Electric energy storage control system

Abstract

The invention relates to the technical field of energy management and control, and discloses an electric energy storage control system, and the system comprises a data collection module which is used for collecting the operation data of electric energy storage equipment; the system modeling module is used for establishing a dynamic power flow model of a power system, a charging and discharging state model of energy storage equipment and an energy conversion model; the optimization control module is used for carrying out multi-objective optimization scheduling according to the operation data and the dynamic model, and optimization objectives comprise operation cost, energy loss and carbon emission cost; and the scheduling execution module is used for executing charging and discharging operation of the energy storage system according to the scheduling result of the optimization control module. Through multi-objective optimization and intelligent scheduling, in combination with real-time data acquisition and visual management, the dynamic optimization control of the energy storage system is realized, the economy, energy efficiency and environmental protection property of the system are effectively improved, the adaptive capacity of the energy storage equipment to complex working conditions is enhanced, and a user is supported to dynamically adjust an optimization objective and scheduling parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management and control, and in particular to an electric energy storage control system. Background Art

[0002] The power storage control system is an important part of the modern power system, which is used to coordinate the energy exchange between the energy storage equipment and the power grid to achieve a dynamic balance between power supply and demand. With the widespread application of renewable energy (such as wind and solar energy) in the power system, its volatility and uncertainty have increased significantly. The energy storage control system improves the stability and security of the power grid through functions such as peak shaving, frequency regulation and backup scheduling.

[0003] Existing power energy storage control systems mainly implement dispatching control of energy storage equipment through rule-based control and simple optimization algorithms. Its typical applications include: Peak shaving and valley filling: The existing system can reasonably dispatch energy storage equipment according to the changes in power load, charge during off-peak hours and discharge during peak hours, effectively balance the grid load and alleviate peak power pressure; Backup power supply: When a sudden failure occurs in the power grid or when the power demand surges, the energy storage control system can quickly start the energy storage equipment to provide power support and improve the reliability of the power grid; Basic frequency regulation: The existing system can respond to grid frequency fluctuations and maintain grid frequency stability by adjusting the power output of energy storage devices; The above technologies have improved the operating efficiency of the power system and the stability of the power grid to a certain extent, but their control methods are mostly based on rules or static models, with relatively low flexibility and intelligence, and are difficult to adapt to the complex and changeable power grid operating environment.

[0004] Although the existing power storage control system has achieved certain results in peak load shaving, backup power supply and basic frequency regulation, it still has the following shortcomings: Insufficient economic efficiency: The existing system lacks multi-objective optimization functions and only focuses on a single goal (such as peak shaving or frequency regulation). It is unable to comprehensively weigh operating costs, energy losses and carbon emissions, resulting in poor overall economic efficiency of the system. Low energy efficiency: Rule-based control cannot accurately and dynamically adjust the charging and discharging behavior of energy storage devices, resulting in low energy conversion efficiency and inability to fully tap the potential of energy storage devices; Lack of intelligence and flexibility: The existing system is difficult to dynamically adjust optimization goals and scheduling strategies based on real-time data, lacks the ability to adapt to complex working conditions, and cannot meet users' needs for personalized energy management; Limited operability: The existing system lacks comprehensive operating status display and human-computer interaction functions. It is difficult for users to intuitively grasp the operating status, power distribution and scheduling results of energy storage equipment, and the operating experience is insufficient. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides an electric energy storage control system, which solves the problems of insufficient economy, low energy efficiency, insufficient intelligence and flexibility, and limited operability existing in the existing electric energy storage control systems.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an electric energy storage control system, the system comprising: A data acquisition module, used to collect operating data of the power storage equipment; System modeling module, used to establish the dynamic power flow model of the power system, the charging and discharging state model of the energy storage equipment, and the energy conversion model; The optimization control module is used to perform multi-objective optimization scheduling based on operating data and dynamic models. The optimization targets include operating cost, energy loss and carbon emission cost. A scheduling execution module, used to execute the charging and discharging operations of the energy storage system according to the scheduling results of the optimization control module; Visual management module, used to display the operating status and dispatch results of the energy storage system.

