A pumped storage and flywheel energy storage combined peak regulation and frequency modulation optimization control method

Abstract

The application discloses a peak regulation and frequency modulation optimization control method combining pumped storage and flywheel energy storage, and belongs to the technical field of power systems. The technical problem to be solved is that the existing single energy storage system has insufficient scheduling capability, slow response speed and poor frequency regulation flexibility. The technical solution points are that a dynamic model of a power grid frequency and an energy storage system is established, a joint scheduling optimization objective function of minimizing the weighted sum of energy storage power consumption and power grid frequency deviation is constructed, system constraints are set, scheduling strategies are derived and dynamically adjusted in combination with optimal control, robust control and reinforcement learning, and after simulation verification, the scheduling strategies are applied to actual power grids for real-time scheduling. The application realizes efficient scheduling of energy storage equipment, and significantly improves the power grid frequency stability and load balancing capability.

Description

Technical Field

[0001] This invention belongs to the field of power system technology, specifically relating to a peak-shaving and frequency regulation optimization control method that combines pumped storage and flywheel energy storage. Background Technology

[0002] With the large-scale integration of renewable energy, fluctuations in grid frequency and load have become major challenges to the stable operation of the power system. To address this challenge, traditional grid dispatching methods primarily rely on conventional power generation methods (such as thermal power and hydropower) for frequency regulation. In addition, energy storage technologies such as pumped storage and flywheel energy storage are widely used in frequency regulation and peak shaving tasks to provide auxiliary frequency regulation and load balancing. However, as the proportion of renewable energy continues to increase, traditional dispatching methods are gradually revealing their inability to fully meet grid demand.

[0003] In existing technologies, traditional grid frequency regulation methods mainly rely on single energy storage technologies, such as pumped hydro storage systems or flywheel energy storage systems. While pumped hydro storage systems have high power output capabilities, their slow response speed makes them difficult to cope with sudden frequency fluctuations in the grid. Flywheel energy storage systems, on the other hand, have a faster response speed, but their power output is relatively small, making it difficult to provide sufficient regulation support. Furthermore, traditional dispatch optimization methods are mostly based on fixed rules or preset models for grid frequency regulation, but this is insufficient in terms of dispatch flexibility and response speed when faced with large load fluctuations and frequent fluctuations in renewable energy generation. In actual operation, problems such as slow regulation and excessive frequency fluctuations frequently occur, resulting in the inability to effectively guarantee the stability of the grid frequency and the utilization efficiency of energy storage devices.

[0004] In view of this, the present invention is hereby proposed. Summary of the Invention

[0005] In order to solve the above-mentioned technical problems in the prior art, the present invention provides a peak-shaving and frequency regulation optimization control method that combines pumped storage and flywheel energy storage, which solves the problems of insufficient scheduling capability, slow response speed and poor frequency regulation flexibility of single energy storage systems in the prior art.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows: A peak-shaving and frequency-regulating optimization control method combining pumped hydro storage and flywheel energy storage includes: S1. Establish a dynamic model of the power grid frequency and the energy storage system to describe the relationship between the power grid frequency and the power input and output of the energy storage device; S2. Based on the needs of frequency regulation and peak shaving, a joint scheduling optimization objective function is constructed. This objective function minimizes the weighted sum of the power consumption of energy storage devices and the frequency deviation of the power grid. S3. Set system constraints, including energy state constraints for the energy storage device, grid frequency constraints, and energy storage power balance constraints, wherein the energy state constraints for the energy storage device satisfy the following formula:

[0007] in, and These represent the energy states of the pumped-storage and flywheel energy storage systems at time t, respectively, in kilowatt-hours. and These are the minimum and maximum energy capacities of the energy storage device, respectively, in kilowatt-hours. S4. Using optimal control theory, derive and implement the optimal scheduling strategy, specifically including selecting the scheduling target, optimizing the algorithm, and configuring parameters; S5. Apply robust control methods to design the control input of the energy storage system to suppress disturbances caused by grid load fluctuations and renewable energy generation fluctuations. The robust control optimization problem satisfies the following mathematical model:

[0008] in, This represents the disturbance set, which includes grid load fluctuations and instability factors in renewable energy generation. This represents the total power output of the energy storage device at time t, in kilowatts. and The weighting coefficients are set to balance the control of energy storage power output and grid frequency deviation; This represents the total time period for optimized scheduling and control; Represents a time variable; Indicates time The power grid frequency deviation at any given time, in Hertz; S6. Employ reinforcement learning or deep reinforcement learning methods to dynamically adjust the scheduling strategy of the energy storage system based on the real-time operation status and feedback information of the power grid. S7. Conduct simulation verification, using power system simulation tools to test the effectiveness of the dispatch strategy under different load fluctuations and renewable energy generation conditions; S8. Apply the optimized control method to the actual power grid and adjust the operating status of the energy storage equipment through real-time dispatch feedback.

[0009] Furthermore, the dynamic model of the power grid frequency and the energy storage system includes a power grid frequency response model, which satisfies the following formula:

[0010] in, This indicates the deviation of the power grid frequency, measured in Hertz. It is the inertial time constant of the power grid, with units of seconds per kilowatt-hour; This represents the power grid frequency regulation coefficient, with units of Hertz per kilowatt. This indicates the power input of the energy storage system, measured in kilowatts (kW). This indicates the power output of the energy storage system, measured in kilowatts (kW).

