Power optimization method of integrated energy system, energy management equipment and energy storage cabinet

Abstract

The invention provides a power optimization method of an integrated energy system, energy management equipment and an energy storage cabinet, and relates to the technical field of energy management. The method comprises the following steps: when the integrated energy system enters any target mode, obtaining a target function corresponding to the target mode; the target mode is determined by the current power demand of the integrated energy system; and according to the target function, optimizing the current output power of each power-adjustable device in the integrated energy system, so that each power-adjustable device operates at the optimal output power meeting the target function. According to the method, the comprehensive performance of the comprehensive energy system can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of energy management, and particularly relates to a power optimization method for an integrated energy system, an energy management device, and a storage cabinet. Background Art

[0002] With the transformation of the energy structure and the wide application of renewable energy, the source-load-storage-charging collaborative integrated energy system that integrates energy supply, storage, and consumption has become the core hub for achieving efficient energy utilization.

[0003] Currently, most known technologies adopt rule-based control strategies to manage and optimize the above-mentioned integrated energy system. Specifically, the rule-based control strategy realizes energy scheduling through pre-set logical rules. For example, when the photovoltaic power generation is sufficient, it preferentially charges the energy storage device and meets the load demand, and the diesel generator starts only when the energy storage power is insufficient and the load demand is at a peak.

[0004] However, although the above-mentioned rule-based control strategy can meet the basic operation requirements, when facing a complex integrated energy system, it has the defect that it is difficult to achieve global optimal energy distribution and scheduling, which is not conducive to ensuring the comprehensive performance of the system. Summary of the Invention

[0005] The present application provides a power optimization method for an integrated energy system, an energy management device, and a storage cabinet to ensure the comprehensive performance of the system.

[0006] In a first aspect, the present application provides a power optimization method for an integrated energy system, the method comprising:

[0007] When the integrated energy system enters any target mode, obtaining the target function corresponding to the target mode; the target mode is determined by the current power demand of the integrated energy system;

[0008] According to the target function, optimizing the current output power of each power adjustable device in the integrated energy system, so that each power adjustable device operates at the optimal output power that satisfies the target function.

[0009] In another possible implementation manner, the step of optimizing the current output power of each power adjustable device in the integrated energy system according to the target function, so that each power adjustable device operates at the optimal output power that satisfies the target function, includes:

[0010] Generating initial parameters including the optimal target function value, as well as the minimum output power, the maximum output power, and the desired output power of each power adjustable device;

[0011] Calculate the current objective function value using the objective function based on the current output power of each power-adjustable device;

[0012] According to a preset optimization algorithm, a first constraint condition, and a second constraint condition, update the current output power of each power-adjustable device until the iteration termination condition is reached, and then use the current output power as the optimal output power; the first constraint condition is used to indicate that the current output power of each power-adjustable device is greater than or equal to the corresponding minimum output power and less than or equal to the corresponding maximum output power; the second constraint condition is used to indicate that the sum of the current output power of each power-adjustable device and the current output power of the power-adjustable devices belonging to the same category is equal to the current output power of the superior device to which the power-adjustable device belongs.

[0013] In another possible implementation manner, optimizing the current output power of each power-adjustable device in the integrated energy system according to the objective function so that each power-adjustable device operates at the optimal output power that satisfies the objective function includes:

[0014] For any target group in the network structure of the integrated energy system, generate target initial parameters and simulated annealing parameters; the target initial parameters include the optimal objective function value, as well as the minimum output power, maximum output power, and expected output power of each power-adjustable device in the target group; the simulated annealing parameters include the initial temperature and the cooling rate;

[0015] Calculate the current objective function value using the objective function based on the current output power of each power-adjustable device;

[0016] According to the current objective function value and / or the initial temperature, update the current output power of each power-adjustable device through at least one iteration process to obtain the optimal output power of each power-adjustable device.

[0017] In another possible implementation manner, any iteration process includes:

[0018] When the current temperature does not meet the iteration termination condition and / or the current objective function value has not reached the optimal objective function value, update the current output power of each power-adjustable device according to the first constraint condition and the second constraint condition corresponding to the target group; in the first iteration process, the current temperature is the initial temperature;

[0019] Calculate the updated current objective function value using the objective function based on the updated current output power of each power-adjustable device;

[0020] When the updated current objective function value is closer to the optimal objective function value than the current objective function value in the previous iteration, the updated current output power is taken as the optimal output power in the current iteration;

[0021] Reduce the current temperature based on the cooling rate.

[0022] In another possible implementation, the updating the current output power of each power adjustable device according to the first constraint condition and the second constraint condition corresponding to the target group includes:

[0023] According to the first constraint condition corresponding to the target group, randomly update the current output power of any target power adjustable device by using a neighborhood search mechanism;

[0024] Update the current output power of other power adjustable devices in the target group based on the first constraint condition and the second constraint condition.

[0025] In another possible implementation, the method further includes:

[0026] Obtain the grid data of the integrated energy system through a collection device, parse the grid data according to a preset tree - type topological structure to obtain the grid structure of the integrated energy system, and make the power adjustable devices in the integrated energy system all be leaf nodes of the grid structure; the grid data includes the device information, connection relationship, and power information of the integrated energy system;

[0027] Determine at least one target group according to the grid structure; the target group includes the target device corresponding to a parent node and the power adjustable devices corresponding to at least one leaf node included in the parent node.

[0028] In another possible implementation, the target mode is any one of a peak - shaving and valley - filling mode, a green - electricity - priority mode, an island operation mode, a three - phase imbalance adjustment mode, and a standby power supply mode.

