and prediction algorithms should be reviewed and adjusted regularly to ensure their accuracy and adaptability.

[0249] Output power and operating cost are key factors in determining the charging and discharging strategy of an energy storage system. Cost is reflected in the dynamics of the electricity market and the operating efficiency of the energy storage system. This model can help operators maximize cost-effectiveness and ensure the economical and efficient operation of the energy storage system.

[0250] These decisions reflect the optimal amount of charging or discharging that each energy storage system should do within a specific time period, as well as the corresponding power level.

[0251] Data parsing: The model output includes the charge or discharge amount (kWh) and power (kW) of each energy storage unit in each time period. This data needs to be parsed and converted into executable control commands.

[0252] 2. Formulation of scheduling strategy

[0253] Based on the model's output, specific scheduling strategies are developed. This involves determining the operating schedule and power settings for each energy storage system.

[0254] Schedule creation: Determine when to initiate charging or discharging operations and schedule the duration of these operations. For example, if the model recommends charging during the nighttime hours when electricity prices are low, the scheduling strategy will specify the exact start and end times.

[0255] Power adjustment: Adjust the input or output power of the energy storage system according to the power level recommended by the model. This needs to be matched with the technical specifications and operating limitations of the energy storage equipment.

[0256] 3. In accordance with power grid demand and conditions

[0257] The charging and discharging strategy needs to be flexibly adjusted to adapt to the actual needs and operating conditions of the power grid.

[0258] Demand Response: If grid demand changes unexpectedly, charging and discharging strategies may need to be adjusted immediately. For example, a sudden surge in demand may require an immediate increase in discharge.

[0259] Renewable energy integration: When renewable energy output is high, it may be necessary to increase charging to store excess energy; conversely, when output is low, it may be necessary to increase discharging to replenish energy.

[0260] 4. Implementation of the control system

[0261] The scheduling strategy is translated into control commands for the energy storage system and implemented through an automated control system.

[0262] Automated control: Advanced control systems (such as PLCs or DCSs) are used to automatically execute charging and discharging strategies. The system automatically adjusts the operating status of the energy storage devices according to the scheduling strategy.

[0263] Real-time monitoring and adjustment: Monitor the actual operation of the energy storage system, compare it with the predictions of the optimization model, and adjust the control strategy as needed.

[0264] 5. Performance Evaluation and Feedback

[0265] Regularly evaluate the effectiveness of the charging and discharging strategy and feed the results back into the optimization model to improve future scheduling strategies.

[0266] Performance evaluation: Analyze the operating data of the energy storage system to evaluate whether the charging and discharging strategy achieves the expected economic and technical results.

[0267] Model iteration: Based on the execution results and new data input, the scheduling model is continuously iterated and optimized.

[0268] Monitoring and Adjustment: Monitor the implementation effect and adjust the prediction and optimization models based on real-time operational data and external changes to optimize subsequent scheduling strategies.

[0269] Case Analysis:

[0270] Three case scenarios were set up: single gravity energy storage, single electrochemical energy storage, and a synergistic optimization system of gravity energy storage and electrochemical energy storage. The system parameters were: gravity energy storage system efficiency 85%, cost 0.6 yuan / kWh; electrochemical energy storage system efficiency 92%, cost 1.2 yuan / kWh; renewable energy accounted for 40%, and the prediction error was ±15%.

[0271] The results show that by adopting the collaborative optimization scheme of this invention, the total cost, renewable energy absorption rate, and grid stability are effectively improved by 50%.

[0272]

[0273] On the other hand, such as Figure 1 As shown, the present invention also provides a collaborative optimization scheduling system between a gravity energy storage system and a conventional energy storage system, the system comprising:

[0274] A configuration module is used to configure a gravity energy storage system and a conventional energy storage system on the power supply side of a large power grid. The gravity energy storage system adopts a vertical frame type gravity energy storage, and the conventional energy storage system is an electrochemical energy storage system.