[0011] Furthermore, the dynamic model of the power grid frequency and the energy storage system includes a power model for pumped storage equipment and a power model for flywheel energy storage equipment; the power model for pumped storage equipment satisfies the following formula:

[0012] in, This indicates the power output of the pumped storage system, measured in kilowatts (kW). This indicates the power generation efficiency of the pumped storage system; This indicates the input power of the water pump, measured in kilowatts (kW). The power model of the flywheel energy storage device satisfies the following formula:

[0013] in, This indicates the power output of the flywheel energy storage system, measured in kilowatts (kW). The moment of inertia of a flywheel is expressed in kilograms. square meters; This indicates the angular velocity of the flywheel, measured in radians per second. It represents the rate of change of the flywheel's angular velocity, measured in radians per second squared.

[0014] Furthermore, the joint scheduling optimization objective function satisfies the following formula:

[0015] in, and These represent the power output of pumped storage and flywheel energy storage systems, respectively, in kilowatts. This indicates the deviation of the power grid frequency, measured in Hertz. and These represent weighting coefficients, used to adjust the contribution of power output and frequency deviation to the optimization objective, respectively. This indicates the time period for optimization calculations, in seconds.

[0016] Furthermore, the power grid frequency constraint satisfies the following formula:

[0017] in, This indicates the deviation of the power grid frequency, measured in Hertz. This indicates the maximum permissible deviation of the power grid frequency, measured in Hertz.

[0018] Furthermore, the energy storage power balance constraint satisfies the following formula:

[0019] in, This indicates the power input of the energy storage system, measured in kilowatts (kW). This indicates the power output of the energy storage system, measured in kilowatts (kW). This indicates the net load demand of the power grid, expressed in kilowatts.

[0020] Furthermore, the optimal scheduling strategy is derived using the Pontryagin minimum principle, and the Hamiltonian function constructed during the derivation process satisfies the following formula:

[0021] in, , , These represent the Lagrange multipliers, corresponding to the dynamic response of grid frequency deviation, energy storage power balance constraints, and power consumption weighting, respectively. The inertial time constant of the power grid is expressed in seconds per kilowatt-hour. This represents the power grid frequency regulation coefficient, with units of Hertz per kilowatt. and These represent the power input and output of the energy storage system, respectively, in kilowatts. and These represent the power output of pumped storage and flywheel energy storage devices, respectively, in kilowatts.

[0022] Furthermore, the robust control method optimizes the control input of the energy storage device by considering a set of disturbances that includes grid load fluctuations and unstable factors of renewable energy generation, so that the system can still maintain grid frequency stability within the disturbance range.

[0023] Furthermore, the reinforcement learning method dynamically adjusts the scheduling strategy of energy storage devices based on grid frequency fluctuations and load demand, and continuously updates the scheduling model based on historical data.

[0024] Furthermore, the simulation verification was conducted using the MATLAB / Simulink power system simulation tool to verify the stability and regulation effect of the optimized control method under different power grid operating conditions.

[0025] The present invention has the following beneficial effects: 1. This invention achieves efficient scheduling of energy storage devices, realizing the technical effects of maintaining grid frequency stability, load balance, and optimizing energy storage device efficiency under different grid operating environments. Compared with the scheduling schemes of single energy storage methods in the prior art, this invention can more flexibly cope with frequency fluctuations and load fluctuations, overcoming the shortcomings of slow response and insufficient regulation capacity of single energy storage systems.

[0026] 2. This invention can adjust the power output of energy storage devices in real time according to grid load fluctuations and the instability of renewable energy generation. It achieves real-time optimization and adaptive capabilities for the scheduling strategy. Compared to traditional rule-based scheduling methods, this invention can dynamically adjust the scheduling strategy according to the actual operating conditions of the grid, overcoming the shortcomings of traditional methods in adapting to complex grid environments.

[0027] 3. This invention can quickly respond to frequency deviations and load fluctuations, maintaining the stability of the power grid frequency. It achieves efficient interaction between the energy storage system and the power grid. Compared to existing technologies that rely solely on traditional frequency regulation methods, this invention significantly improves the utilization rate of energy storage devices, avoids over-regulation or energy waste, and solves the shortcomings of traditional methods such as slow regulation and poor adaptability.

[0028] 4. This invention ensures the feasibility and stability of the scheduling optimization scheme under different grid load and renewable energy fluctuation conditions through multi-scenario verification in a simulation environment. It achieves reliable optimized control under complex grid operation conditions. Compared to existing technologies that lack simulation verification and practical deployment, this invention solves the problems of insufficient verification and inability to cope with real-world environmental challenges by fully verifying the scheduling effect. Attached Figure Description

[0029] Figure 1 A flowchart of a peak-shaving and frequency-regulating optimization control method combining pumped storage and flywheel energy storage provided in an embodiment of the present invention. Detailed Implementation

[0030] The technical solution of the present invention will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention. All other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0031] It should be noted that, unless otherwise specifically stated, the relative arrangement and numerical expressions of the components and steps described in these embodiments should not be construed as limiting the scope of the invention.

[0032] The following description of exemplary embodiments is merely illustrative and is not intended to limit the invention or its application or use in any way. Techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail herein, but where applicable, such techniques, methods, and apparatus should be considered part of this specification.

[0033] Example See Figure 1 , Figure 1 This is a flowchart of a peak-shaving and frequency-regulating optimization control method combining pumped hydro storage and flywheel energy storage proposed in this invention. Specific steps may include: S1. Establish a dynamic model of the power grid frequency and the energy storage system to describe the relationship between the power grid frequency and the power input and output of the energy storage device; To achieve effective peak shaving and frequency regulation, this embodiment first establishes a dynamic model between the grid frequency and the energy storage system, describing the relationship between the grid frequency and the power input and output of the energy storage device. This is the foundation for subsequent optimized control. Through this model, grid frequency fluctuations and the power response of the energy storage device can be tracked in real time, ensuring that the stability of the grid frequency and the efficiency of the energy storage system are maximized in actual operation.