[0029] In another possible implementation, when the target mode is the peak - shaving and valley - filling mode, the target function is: , and, , ; where, is the objective function value in the peak - shaving and valley - filling mode, the optimal objective function value is the minimum objective function value, i is used to represent any power adjustable device in the integrated energy system, w is the weight of the power adjustable device i, M is the number of power adjustable devices, is the current output power of the power adjustable device i, is the expected output power of the power adjustable device i, is the maximum output power of the power adjustable device i, is the minimum output power of the power adjustable device i, is the expected output power of the energy storage device, is the expected output power of the transformer;

[0030] And / or, when the target mode is the green power priority mode or the island operation mode, the target function is: , where, is the target function value in the green power priority mode, the optimal target function value is the maximum target function value, i is used to represent any green power device in the integrated energy system, and N is used to represent the number of green power devices in the integrated energy system, is used to represent the current power of the green power device i;

[0031] And / or, when the target mode is the three-phase unbalance adjustment mode, the target function is: , and , where, is the target function value in the three-phase unbalance adjustment mode, the optimal target function value is the minimum target function value, is the average value of the three-phase power of the root node of the grid structure of the integrated energy system, are the powers of phases A, B, and C respectively;

[0032] And / or, when the target mode is the standby power supply mode, the target function is: , and, , , , where, is the generator start-stop signal in the standby power supply mode, is 1 when the generator starts, is 0 when the generator shuts down, is the apparent power threshold for generator startup, is the SOC threshold for generator startup, is the voltage threshold for generator startup, is the apparent power threshold for generator shutdown, is the SOC threshold for generator shutdown, is the energy storage node with the largest apparent power in the integrated energy system, is the energy storage node with the smallest SOC in the integrated energy system, c is the number of energy storage nodes in the integrated energy system, is the apparent power of the cth energy storage node, is the SOC of the cth energy storage node, is the voltage of the cth energy storage node.

[0033] In a second aspect, the present application provides a power optimization device for an integrated energy system, and the device includes:

[0034] An acquisition module, configured to acquire an objective function corresponding to a target mode when the integrated energy system enters any target mode; the target mode is determined by the current power demand of the integrated energy system;

[0035] An optimization module, configured to optimize the current output power of each power adjustable device in the integrated energy system according to the objective function, so that each power adjustable device operates at an optimal output power that meets the objective function.

[0036] In a third aspect, the present application provides an energy management device, including: at least one processor and a memory;

[0037] The memory stores computer-executable instructions;

[0038] The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor executes the method according to any one of the above first aspects.

[0039] In a fourth aspect, the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a central processing unit, they are used to implement the method according to any one of the above first aspects.

[0040] In a fifth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a central processing unit, it implements the method according to any one of the first aspects.

[0041] In a sixth aspect, the present application provides a energy storage cabinet, including the energy management device according to the above third aspect.

[0042] The present application provides a power optimization method, an energy management device and an energy storage cabinet for an integrated energy system. Among them, the method of the present application proposes to acquire an objective function corresponding to a target mode when the integrated energy system enters the target mode, and optimize the current output power of each power adjustable device in the integrated energy system according to the objective function, so that each power adjustable device operates at a corresponding optimal output power. In the method of the present application, since the objective function corresponding to the current target mode of the integrated energy system is used to optimize the current output power of each power adjustable device, it can accurately match the requirements of different operation scenarios, effectively break through the limitations of traditional single optimization rules, thereby ensuring the stable operation of the complex integrated energy system in each mode, and significantly improving the energy utilization efficiency and system comprehensive performance. Description of the Drawings

[0043] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with this application, and are used together with the specification to explain the principles of this application.

[0044] Figure 1 A schematic diagram of an application scenario of a power optimization method for an integrated energy system provided by an embodiment of this application;

[0045] Figure 2 A flowchart of a power optimization method for an integrated energy system provided by an embodiment of this application Figure 1 ;

[0046] Figure 3A A flowchart of a power optimization method for an integrated energy system provided by an embodiment of this application Figure 2 ;

[0047] Figure 3B A flowchart block diagram of a power optimization method for an integrated energy system provided by an embodiment of this application Figure 1 ;

[0048] Figure 3C A flowchart block diagram of a power optimization method for an integrated energy system provided by an embodiment of this application Figure 2 ;

[0049] Figure 4 A structural diagram of a power optimization device for an integrated energy system provided by an embodiment of this application;

[0050] Figure 5 A structural diagram of an energy management device provided by an embodiment of this application.

[0051] Through the above accompanying drawings, specific embodiments of this application have been shown, and there will be more detailed descriptions hereinafter. These accompanying drawings and the written descriptions are not intended to limit the scope of the concept of this application in any way, but to illustrate the concept of this application to those skilled in the art by referring to specific embodiments. Detailed Embodiments

[0052] Exemplary embodiments will be described in detail here, and the examples are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. On the contrary, they are merely examples of devices and methods consistent with some aspects of this application as detailed in the appended claims.

[0053] With the transformation of the energy structure and the wide application of renewable energy, the source-load-storage-charging collaborative energy storage system that integrates energy supply, storage, and consumption has become the core hub for achieving efficient energy utilization. Among them, "source" refers to energy supply devices such as diesel generators and photovoltaic power generation equipment, "load" refers to the load demand generated by load devices, "storage" refers to energy storage devices, and "charging" covers charging facilities such as electric vehicles.

[0054] Currently, for the above-mentioned integrated energy system, known technologies mostly adopt rule-based control strategies to manage and optimize the output power of each power-adjustable device. It should be noted that the power-adjustable devices mentioned in this application specifically refer to the energy supply devices and energy storage devices (or energy storage nodes) with power adjustment capabilities in the integrated energy system. Specifically, the rule-based control strategy realizes energy scheduling through pre-set logical rules. For example, when the photovoltaic power generation is sufficient, it preferentially charges the energy storage device and meets the load demand, and the diesel generator only starts when the energy storage power is insufficient and the load demand is at a peak. In addition, some known technologies also use simple optimization algorithms or heuristic algorithms for system management.

[0055] However, although the methods in known technologies can meet the basic operation requirements, they have significant defects in complex scenarios: First, fixed rules are difficult to adapt to the volatility of renewable energy generation (such as unstable photovoltaic power due to weather changes) and the dynamic changes of load demand; Second, this strategy is mostly single-objective or sequential optimization, and it cannot effectively balance multi-objective conflicts such as economy (such as power generation cost), reliability (such as power supply stability), and environmental protection (such as carbon emissions), resulting in difficulty in achieving the global optimal energy allocation and scheduling, and thus affecting the comprehensive performance of the system.

[0056] Therefore, this application provides a power optimization method, an energy management device, and an energy storage cabinet for an integrated energy system to solve the above problems. Specifically, the power optimization method for the integrated energy system in this application proposes to use different objective functions to optimize the current output power of each power-adjustable device in the integrated energy system when the integrated energy system is in different modes, so that each power-adjustable device operates at the corresponding optimal output power.

