Method for Determining the Operation Optimization of Distributed Energy Systems Including Multiple Types of Energy Storage Equipment

Abstract

This application proposes a method for optimizing the operation of a distributed energy system containing multiple types of energy storage devices. The method includes: constructing an objective function to optimize the operation of the distributed energy system with multiple types of energy storage devices, aiming to improve the absorption of renewable energy and the independent operation capability of the system; clustering energy storage devices with similar total operating times into a single energy storage unit for overall operation, and proposing SOC difference constraints between the energy storage devices within each unit; setting multiple constraints on the objective parameters in the objective function, including restrictions on the charging, discharging, and energy storage of each energy storage unit; solving the objective function based on these constraints, and controlling the energy system to operate under these constraints according to the solution results; this method ensures the charging and discharging synchronization of each energy storage device within multiple energy storage units, maximizing the overall regulation capability of the energy storage devices.

Description

Technical Field

[0001] This application relates to the field of power system energy storage technology, and in particular to a method for determining the operation optimization of a distributed energy system containing multiple types of energy storage devices. Background Technology

[0002] With the development of renewable energy technologies, wind power and photovoltaic power generation are accounting for an increasing proportion of the power supply system. Through energy storage systems, such as energy storage battery packs, the electricity output by renewable energy power generation systems can be stored and discharged at appropriate times.

[0003] Based on this, the adoption rate of distributed energy systems incorporating multiple types of energy storage batteries is gradually increasing. These systems can maximize the utilization of renewable energy output by adjusting the charging and discharging control of each energy storage unit, thereby absorbing surplus renewable energy and providing power to meet load power shortages. In practical applications, since distributed energy systems have multiple energy storage units, optimizing the operation of the distributed energy system is necessary to improve the rationality of controlling each unit.

[0004] In related technologies, when performing optimization, since each energy storage device in a system containing multiple types of hybrid energy storage devices often has different maximum charging and discharging powers and charging and discharging times, treating all energy storage units as independent optimization variables would greatly increase the complexity of the problem, making it unsolvable. Therefore, related technologies typically treat all energy storage devices in the system as equivalent to a single energy storage system, simplifying its maximum charging and discharging power, charging and discharging time, and other parameters to a single equivalent, and using them as constraints for the scheduling optimization problem to solve the optimization model.

[0005] However, the above optimization methods may result in low equipment utilization of individual energy storage devices. Furthermore, uneven charging and discharging constraints on various energy storage units may cause some energy storage devices to operate beyond their rated operating conditions, ultimately leading to damage to the energy storage devices. Summary of the Invention

[0006] This application aims to at least partially address one of the technical problems in the related art.

[0007] Therefore, the first objective of this application is to propose a method for determining the operation optimization of a distributed energy system containing multiple types of energy storage devices. This method can ensure the charging and discharging synchronization among multiple energy storage devices, effectively reduce the computational complexity of system operation optimization, maximize the overall regulation capability of the energy storage system, ensure the safety of energy storage devices and the utilization rate of each energy storage device, and improve the output of the energy system.

[0008] The second objective of this application is to propose a system for determining the operation optimization of a distributed energy system containing multiple types of energy storage devices;

[0009] The third objective of this application is to provide a non-transitory computer-readable storage medium.

[0010] To achieve the above objectives, a first aspect of this application provides a method for determining the operation optimization of a distributed energy system containing multiple types of energy storage devices, the method comprising the following steps:

[0011] With the goal of improving the absorption of renewable energy and the independent operation capability of distributed energy systems containing multiple types of energy storage devices, an objective function is constructed to optimize the operation mode of the distributed energy system containing multiple types of energy storage devices.

[0012] Energy storage devices with similar total operating time are clustered into one energy storage unit for overall operation, and multiple constraints are set for the target parameters in the objective function. These multiple constraints include conditions that limit the charging, discharging, and energy storage of each energy storage unit.

[0013] The objective function is solved based on the multiple constraints, and the distributed energy system containing multiple types of energy storage devices is controlled to operate under the multiple constraints according to the solution results.

[0014] Optionally, in one embodiment of this application, constructing an objective function to optimize the operation of the distributed energy system containing multiple types of energy storage devices includes: constructing a first objective function with the goal of maximizing renewable energy absorption, and minimizing the first objective function; constructing a second objective function with the goal of minimizing the energy transmitted between the distributed energy system containing multiple types of energy storage devices and the power grid; and combining the first objective function and the second objective function to transform the multi-objective optimization problem of optimizing the operation of the distributed energy system containing multiple types of energy storage devices into a single-objective optimization problem.

[0015] Optionally, in one embodiment of this application, the objective function for optimizing the operation of the distributed energy system containing multiple types of energy storage devices is represented by the following formula:

[0016]

[0017] Where N is the number of time periods into which each day is divided, and P wi Let P be the wind power generation capacity in the i-th time period. pvi Let P be the photovoltaic power generation in the i-th time period, a be the priority coefficient for wind power consumption, b be the priority coefficient for photovoltaic power consumption, and P' be the photovoltaic power generation output. ni Let be the transmission power between the distributed energy system containing multiple types of energy storage devices and the power grid during the i-th time period, and c be the energy storage charging and discharging determination coefficient.