[0034] In this embodiment, the power grid frequency response model is described by the power grid's inertia time constant and frequency regulation coefficient. The rate and magnitude of power grid frequency changes are affected by the power grid's inertia (M) and frequency regulation coefficient (D): the power grid's inertia determines the delay in frequency response; the greater the inertia, the slower the frequency change. The frequency regulation coefficient determines the power grid's frequency regulation rate; the larger the coefficient, the stronger the power grid's frequency regulation capability.

[0035] The dynamic relationship between grid frequency and the power input and output of energy storage devices is expressed by the following formula:

[0036] in, This indicates the deviation of the power grid frequency, measured in Hertz. It is the inertial time constant of the power grid, with units of seconds per kilowatt-hour; This represents the power grid frequency regulation coefficient, with units of Hertz per kilowatt. This indicates the power input of the energy storage system, measured in kilowatts (kW). This indicates the power output of the energy storage system, measured in kilowatts (kW).

[0037] The physical meaning of this formula is: when an energy storage system outputs power to the grid, it increases the grid frequency; when it absorbs power from the grid, it decreases the grid frequency. By precisely controlling the power input and output of energy storage devices, the grid frequency can be adjusted in real time, achieving peak shaving and frequency regulation.

[0038] The energy storage system in this embodiment includes a pumped hydro storage system and a flywheel energy storage system. The combined use of these two systems can simultaneously address different types of frequency fluctuations and load demands. The power models for the two energy storage devices are as follows: (1) Power model of pumped storage system The energy storage and output power of a pumped storage system are closely related to the input power of the pump and the water level changes in the reservoir. Its power output under power generation conditions satisfies the following relationship: The dynamic model of the power grid frequency and the energy storage system includes a power model for pumped storage equipment and a power model for flywheel energy storage equipment; the power model for pumped storage equipment satisfies the following formula:

[0039] in, This indicates the power output of the pumped storage system, measured in kilowatts (kW). This indicates the power generation efficiency of the pumped storage system; This indicates the input power of the water pump, measured in kilowatts (kW). (2) Power model of flywheel energy storage system Flywheel energy storage systems store and release energy by adjusting the speed of the flywheel. Their power output is the derivative of the rotational kinetic energy with respect to time, satisfying the following formula:

[0040] in, This indicates the power output of the flywheel energy storage system, measured in kilowatts (kW). The moment of inertia of a flywheel is expressed in kilograms. square meters; This indicates the angular velocity of the flywheel, measured in radians per second. It represents the rate of change of the flywheel's angular velocity, measured in radians per second squared.

[0041] The above model allows for a precise correlation between changes in grid frequency and the power output of the energy storage system, providing a solid theoretical foundation for subsequent scheduling optimization and control strategies.

[0042] In some embodiments, during the process of establishing a dynamic model of the power grid frequency and the energy storage system, the accuracy of the model can be further optimized by incorporating power grid load forecasting data. For example, by predicting the load change trend of the power grid and combining it with the operating status of the energy storage device, the future power grid frequency can be predicted, thereby achieving a more accurate dispatching strategy.

[0043] In one possible implementation, the energy storage system can also continuously adjust its power output through state monitoring and feedback mechanisms to cope with sudden frequency fluctuations or load changes. By continuously optimizing and adjusting the power output of the energy storage system, grid frequency fluctuations can be minimized, and the response speed of grid frequency regulation can be improved.

[0044] S2. Based on the needs of frequency regulation and peak shaving, a joint scheduling optimization objective function is constructed. This objective function minimizes the weighted sum of the power consumption of energy storage devices and the frequency deviation of the power grid. Based on the dynamic model established in step S1, this step constructs a joint scheduling optimization objective function according to the needs of power grid frequency regulation and peak shaving. By optimizing this objective function, efficient scheduling of energy storage devices is achieved, maximizing the stability of power grid frequency, while simultaneously optimizing the energy efficiency of the energy storage system.

[0045] In this embodiment, the scheduling optimization objective function is achieved by minimizing the weighted sum of the power consumption of the energy storage device and the sum of the squares of the grid frequency deviation. The core is to balance the relationship between grid frequency fluctuations and energy storage device power consumption: to ensure grid stability, the frequency deviation needs to be minimized; to ensure the operating efficiency of the energy storage system, the power output of the energy storage device needs to be controlled to avoid unnecessary energy loss.

[0046] The specific expression for the joint scheduling optimization objective function is as follows:

[0047] in, and These represent the power output of pumped storage and flywheel energy storage systems, respectively, in kilowatts. This indicates the deviation of the power grid frequency, measured in Hertz. and These represent weighting coefficients, used to adjust the contribution of power output and frequency deviation to the optimization objective, respectively. This indicates the time period for optimization calculations, in seconds.

[0048] By adjusting the weighting coefficients and This can balance the different needs of the system: A smaller value helps reduce the power consumption of energy storage systems; A larger value helps to enhance the power grid's frequency regulation capability and improve power grid stability.

[0049] Alternatively, the objective function can be further flexibly adjusted. For example, other adjustment parameters can be introduced to adapt to the needs of different power grid operating conditions. In some embodiments, the objective function can also incorporate factors affecting power grid load changes, further improving the adaptability of the scheduling strategy.