[0057] It can be understood that the method of this application can be applied to any integrated energy system, such as a large-scale grid-side integrated energy system, a distributed integrated energy system, and a household integrated energy system. As an example, Figure 1 is a schematic diagram of the application scenario of a power optimization method for an integrated energy system provided by an embodiment of this application. As Figure 1 shown, the power optimization method for the integrated energy system of this application can be applied to an integrated energy system including x energy supply devices, y energy storage devices, and z electrical equipment, where x, y, and z are all integers greater than 2. Specifically, as Figure 1As shown in the figure, the integrated energy system further includes an energy management device and a collection device. The method of this application is executed by the energy management device. The energy management device obtains the current output powers of multiple energy supply devices, multiple power-consuming devices, and multiple energy storage devices through the collection device to determine the current state of the integrated energy system. When it is monitored that the integrated energy system enters the target mode, the energy management device obtains the objective function corresponding to the target mode, and optimizes the current output powers of the multiple energy supply devices and the multiple energy storage devices in the integrated energy system, so that the multiple energy supply devices and the multiple energy storage devices operate at the corresponding optimal output powers.

[0058] In the above setting, for the current target mode of the integrated energy system, the energy management device uses the corresponding objective function to optimize the current output power of each power-adjustable device in the integrated energy system, so that each power-adjustable device can operate at the corresponding optimal output power. Since the optimization process uses the objective function for the target mode, the power adjustment strategy is deeply adapted to the system operation requirements, and can fully consider the energy supply and demand characteristics, equipment constraint conditions, and optimization focuses in different modes, which is beneficial to improving the overall performance of the integrated energy system.

[0059] It can be understood that in practical applications, the method of this application can also be executed by any other electronic device, as long as it can interact with each device included in the integrated energy system. This is not limited in this embodiment.

[0060] Next, in conjunction with the accompanying drawings, some embodiments of this application will be described in detail. In the case where the embodiments do not conflict with each other, the following embodiments and the features in the embodiments can be combined with each other.

[0061] This application provides a power optimization method for an integrated energy system, which is specifically executed by the energy management device of the integrated energy system. Figure 2 It is a schematic flow chart of a power optimization method for an integrated energy system provided by an embodiment of this application Figure 1 As Figure 2 shown, the method provided in this embodiment includes:

[0062] S201, when the integrated energy system enters any target mode, obtain the objective function corresponding to the target mode.

[0063] Among them, the target mode is determined by the current power demand of the integrated energy system.

[0064] In this embodiment, corresponding objective functions are configured for different target modes. When the energy management device monitors that the integrated energy system enters any target mode, it quickly retrieves and loads the matching objective function through the pre-established mode-function mapping relationship stored locally. Optionally, in practical applications, the energy management device can also obtain the objective function corresponding to the target mode through interaction with the user, or obtain the objective function corresponding to the target mode through interaction with the cloud server. The cloud server stores the pre-established mode-function mapping relationship, which is not limited in this embodiment.

[0065] In this embodiment, the target mode is any one of the peak shaving and valley filling mode, green power priority mode, island operation mode, three-phase unbalance adjustment mode, and standby power mode. The energy management device specifically determines whether the integrated energy system enters any target mode according to the current power demand of the integrated energy system. Specifically, as a possible implementation, the energy management device collects power parameters such as voltage, current, power, and the state of charge of energy storage devices in real time through sensors set in the integrated energy system, and based on a preset power demand calculation model, performs weighted calculation or threshold comparison on the collected power parameters to determine the current power demand of the integrated energy system.

[0066] As another possible implementation, the energy management device receives a power demand instruction input by the user through a human-machine interface. The human-machine interface includes but is not limited to a touch screen, a button panel, and a voice interaction module. The user can indicate the power demand in the form of numerical input, mode selection, or voice instruction through the above interfaces, and the system obtains and confirms the power demand indicated by the user.

[0067] As yet another possible implementation, the energy management device uses a collection device to collect the real-time operation data and historical data of the integrated energy system, and adopts time series prediction algorithms, machine learning regression models, or grey prediction models, etc., combined with influencing factors such as date, time period, and weather, to predict the power demand for a period of time in the future.

[0068] Further, when the power demand obtained by any of the above methods meets the power demand threshold or characteristic conditions corresponding to any target mode, it is determined that the integrated energy system enters the corresponding target mode; for example, the power demand condition for the peak shaving and valley filling mode is that the system real-time power or predicted power exceeds the peak power threshold and lasts for a preset duration. It should be understood that in this mode, according to the system real-time power or predicted power, the energy storage device of the integrated energy system supplies power to the load during the peak electricity consumption period, and the transformer and photovoltaic power sources of the integrated energy system charge the energy storage device during the valley electricity consumption period, and the lowest electricity price is achieved through the peak-valley price difference. By applying this mode, compared with directly using grid electricity, the electricity bill can be significantly saved, and peak-valley arbitrage can also be achieved after allowing green power to be connected to the grid.

[0069] Exemplarily, the power demand condition for the green power priority mode is that the proportion of the available power of green power equipment exceeds a set proportion threshold. It should be understood that in the process of generating electricity, the carbon dioxide emission is zero or approaches zero, such as photovoltaic, wind energy, biomass energy, etc. In this mode, priority will be given to using green power to supply power to the load.

[0070] Exemplarily, the power demand condition for the island operation mode is that the abnormal grid voltage is detected (such as the voltage amplitude is lower than the lower limit of the normal range and the duration exceeds the specified time) or the grid frequency exceeds the normal range (such as deviating from 50Hz to a certain extent), and at the same time the integrated energy system meets the conditions for independent power supply (such as the state of charge of the energy storage device is higher than the minimum guarantee value, and the key loads in the system can operate normally). It should be understood that when the power grid is disconnected, this mode can realize the independent operation of the microgrid or the local power grid. This mode is applicable to remote areas or areas with unstable power grids. In the case of insufficient green power or insufficient energy storage, it may switch to the standby power supply mode. Under the condition of ensuring the global constraint conditions, the island operation mode gives priority to ensuring the power stability of the energy storage (except for meeting the load, the green power needs to ensure that the soc of the energy storage cannot be too low to meet the power outage caused by insufficient green power), so the island operation mode follows the green power priority mode.