[0007] Preferably, the data acquisition module includes: Power data acquisition unit, used to collect real-time voltage, current and power of the power system; Energy storage data acquisition unit, used to collect charging status, temperature and capacity change information of energy storage equipment; The data transmission unit is used to transmit the collected data to the edge computing device through the Internet of Things communication protocol.

[0008] Preferably, the system modeling module includes: Power system model, which is used to calculate the power transmission between nodes through dynamic power flow equations, optimize the power distribution between nodes, and improve the adaptability of the power grid under load fluctuations; Energy storage device model, which is used to establish a nonlinear state model based on the dynamic charging and discharging behavior of the energy storage device to achieve regulation of the charging and discharging process; Energy conversion model, used to construct the energy conversion path of cogeneration equipment and achieve multi-objective optimization through real-time energy efficiency calculation.

[0009] Preferably, the dynamic charging and discharging behavior of the energy storage device model is implemented by the following formula: in: SOC t is the charging state of the energy storage device at time t; η c , η dare charging and discharging efficiency respectively; C total is the total capacity of the energy storage device; P charge , P discharge are charging power and discharging power respectively.

[0010] Preferably, the power system model is described by the following power flow equation: P ij =V i V j (G ij cosθ ij +B ij sinθ ij ) Where: P ij is the active power from node i to node j; V i , V j is the voltage between node i and node j; G ij , B ij are the real and imaginary parts of the admittance matrix respectively; θ ij is the phase difference between node i and node j.

[0011] Preferably, the optimization control module includes: Objective function design unit, used to construct multi-objective optimization function, the optimization objectives include operation cost, energy loss and carbon emission cost; A constraint condition setting unit, used to set state constraints, energy balance constraints and equipment capacity limits for energy storage equipment; The optimization algorithm unit is used to solve optimization problems using mixed integer linear programming methods.

[0012] Preferably, the multi-objective optimization function is expressed by the following formula: minJ=α1C op +α2E loss +α3C em Where: J is the optimization target; C op is the operating cost of the energy storage system; E loss is the energy loss of the energy storage system; C em is the carbon emission cost of the energy storage system; α1, α2, α3 are weight coefficients.

[0013] Preferably, the scheduling execution module includes: A scheduling strategy generating unit, used to generate a charging and discharging scheduling strategy for energy storage equipment; The execution unit is used to control the actual operation of the energy storage device according to the scheduling strategy.

[0014] Preferably, the scheduling strategy is generated by a deep Q-network, where: Status tIndicates the current operating status of the energy storage system, including the state of charge (SOC) of the energy storage device, load demand, and grid power; Action a t Indicates the dispatch operation of the energy storage system, including charging, discharging or standby mode; Reward t It is used to measure the scheduling effect and is expressed by the following formula: r t =-(α1C op +α2E loss ) Among them: α1, α2 are weights; C op is the operating cost; E loss For energy loss; In addition, the value function Q(s) of the deep Q network t ,a t )The update formula is: in: γ is a discount factor used to balance the weights of current rewards and future rewards; Q(s t ,a t ) means in state s t Take action a t The expected cumulative return.

[0015] Preferably, the visual management module is used to: Real-time display of the operating status of the energy storage system, including equipment charging status, power distribution and dispatch results; Provides a user interface for adjusting optimization target weights or scheduling strategy parameters.

[0016] The present invention provides an electric energy storage control system. It has the following beneficial effects: 1. The present invention realizes dynamic optimization control of the energy storage system through multi-objective optimization and deep Q network scheduling, which significantly improves the economy, energy efficiency and environmental protection of the system operation, while enhancing the adaptability of the energy storage equipment to complex working conditions, ensuring the load balance of the power grid and efficient use of energy.

[0017] 2. The present invention combines real-time data collection and visual management to comprehensively display the operating status and scheduling results of the energy storage system. Users can dynamically adjust the optimization target weights and scheduling parameters to achieve personalized energy management, thereby improving the flexibility and operability of the system.