[0018] Optionally, in one embodiment of this application, multiple constraints include: SOC difference constraints between energy storage devices within the energy storage unit, active power constraints, power transmission constraints between the energy system and the power grid, energy storage power constraints, state of charge / discharge constraints, energy storage state of charge (SOC) constraints, number of charge / discharge cycles constraints, and power change rate constraints.

[0019] Optionally, in one embodiment of this application, the SOC difference constraint between the energy storage devices within the energy storage unit is expressed by the following formula:

[0020]

[0021] Among them, S b1i Let S be the SOC size of the first energy storage device at the start of the i-th time period. bni Let S be the SOC (State of Charge) of the nth energy storage device at the start of the i-th time period. b,diff This represents the upper limit of the SOC difference between energy storage devices within the energy storage unit, where n is the number of energy storage devices in the energy storage unit.

[0022] Optionally, in one embodiment of this application, when the distributed energy system containing multiple types of energy storage devices includes two clustered energy storage units, the active power constraint is expressed by the following formula:

[0023]

[0024] in, Let be the discharge power of the first energy storage unit in the i-th time period. Let be the charging power of the first energy storage unit in the i-th time period. Let be the discharge power of the second energy storage unit in the i-th time period. P represents the charging power of the second energy storage unit during the i-th time period. i load The magnitude of the load power in the i-th time period;

[0025] The power transmission constraints between the energy system and the power grid are expressed by the following formula:

[0026] |P ni |≤p max

[0027] Where, p max This represents the upper limit of the power allowed for transmission between the system and the power grid.

[0028] Optionally, in one embodiment of this application, the energy storage power constraint is expressed by the following formula:

[0029]

[0030] in,

[0031] in, This is the discharge signal of the first energy storage unit. This is the charging signal for the first energy storage unit. This is the discharge signal for the second energy storage unit. This is the charging signal for the second energy storage unit. This represents the maximum discharge power of the first energy storage unit. This represents the maximum discharge power of the second energy storage unit. This represents the maximum charging power of the first energy storage unit. The maximum charging power of the second energy storage unit;

[0032] The charge / discharge state constraint condition is expressed by the following formula:

[0033] u 1i =u 2i

[0034] Among them, u 1i This is the charge / discharge state control signal for the first energy storage unit, u 2i This is the charging and discharging status control signal for the second energy storage unit.

[0035] Optionally, in one embodiment of this application, the energy storage state of charge (SOC) constraint is expressed by the following formula:

[0036]

[0037] Among them, S 11 =S 1start S 1N =S 1end S 21 =S 2start S 2N =S 2end ,

[0038] Among them, S 1i S represents the state of charge (SOC) of the first energy storage unit at the start of the i-th time period. 2i Let SOC be the size of the second unit system at the start of the i-th time interval. The discharge efficiency of the first unit system. The charging efficiency of the first energy storage unit. The discharge efficiency of the second energy storage unit. For the charging efficiency of the second energy storage unit, S 1start For the initial state of charge (SOC) of the first energy storage unit, S1end S is the end-of-day SOC of the first energy storage unit. 2start For the initial state of charge (SOC) of the second energy storage unit, S 2end SOC at the end of the day for the second energy storage unit 1max and S 1min These are the upper and lower threshold values ​​of the State of Charge (SOC) for the first energy storage unit, respectively. 2max and S 2min S1 and S2 are the upper and lower limits of the SOC of the second energy storage unit, respectively, and the capacities of the first and second energy storage units, respectively.

[0039] Optionally, in one embodiment of this application, the charge / discharge cycle constraint is expressed by the following formula:

[0040]

[0041] Among them, z 1i and z 2i These are the parameters for the auxiliary calculation of the number of cycles for the first and second energy storage units, respectively, and k is the limit parameter for the number of charge and discharge cycles;

[0042] The power change rate constraint is expressed by the following formula:

[0043]

[0044] in, It represents the maximum change in wind power generation between two time periods. It is the maximum change in photovoltaic power generation between two time periods. It is the maximum power change of the first energy storage unit between two time periods. It is the maximum power change of the second energy storage unit between two time periods. It is the maximum change in transmission power between a distributed energy system containing multiple types of energy storage devices and the power grid between two time periods.

[0045] To achieve the above objectives, a second aspect of this application also proposes a distributed energy system operation optimization determination system containing multiple types of energy storage devices, comprising the following modules:

[0046] The module is used to construct an objective function to optimize the operation mode of the distributed energy system containing multiple types of energy storage devices, with the goal of improving the absorption of renewable energy and the independent operation capability of the distributed energy system containing multiple types of energy storage devices.

[0047] The configuration module is used to cluster energy storage devices with similar total operating time into an energy storage unit for overall operation, and to set multiple constraints for the target parameters in the objective function. The multiple constraints include conditions that limit the charging, discharging and energy storage of each energy storage system.

[0048] The control module is used to solve the objective function according to the multiple constraints and control the distributed energy system containing multiple types of energy storage devices to operate under the multiple constraints according to the solution results.

[0049] To implement the above embodiments, a third aspect of this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the method for determining the operation optimization of a distributed energy system containing multiple types of energy storage devices as described in the above embodiments.