[0071] Exemplarily, the power demand condition for the three-phase imbalance adjustment mode is that the imbalance degree of the three-phase current or voltage exceeds the specified standard, for example, the imbalance degree of the three-phase current is greater than 15%; it should be understood that in this mode, during the optimization process, the algorithm adjusts the power of each device so that the three-phase imbalance rate of the final root node is reduced to within the threshold.

[0072] Exemplarily, the power demand condition for the standby power supply mode is that the main power supply interruption signal is detected, or the user manually triggers the standby power supply enabling instruction, and the energy storage device has enough power to maintain the operation of the key load. It should be understood that in this mode, when the main power supply fails or the power grid is abnormal, according to the parameters such as the soc, voltage, and in-power of the integrated energy system, a standby power supply (such as a diesel generator) is automatically provided to support and ensure the stable operation of the system. In this mode, the algorithm will, when an abnormality occurs and before the standby power supply starts, adjust the energy storage power according to the global constraint function to ensure power balance, and realize the automatic start and stop of the standby power supply when the energy storage device meets certain conditions.

[0073] In this embodiment, when it is determined that the integrated energy system enters any target mode, the energy management device immediately executes the optimization algorithm applied with the corresponding target function to optimize and adjust the output power of each power-adjustable device in the integrated energy system to achieve the system operation target in this mode.

[0074] Correspondingly, in this embodiment, when the target mode is the peak shaving and valley filling mode, the objective function is: , and, , ; where is the objective function value in the peak shaving and valley filling mode, the optimal objective function value is the minimum objective function value, i is used to represent any power adjustable device in the integrated energy system, w is the weight of the power adjustable device i, M is the number of power adjustable devices, is the current output power of the power adjustable device i, is the desired output power of the power adjustable device i, is the maximum output power of the power adjustable device i, is the minimum output power of the power adjustable device i, is the desired output power of the energy storage device, is the desired output power of the transformer.

[0075] When the target mode is the green power priority mode or the island operation mode, the objective function is: , where is the objective function value in the green power priority mode, the optimal objective function value is the maximum objective function value, i is used to represent any green power device in the integrated energy system, N is the number of green power devices in the integrated energy system, is used to represent the current power of the green power device i. It should be understood that the green power device specifically refers to the energy supply device in the integrated energy system that uses renewable energy for power generation and is environmentally friendly during the power generation process, with almost no greenhouse gas emissions and other pollutants.

[0076] When the target mode is the three-phase unbalance adjustment mode, the objective function is: , and , where is the objective function value in the three-phase unbalance adjustment mode, the optimal objective function value is the minimum objective function value, is the average value of the three-phase power of the root node of the grid structure of the integrated energy system, are the powers of phases A, B, and C respectively.

[0077] When the target mode is the standby power supply mode, the objective function is: , and, , , , where is the generator start-stop signal in the standby power supply mode, means the generator starts when it is 1, means the generator shuts down when it is 0, is the apparent power threshold for generator startup, The SOC threshold for generator startup The voltage threshold for generator startup The apparent power threshold for generator shutdown The SOC threshold for generator shutdown The energy storage node with the largest apparent power in the integrated energy system The energy storage node with the smallest SOC in the integrated energy system, where c is the number of energy storage nodes in the integrated energy system The apparent power of the c-th energy storage node The SOC of the c-th energy storage node The voltage of the c-th energy storage node

[0078] S202. According to the objective function, optimize the current output power of each power-adjustable device in the integrated energy system so that each power-adjustable device operates at the optimal output power that satisfies the objective function

[0079] Specifically, in this embodiment, after the energy management device determines the objective function of the current target mode, it first generates initial parameters including the optimal objective function value, as well as the minimum output power, maximum output power, and expected output power of each power-adjustable device; then uses the objective function to calculate the current objective function value based on the current output power and the corresponding expected output power of each power-adjustable device; finally, according to the preset optimization algorithm, the first constraint condition, and the second constraint condition, update the current output power of each power-adjustable device until the iteration termination condition is reached, and use the current output power as the corresponding optimal output power

[0080] Among them, the first constraint condition is used to indicate that the current output power of each power-adjustable device is greater than or equal to the corresponding minimum output power and less than or equal to the corresponding maximum output power; the second constraint condition is used to indicate that the sum of the current output power of each power-adjustable device and the current output power of the power-adjustable devices belonging to the same category is equal to the current output power of the superior device to which the power-adjustable device belongs

[0081] As a further explanation, the first constraint condition is expressed as: , and the second constraint condition is expressed as: . Among them, x is used to represent any power-adjustable device in the integrated energy system, y is used to represent the superior device corresponding to x, and M is used to represent the number of power-adjustable devices in the integrated energy system that have the same superior device. On this basis, is used to represent the minimum output power of the power-adjustable device is used to represent the current output power of the power-adjustable device is used to represent the maximum output power of the power-adjustable device For representing the current output power of a superior device, For representing the current output power of any power adjustable device belonging to the same superior device y.

[0082] More specifically, the preset optimization algorithm can be a simulated annealing algorithm, or it can be a particle swarm optimization algorithm, a genetic algorithm, an ant colony algorithm, etc. Taking the genetic algorithm as an example, it iteratively optimizes the output power parameters of the power adjustable devices by simulating the selection, crossover, and mutation operations in the biological evolution process. In each iteration, the output power combination of each power adjustable device is regarded as an "individual", and the fitness value of each individual is calculated according to the objective function. The higher the fitness, the closer the combination is to the optimal solution. By selecting individuals with high fitness values for crossover and mutation operations, a new set of individuals is generated, and the iteration continues until the iteration termination condition is met (such as reaching the preset maximum number of iterations, the objective function value converges within a certain accuracy range, etc.). If the particle swarm optimization algorithm is adopted, the output power combination of each power adjustable device is regarded as a "particle" in the search space. Each particle adjusts its flight direction and speed according to its own historical optimal position and the historical optimal position of the entire group, continuously updates its position in the search space, and gradually approaches the optimal solution of the objective function, thereby realizing the optimization of the output power of the power adjustable devices. During the optimization process, the first constraint condition and the second constraint condition are always followed to ensure that the output power of each power adjustable device is within a reasonable and feasible range and meets the power balance requirements of the entire integrated energy system.