[0018] 3. Through mathematical models and intelligent control algorithms, the present invention can effectively control the charging and discharging process of energy storage equipment, optimize power distribution and energy conversion efficiency, extend equipment life, reduce system operating costs and carbon emissions, and meet the application needs of multiple scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a system structure diagram of the present invention; Figure 2 It is a system structure diagram of the data acquisition module of the present invention; Figure 3 A system diagram of a system modeling module of the present invention; Figure 4 A system diagram of the optimized control module of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Please see attached Figure 1 -Attached Figure 4 The embodiment of the present invention provides an electric energy storage control system, the system comprising: The data acquisition module is used to collect the operating data of the power energy storage equipment. Through real-time and comprehensive data acquisition, the system can accurately perceive the operating status of the energy storage equipment and its surrounding environment, provide high-quality data support for subsequent modeling and optimization, and improve the system's response speed and optimization accuracy; System modeling module is used to establish the dynamic power flow model of the power system, the charging and discharging state model of the energy storage equipment, and the energy conversion model. Through system modeling, the dynamic characteristics of the energy storage system and the power grid can be accurately reflected, providing a scientific basis for subsequent optimization control, while improving the safety and stability of system operation; The optimization control module is used to perform multi-objective optimization scheduling based on operating data and dynamic models. The optimization targets include operating costs, energy losses, and carbon emission costs. Through multi-objective optimization, the system can strike a balance between operating costs, energy losses, and carbon emissions, achieving both economic efficiency and environmental protection. The optimization results can adapt to different load requirements and external environmental changes; The scheduling execution module is used to execute the charging and discharging operations of the energy storage system according to the scheduling results of the optimization control module. Through precise scheduling execution, the system can flexibly adapt to load changes and respond to sudden demands in a timely manner, thereby improving the utilization efficiency and operation stability of the energy storage system; The visual management module is used to display the operating status and scheduling results of the energy storage system. The visual management module improves the system's operability and user experience, allowing users to intuitively understand the operation of the energy storage system and quickly adjust system parameters according to needs to achieve customized energy management.

[0022] Please see attached Figure 1 and attached Figure 2 In a preferred embodiment of the present invention, the data acquisition module includes: The power data acquisition unit is used to collect the real-time voltage, current and power of the power system. By collecting electrical parameters such as voltage, current and power, the system can fully grasp the operating status of the energy storage equipment and the power grid, and provide data support for subsequent scheduling and control. Real-time power parameter acquisition improves the response speed of the energy storage system to grid load fluctuations, ensuring the balance of grid supply and demand, while avoiding energy waste caused by overload or inefficient operation; The energy storage data acquisition unit is used to collect the charging status, temperature and capacity change information of the energy storage equipment. The energy storage data acquisition unit obtains the dynamic operation information of the energy storage equipment through a high-precision sensor network, providing a data basis for optimizing scheduling and equipment maintenance. Real-time collection of energy storage equipment status data can significantly improve the safety of the energy storage system and extend the service life of the equipment; the data transmission unit is used to transmit the collected data to the edge computing device through the Internet of Things communication protocol. Through the Internet of Things communication protocol, the data transmission unit can efficiently integrate the data of the distributed energy storage system, realize centralized information processing, and provide reliable support for system modeling and optimized scheduling. The reliable data transmission mechanism improves the response speed and safety of the energy storage control system, while reducing latency and improving data processing efficiency through edge computing.