[0050] In one possible implementation, the objective function not only considers the weighted sum of the power output and frequency deviation of the energy storage device, but also adjusts the scheduling strategy by combining the grid's frequency regulation capability with the charging / discharging rate of the energy storage device. Specifically, when frequency fluctuations are large, the system will prioritize the scheduling of flywheel energy storage devices to respond quickly to frequency changes; while when load fluctuations are large, the system will rely more on pumped hydro storage devices to balance the grid load through longer-term power output regulation.

[0051] In some embodiments, in addition to considering frequency and power output, constraints on the energy state of the energy storage device can be added to the design of the scheduling objective. By adjusting the constraints in the objective function, the energy management of the energy storage device can be further optimized, avoiding excessive consumption or accumulation of energy in the energy storage system and ensuring that the energy storage device is scheduled under optimal operating conditions.

[0052] S3. Set system constraints, including energy state constraints for the energy storage device, grid frequency constraints, and energy storage power balance constraints, wherein the energy state constraints for the energy storage device satisfy the following formula:

[0053] in, and These represent the energy states of the pumped-storage and flywheel energy storage systems at time t, respectively, in kilowatt-hours. and These are the minimum and maximum energy capacities of the energy storage device, respectively, in kilowatt-hours; this constraint prevents the energy storage device from being damaged due to overcharging or over-discharging.

[0054] To ensure the stable operation of the power grid, the frequency deviation must not exceed a certain range, satisfying the following constraints:

[0055] in, This indicates the deviation of the power grid frequency, measured in Hertz. This indicates the maximum permissible deviation of the power grid frequency, measured in Hertz.

[0056] In the system, the power input and output of energy storage devices must be balanced with the net load demand of the power grid, satisfying the following relationship:

[0057] in, This indicates the power input of the energy storage system, measured in kilowatts (kW). This indicates the power output of the energy storage system, measured in kilowatts (kW). This represents the net load demand of the power grid (i.e., the power grid load minus the conventional power generation capacity), and the unit is kilowatts.

[0058] This constraint ensures that energy storage devices can play a peak-shaving role during grid load fluctuations: when the load increases, the energy storage devices provide additional power output; when the load decreases, the energy storage devices absorb excess energy and avoid excessive fluctuations in grid frequency.

[0059] In some embodiments, to further enhance system stability, other factors can be added to the constraints, such as the energy storage system's response time and dispatch efficiency. By adding these additional constraints, it is possible to ensure that the energy storage system operates stably and efficiently in more complex grid environments.

[0060] In one possible implementation, these constraints can be dynamically adjusted. For example, when the grid frequency deviation is large, the power balance constraints can be appropriately relaxed to allow the energy storage system to respond quickly and adjust the frequency; while when the grid load is relatively stable, the constraints can be more stringent to ensure that the energy management efficiency of the energy storage system is optimized.

[0061] S4. Using optimal control theory, derive and implement the optimal scheduling strategy, specifically including selecting the scheduling target, optimizing the algorithm, and configuring parameters; This step uses optimal control theory to derive the optimal scheduling strategy for energy storage systems during frequency regulation and peak shaving. The goal is to maximize the energy efficiency and stability of the system while meeting the power output of the energy storage devices and the grid frequency and load requirements.

[0062] In this embodiment, optimal control theory derives the optimal scheduling of the energy storage system by establishing a control objective function and combining the response characteristics of the power grid frequency and the power output of the energy storage device. Based on the scheduling optimization objective function established in step S2, it is solved using the Pontryagin minimum principle, first constructing the following Hamiltonian function:

[0063] in, , , These represent the Lagrange multipliers, corresponding to the dynamic response of grid frequency deviation, energy storage power balance constraints, and power consumption weighting, respectively. The inertial time constant of the power grid is expressed in seconds per kilowatt-hour. This represents the power grid frequency regulation coefficient, with units of Hertz per kilowatt. and These represent the power input and output of the energy storage system, respectively, in kilowatts. and These represent the power output of pumped storage and flywheel energy storage devices, respectively, in kilowatts.

[0064] By optimizing the Hamiltonian function, the optimal power output strategy for energy storage devices can be obtained. This strategy not only ensures that grid frequency fluctuations are minimized, but also reduces the energy consumption of energy storage devices.

[0065] Alternatively, optimal control can be solved using numerical optimization algorithms. For example, numerical methods such as dynamic programming or gradient descent can be used to gradually approximate the optimal solution, thereby determining the optimal power output of each energy storage device at different times.

[0066] In one possible implementation, the derivation of the optimal scheduling strategy also needs to consider the response time and power output smoothness of the energy storage system. In actual grid operation, the power adjustment of energy storage devices should be smooth to avoid frequent adjustments that could adversely affect the grid. Therefore, in addition to frequency regulation and load demand, the scheduling strategy should also consider the response rate and power fluctuation limits of energy storage devices.

[0067] Specifically, the implementation process of this optimal control strategy is as follows: First, by acquiring the frequency deviation of the power grid in real time. and load demand Determine the current scheduling objectives of the energy storage system; Then, using the scheduling strategy derived from optimal control theory, the power that the energy storage device should provide at each moment is calculated. and In order to maintain the stability of the power grid frequency; Finally, the charging and discharging process of the energy storage system is adjusted to ensure that the system can flexibly cope with grid frequency fluctuations and load fluctuations.

[0068] In some embodiments, to improve the efficiency and response speed of system scheduling, a real-time feedback mechanism can be combined with the optimal control strategy. The real-time feedback mechanism can quickly adjust the power output of energy storage devices based on the instantaneous frequency deviation of the power grid and load demand, thereby further optimizing the scheduling effect.