[0083] In the method provided in this embodiment, when the energy management device monitors that the integrated energy system enters the target mode, it obtains the objective function corresponding to the target mode, and then optimizes the current output power of each power adjustable device in the integrated energy system according to the objective function, and finally makes each power adjustable device operate at the corresponding target output power (that is, the optimal output power that satisfies the corresponding objective function). Through the method of this embodiment, for different target modes, the corresponding objective function can be used to optimize the current output power of each power adjustable device, so as to accurately match the requirements of different operation scenarios. Compared with the traditional single optimization rule, it effectively ensures the stable operation of the complex integrated energy system in each mode and significantly improves the energy utilization efficiency and system comprehensive performance.

[0084] In addition, in this embodiment, the energy management device can adopt different preset optimization algorithms based on different requirements to optimize the current output power of each power adjustable device based on the objective function, effectively improving the flexibility of the system.

[0085] As a possible design, taking the simulated annealing algorithm as an example of the preset optimization algorithm, the process of obtaining the optimal output power of each power adjustable device in the method of this application will be described in detail below. Figure 3AFlow schematic of a power optimization method for an integrated energy system provided by an embodiment of the present application Figure 2 , as Figure 3A shown, the method of this embodiment includes:

[0086] S301, for any target group in the grid structure of the integrated energy system, generate target initial parameters and simulated annealing parameters.

[0087] Among them, the target initial parameters include the optimal objective function value, as well as the minimum output power, maximum output power, and expected output power of each power adjustable device in the target group; the simulated annealing parameters include the initial temperature and the cooling rate.

[0088] In this embodiment, the energy management device groups multiple power adjustable devices according to the different superior devices to which each power adjustable device belongs, so that the power adjustable devices belonging to the same superior device are in one group.

[0089] Specifically, in this embodiment, the energy management device groups multiple power adjustable devices of the integrated energy system in the following specific way: obtaining the grid data of the integrated energy system through the acquisition device, and parsing the grid data according to the preset tree topology structure to obtain the grid structure of the integrated energy system, and making the power adjustable devices in the integrated energy system all be leaf nodes of the grid structure; the grid data includes the device information, connection relationship, and power information of the integrated energy system; determining at least one target group according to the grid structure; the target group includes the target device corresponding to a parent node and the power adjustable devices corresponding to at least one leaf node included in the parent node.

[0090] More specifically, in this embodiment, when the acquisition device obtains the grid data, the grid data is transmitted to a custom JSON structure and uploaded to Redis for interactive use. The energy management device reads the grid data in Redis and parses it into a custom preset tree topology structure to obtain the grid structure of the integrated energy system. The grid structure includes at least two layers of structures, and the leaf nodes at the bottom layer correspond to the power adjustable devices in the integrated energy system. It can be understood that in actual applications, the integrated energy system can also adopt data structures other than the JSON format and storage structures other than Redis, and this embodiment does not limit this.

[0091] On this basis, as a preferred example, Figure 3B Flow block diagram schematic of a power optimization method for an integrated energy system provided by an embodiment of the present application Figure 1 , as Figure 3BAs shown, the acquisition device collects data from the integrated energy system, obtains the network framework data of the integrated energy system, and writes it into a custom JSON file. Further, the energy management device acquires and parses the network framework data to obtain the network framework structure of the integrated energy system. When the energy management device detects that the integrated energy system enters any target mode, it first uses the anomaly detection module to detect whether there are abnormal nodes in the network framework structure, such as reverse flow nodes, overloaded nodes, and out-of-limit nodes. When there are abnormal nodes, the energy management device calls the anomaly elimination module to adjust the power of the abnormal nodes in the network framework structure to eliminate the abnormal nodes. When there are no abnormal nodes in the network framework structure, it then calls the target function corresponding to the target mode to optimize the power of the power adjustable devices corresponding to each leaf node in the network framework structure so that they operate at their respective optimal output powers.

[0092] Through the above settings, converting the network framework data into a custom JSON structure and storing it in Redis not only facilitates data interaction and invocation but also improves the flexibility and compatibility of data processing, meeting the data storage and transmission requirements under different application scenarios. In addition, the detection and processing mechanism for abnormal nodes in the network framework structure before optimization can effectively avoid system operation risks caused by node anomalies, such as equipment damage and power supply interruption, ensuring the safety and stability of the integrated energy system operation.

[0093] S302, Calculate the current target function value using the target function based on the current output power of each power adjustable device.

[0094] Specifically, the energy management device substitutes the current output power of the power adjustable devices in the target mode into the target function to obtain the current target function value. For example, for the peak shaving and valley filling mode, substitute the current output power and the desired output power of the power adjustable devices into the corresponding target function to obtain the current target function value. For the green electricity priority mode or the island operation mode, substitute the current output power of each green electricity device in the integrated energy system into the corresponding target function to obtain the current target function value. For the three-phase unbalance adjustment mode, determine the A, B, and C phase powers of the root node based on the current output power of the leaf nodes in the network framework structure of the integrated energy system, and then substitute the A, B, and C phase powers of the root node in the network framework structure of the integrated energy system into the corresponding target function to obtain the current target function value. For the standby power supply mode, find the device power with the largest apparent power and the device state of charge with the smallest state of charge based on the current output power of the power adjustable devices, and then combine the generator start-stop signal and the system voltage. Substitute these parameters into the target function corresponding to the standby power supply mode and obtain the target function value by judging whether the relevant conditions are met.

[0095] S303. Update the current output power of each power-adjustable device through at least one iteration process according to the current objective function value and / or the initial temperature, so as to obtain the optimal output power of each power-adjustable device.

[0096] Specifically, Figure 3C is a flowchart schematic of a power optimization method for an integrated energy system provided by an embodiment of the present application Figure 2 . As Figure 3C shown, in this embodiment, any iteration process includes:

[0097] When the current temperature does not meet the iteration termination condition and / or the current objective function value has not reached the optimal objective function value, update the current output power of each power-adjustable device according to the first constraint condition and the second constraint condition corresponding to the target group; in the first iteration process, the current temperature is the initial temperature; use the objective function to calculate the updated current objective function value based on the updated current output power of each power-adjustable device; when the updated current objective function value is closer to the optimal objective function value than the current objective function value in the previous iteration process, use the updated current output power as the optimal output power in the current iteration process; reduce the current temperature based on the cooling rate.