[0023] Please see attached Figure 1 and attached Figure 3 In a preferred embodiment of the present invention, the system modeling module includes: The power system model is used to calculate the power transmission between nodes through the dynamic power flow equation, optimize the power distribution between nodes, and improve the adaptability of the power grid under load fluctuations. The dynamic power flow equation quantifies the power interaction between the energy storage device and the power grid in real time. The power distribution optimization effectively improves the stability and efficiency of the power grid by balancing the node load, thereby improving the adaptability of the energy storage device to the power grid under load fluctuations, reducing the risk of overload or unbalanced operation of the power grid, and improving the response efficiency of the energy storage system; Energy storage equipment model, which is used to establish a nonlinear state model based on the dynamic charging and discharging behavior of the energy storage equipment to achieve regulation of the charging and discharging process. The energy storage equipment model is based on the dynamic state equation and nonlinear correction model, accurately describes the operating characteristics of the energy storage equipment, and provides an optimized charging and discharging regulation strategy, thereby improving the equipment's operating efficiency and lifespan, while providing more accurate energy support when load demand fluctuates; The energy conversion model is used to construct the energy conversion path of the cogeneration equipment and realize multi-objective optimization through real-time energy efficiency calculation. The energy conversion model realizes efficient coordination between energy storage equipment and cogeneration equipment through real-time energy efficiency calculation and multi-objective optimization, maximizes energy utilization efficiency, thereby improving the comprehensive utilization efficiency of electrical energy and thermal energy, while reducing system operating costs and carbon emissions, providing a green and efficient operation mode for the energy storage system.

[0024] Please see attached Figure 1 and attached Figure 3 In a preferred embodiment of the present invention, the dynamic charging and discharging behavior of the energy storage device model is implemented by the following formula: in: SOC t is the charging state of the energy storage device at time t; η c , η d are charging and discharging efficiency respectively; C total is the total capacity of the energy storage device; P charge , P discharge They are charging power and discharging power respectively. Through the dynamic charging and discharging formula of the energy storage device, the system realizes precise control of the charging and discharging process and can flexibly respond to various operating conditions: Charging process: optimize charging efficiency and avoid equipment aging caused by overcharging; discharging process: dynamically respond to load demand to avoid damage to equipment due to overdischarge; cycle control: update model parameters in real time to improve the operating stability of equipment under complex working conditions. The dynamic charging and discharging model increases the utilization rate of energy storage equipment by 20% to 30%, reduces operating costs by 15% to 20%, and extends the service life of energy storage equipment, providing solid support for the stable operation of the power grid.

[0025] Please see attached Figure 1 and attached Figure 3 In a preferred embodiment of the present invention, the power system model is described by the following power flow equation: P ij =V i V j (G ij cosθ ij +B ij sinθ ij ) Where: P ij is the active power from node i to node j; V i , V j is the voltage between node i and node j; G ij , B ij are the real and imaginary parts of the admittance matrix respectively; θ ij The phase difference between node i and node j is calculated by using the power flow equation to minimize power loss and balance node loads, thereby improving the operation efficiency of the power grid. The real-time analysis of power flow can quickly respond to power grid emergencies and enhance the stability of the system. At the same time, the power flow equation provides a scientific basis for the optimal access and scheduling of energy storage equipment, reduces energy consumption, and improves the economy of the power grid.

[0026] Please see attached Figure 1 and attached Figure 4 In a preferred embodiment of the present invention, the optimization control module includes: The objective function design unit is used to construct a multi-objective optimization function. The optimization objectives include operating costs, energy losses, and carbon emission costs. By constructing a multi-objective optimization function, the objective function design unit provides a scientific optimization direction based on comprehensive consideration of economy, energy efficiency, and environmental protection. The multi-objective optimization function provides flexibility and adaptability for system operation. Users can adjust the objective weights according to actual needs to achieve the dual effects of energy conservation, emission reduction, and cost optimization. The constraint setting unit is used to set the state constraints, energy balance constraints and equipment capacity limits of the energy storage equipment. The constraint setting unit limits the optimization process to the safe operation range of the energy storage equipment and the power grid to avoid problems such as overcharging, over-discharging or load imbalance. The setting of constraints ensures the safe operation of the energy storage system, avoids system failures caused by equipment overload or unbalanced operation, and improves the executableness of the optimization results. The optimization algorithm unit is used to solve the optimization problem through the mixed integer linear programming method. The mixed integer linear programming achieves the optimality of system scheduling under dynamic constraints by accurately solving the optimization problem. The optimization algorithm unit ensures the accuracy and real-time performance of the scheduling results, reduces the system operating costs, and maximizes the utilization efficiency of the energy storage equipment.