[0069] S5. Apply robust control methods to design the control input of the energy storage system to suppress disturbances caused by grid load fluctuations and renewable energy generation fluctuations; In the preceding steps, a dynamic model of the grid frequency and the energy storage system has been constructed, a scheduling optimization objective has been set, and the optimal scheduling strategy has been derived using optimal control theory. The key task of this next step is to address disturbances such as grid load fluctuations and the instability of renewable energy generation by enhancing the system's stability and reliability through robust control methods. Robust control ensures that the energy storage system can still operate stably and effectively regulate the grid frequency when facing uncertainties and external disturbances.

[0070] In this embodiment, the core of the robust control method lies in optimizing the control input of the energy storage device to ensure that the system can maintain a stable state when facing uncertainties such as grid load fluctuations and fluctuations in wind and solar power generation. Specifically, robust control takes into account the modeling errors of the grid system and external disturbances, and adjusts the scheduling strategy of the energy storage device based on these factors to ensure that the grid frequency does not fluctuate significantly due to disturbances.

[0071] In its implementation, the robust control method proceeds through the following steps: Modeling Errors and Disturbance Prediction: First, the system predicts the possible range of grid load and power generation fluctuations based on the grid load forecasting model and the renewable energy generation model. Grid frequency variations are affected not only by the power of energy storage devices but also by grid load fluctuations and renewable energy generation fluctuations. Therefore, robust control methods need to consider these factors and predict potential frequency fluctuations in the grid.

[0072] Control Input Optimization: Based on predicted disturbance information, robust control methods optimize the control input of the energy storage device. By adjusting the power output of the energy storage system, the system is protected from external disturbances, ensuring grid frequency stability. The goal of this optimization process is to enable the system to tolerate disturbances within a certain range without causing significant frequency fluctuations or grid instability.

[0073] Solving robust optimization problems: When facing uncertainty, robust control is typically optimized by setting a worst-case scenario. Specifically, the robust control optimization problem can be represented by the following mathematical model:

[0074] in, This represents the disturbance set, which includes grid load fluctuations and instability factors in renewable energy generation. This represents the total power output of the energy storage device at time t, in kilowatts. and The weighting coefficients are set to balance the control of energy storage power output and grid frequency deviation; This represents the total time period for optimized scheduling and control; Represents a time variable; Indicates time The power grid frequency deviation at any given time, in Hertz; Alternatively, this optimization problem can be solved using robust optimization algorithms, such as interval optimization methods based on uncertainty modeling or stochastic optimization methods based on probability theory. These methods allow the system to optimize the scheduling strategy of energy storage devices while minimizing the impact of disturbances.

[0075] In one possible implementation, robust control methods can also be combined with real-time feedback mechanisms for dynamic adjustment. During actual operation, the load and power generation of the power grid are constantly changing. Therefore, through real-time feedback, the control input can be adjusted based on the observed frequency deviation and the output of the energy storage device, ensuring that the system remains stable under disturbance conditions.

[0076] Specifically, when implementing robust control, the system considers not only the prediction of grid load and generation fluctuations, but also the dynamic response characteristics of the energy storage system. For example, when the load changes drastically, the system may need to adjust the power output of the energy storage device more quickly to cope with sudden frequency deviations. In this case, robust control will automatically increase the output of the flywheel energy storage device, because the flywheel has a faster response speed and can quickly absorb or release power.

[0077] In some embodiments, by introducing robust control, the system can quickly adjust the power output of the energy storage system when faced with large load fluctuations or unstable renewable energy generation, thereby effectively stabilizing the grid frequency. For example, when wind power generation fluctuates significantly, the energy storage system will dynamically adjust the output of the energy storage device based on the results of robust control optimization, absorbing excess electricity and preventing the grid frequency from rising excessively; while when wind power generation decreases, the system will release the stored energy to maintain grid frequency stability.

[0078] In one possible implementation, robust control methods can effectively handle not only known disturbances but also unknown and uncertain disturbances through adaptive algorithms. For example, as the power grid becomes increasingly reliant on renewable energy, energy storage systems will require greater adaptability to handle the complex uncertainties of grid load and renewable energy generation. By introducing these adaptive mechanisms, the system can continuously improve its ability to cope with disturbances and maintain the stable operation of the power grid.

[0079] In summary, the robust control method employed in this step ensures that the energy storage system can maintain grid frequency stability even in the face of external disturbances, prediction errors, or system uncertainties, playing a crucial role in frequency regulation and peak shaving. By optimizing control inputs, the system can not only cope with known disturbances but also adapt to potential new challenges in the future, ensuring the efficient and stable operation of the power grid.

[0080] S6. Employ reinforcement learning or deep reinforcement learning methods to dynamically adjust the scheduling strategy of the energy storage system based on the real-time operation status and feedback information of the power grid. In the preceding steps, a scheduling optimization model was constructed based on grid frequency, energy storage system power model, and disturbance control theory, and robust control was implemented on the system. However, with grid load fluctuations and uncertainties in renewable energy generation, traditional optimization and control methods may not be adequately adapted to the complex grid operating environment. Therefore, this step employs reinforcement learning to further enhance the adaptive capability of the scheduling system. By continuously learning from the grid's operating conditions, reinforcement learning can dynamically adjust the scheduling strategy of energy storage devices to cope with grid load fluctuations and renewable energy generation fluctuations.

[0081] In this embodiment, reinforcement learning is used to dynamically adjust the scheduling strategy of the energy storage system. This method continuously adjusts the power output of the energy storage devices through interaction with the power grid. Specifically, reinforcement learning employs a reward-penalty-based learning strategy, using environmental feedback to improve scheduling decisions. The control input of the energy storage devices is considered an action, and changes in grid frequency and load are considered environmental states. The system selects an action (i.e., the power output of the energy storage devices) based on the current state and optimizes based on the feedback reward-penalty information.