[0098] It can be understood that, as Figure 3C shown, when the updated current objective function value is not closer to the optimal objective function value than the current objective function value in the previous iteration process, do not update the optimal output power in the current iteration process according to the updated current output power, and directly reduce the current temperature based on the cooling rate. When the current temperature meets the iteration termination condition and / or the current objective function value reaches the optimal objective function value, output the optimal output power of each power-adjustable device according to the optimal output power in the current iteration process. Among them, when the current temperature is not greater than the preset temperature threshold (such as 0.01), it is considered that the current temperature meets the iteration termination condition.

[0099] Specifically, the energy manager updates the current output power of each power-adjustable device according to the first constraint condition and the second constraint condition corresponding to the target group in the following manner: according to the first constraint condition corresponding to the target group, randomly update the current output power of any target power-adjustable device by using a neighborhood search mechanism; update the current output power of other power-adjustable devices in the target group based on the first constraint condition and the second constraint condition.

[0100] It should be understood that the neighborhood search mechanism performs a random search within a small neighborhood range of the output power of the current target power-adjustable device. For example, let the output power of the current target power-adjustable device be P current , and its neighborhood range can be set as |P current - δP, Pcurrent +δP, where δP is a preset small power increment. Randomly select a new power value P in this domain new , but ensure that P new satisfies the first constraint condition.

[0101] It can be understood that after updating the power of a device, in order to satisfy the power balance relationship, the power of other devices needs to be adjusted accordingly. Therefore, in this embodiment, after updating the current output power of any target power-adjustable device, further update the current output power of other power-adjustable devices in the target group according to the first constraint condition and the second constraint condition. The second constraint condition requires that the sum of the current output powers of each power-adjustable device and the current output powers of the power-adjustable devices belonging to the same group is equal to the current output power of the superior device to which the power-adjustable device belongs, that is, the sum of the current output powers of the power-adjustable devices corresponding to each leaf node under the same parent node is equal to the current output power of the superior device corresponding to the parent node.

[0102] After each iteration ends, the updated current objective function value is compared with the current objective function value of the previous iteration. If the updated current objective function value is closer to the optimal objective function value, it means that this iteration has achieved a better result, and the updated current output power is used as the optimal output power in the current iteration process. As the iteration progresses, continuously reduce the current temperature based on the cooling rate. When the current temperature satisfies the iteration termination condition (such as being lower than a set minimum temperature), or the current objective function value reaches the optimal objective function value, the iteration process ends. At this time, the obtained current output power is the final optimal output power of each power-adjustable device. In this way, through continuous iterative optimization, the output power of each power-adjustable device in the integrated energy system can reach the optimal configuration to better meet the system operation requirements in the target mode.

[0103] In addition, in the method provided in this embodiment, during each iteration process, the neighborhood search mechanism is used to update the current output power of the power-adjustable device, which can effectively reduce the ineffective search by dynamically adjusting the neighborhood range, is beneficial to quickly locate the optimal solution area, improve the search efficiency of the output power of the power-adjustable device, accelerate the convergence speed of the iteration process, enable the integrated energy system to more efficiently achieve power optimization configuration under each target mode, and improve the overall operation performance and energy utilization efficiency.

[0104] It should be understood that the above-mentioned efficient iterative optimization process is carried out for each group in the grid structure of the integrated energy system. Specifically, in the method of this embodiment, for each target group, the energy management device executes the process of S301-S303 to obtain the optimal output power of each power-adjustable device in the integrated energy system. Among them, the energy management device can optimize each group in a random order or in the order of the number of power-adjustable devices included. This embodiment does not limit this, as long as it is ensured that the power-adjustable devices included in all groups of the integrated energy system can be optimized finally.

[0105] As can be seen from the above, the method of this application introduces a global objective function, comprehensively considers the balance, reliability, and environmental protection of the power of the integrated energy system, ensures that the optimization result meets the global optimal objective, and avoids the problem of low overall efficiency caused by the traditional method only focusing on local optimization. The method of this application effectively improves the efficiency of optimizing each power-adjustable device through the simulated annealing algorithm and the neighborhood search mechanism to meet the real-time requirements. The method of this application introduces a multi-objective function to balance the relationship between various objectives, ensures that the optimization result reaches the best balance among multiple objectives (such as economy, environmental protection, reliability, etc.), and improves the comprehensive performance of the integrated energy system. The method of this application efficiently allocates various energy resources (such as diesel generators, photovoltaic power generation, energy storage devices, load demands, electric vehicle charging facilities, etc.) in the integrated energy system under different modes to maximize the overall benefit of the system, effectively reduces the system operation cost, reduces environmental pollution, and improves the energy utilization efficiency.

[0106] It should be understood that the method of this application is applicable to fixed-type multi-energy systems and can also be extended and applied to other complex multi-energy systems, thus having a wide range of application prospects and important practical significance.

[0107] The above embodiment introduces a power optimization method for an integrated energy system from the perspective of the method flow. The following embodiment introduces a power optimization device for an integrated energy system from the perspective of virtual modules or virtual units. For details, see the following embodiment.

[0108] The embodiment of this application provides a power optimization device for an integrated energy system, Figure 4 which is a schematic structural diagram of a power optimization device for an integrated energy system provided by the embodiment of this application. As Figure 4 shown, the device includes:

[0109] An acquisition module 41, configured to acquire an objective function corresponding to a target mode when the integrated energy system enters any target mode; the target mode is determined by the current power demand of the integrated energy system;

[0110] Optimization module 42, which is used to optimize the current output power of each power-adjustable device in the integrated energy system according to the objective function, so that each power-adjustable device operates at the optimal output power that meets the objective function.

[0111] Another possible implementation manner of the embodiment of the present application, the optimization module 42 is specifically used for:

[0112] Generate initial parameters including the optimal objective function value, as well as the minimum output power, maximum output power, and expected output power of each power-adjustable device;

[0113] Use the objective function to calculate the current objective function value based on the current output power of each power-adjustable device;

[0114] According to the preset optimization algorithm, the first constraint condition, and the second constraint condition, update the current output power of each power-adjustable device until the iteration termination condition is reached, and then use the current output power as the optimal output power; the first constraint condition is used to indicate that the current output power of each power-adjustable device is greater than or equal to the corresponding minimum output power and less than or equal to the corresponding maximum output power; the second constraint condition is used to indicate that the sum of the current output powers of each power-adjustable device belonging to the same power-adjustable device group is equal to the current output power of the superior device to which the power-adjustable device belongs.