[0027] Please see attached Figure 1 and attached Figure 4 In a preferred embodiment of the present invention, the multi-objective optimization function is expressed by the following formula: minJ=α1C op +α2E loss +α3C em Where: J is the optimization target; C op is the operating cost of the energy storage system; Eloss is the energy loss of the energy storage system; C em is the carbon emission cost of the energy storage system; α1, α2, α3 are weight coefficients. The multi-objective optimization function constructs a scientific optimization model for the operation of the energy storage system by comprehensively considering economy, energy efficiency and environmental protection. Each target is quantified through a precise formula, and its impact is incorporated into the overall optimization framework. Combined with dynamically adjusted weight coefficients, the optimization function can adapt to a variety of operating environments and user needs. The multi-objective optimization function not only realizes the organic combination of economy, energy efficiency and environmental protection, but also improves the adaptability of the energy storage system to complex environments.

[0028] Please see attached Figure 1 In a preferred embodiment of the present invention, the scheduling execution module includes: The dispatching strategy generation unit is used to generate the charging and discharging dispatching strategy of the energy storage equipment. The dispatching strategy generation unit generates a scientific charging and discharging dispatching plan by real-time monitoring the supply and demand balance of the power grid and combining the status information and optimization results of the energy storage equipment to ensure the efficiency and reliability of the system operation; The execution unit is used to control the actual operation of the energy storage equipment according to the scheduling strategy. The execution unit executes the operation of the energy storage equipment through precise control instructions, combined with the safety protection mechanism and real-time status feedback, to ensure the reliability and accuracy of the equipment operation, thereby improving the execution efficiency of the scheduling plan, ensuring the safe operation of the energy storage equipment, and enhancing the adaptability of the energy storage system to grid fluctuations.

[0029] Please see attached Figure 1 In a preferred embodiment of the present invention, the scheduling strategy is generated by a deep Q network, where: the state s t Indicates the current operating status of the energy storage system, including the state of charge (SOC) of the energy storage device, load demand, and grid power; Action a t Indicates the dispatch operation of the energy storage system, including charging, discharging or standby mode; Reward t It is used to measure the scheduling effect and is expressed by the following formula: r t =-(α1C op +α2E loss ) Among them: α1, α2 are weights; C op is the operating cost; E loss For energy loss; In addition, the value function Q(s) of the deep Q network t ,a t )The update formula is: in: γ is a discount factor used to balance the weights of current rewards and future rewards; Q(s t ,a t ) means in state s t Take action a t The expected cumulative return.

[0030] Specifically: The scheduling strategy is generated by the deep Q network, using the state s t Characterizes the operation information of the energy storage system, action a t represents the dispatch mode of energy storage equipment, and the reward r t To evaluate the scheduling effect, the deep Q network uses the value function Q(s t ,a t ) is iteratively updated and the optimal scheduling strategy is gradually learned to ensure the efficiency and adaptability of the energy storage system. The deep Q network can dynamically adjust the operation mode of the energy storage equipment according to real-time data to achieve refined scheduling, and the design of the reward function can prompt the scheduling strategy to achieve a balance between economy and energy efficiency, significantly reducing system operating costs and energy losses.

[0031] Please see attached Figure 1 In a preferred embodiment of the present invention, the visualization management module is used to: Real-time display of the operating status of the energy storage system, including the device charging status, power distribution and dispatch results. By displaying the SOC value and its dynamic changes in real time, users can intuitively understand the remaining energy and current operating mode of the energy storage device. The intuitive device status display improves the user's ability to grasp the operation of the energy storage system, making it easier to quickly discover anomalies and adjust the dispatch strategy. The visualization of power flow helps users optimize energy dispatch and improve the energy storage system's ability to adapt to grid fluctuations. A user operation interface is provided for adjusting the optimization target weight or scheduling strategy parameters. The optimization target weight adjustment function dynamically changes the calculation logic of the optimization function through user input, so that the scheduling strategy of the energy storage system can adapt to the needs of different scenarios. The scheduling strategy parameter adjustment dynamically changes the operating boundaries of the energy storage device through user intervention, enhancing the flexibility and responsiveness of the system.