[0050] The technical solution provided by the embodiments of this application brings at least the following beneficial effects: This application optimizes the operation of a distributed energy system by solving a set objective function and setting multiple types of constraints for the operation of the energy system. To effectively reduce the complexity of optimization calculations, energy storage devices with similar total operating times are clustered into one energy storage unit for overall operation. This ensures the charging and discharging synchronization of similar energy storage devices in the distributed energy system, avoids the operating state of energy storage devices exceeding the rated operating conditions, and helps ensure the safety of energy storage devices. Furthermore, it maximizes the overall regulation capability of the energy storage system by limiting the charging, discharging, and energy storage operations of each energy storage unit through corresponding constraints, ensuring the equipment utilization rate of each energy storage unit and maximizing renewable energy output. Moreover, by transforming the multi-objective optimization problem into a single-objective optimization problem, the complexity of the optimization model can be greatly simplified in practical applications, improving the solution efficiency of the multi-objective optimization problem.

[0051] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0052] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0053] Figure 1 This is a flowchart illustrating a method for optimizing the operation of a distributed energy system containing multiple types of energy storage devices, as proposed in an embodiment of this application.

[0054] Figure 2 This is a schematic diagram of the structure of a specific distributed energy system containing multiple types of energy storage devices, as proposed in an embodiment of this application.

[0055] Figure 3 This is a flowchart illustrating a method for setting an objective function for optimizing the operation of an energy system, as proposed in an embodiment of this application.

[0056] Figure 4 This is a schematic diagram of the structure of a distributed energy system operation optimization determination system containing multiple types of energy storage devices proposed in an embodiment of this application. Detailed Implementation

[0057] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0058] The following description, with reference to the accompanying drawings, illustrates a method and system for determining the operation optimization of a distributed energy system containing multiple types of energy storage devices, as proposed in an embodiment of the present invention.

[0059] Figure 1 This is a flowchart illustrating a method for determining the operation optimization of a distributed energy system containing multiple types of energy storage devices, as proposed in an embodiment of this application. Figure 1 As shown, the method includes the following steps:

[0060] Step S101: With the goal of improving the absorption of renewable energy and the independent operation capability of distributed energy systems containing multiple types of energy storage devices, construct an objective function to optimize the operation mode of distributed energy systems containing multiple types of energy storage devices.

[0061] The distributed energy system with multiple types of energy storage devices targeted in this application refers to a distributed energy system that includes multiple types of renewable energy power generation systems (e.g., wind power generation systems and photovoltaic power generation systems) and multiple energy storage units. Each energy storage unit contains multiple energy storage devices, such as energy storage batteries. Each energy storage unit can store the electricity output by a specific renewable energy power generation system, and the types of energy storage devices in each energy storage unit can also be different.

[0062] It should be noted that the number of renewable energy generation systems and energy storage units included in the distributed energy system containing multiple types of energy storage devices in this application is set according to actual application needs and is not limited here. For example, this application describes an energy system containing two energy storage units as an example, such as... Figure 2As shown, a distributed energy system with multiple types of energy storage devices proposed in one embodiment of this application includes: a first energy storage unit 10, a second energy storage unit 20, two wind power generation systems 30, two photovoltaic power generation systems 40, a power grid 50, and a power consumption side 60. The first energy storage unit 10 and the second energy storage unit 20 are connected to the power grid via energy storage converters, and the photovoltaic power generation systems 40 are connected to the power grid via photovoltaic converters. It is understood that in other embodiments of this application, multiple energy storage units (more than two) and other types of renewable energy systems may also be provided.

[0063] Specifically, this application constructs a multi-objective optimization model aimed at improving the absorption of renewable energy and the independent operation capability of the energy system. This multi-objective optimization model includes two objectives, one of which is minimizing wind and solar curtailment and the other being minimizing the amount of electricity exchanged between the energy system and the grid. In practice, the independent operation capability of the energy system can be determined based on the amount of electricity exchanged between the energy system and the grid.

[0064] To more clearly illustrate the specific implementation process of the objective function for constructing and optimizing the operation of a distributed energy system containing multiple types of energy storage devices, an exemplary construction method proposed in one embodiment of this application is provided below. Figure 3 This is a flowchart illustrating a method for setting the objective function for optimizing the operation of an energy system, as proposed in an embodiment of this application. Figure 3 As shown, the method includes the following steps:

[0065] Step S301: Construct a first objective function with the goal of maximizing the absorption of renewable energy, and minimize the first objective function.

[0066] In this embodiment, the objective function formula can be expressed as follows:

[0067] minF1={f1,f2}

[0068] Here, f1 is the objective function aimed at maximizing the absorption of renewable energy, i.e., the first objective function. f2 is the objective function aimed at minimizing the power exchange with the grid, i.e., the second objective function.