[0082] During reinforcement learning, the system achieves adaptive scheduling through the following steps: Definition of State Space: The system first defines the state space of the power grid, including factors such as grid frequency deviation, load demand, and energy state of energy storage devices. The definition of the state space should be as comprehensive as possible to accurately reflect the operating state of the power grid.

[0083] Definition of action space: Action space includes the power output decisions of energy storage devices, such as the charging and discharging power of energy storage devices. Each action corresponds to the control strategy that the energy storage system should take at a certain moment.

[0084] Reward and Penalty Mechanism: To optimize the scheduling strategy, the system needs to design a reasonable reward and penalty mechanism. For example, when the grid frequency deviation is less than a certain threshold, the system provides a reward; when the frequency deviation exceeds a predetermined range, the system imposes a penalty. By continuously optimizing the reward and penalty mechanism, the system can adjust the power output of the energy storage equipment to achieve the expected frequency regulation effect.

[0085] Policy Learning and Updates: During reinforcement learning, the system optimizes scheduling decisions through continuous learning and policy updates. After each interaction, the system adjusts its control strategy based on actual feedback (i.e., grid frequency, load demand, etc.). Based on historical experience and real-time data, the system continuously improves scheduling efficiency and reduces frequency fluctuations.

[0086] Specifically, the core objective of reinforcement learning is to select the optimal energy storage dispatch strategy in a dynamically changing power grid environment through an adaptive learning process. In the initial stage, the system may adopt a relatively simple strategy, but as the learning process deepens, the system can gradually select more effective dispatch schemes, thereby improving the frequency stability of the power grid.

[0087] In some embodiments, reinforcement learning methods can also be combined with deep learning (deep reinforcement learning), especially in complex or large-scale power grid environments. By using deep neural networks to model the power grid state, deep reinforcement learning can handle more complex state and action spaces, improving the accuracy and adaptability of scheduling strategies.

[0088] As an alternative, reinforcement learning can be combined with other optimization methods, such as genetic algorithms or particle swarm optimization, for multi-strategy combinatorial optimization. In some power grid operating environments, a single reinforcement learning strategy may not be sufficient to cope with all changes. Therefore, combining multiple optimization algorithms can make the system more stable and flexible in complex environments.

[0089] In one possible implementation, reinforcement learning systems can further improve processing speed through parallel computing techniques, especially in applications requiring real-time scheduling decisions. By using multi-threaded or distributed computing, scheduling decisions for multiple energy storage devices can be processed simultaneously, improving system response speed and computational efficiency.

[0090] Overall, the introduction of reinforcement learning enables this system to respond quickly to changes in frequency and load during grid operation. By continuously learning and adjusting scheduling strategies, reinforcement learning improves the system's adaptability and optimization capabilities, especially in situations with large grid frequency fluctuations or unstable loads. This approach allows the system to achieve more precise frequency regulation, improve grid stability, and ensure the efficient utilization of energy storage devices.

[0091] S7. Conduct simulation verification, using power system simulation tools to test the effectiveness of the dispatch strategy under different load fluctuations and renewable energy generation conditions; In previous steps, a dynamic model of the power grid frequency and the energy storage system was established, a scheduling optimization objective function was constructed, and various control strategies, such as optimal control, robust control, and reinforcement learning, were adopted. These strategies have provided a theoretical foundation for the scheduling of the energy storage system. Building upon this foundation, the goal of this step is to verify the effectiveness of the previously designed scheduling optimization scheme under different power grid operating conditions through simulation. Simulation can verify the accuracy of the model and the actual performance of the control strategies.

[0092] In this embodiment, simulation verification is primarily conducted using power system simulation tools (such as MATLAB / Simulink). During the simulation, various scenarios, including grid load fluctuations and renewable energy generation fluctuations, are set up to simulate various possible situations during actual grid operation. By testing the scheduling strategy of the energy storage system, the simulation can provide detailed feedback information to help optimize the scheduling scheme.

[0093] The simulation verification process mainly consists of the following steps: Establishing the power grid model: First, a power grid model is established in the simulation tool to simulate the operation of the power grid. This model includes multiple parameters such as the grid frequency, load, power input and output of the generation and energy storage systems. To ensure the accuracy of the simulation results, different power grid structures, operating environments, and the dynamic response characteristics of energy storage devices need to be considered.

[0094] Simulation scenarios are set up: During the simulation, different scenarios such as load fluctuations, renewable energy generation fluctuations, and grid frequency deviations are set. For example, during high-load periods, the grid frequency may fluctuate significantly; while during low-load periods, energy storage devices may overcharge. By setting these different scenarios, the adaptability of the energy storage system scheduling strategy under different environments can be tested.

[0095] Verifying the scheduling effect: During the simulation, the scheduling strategy of the energy storage equipment will be adjusted according to the set optimization objectives, and the simulation system will provide real-time feedback on the scheduling results. For example, when the grid frequency deviates, the energy storage system will adjust its power output, utilizing pumped hydro storage or flywheel energy storage for frequency regulation. After each simulation cycle, the effectiveness of the scheduling strategy will be verified by comparing the actual grid frequency fluctuations and the power output of the energy storage equipment.

[0096] Specifically, the core of simulation verification is to test the response capability of the energy storage system under different load fluctuations and renewable energy generation fluctuations. The parameters of the dispatch strategy, such as the weighting coefficients α and β, and the power output range of the energy storage devices, will be adjusted according to different simulation scenarios. In this way, it can be ensured that the energy storage system maintains a stable grid frequency under various uncertain conditions.