[0115] Another possible implementation manner of the embodiment of the present application, the optimization module 42 is specifically used for:

[0116] For any target group in the grid structure of the integrated energy system, generate target initial parameters and simulated annealing parameters; the target initial parameters include the optimal objective function value, as well as the minimum output power, maximum output power, and expected output power of each power-adjustable device in the target group; the simulated annealing parameters include the initial temperature and the cooling rate;

[0117] Use the objective function to calculate the current objective function value based on the current output power of each power-adjustable device;

[0118] According to the current objective function value and / or the initial temperature, update the current output power of each power-adjustable device through at least one iteration process to obtain the optimal output power of each power-adjustable device.

[0119] Another possible implementation manner of the embodiment of the present application, any iteration process includes:

[0120] When the current temperature does not meet the iteration termination condition and / or the current objective function value has not reached the optimal objective function value, update the current output power of each power-adjustable device according to the first constraint condition and the second constraint condition corresponding to the target group; in the first iteration process, the current temperature is the initial temperature;

[0121] Calculate the updated current objective function value using the objective function based on the updated current output power of each power adjustable device;

[0122] When the updated current objective function value is closer to the optimal objective function value than the current objective function value in the previous iteration, take the updated current output power as the optimal output power in the current iteration;

[0123] Reduce the current temperature based on the cooling rate.

[0124] In another possible implementation manner of the embodiment of the present application, the optimization module 42 is specifically configured to:

[0125] Randomly update the current output power of any target power adjustable device according to the first constraint condition corresponding to the target group by using a neighborhood search mechanism;

[0126] Update the current output power of other power adjustable devices in the target group based on the first constraint condition and the second constraint condition.

[0127] In another possible implementation manner of the embodiment of the present application, the optimization module 42 is further configured to:

[0128] Obtain the grid data of the integrated energy system through the acquisition device, parse the grid data according to the preset tree - type topology structure to obtain the grid structure of the integrated energy system, and make the power adjustable devices in the integrated energy system all be leaf nodes of the grid structure; the grid data includes the device information, connection relationship, and power information of the integrated energy system;

[0129] Determine at least one target group according to the grid structure; the target group includes the target device corresponding to a parent node and the power adjustable devices corresponding to at least one leaf node included in the parent node.

[0130] In another possible implementation manner of the embodiment of the present application, the target mode is any one of a peak - shaving and valley - filling mode, a green power priority mode, an island operation mode, a three - phase unbalance adjustment mode, and a standby power supply mode.

[0131] In another possible implementation manner of the embodiment of the present application, when the target mode is a peak - shaving and valley - filling mode, the objective function is: , and, , ; where, is the objective function value in the peak - shaving and valley - filling mode, the optimal objective function value is the minimum objective function value, i is used to represent any power adjustable device in the integrated energy system, w is the weight of the power adjustable device i, M is the number of power adjustable devices, is the current output power of the power adjustable device i, is the desired output power of the power adjustable device i, is the maximum output power of the power adjustable device i, is the minimum output power of the power adjustable device i, is the desired output power of the energy storage device, is the desired output power of the transformer;

[0132] And / or, when the target mode is the green power priority mode or the island operation mode, the objective function is: , where, is the objective function value in the green power priority mode, the optimal objective function value is the maximum objective function value, i is used to represent any green power device in the integrated energy system, and N is used to represent the number of green power devices in the integrated energy system, is used to represent the current power of the green power device i;

[0133] And / or, when the target mode is the three-phase unbalance adjustment mode, the objective function is: , and , where, is the objective function value in the three-phase unbalance adjustment mode, the optimal objective function value is the minimum objective function value, is the average value of the three-phase power of the root node of the grid structure of the integrated energy system, are the powers of phases A, B, and C respectively;

[0134] And / or, when the target mode is the standby power supply mode, the objective function is: , and, , , , where, is the generator start-stop signal in the standby power supply mode, means the generator starts when it is 1, means the generator shuts down when it is 0, is the apparent power threshold for generator startup, is the SOC threshold for generator startup, is the voltage threshold for generator startup, is the apparent power threshold for generator shutdown, is the SOC threshold for generator shutdown, is the energy storage node with the largest apparent power in the integrated energy system, is the energy storage node with the smallest SOC in the integrated energy system, c is the number of energy storage nodes in the integrated energy system, is the apparent power of the c-th energy storage node, is the SOC of the c-th energy storage node, is the voltage of the c-th energy storage node.

[0135] A power optimization device for an integrated energy system provided in an embodiment of the present application is applicable to the above method embodiment and will not be elaborated herein.

[0136] An energy management device is provided in an embodiment of the present application. Figure 5 It is a schematic structural diagram of an energy management device provided in an embodiment of the present application, as Figure 5 shown. Figure 5 The energy management device shown includes: a processor 51 and a memory 52. Among them, the processor 51 and the memory 52 are connected, such as connected through a bus 53. Optionally, the energy management device may further include a transceiver 54. It should be noted that in practical applications, the number of transceivers 54 is not limited to one, and the structure of this energy management device does not constitute a limitation to the embodiment of the present application.

[0137] The processor 51 may be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logical blocks, modules, and circuits described in combination with the disclosure of the present application. The processor 51 may also be a combination for implementing computing functions, such as a combination including one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0138] The bus 53 may include a path for transmitting information between the above components. The bus 53 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 53 may be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 5 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus 53 or one type of bus 53.

[0139] The memory 52 can be a read only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or can also be an electrically erasable programmable read only memory (EEPROM), a compact disc read only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0140] The memory 52 is used to store the application program code for implementing the solution of this application and is controlled by the processor 51 for execution. The processor 51 is used to execute the application program code stored in the memory 52 to implement the content shown in the foregoing method embodiments.

[0141] This application also provides a computer-readable storage medium, which may include: various media that can store program code such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks or optical discs, etc. Specifically, the computer-readable storage medium stores program instructions for implementing the methods in the above embodiments.

[0142] This application embodiment also provides a computer program product, including a computer program that implements the technical solutions of the above method embodiments when executed by a processor. The implementation principle and technical effects are similar and will not be elaborated here.