[0069] Specifically, when setting the first objective function, the renewable energy sources included in the function are wind power and photovoltaic power. The minimization of the first objective function is expressed by the following formula:

[0070]

[0071] Where N is the number of time periods divided into days, and in this embodiment, the optimized granularity is 15 minutes, so N is 96 in this embodiment; P wi Let P be the wind power output in the i-th time period.pvi is the photovoltaic power generation in the i-th time period; the coefficients a and b are used to limit the priority of consuming photovoltaic and wind power. When a > b, wind power is preferentially used; when a < b, photovoltaic power generation is preferentially used. In the embodiment of the present application, wind power is preferentially used, so a > b is set.

[0072] Step S302, construct a second objective function with the minimum energy transmission between the distributed energy system with multiple types of energy storage devices and the power grid as the goal.

[0073] In the embodiment of the present application, the objective function f2 of the minimum energy transmission with the power grid needs to be expressed in an absolute value manner. Since the energy transmission between the distributed energy system and the power grid is related to the transmission power between the distributed energy system and the power grid, the second objective function set can be expressed by the following formula:

[0074]

[0075] where, P ni is the transmission power between the distributed energy system and the power grid in the i-th time period, and the inflow into the distributed energy system is set as positive; the coefficient c is used to limit whether to preferentially feed the surplus power into the grid or preferentially charge the energy storage when wind power and photovoltaic power are sufficient. When c < a, b, if the wind and light are excessive, the energy storage is preferentially charged. If the energy storage has reached the upper limit, the wind and light power can be guaranteed to be fed into the grid. If the wind and light are insufficient, the energy storage discharge operation is preferentially controlled.

[0076] Step S303, combine the first objective function and the second objective function to transform the multi-objective optimization problem of optimizing the operation mode of the distributed energy system with multiple types of energy storage devices into a single-objective optimization problem.

[0077] Specifically, considering the above two objective functions comprehensively, combining and arranging the first objective function and the second objective function, the multi-objective optimization problem can be transformed into a single-objective optimization problem. The finally constructed objective function for optimizing the operation mode of the distributed energy system with multiple types of energy storage devices is expressed by the following formula:

[0078]

[0079] where, N is the number of time periods divided daily, P wi is the wind power generation in the i-th time period, P pvi is the photovoltaic power generation in the i-th time period, a is the priority coefficient for consuming wind power generation, b is the priority coefficient for consuming photovoltaic power generation, P ni is the transmission power between the distributed energy system with multiple types of energy storage devices and the power grid in the i-th time period, and c is the energy storage charge and discharge determination coefficient.

[0080] Step S102: Cluster energy storage devices with similar total operating time into an energy storage unit for overall operation, and set multiple constraints for the objective parameters in the objective function. Among these constraints, there are conditions that limit the charging, discharging and energy storage of each energy storage system.

[0081] It should be noted that in practical applications, the State of Charge (SOC) and operating time of various energy storage devices in a distributed energy system differ during charging / discharging. If traditional unified optimization control principles are adopted, some energy storage devices may prematurely shut down. Optimizing each device independently may lead to complex solutions or even no solutions at all. Therefore, this application clusters and reduces the dimensionality of multiple energy storage devices in the distributed energy system when establishing the optimization scheduling model.

[0082] As a possible implementation approach, to effectively reduce the computational complexity of optimization, energy storage devices with similar total operating times are clustered into a single energy storage unit. Within each unit, the individual energy storage devices are then operated as a whole. An energy storage unit can be viewed as a collection of energy storage devices. This approach ensures the synchronization of charging and discharging for devices of the same type, improves the utilization rate of individual devices, and provides clear and consistent conditions for future energy management optimization.

[0083] In subsequent embodiments of this application, constraints are also imposed on the SOC difference between each energy storage device within each clustered energy storage unit, as detailed in the description of the subsequent embodiments.

[0084] The objective parameters in the objective function are the key parameters for solving the objective function. For example, in the example constructed in step S101, the objective parameters include: P wi P pvi and P ni That is, setting constraints that include the above target parameters.

[0085] In one embodiment of this application, the set constraints include: SOC difference constraints between energy storage devices within the energy storage unit, active power constraints, power transmission constraints between the energy system and the power grid, energy storage power constraints, charge / discharge state constraints, energy storage state of charge (SOC) constraints, charge / discharge cycle constraints, and power change rate constraints.

[0086] The SOC difference constraint between energy storage devices within an energy storage unit is expressed by the following formula:

[0087]

[0088] Among them, S b1i Let S be the SOC size of the first energy storage device at the start of the i-th time period. bniLet S be the SOC (State of Charge) of the nth energy storage device at the start of the i-th time period. b,diff This represents the upper limit of the SOC difference between energy storage devices within the energy storage unit, where n is the number of energy storage devices in the energy storage unit.

[0089] Furthermore, to more clearly explain the various constraints set, please refer to the following... Figure 2 The example shown illustrates the remaining constraints when a distributed energy system containing multiple types of energy storage devices includes two energy storage units after the above clustering, namely the first energy storage unit and the second energy storage unit.

[0090] As a first example, regarding active power constraints, this distributed energy system needs to satisfy the equality constraint of active power balance. Therefore, the active power constraint can be expressed by the following formula:

[0091]

[0092] in, Let be the discharge power of the first energy storage unit in the i-th time period, and keep it as a non-negative value. The charging power of the first energy storage unit in the i-th time period is kept as a non-positive value. Let be the discharge power of the second energy storage unit during the i-th time period, and keep it as a non-negative value. P represents the charging power of the second energy storage unit during the i-th time period, and remains a non-positive value. i load Let represent the load power during the i-th time period.