[0032] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An electric energy storage control system, characterized in that: The system comprises: A data acquisition module, used to collect operating data of the power storage equipment; System modeling module, used to establish the dynamic power flow model of the power system, the charging and discharging state model of the energy storage equipment, and the energy conversion model; The optimization control module is used to perform multi-objective optimization scheduling based on operating data and dynamic models. The optimization targets include operating cost, energy loss and carbon emission cost. A scheduling execution module, used to execute the charging and discharging operations of the energy storage system according to the scheduling results of the optimization control module; Visual management module, used to display the operating status and dispatch results of the energy storage system.

2. A power storage control system according to claim 1, characterized in that: The data acquisition module includes: Power data acquisition unit, used to collect real-time voltage, current and power of the power system; Energy storage data acquisition unit, used to collect charging status, temperature and capacity change information of energy storage equipment; The data transmission unit is used to transmit the collected data to the edge computing device through the Internet of Things communication protocol.

3. The power storage control system according to claim 1, characterized in that: The system modeling module includes: Power system model, which is used to calculate the power transmission between nodes through dynamic power flow equations, optimize the power distribution between nodes, and improve the adaptability of the power grid under load fluctuations; Energy storage device model, which is used to establish a nonlinear state model based on the dynamic charging and discharging behavior of the energy storage device to achieve regulation of the charging and discharging process; Energy conversion model, used to construct the energy conversion path of cogeneration equipment and achieve multi-objective optimization through real-time energy efficiency calculation.

4. The power storage control system according to claim 3, characterized in that: The dynamic charging and discharging behavior of the energy storage device model is implemented by the following formula: in: SOC t is the charging state of the energy storage device at time t; η c , η d are charging and discharging efficiency respectively; C total is the total capacity of the energy storage device; P charge , P discharge are charging power and discharging power respectively.

5. The power storage control system according to claim 3, characterized in that: The power system model is described by the following power flow equation: P ij =V i V j (G ij cosθ ij +B ij sinθ ij ) Where: P ij is the active power from node i to node j; V i , V j is the voltage between node i and node j; G ij , B ij are the real and imaginary parts of the admittance matrix respectively; θ ij is the phase difference between node i and node j.

6. The power storage control system according to claim 1, characterized in that: The optimization control module includes: Objective function design unit, used to construct multi-objective optimization function, the optimization objectives include operation cost, energy loss and carbon emission cost; A constraint condition setting unit, used to set state constraints, energy balance constraints and equipment capacity limits for energy storage equipment; The optimization algorithm unit is used to solve optimization problems using mixed integer linear programming methods.

7. The power storage control system according to claim 6, characterized in that: The multi-objective optimization function is expressed by the following formula: minJ=α1C op +α2E loss +α3C em Where: J is the optimization target; C op is the operating cost of the energy storage system; E loss is the energy loss of the energy storage system; C em is the carbon emission cost of the energy storage system; α1, α2, α3 are weight coefficients.

8. The power storage control system according to claim 1, characterized in that: The scheduling execution module includes: A scheduling strategy generating unit, used to generate a charging and discharging scheduling strategy for energy storage equipment; The execution unit is used to control the actual operation of the energy storage device according to the scheduling strategy.

9. The power storage control system according to claim 8, characterized in that: The scheduling policy is generated through a deep Q-network, where: Status t Indicates the current operating status of the energy storage system, including the state of charge (SOC) of the energy storage device, load demand, and grid power; Action a t Indicates the dispatch operation of the energy storage system, including charging, discharging or standby mode; Reward t It is used to measure the scheduling effect and is expressed by the following formula: r t =-(α1C op +α2E loss ) Among them: α1, α2 are weights; C op is the operating cost; E loss For energy loss; In addition, the value function Q(s) of the deep Q network t ,a t )The update formula is: in: γ is a discount factor used to balance the weights of current rewards and future rewards; Q(s t ,a t ) means in state s t Take action a t The expected cumulative return.

10. The electric energy storage control system according to claim 1, characterized in that: The visual management module is used to: Real-time display of the operating status of the energy storage system, including equipment charging status, power distribution and dispatch results; Provides a user interface for adjusting optimization target weights or scheduling strategy parameters.

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