[0275] The data acquisition and prediction module is used to collect the operating data of the gravity energy storage system and the conventional energy storage system over a period of time, respectively, to form historical operating data, and to use the demand prediction model to predict the electricity demand value for a specific future period based on the historical operating data.

[0276] An optimization module is used to input the electricity demand value for a specific future time period into the system optimization model and iteratively train the system optimization model to finally obtain the charging and discharging plans of the gravity energy storage system and the conventional energy storage system for each time period. The system optimization model includes an optimization model and a coordination model. The optimization model applies a decomposition and coordination method to the collaborative optimization of the gravity energy storage system and the electrochemical energy storage system. The decomposition and coordination method decomposes the overall optimization problem into two sub-problems: one for the gravity energy storage system and the other for the conventional energy storage system. Each sub-problem independently optimizes its own operating strategy and satisfies balance constraints. The balance constraints are used to balance the operational decisions of the two subsystems to ensure that the overall optimization problem is achieved. The balance constraints are implemented through a two-layer coordination model, specifically:

[0277] The upper-layer coordination model is responsible for global optimization, while the lower-layer model is responsible for real-time control. The upper-layer model sets constraints and corresponding objective functions based on the global variables corresponding to the gravity energy storage system and the conventional energy storage system. The lower-layer model constructs constraints and corresponding objective functions based on the control variables corresponding to the gravity energy storage system and the conventional energy storage system.

[0278] The other technical features of the collaborative optimization scheduling system between a gravity energy storage system and a conventional energy storage system described in this embodiment are similar to those of the collaborative optimization scheduling method between a gravity energy storage system and a conventional energy storage system, and will not be repeated here.

[0279] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. "A plurality of" means two or more, unless otherwise explicitly specified.

[0280] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0281] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "over," and "on top" of the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0282] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0283] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0284] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0285] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0286] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0287] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0288] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for coordinated optimization scheduling between a gravity energy storage system and a conventional energy storage system, characterized in that, The method includes: Gravity energy storage system and conventional energy storage system are configured on the power source side of the large power grid. The gravity energy storage system adopts a vertical frame type gravity energy storage system, and the conventional energy storage system is an electrochemical energy storage system. Operational data of the gravity energy storage system and the conventional energy storage system are collected over a period of time to form historical operational data. Based on this historical operational data, a demand forecasting model is used to predict the electricity demand for a specific future period. The electricity demand values ​​for a specific future time period are input into the system optimization model, and the model is iteratively trained to obtain the charging and discharging plans for the gravity energy storage system and the conventional energy storage system in each time period. The system optimization model includes an optimization model and a coordination model. The optimization model applies a decomposition and coordination method to the collaborative optimization of the gravity energy storage system and the electrochemical energy storage system. This decomposition and coordination method decomposes the overall optimization problem into two sub-problems: one for the gravity energy storage system and the other for the conventional energy storage system. Each sub-problem independently optimizes its own operating strategy while satisfying balance constraints. The overall optimization problem sets the overall optimization objective as minimizing the total system cost. The balance constraints are used to balance the operational decisions of the two subsystems to ensure the overall optimization problem is achieved. The balance constraints are implemented through a two-layer coordination model, specifically: The two-layer coordination model consists of an upper-layer model responsible for global optimization and a lower-layer model responsible for real-time control. The upper-layer model sets constraints and corresponding objective functions based on the global variables of the gravity energy storage system and the conventional energy storage system. The lower-layer model constructs constraints and corresponding objective functions based on the control variables of the gravity energy storage system and the conventional energy storage system. The global variables include the charging and discharging power, energy storage capacity, and integer variables in the gravity energy storage system and the conventional energy storage system. The integer variables are the on / off states of the equipment in the gravity energy storage system and the conventional energy storage system. The control variables include the real-time charging and discharging power adjustment amounts of the gravity energy storage system and the conventional energy storage system.

2. The collaborative optimization scheduling method between gravity energy storage system and conventional energy storage system according to claim 1, characterized in that, The demand forecasting model predicts the electricity demand for a specific future time period based on the historical operating data, including: By inputting past electricity consumption data, weather conditions, calendar information, and relevant economic indicators into a neural network model, the model can obtain information on electricity demand, demand volatility and uncertainty at a specific future point in time, as well as the maximum demand forecast for the future period.