[0093] It is understood that in other embodiments of this application, when a distributed energy system containing multiple types of energy storage devices includes other numbers of energy storage systems, it is only necessary to modify the formula for the above active power constraint. For example, for n energy storage units, the active power constraint is expressed by the following formula:

[0094]

[0095] It should be noted that in the subsequent examples of this application, the formulas for the constraints can only be modified in the above manner to be applicable to n energy storage units, and will not be elaborated further thereafter. Therefore, the method for determining the operation optimization of a distributed energy system containing multiple types of energy storage devices in this application can be applied to multiple energy storage units, improving the applicability of the optimization method in this application.

[0096] As a second example, regarding the power transmission constraints between the energy system and the power grid, these constraints are limited by factors such as the capacity of distributed energy system equipment and transmission lines. Therefore, the power exchange between the system and the power grid is restricted. The power transmission constraints between the energy system and the power grid can be expressed by the following formula:

[0097] |P ni |≤p max

[0098] Where, p max This represents the upper limit of the power allowed for transmission between the system and the power grid.

[0099] As a third example, for energy storage power constraints, the operation of energy storage requires the introduction of charge and discharge state variables: These four variables, represented by 0 and 1 values, are used to express the energy storage power constraint conditions using the following formula:

[0100]

[0101] in,

[0102] in, This is the discharge signal of the first energy storage unit. This is the charging signal for the first energy storage unit. This is the discharge signal for the second energy storage unit. This is the charging signal for the second energy storage unit. This represents the maximum discharge power of the first energy storage unit. This represents the maximum discharge power of the second energy storage unit. This represents the maximum charging power of the first energy storage unit. This is the maximum charging power of the second energy storage unit.

[0103] Specifically, when When the value is 1, the first energy storage unit is in a discharging state. =0; when When the value is 1, the first energy storage unit is in a charging state. It is 0; similarly, and The second energy storage unit is controlled in the same way.

[0104] As a fourth example, regarding the charge / discharge state constraints, to prevent the inefficient situation of equipment groups charging each other, charge / discharge constraints need to be applied to the two energy storage systems to maintain the same charge / discharge state. Therefore, the charge / discharge state constraints can be expressed by the following formula:

[0105] u 1i =u 2i

[0106] Among them, u 1i This is the charge / discharge state control signal for the first energy storage unit, u 2i This is the charging and discharging status control signal for the second energy storage unit.

[0107] As a fifth example, the State of Charge (SOC) constraint for energy storage can be expressed by the following formula:

[0108]

[0109] Among them, S 11 =S 1start S 1N =S 1end S 21 =S 2start S 2N =S 2end ,

[0110] Among them, S 1i S represents the state of charge (SOC) of the first energy storage unit at the start of the i-th time period. 2i Let SOC be the value of the second energy storage unit at the start of the i-th time period. The discharge efficiency of the first energy storage unit. The charging efficiency of the first energy storage unit. The discharge efficiency of the second energy storage unit. For the charging efficiency of the second energy storage unit, S 1start For the initial state of charge (SOC) of the first energy storage unit, S 1end S is the end-of-day SOC of the first energy storage unit. 2start For the initial state of charge (SOC) of the second energy storage unit, S 2end SOC at the end of the day for the second energy storage unit 1max and S 1min These are the upper and lower threshold values ​​of the State of Charge (SOC) for the first energy storage unit, respectively. 2max and S 2min S1 and S2 are the upper and lower limits of the SOC of the second energy storage unit, respectively, and the capacities of the first and second energy storage units, respectively.

[0111] As a sixth example, the charge / discharge cycle constraint, which limits the number of charge / discharge cycles per day, can be expressed by the following formula:

[0112]

[0113] Among them, z 1i and z 2iThese are the parameters used by the first and second energy storage units to assist in calculating the number of cycles, respectively. 1i and z 2i The variable is set to a value of 0 or 1, and k is the limit parameter for the number of charge / discharge cycles.

[0114] As a seventh example, to prevent sudden power changes due to power change rate constraints, the following constraint formula should be applied to limit the power change rate of each part of the system:

[0115]

[0116] in, It represents the maximum change in wind power generation between two time periods. It is the maximum change in photovoltaic power generation between two time periods. It is the maximum power change of the first energy storage unit between two time periods. It is the maximum power change of the second energy storage unit between two time periods. It is the maximum change in transmission power between a distributed energy system containing multiple types of energy storage devices and the power grid between two time periods.

[0117] Step S103: Solve the objective function according to multiple constraints, and control the distributed energy system containing multiple types of energy storage devices to operate under multiple constraints according to the solution results.

[0118] Specifically, the aforementioned constraints are used as limitations on the optimization problem to solve the optimization model, that is, to find the values ​​of the undetermined parameters controlling the operation of the distributed energy system containing multiple types of energy storage devices under the constraints. Then, the distributed energy system is controlled to operate according to the solved parameters, and the operating state of each unit within the distributed energy system is controlled to meet the aforementioned constraints.