[0097] In some embodiments, to improve the accuracy of simulation verification, real power grid data can be introduced for calibration. By inputting historical load data and renewable energy generation data from the actual power grid into the simulation model, the power grid operation can be simulated more realistically, and the dispatching strategy can be further optimized.

[0098] Alternatively, simulation verification can also be combined with an evaluation of the system's robustness. Systems need to respond quickly to grid load fluctuations or uncertainties in renewable energy generation. By incorporating disturbances into the simulation, the emergency response capabilities of the energy storage system can be tested, and the system's ability to operate stably under various complex conditions can be ensured.

[0099] In one possible implementation, simulation results are used not only to verify the effectiveness of the scheduling strategy, but also as a basis for further adjusting and optimizing the objective function and control parameters. If certain parameter settings lead to large frequency fluctuations or excessive energy consumption by the energy storage device, the simulation results will provide important information for adjustments.

[0100] Overall, simulation verification allows for a comprehensive evaluation of the energy storage system's performance in a real-world power grid environment, ensuring that the designed dispatching strategy achieves its intended effect under various power grid operating conditions. This process is a crucial step in the overall dispatching system verification, helping to identify potential problems and optimize control schemes, ultimately achieving stable regulation of the power grid frequency and efficient utilization of energy storage devices.

[0101] S8. Apply the optimized control method to the actual power grid and adjust the operating status of the energy storage equipment through real-time dispatch feedback.

[0102] In the preceding steps, dynamic modeling of the grid frequency and energy storage system was completed, a scheduling optimization objective function was set, and an optimal scheduling strategy for the energy storage system was formulated using methods such as reinforcement learning and robust control. Simulation verification shows that the proposed scheduling strategy has good performance in both theoretical and simulated environments. The next step is to apply these optimized control methods to a real power grid for real-time scheduling. By applying the optimized strategy to a real power grid, the effectiveness and adaptability of the system in a dynamic power grid environment will be further verified.

[0103] In this embodiment, deploying the optimized control method into the actual power grid first requires interfacing with the power grid's real-time data acquisition system. The real-time status of the power grid, including load demand, frequency deviation, and renewable energy generation, is fed back to the dispatch system in real time. By acquiring this data in real time, the optimized control system can continuously adjust the power output of the energy storage devices to ensure the grid frequency remains stable within a specified range.

[0104] In practical applications, energy storage systems adjust the operating status of pumped hydro storage and flywheel energy storage in real time according to the pre-designed scheduling strategy. For example, when there are large fluctuations in the grid frequency, the energy storage devices can respond quickly, absorbing excess electricity or releasing stored energy to maintain the stability of the grid frequency.

[0105] Specifically, during real-time dispatch, the power output of the energy storage device will be adjusted according to real-time data on grid frequency fluctuations. If the grid frequency is lower than the preset normal range, the energy storage device will prioritize releasing the stored energy to provide necessary peak-shaving support; conversely, if the grid frequency is too high, the energy storage system will regulate the frequency by absorbing excess electricity to avoid excessive fluctuations in the grid frequency.

[0106] In some embodiments, to improve the response speed and reliability of the dispatching system, the optimized control method can also combine grid load forecasting and renewable energy generation forecasting. By predicting load and generation changes before grid frequency fluctuations occur, the system can prepare for power dispatch in advance, reducing the difficulty of real-time adjustments. This combination of forecasting and real-time dispatching can effectively reduce the amplitude of grid frequency fluctuations and enhance grid stability.

[0107] As an alternative, during actual dispatching, the system can also dynamically adjust the weighting coefficients α and β based on the grid load demand and renewable energy generation to optimize the balance between frequency regulation and energy storage device efficiency. For example, when the grid load fluctuates significantly, the system may increase the regulation of frequency deviation, while when the load is relatively stable, it can reduce the intervention of frequency regulation and reduce the power consumption of the energy storage system.

[0108] In one possible implementation, to better adapt to the actual power grid environment, the optimized control system can incorporate advanced fault diagnosis and early warning mechanisms. By monitoring the operating status of energy storage devices and the power grid operation in real time, the system can quickly adjust its dispatch strategy upon detecting device failures or abnormal fluctuations, avoiding negative impacts on the power grid. Such a fault detection and adaptive adjustment mechanism can further improve the system's stability and emergency response capabilities.

[0109] Overall, by applying optimized control methods to the actual power grid, the dispatching of energy storage systems can not only maintain the stability of the grid frequency but also flexibly adjust under different operating conditions, ensuring the efficient and stable operation of the grid. The successful deployment of the real-time dispatching system will greatly enhance the grid's adaptability, especially when renewable energy generation fluctuates significantly, ensuring the grid can maintain stable operation in complex environments and providing solid technical support for the construction of smart grids.