3. The collaborative optimization scheduling method between a gravity energy storage system and a conventional energy storage system according to claim 1, characterized in that, The optimization model applies the decomposition and coordination method to the synergistic optimization of gravity energy storage systems and electrochemical energy storage systems, specifically including the following steps: Data from different sources is integrated into a unified data framework, including electricity demand values ​​for a specific future time period and operational data collected from the gravity energy storage system and conventional energy storage system. The decomposition and coordination method includes: The overall optimization problem is decomposed into two sub-problems: the optimization problem of gravity energy storage system and the optimization problem of conventional energy storage system, and the objective functions and constraints of the optimization problems of gravity energy storage system and conventional energy storage system are set respectively. To balance the operational decisions of the two subsystems by setting shared variables or parameters, ensuring that the overall system objective is achieved; specifically: setting power balance constraints to ensure that the total output power of the two systems meets the power demand in each time period; and setting a common objective function to adjust their respective strategies, wherein the common objective function is to minimize the total cost.

4. The collaborative optimization scheduling method between a gravity energy storage system and a conventional energy storage system according to claim 3, characterized in that, Iterative training of the system optimization model includes: The process iteratively solves each subproblem and updates the Lagrange multipliers, gradually adjusting the strategies for each subproblem to minimize the overall cost and satisfy power balance. Specifically, for each iteration, two subproblems are solved independently first. Then, the magnitude of the power imbalance is determined based on the solution results of the subproblems. If a power imbalance problem exists, the Lagrange multipliers are adaptively adjusted until the power balance constraint is met. Finally, the constraints of the two subproblems are adjusted through the common objective function.

5. The collaborative optimization scheduling method between a gravity energy storage system and a conventional energy storage system according to claim 4, characterized in that, The upper-level model sets constraints and corresponding objective functions based on the global variables corresponding to the gravity energy storage system and the conventional energy storage system, including: Set global variables, i.e., continuous variables: time in gravity energy storage systems and conventional energy storage systems. t Charge and discharge power Time in gravity energy storage systems and conventional energy storage systems t Energy storage capacity Integer variables: the on / off states of equipment in gravity energy storage systems and conventional energy storage systems; Setting constraints based on the global variables includes: Static constraints, denoted as Between the minimum and maximum values Between the minimum and maximum values; Renewable energy consumption constraints are expressed as: ; The long-term constraint on power balance is expressed as: ; in, Indicates the output power of renewable energy. Indicates tolerance for fluctuations in renewable energy. This represents the electricity demand value for a specific time period. T Indicates a time range or the total number of items within a certain time period; Construct the corresponding objective function, expressed as: ; in, For time t Operating costs of gravity energy storage systems; For time t The operating cost of conventional energy storage systems, For time t Penalties for imbalances in electricity supply and demand; For time t The cost of equipment aging; The weighting coefficient for the penalty term of power supply and demand imbalance. This is the weighting coefficient for equipment aging costs.

6. The collaborative optimization scheduling method between a gravity energy storage system and a conventional energy storage system according to claim 5, characterized in that, The lower-level model is constructed based on the control variables of the gravity energy storage system and the conventional energy storage system, establishing constraints and corresponding objective functions, specifically including: Set control variables, namely the real-time charging and discharging power adjustment of the gravity energy storage system and the conventional energy storage system. , ; Setting constraints based on the control variables includes: Dynamic electricity price response, expressed as: ; in, Indicates real-time electricity price. Indicates the benchmark electricity price. This represents the dynamic electricity price response coefficient, which determines the intensity of the impact of electricity price fluctuations on power adjustment; Power rate limiting, expressed as: ; in, Indicates the amount of charging power adjustment. This indicates the electricity price threshold; power adjustments are triggered when the real-time electricity price exceeds this value. This indicates the maximum allowable charging and discharging power of the energy storage system; Power grid frequency regulation is represented as: ; in, K Indicates the frequency adjustment coefficient. Indicates the real-time frequency of the power grid. Indicates the nominal frequency; Construct the corresponding objective function, expressed as: ; in, H For future prediction time domain; C Grid ( k (This refers to the real-time electricity price.) This is the power balance weighting coefficient.