[0119] Therefore, this application takes distributed energy systems incorporating wind and solar renewable energy generation and energy storage as the research object, and proposes a method for determining the operation optimization of distributed energy systems containing multiple types of hybrid energy storage devices. This method ensures the charging and discharging synchronization of multiple energy storage units and maximizes the overall regulation capability of the energy storage system. Its benefits include simplifying the complexity of the optimization problem and improving the efficiency of solving the optimization problem. This method is applicable not only to systems with two energy storage units but also to systems with multiple types of energy storage devices connected.

[0120] In summary, the method for optimizing the operation of a distributed energy system containing multiple types of energy storage devices, as described in this application, optimizes the operation of the distributed energy system by solving a set objective function and setting multiple types of constraints for the system's operation. To effectively reduce the computational complexity of optimization, energy storage devices with similar total operating times are clustered into one energy storage unit for overall operation. This ensures the charging and discharging synchronization of similar energy storage devices in the distributed energy system, preventing the operating state of energy storage devices from exceeding the rated operating conditions and thus contributing to the safety of the energy storage devices. Furthermore, this method maximizes the overall regulation capability of the energy storage system by restricting the charging, discharging, and energy storage operations of each energy storage unit through appropriate constraints, ensuring the equipment utilization rate of each energy storage unit and maximizing renewable energy output. Moreover, this method also significantly simplifies the complexity of the optimization model and improves the solution efficiency of the multi-objective optimization problem in practical applications by transforming the multi-objective optimization problem into a single-objective optimization problem.

[0121] To achieve the above embodiments, this application also proposes a distributed energy system operation optimization determination system containing multiple types of energy storage devices. Figure 4 This is a schematic diagram of the structure of a distributed energy system operation optimization determination system containing multiple types of energy storage devices proposed in an embodiment of this application, as shown below. Figure 4 As shown, the system includes: a construction module 100, a setting module 200, and a control module 300.

[0122] Among them, the construction module 100 is used to construct an objective function to optimize the operation mode of a distributed energy system containing multiple types of energy storage devices, with the goal of improving the absorption of renewable energy and the independent operation capability of the distributed energy system containing multiple types of energy storage devices.

[0123] The setting module 200 is used to cluster energy storage devices with similar total operating time into an energy storage unit for overall operation, and to set multiple constraints for the objective parameters in the objective function. Among these constraints are conditions that limit the charging, discharging and energy storage of each energy storage unit.

[0124] The control module 300 is used to solve the objective function according to multiple constraints and control the distributed energy system containing multiple types of energy storage devices to operate under multiple constraints according to the solution results.

[0125] Optionally, in one embodiment of this application, the construction module 100 is specifically used to: construct a first objective function with the goal of maximizing the absorption of renewable energy, and minimize the first objective function; construct a second objective function with the goal of minimizing the energy transmitted between the distributed energy system containing multiple types of energy storage devices and the power grid; and combine the first objective function and the second objective function to transform the multi-objective optimization problem of optimizing the operation mode of the distributed energy system containing multiple types of energy storage devices into a single-objective optimization problem.

[0126] Optionally, in one embodiment of this application, the construction module 100 is specifically used to represent the objective function for optimizing the operation of a distributed energy system containing multiple types of energy storage devices through the following formula:

[0127]

[0128] Where N is the number of time periods into which each day is divided, and P wi Let P be the wind power generation capacity in the i-th time period. pvi Let P be the photovoltaic power generation in the i-th time period, a be the priority coefficient for wind power consumption, b be the priority coefficient for photovoltaic power consumption, and P' be the photovoltaic power generation output. ni Let be the transmission power between the distributed energy system containing multiple types of energy storage devices and the power grid during the i-th time period, and c be the energy storage charging and discharging determination coefficient.

[0129] Optionally, in one embodiment of this application, multiple constraints include: active power constraints, energy system and grid transmission power constraints, energy storage power constraints, state of charge and discharge constraints, energy storage state of charge (SOC) constraints, number of charge and discharge cycles constraints, and power change rate constraints.

[0130] Optionally, in one embodiment of this application, the setting module 200 is specifically used to represent the SOC difference constraint condition between each energy storage device in the energy storage unit using the following formula:

[0131]

[0132] Among them, S b1i Let S be the SOC size of the first energy storage device at the start of the i-th time period. bni Let S be the SOC (State of Charge) of the nth energy storage device at the start of the i-th time period. b,diff This represents the upper limit of the SOC difference between energy storage devices within the energy storage unit, where n is the number of energy storage devices in the energy storage unit.

[0133] Optionally, in one embodiment of this application, when the distributed energy system containing multiple types of energy storage batteries includes two clustered energy storage units, the setting module 200 is specifically used to express the active power constraint condition through the following formula:

[0134]

[0135] in, Let be the discharge power of the first energy storage unit in the i-th time period. Let be the charging power of the first energy storage unit in the i-th time period. Let be the discharge power of the second energy storage unit in the i-th time period. P represents the charging power of the second energy storage unit during the i-th time period. i load The magnitude of the load power in the i-th time period;

[0136] The power transmission constraints between the energy system and the power grid are expressed by the following formula:

[0137] |P ni |≤p max

[0138] Where, p max This represents the upper limit of the power allowed for transmission between the system and the power grid.