[0110] The above specific embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A peak-shaving and frequency-regulating optimization control method combining pumped hydro storage and flywheel energy storage, characterized in that, include: S1. Establish a dynamic model of the power grid frequency and the energy storage system to describe the relationship between the power grid frequency and the power input and output of the energy storage device; S2. Based on the needs of frequency regulation and peak shaving, a joint scheduling optimization objective function is constructed. This objective function minimizes the weighted sum of the power consumption of energy storage devices and the frequency deviation of the power grid. S3. Set system constraints, including energy state constraints for the energy storage device, grid frequency constraints, and energy storage power balance constraints, wherein the energy state constraints for the energy storage device satisfy the following formula: in, and These represent the energy states of the pumped-storage and flywheel energy storage systems at time t, respectively, in kilowatt-hours. and These are the minimum and maximum energy capacities of the energy storage device, respectively, in kilowatt-hours. S4. Using optimal control theory, derive and implement the optimal scheduling strategy, specifically including selecting the scheduling target, optimizing the algorithm, and configuring parameters; S5. Apply robust control methods to design the control input of the energy storage system to suppress disturbances caused by grid load fluctuations and renewable energy generation fluctuations. The robust control optimization problem satisfies the following mathematical model: in, This represents the disturbance set, which includes grid load fluctuations and instability factors in renewable energy generation. This represents the total power output of the energy storage device at time t, in kilowatts. and The weighting coefficients are set to balance the control of energy storage power output and grid frequency deviation; This represents the total time period for optimized scheduling and control; Represents a time variable; Indicates time The power grid frequency deviation at any given time, in Hertz; S6. Employ reinforcement learning or deep reinforcement learning methods to dynamically adjust the scheduling strategy of the energy storage system based on the real-time operation status and feedback information of the power grid. S7. Conduct simulation verification, using power system simulation tools to test the effectiveness of the dispatch strategy under different load fluctuations and renewable energy generation conditions; S8. Apply the optimized control method to the actual power grid and adjust the operating status of the energy storage equipment through real-time dispatch feedback.

2. The peak-shaving and frequency-regulating optimization control method combining pumped hydro storage and flywheel energy storage according to claim 1, characterized in that, The dynamic model of the power grid frequency and energy storage system includes a power grid frequency response model, which satisfies the following formula: in, This indicates the deviation of the power grid frequency, measured in Hertz. It is the inertial time constant of the power grid, with units of seconds per kilowatt-hour; This represents the power grid frequency regulation coefficient, with units of Hertz per kilowatt. This indicates the power input of the energy storage system, measured in kilowatts (kW). This indicates the power output of the energy storage system, measured in kilowatts (kW).

3. The peak-shaving and frequency-regulating optimization control method combining pumped hydro storage and flywheel energy storage according to claim 1, characterized in that, The dynamic model of the power grid frequency and the energy storage system includes a power model for pumped storage equipment and a power model for flywheel energy storage equipment; the power model for pumped storage equipment satisfies the following formula: in, This indicates the power output of the pumped storage system, measured in kilowatts (kW). This indicates the power generation efficiency of the pumped storage system; This indicates the input power of the water pump, measured in kilowatts (kW). The power model of the flywheel energy storage device satisfies the following formula: in, This indicates the power output of the flywheel energy storage system, measured in kilowatts (kW). The moment of inertia of a flywheel is expressed in kilograms. square meters; This indicates the angular velocity of the flywheel, measured in radians per second. It represents the rate of change of the flywheel's angular velocity, measured in radians per second squared.

4. The peak-shaving and frequency-regulating optimization control method combining pumped hydro storage and flywheel energy storage according to claim 1, characterized in that, The joint scheduling optimization objective function satisfies the following formula: in, and These represent the power output of pumped storage and flywheel energy storage systems, respectively, in kilowatts. This indicates the deviation of the power grid frequency, measured in Hertz. and These represent weighting coefficients, used to adjust the contribution of power output and frequency deviation to the optimization objective, respectively. This indicates the time period for optimization calculations, in seconds.

5. The peak-shaving and frequency-regulating optimization control method combining pumped hydro storage and flywheel energy storage according to claim 1, characterized in that, The power grid frequency constraint satisfies the following formula: in, This indicates the deviation of the power grid frequency, measured in Hertz. This indicates the maximum permissible deviation of the power grid frequency, measured in Hertz.

6. The peak-shaving and frequency-regulating optimization control method combining pumped hydro storage and flywheel energy storage according to claim 1, characterized in that, The energy storage power balance constraint satisfies the following formula: in, This indicates the power input of the energy storage system, measured in kilowatts (kW). This indicates the power output of the energy storage system, measured in kilowatts (kW). This indicates the net load demand of the power grid, expressed in kilowatts.

7. The peak-shaving and frequency-regulating optimization control method combining pumped hydro storage and flywheel energy storage according to claim 1, characterized in that, The optimal scheduling strategy is derived using the Pontryagin minimum principle, and the Hamiltonian function constructed during the derivation process satisfies the following formula: in, , , These represent the Lagrange multipliers, corresponding to the dynamic response of grid frequency deviation, energy storage power balance constraints, and power consumption weighting, respectively. The inertial time constant of the power grid is expressed in seconds per kilowatt-hour. This represents the power grid frequency regulation coefficient, with units of Hertz per kilowatt. and These represent the power input and output of the energy storage system, respectively, in kilowatts. and These represent the power output of pumped storage and flywheel energy storage devices, respectively, in kilowatts.

8. The peak-shaving and frequency-regulating optimization control method combining pumped hydro storage and flywheel energy storage according to claim 1, characterized in that, The robust control method optimizes the control input of the energy storage device by considering a set of disturbances that includes grid load fluctuations and unstable factors of renewable energy generation, so that the system can still maintain grid frequency stability within the disturbance range.

9. The peak-shaving and frequency-regulating optimization control method combining pumped hydro storage and flywheel energy storage according to claim 1, characterized in that, The reinforcement learning method dynamically adjusts the scheduling strategy of energy storage devices based on grid frequency fluctuations and load demand, and continuously updates the scheduling model based on historical data.

10. The peak-shaving and frequency-regulating optimization control method combining pumped hydro storage and flywheel energy storage according to claim 1, characterized in that, The simulation verification was conducted using the MATLAB / Simulink power system simulation tool to verify the stability and regulation effect of the optimized control method under different power grid operating conditions.

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