7. The method for coordinated optimization scheduling between a gravity energy storage system and a conventional energy storage system according to claim 6, characterized in that, The overall optimization problem includes: defining the overall optimization problem as minimizing the total system cost while satisfying power demand and technical constraints, wherein the total system cost includes operating costs, charging and discharging costs, and possible maintenance and depreciation costs; Overall optimization goal: ; in, and They are in time The operating costs of gravity energy storage systems and electrochemical energy storage systems, This represents a penalty term for power imbalance, used to characterize the penalty imposed when the deviation between actual output and demand exceeds a threshold, in order to improve grid stability. This represents the cost of equipment aging, used to quantify the impact of charge-discharge cycles on energy storage lifespan. The weighting coefficient for the penalty item of power supply and demand imbalance; This is the weighting coefficient for equipment aging costs, and the specific value is set according to the power grid stability requirements and equipment lifespan priorities.

8. The method for coordinated optimization scheduling between a gravity energy storage system and a conventional energy storage system according to claim 6, characterized in that, The Lagrange multipliers Used for relaxing the power balance, it is represented as: ; in, and They are in time The charging and discharging power of gravity energy storage systems and conventional energy storage systems, while It is time The electricity demand value is obtained through the demand forecasting model. T It indicates the total number of time ranges or time periods.

9. The method for coordinated optimization scheduling between a gravity energy storage system and a conventional energy storage system according to claim 8, characterized in that, If an electrical imbalance exists, the Lagrange multipliers are adaptively adjusted until the electrical balance constraint condition is met. Represented as: ; in, The step size parameter is equal to The step size parameter changes over time, which accelerates model convergence.

10. A collaborative optimization scheduling system between a gravity energy storage system and a conventional energy storage system, characterized in that, The system includes: A configuration module is used to configure a gravity energy storage system and a conventional energy storage system on the power supply side of a large power grid. The gravity energy storage system adopts a vertical frame type gravity energy storage, and the conventional energy storage system is an electrochemical energy storage system. The data acquisition and prediction module is used to collect the operating data of the gravity energy storage system and the conventional energy storage system over a period of time, respectively, to form historical operating data, and to use the demand prediction model to predict the electricity demand value for a specific future period based on the historical operating data. An optimization module is used to input the electricity demand value for a specific future time period into the system optimization model and iteratively train the system optimization model to finally obtain the charging and discharging plans of the gravity energy storage system and the conventional energy storage system for each time period. The system optimization model includes an optimization model and a coordination model. The optimization model applies a decomposition and coordination method to the collaborative optimization of the gravity energy storage system and the electrochemical energy storage system. The decomposition and coordination method decomposes the overall optimization problem into two sub-problems: one for the gravity energy storage system and the other for the conventional energy storage system. Each sub-problem independently optimizes its own operating strategy and satisfies balance constraints. The overall optimization problem is to set the overall optimization objective as minimizing the total system cost. The balance constraints are used to balance the operational decisions of the two subsystems to ensure that the overall optimization problem is achieved. The balance constraints are implemented through a two-layer coordination model, specifically: The two-layer coordination model consists of an upper-layer model responsible for global optimization and a lower-layer model responsible for real-time control. The upper-layer model sets constraints and corresponding objective functions based on the global variables of the gravity energy storage system and the conventional energy storage system. The lower-layer model constructs constraints and corresponding objective functions based on the control variables of the gravity energy storage system and the conventional energy storage system. The global variables include the charging and discharging power, energy storage capacity, and integer variables in the gravity energy storage system and the conventional energy storage system. The integer variables are the on / off states of the equipment in the gravity energy storage system and the conventional energy storage system. The control variables include the real-time charging and discharging power adjustment amounts of the gravity energy storage system and the conventional energy storage system.

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