[0139] Optionally, in one embodiment of this application, the setting module 200 is specifically used to represent the energy storage power constraint condition by the following formula:

[0140]

[0141] in,

[0142] in, This is the discharge signal of the first energy storage unit. This is the charging signal for the first energy storage unit. This is the discharge signal for the second energy storage unit. This is the charging signal for the second energy storage unit. This represents the maximum discharge power of the first energy storage unit. This represents the maximum discharge power of the second energy storage unit. This represents the maximum charging power of the first energy storage unit. The maximum charging power of the second energy storage unit;

[0143] The charge / discharge state constraint condition is expressed by the following formula:

[0144] u 1i =u 2i

[0145] Among them, u 1i This is the charge / discharge state control signal for the first energy storage unit, u 2i This is the charging and discharging status control signal for the second energy storage unit.

[0146] Optionally, in one embodiment of this application, the setting module 200 is specifically used to represent the energy storage state of charge (SOC) constraint condition using the following formula:

[0147]

[0148] Among them, S 11 =S 1start S 1N =S 1end S 21 =S 2start S 2N =S 2end , among which, S 1i S represents the state of charge (SOC) of the first energy storage unit at the start of the i-th time period. 2i Let SOC be the value of the second energy storage unit at the start of the i-th time period. The discharge efficiency of the first energy storage unit. The charging efficiency of the first energy storage unit. The discharge efficiency of the second energy storage unit. For the charging efficiency of the second energy storage unit, S 1start For the initial state of charge (SOC) of the first energy storage unit, S 1end S is the end-of-day SOC of the first energy storage unit. 2start For the initial state of charge (SOC) of the second energy storage unit, S 2end For the end-of-day SOC of the second energy storage system, S 1max and S 1min These are the upper and lower threshold values ​​of the State of Charge (SOC) for the first energy storage unit, respectively. 2max and S 2min S1 and S2 are the upper and lower limits of the SOC of the second energy storage system, respectively, and the capacities of the first and second energy storage units are the same.

[0149] The charge / discharge cycle constraint is expressed by the following formula:

[0150]

[0151] Among them, z 1i and z 2i These are the parameters for the auxiliary calculation of the number of cycles for the first and second energy storage units, respectively, and k is the limit parameter for the number of charge and discharge cycles;

[0152] The power change rate constraint is expressed by the following formula:

[0153]

[0154] in, It represents the maximum change in wind power generation between two time periods. It is the maximum change in photovoltaic power generation between two time periods. It is the maximum power change of the first energy storage unit between two time periods. It is the maximum power change of the second energy storage unit between two time periods. It is the maximum change in transmission power between a distributed energy system containing multiple types of energy storage batteries and the power grid between two time periods.

[0155] It should be noted that the explanation of the aforementioned embodiment of the method for determining the operation optimization of a distributed energy system containing multiple types of energy storage devices also applies to the system in this embodiment, and will not be repeated here.

[0156] In summary, the distributed energy system operation optimization determination system containing multiple types of energy storage devices in this application optimizes the operation of the distributed energy system by solving a set objective function and setting multiple types of constraints for the operation of the energy system. This ensures the charging and discharging synchronization of energy storage devices of the same type within the distributed energy system, preventing the operating state of energy storage devices from exceeding rated operating conditions, thus contributing to the safety of the energy storage devices. Furthermore, the system maximizes the overall regulation capability of the energy storage system by limiting the charging, discharging, and energy storage operations of each energy storage unit through appropriate constraints, ensuring the equipment utilization rate of each energy storage unit and maximizing renewable energy output. Moreover, by transforming the multi-objective optimization problem into a single-objective optimization problem, the system can significantly simplify the complexity of the optimization model in practical applications, improving the solution efficiency of the multi-objective optimization problem.

[0157] To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method for determining the operation optimization of a distributed energy system containing multiple types of energy storage devices as described in any of the above embodiments.

[0158] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0159] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0160] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0161] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0162] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0163] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0164] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0165] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for optimizing the operation of a distributed energy system containing multiple types of energy storage devices, characterized in that, Includes the following steps: With the goal of improving the absorption of renewable energy and the independent operation capability of distributed energy systems containing multiple types of energy storage devices, an objective function is constructed to optimize the operation mode of the distributed energy system containing multiple types of energy storage devices. Energy storage devices with similar total operating times are clustered into one energy storage unit for overall operation. Multiple constraints are set for the objective parameters in the objective function, including the SOC difference constraint between the energy storage devices within the energy storage unit. The SOC difference constraint between the energy storage devices within the energy storage unit is expressed by the following formula: in, For the first energy storage device i The SOC size at the start of each time period. For the first n The first energy storage device i The SOC size at the start of each time period. This represents the upper limit of the SOC difference between energy storage devices within an energy storage unit. n This represents the number of energy storage devices in the energy storage unit. The objective function is solved based on the multiple constraints, and the distributed energy system containing multiple types of energy storage devices is controlled to operate under the multiple constraints according to the solution results.