[0143] This application embodiment also provides an energy storage cabinet, including the energy management device in the above content.

[0144] Those skilled in the art will readily conceive of other implementations of this application after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include known common knowledge or conventional technical means in the technical field not disclosed in this application. The specification and embodiments are only regarded as exemplary, and the true scope and spirit of this application are pointed out by the claims.

[0145] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

Claims

1. A power optimization method for an integrated energy system, characterized in that, The method includes: When the integrated energy system enters any target mode, obtaining the target function corresponding to the target mode; the target mode is determined by the current power demand of the integrated energy system; According to the target function, optimizing the current output power of each power adjustable device in the integrated energy system, so that each power adjustable device operates at the optimal output power that satisfies the target function.

2. The method according to claim 1, characterized in that, The optimizing the current output power of each power adjustable device in the integrated energy system according to the target function, so that each power adjustable device operates at the optimal output power that satisfies the target function includes: Generating initial parameters including the optimal target function value, and the minimum output power, maximum output power, and expected output power of each power adjustable device; Calculating the current target function value using the target function based on the current output power of each power adjustable device; According to a preset optimization algorithm, a first constraint condition, and a second constraint condition, updating the current output power of each power adjustable device until the iteration termination condition is reached, and taking the current output power as the optimal output power; the first constraint condition is used to indicate that the current output power of each power adjustable device is greater than or equal to the corresponding minimum output power and less than or equal to the corresponding maximum output power; the second constraint condition is used to indicate that the sum of the current output powers of each power adjustable device belonging to the same power adjustable device group is equal to the current output power of the superior device to which the power adjustable device belongs.

3. The method according to claim 1, characterized in that, The optimizing the current output power of each power adjustable device in the integrated energy system according to the target function, so that each power adjustable device operates at the optimal output power that satisfies the target function includes: For any target group in the grid structure of the integrated energy system, generating target initial parameters and simulated annealing parameters; the target initial parameters include the optimal target function value, and the minimum output power, maximum output power, and expected output power of each power adjustable device in the target group; the simulated annealing parameters include the initial temperature and the cooling rate; Calculating the current target function value using the target function based on the current output power of each power adjustable device; According to the current target function value and / or the initial temperature, through at least one iteration process, updating the current output power of each power adjustable device to obtain the optimal output power of each power adjustable device.

4. The method according to claim 3, characterized in that, Any iteration process includes: When the current temperature does not meet the iteration termination condition, and / or, the current target function value has not reached the optimal target function value, updating the current output power of each power adjustable device according to the first constraint condition and the second constraint condition corresponding to the target group; in the first iteration process, the current temperature is the initial temperature; Calculating the updated current target function value using the target function based on the updated current output power of each power adjustable device; When the updated current objective function value is closer to the optimal objective function value than the current objective function value in the previous iteration process, the updated current output power is used as the optimal output power in the current iteration process; Reduce the current temperature based on the cooling rate.

5. The method according to claim 4, characterized in that, Updating the current output power of each power adjustable device according to the first constraint condition and the second constraint condition corresponding to the target group includes: Randomly update the current output power of any target power adjustable device according to the first constraint condition corresponding to the target group by using a neighborhood search mechanism; Update the current output power of other power adjustable devices in the target group except the target power adjustable device based on the first constraint condition and the second constraint condition.

6. The method according to any one of claims 3 - 5, characterized in that, The method further includes: Obtain the grid data of the integrated energy system through a collection device, parse the grid data according to a preset tree - type topological structure to obtain the grid structure of the integrated energy system, and make the power adjustable devices in the integrated energy system all be leaf nodes of the grid structure; the grid data includes the device information, connection relationship, and power information of the integrated energy system; Determine at least one target group according to the grid structure; the target group includes the target device corresponding to a parent node and the power adjustable devices corresponding to at least one leaf node included in the parent node.

7. The method according to any one of claims 1 - 5, characterized in that, The target mode is any one of a peak - shaving and valley - filling mode, a green - electricity - priority mode, an island operation mode, a three - phase imbalance adjustment mode, and a standby power supply mode.

8. The method according to claim 7, characterized in that, When the target mode is the peak shaving and valley filling mode, the target function is: , and, , ; where, is the target function value in the peak shaving and valley filling mode, the optimal target function value is the minimum target function value, i is used to represent any power adjustable device in the integrated energy system, w is the weight of the power adjustable device i, M is the number of power adjustable devices, is the current output power of the power adjustable device i, is the expected output power of the power adjustable device i, is the maximum output power of the power adjustable device i, is the minimum output power of the power adjustable device i, is the expected output power of the energy storage device, is the expected output power of the transformer; When the target mode is the green power priority mode and / or the island operation mode, the objective function is: , where is the objective function value in the green power priority mode. The optimal objective function value is the maximum objective function value. i is used to represent any green power device in the integrated energy system, and N is used to represent the number of green power devices in the integrated energy system. is used to represent the current power of the green power device i; When the target mode is the three-phase unbalance adjustment mode, the objective function is: and where is the objective function value under the three-phase unbalance adjustment mode, and the optimal objective function value is the minimum objective function value. is the average value of the three-phase power of the root node of the grid structure of the integrated energy system. are the powers of phases A, B, and C respectively. When the target mode is the standby power supply mode, the objective function is: , and 、 、 , where is the generator start-stop signal in the standby power supply mode, represents that the generator starts when it is 1, represents that the generator shuts down when it is 0, is the apparent power threshold for generator start-up, is the SOC threshold for generator start-up, is the voltage threshold for generator start-up, is the apparent power threshold for generator shutdown, is the SOC threshold for generator shutdown, is the energy storage node with the largest apparent power in the integrated energy system, is the energy storage node with the smallest SOC in the integrated energy system, c is the number of energy storage nodes in the integrated energy system, is the apparent power of the c-th energy storage node, is the SOC of the c-th energy storage node, is the voltage of the c-th energy storage node.

9. An energy management device, characterized in that, including: At least one processor and a memory; The memory stores computer - executable instructions; The at least one processor executes the computer - executable instructions stored in the memory, so that the at least one processor executes the method according to any one of claims 1 to 7.

10. A energy storage cabinet, characterized in that, The energy storage cabinet includes the energy management device according to claim 9.

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