2. The method according to claim 1, characterized in that, The objective function for constructing and optimizing the operation of the distributed energy system containing multiple types of energy storage devices includes: Construct a first objective function with the goal of maximizing the absorption of renewable energy, and minimize the first objective function; Construct a second objective function with the goal of minimizing the energy transmitted between the distributed energy system containing multiple types of energy storage devices and the power grid; By combining the first objective function and the second objective function, the multi-objective optimization problem of optimizing the operation mode of the distributed energy system containing multiple types of energy storage devices is transformed into a single-objective optimization problem.

3. The method according to claim 2, characterized in that, The objective function for optimizing the operation of the distributed energy system containing multiple types of energy storage devices is expressed by the following formula: in, N To divide each day into time periods, For the first i Wind power generation capacity over a given time period For the first i Photovoltaic power generation capacity over a given time period a To absorb the priority coefficient for wind power generation, b Priority coefficients for absorbing photovoltaic power generation, For the first i The power transmission between a distributed energy system containing multiple types of energy storage devices and the power grid over a given time period. c It is the energy storage charging and discharging determination coefficient.

4. The method according to claim 3, characterized in that, The multiple constraints also include: active power constraints, energy system and grid transmission power constraints, energy storage power constraints, charge and discharge state constraints, energy storage state of charge (SOC) constraints, charge and discharge cycles constraints, and power change rate constraints.

5. The method according to claim 4, characterized in that, When the distributed energy system containing multiple types of energy storage batteries includes two clustered energy storage units, the active power constraint is expressed by the following formula: in, For the first i The discharge power of the first energy storage unit in a given time period. For the first i The charging power of the first energy storage unit in a given time period For the first i The discharge power of the second energy storage unit during a given time period. For the first i The charging power of the second energy storage unit during a given time period. For the first i The magnitude of the load power over a time period; The power transmission constraints between the energy system and the power grid are expressed by the following formula: in, This represents the upper limit of the power allowed for transmission between the system and the power grid.

6. The method according to claim 5, characterized in that, The energy storage power constraint is expressed by the following formula: in, , , in, This is the discharge signal of the first energy storage unit. This is the charging signal for the first energy storage unit. This is the discharge signal for the second energy storage unit. This is the charging signal for the second energy storage unit. This represents the maximum discharge power of the first energy storage unit. This represents the maximum discharge power of the second energy storage unit. This represents the maximum charging power of the first energy storage unit. The maximum charging power of the second energy storage unit; The charge / discharge state constraint condition is expressed by the following formula: in, This is the charge / discharge status control signal for the first energy storage unit. This is the charging and discharging status control signal for the second energy storage unit.

7. The method according to claim 5, characterized in that, The SOC constraint condition for the energy storage is expressed by the following formula: in, , , , ,in, For the first energy storage unit i The SOC size at the start of each time period. For the second energy storage unit i The SOC size at the start of each time period. The discharge efficiency of the first energy storage unit. The charging efficiency of the first energy storage unit. The discharge efficiency of the second energy storage unit. The charging efficiency of the second energy storage unit, The initial state of charge (SOC) of the first energy storage unit. The end-of-day SOC of the first energy storage unit. The initial state of charge (SOC) of the second energy storage unit. For the end-of-day SOC of the second energy storage system, and These are the upper and lower threshold values ​​of the SOC for the first energy storage unit, respectively. and These are the upper and lower threshold values ​​of the SOC for the second energy storage system, respectively. and These are the capacities of the first energy storage unit and the second energy storage unit, respectively. The charge / discharge cycle constraint is expressed by the following formula: in, and These are the parameters for the auxiliary calculation of the number of cycles for the first and second energy storage units, respectively. k This is a parameter limiting the number of charge / discharge cycles; The power change rate constraint is expressed by the following formula: in, It represents the maximum change in wind power generation between two time periods. It is the maximum change in photovoltaic power generation between two time periods. It is the maximum power change of the first energy storage unit between two time periods. It is the maximum power change of the second energy storage unit between two time periods. It is the maximum change in transmitted power between a distributed energy system containing multiple types of energy storage batteries and the power grid between two time periods.

8. A system for determining the operation optimization of a distributed energy system containing multiple types of energy storage devices, characterized in that, include: The module is used to construct an objective function to optimize the operation mode of the distributed energy system containing multiple types of energy storage devices, with the goal of improving the absorption of renewable energy and the independent operation capability of the distributed energy system containing multiple types of energy storage devices. The configuration module is used to cluster energy storage devices with similar total operating times into a single energy storage unit for overall operation, and to set multiple constraints for the objective parameters in the objective function. These constraints include a SOC difference constraint between the energy storage devices within the energy storage unit, which is expressed by the following formula: in, For the first energy storage device i The SOC size at the start of each time period. For the first n The first energy storage device i The SOC size at the start of each time period. This represents the upper limit of the SOC difference between energy storage devices within an energy storage unit. n This represents the number of energy storage devices in the energy storage unit. The control module is used to solve the objective function according to the multiple constraints and control the distributed energy system containing multiple types of energy storage devices to operate under the multiple constraints according to the solution results.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for determining the operation optimization of a distributed energy system containing multiple types of energy storage devices as described in any one of claims 1-7.

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