Island micro-grid energy storage configuration method and device for wind-solar-storage-fuel bundling

Abstract

The application provides an island micro-grid energy storage configuration method and device for wind, light, storage and bundling, and the method comprises the following steps: constructing an energy storage configuration constraint condition, which is used for constraining the rated power and rated energy of the configured energy storage device; solving an optimization problem based on the constraint condition to obtain an energy storage configuration result of the island micro-grid, wherein the energy storage configuration result comprises the rated power and rated energy of the energy storage device, and a target function of the optimization problem is determined based on the cost corresponding to the energy storage configuration result; wherein the island micro-grid comprises a plurality of energy management nodes, each energy management node corresponds to a turbine generator, and the photovoltaic power generation device, the wind power generation device and the energy storage device in the island micro-grid are connected to the AC grid connection point of the turbine generator in the energy management node through a converter. The application can reduce the energy storage configuration cost of the offshore island micro-grid.

Description

Technical Field

[0001] This invention relates to the field of microgrids, and more particularly to a method and apparatus for configuring energy storage in isolated microgrids that bundle wind, solar, energy storage, and natural gas. Background Technology

[0002] Offshore microgrids have diverse application scenarios, typically including artificial energy islands, natural islands, large ships, offshore ranches, and various offshore work platform clusters. Most offshore microgrids are located far from land, and connecting them to the onshore power grid via submarine cables is not economically feasible. Therefore, they operate as isolated microgrids, requiring stable power supply under off-grid conditions. Traditional turbine generators suffer from high power supply costs and large carbon emissions, making the introduction of new energy sources such as wind and solar power crucial for addressing the energy needs of these isolated offshore microgrids. However, deep-sea wind and solar power exhibit highly volatile characteristics due to extreme weather and complex sea conditions, necessitating the integration of energy storage devices to respond to these fluctuations.

[0003] In existing technologies, energy storage devices in isolated microgrids are often configured using a fixed ratio. However, the operation of energy storage devices is limited by energy storage constraints and power operating limits. The larger the rated energy and rated power of the energy storage devices, the stronger their regulation capability, but the investment cost of energy storage will also increase accordingly. The traditional fixed-ratio energy storage configuration mode is likely to lead to redundant energy storage capacity and high investment costs. Summary of the Invention

[0004] This invention provides a method and apparatus for configuring energy storage in isolated microgrids that bundle wind, solar, energy storage, and gas storage, in order to solve the problem of high cost in the prior art by using a fixed ratio of energy storage equipment, and to reduce the energy storage configuration cost of isolated microgrids.

[0005] This invention provides a method for configuring energy storage in isolated microgrids that bundle wind, solar, energy storage, and gas. The isolated microgrid includes multiple energy management nodes, each of which corresponds to a turbine generator. The photovoltaic power generation equipment, wind power generation equipment, and energy storage equipment in the isolated microgrid are connected to the AC grid connection point of the turbine generator in the energy management node through a converter.

[0006] The method includes:

[0007] Establish energy storage configuration constraints, which are used to constrain the rated power and rated energy of the configured energy storage devices;

[0008] The optimization problem is solved based on the constraints to obtain the energy storage configuration result of the islanded microgrid. The energy storage configuration result includes the rated power and rated energy of the energy storage devices. The objective function of the optimization problem is determined based on the cost corresponding to the energy storage configuration result.

[0009] According to the present invention, an energy storage configuration method for islanded microgrids bundled with wind, solar, energy storage and gas storage is provided, wherein the constraints include a first constraint and a second constraint.

[0010] The first constraint condition is used to ensure that the rated power and rated energy of the configured energy storage device do not exceed the limit value;

[0011] The second constraint is used to ensure that the energy-to-power ratio of the configured energy storage device is within a limited range.

[0012] According to the present invention, an energy storage configuration method for islanded microgrids bundled with wind, solar, energy storage and gas is provided. The constraint conditions further include a third constraint condition, which is used to ensure that the power deviation of the energy management node configured with energy storage does not exceed the allowable limit under wind power and photovoltaic fluctuation test scenarios.

[0013] According to the present invention, a method for configuring energy storage in an islanded microgrid that bundles wind, solar, energy storage, and natural gas energy, the third constraint condition is:

[0014] ;

[0015] in, The root mean square error between the power of the energy management node and the target power setting value of the energy management node; The output power of the energy management node at the i-th power measurement point during the response process; The reference value for the target tracking power of the energy management node; n is the number of power measurement points; for The upper limit.

[0016] According to the present invention, a method for configuring energy storage in islanded microgrids bundled with wind, solar, energy storage, and natural gas storage is provided. The step of solving the objective function based on the constraints includes:

[0017] The objective function is solved under the constraints using a particle swarm optimization algorithm.

[0018] In the particle swarm optimization algorithm, the position of each particle corresponds to a set of rated power and rated energy values ​​for energy storage, and the fitness value of each particle is determined based on the objective function.

[0019] According to the present invention, a method for configuring energy storage in islanded microgrids bundled with wind, solar, energy storage, and natural gas is provided, wherein solving the objective function under the constraints using a particle swarm optimization algorithm includes:

[0020] A penalty function is generated based on the second and third constraints. The objective function is then rewritten based on the penalty function to obtain an augmented objective function. This augmented objective function is then used as the fitness function in the particle swarm optimization algorithm.

[0021] Under the first constraint, the particle position and velocity are updated based on the fitness function.

[0022] The present invention also provides an energy storage configuration device for isolated microgrids bundled with wind, solar, energy storage and gas. The isolated microgrid includes multiple energy management nodes, each energy management node corresponding to a turbine generator. The photovoltaic power generation equipment, wind power generation equipment and energy storage equipment in the isolated microgrid are connected to the AC grid connection point of the turbine generator in the energy management node through a converter.

[0023] The device includes:

[0024] A constraint module is used to construct energy storage configuration constraints, which constrain the rated power and rated energy of the configured energy storage devices.

[0025] The solution module is used to solve the optimization problem based on the constraints to obtain the energy storage configuration result of the islanded microgrid. The energy storage configuration result includes the rated power and rated energy of the energy storage devices. The objective function of the optimization problem is determined based on the cost corresponding to the energy storage configuration result.

[0026] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the energy storage configuration method for islanded microgrids bundled with wind, solar, energy storage, and gas as described above.

[0027] The present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the energy storage configuration method for islanded microgrids bundled with wind, solar, energy storage and gas as described above.

[0028] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the energy storage configuration method for islanded microgrids bundled with wind, solar, energy storage and gas as described above.

[0029] The present invention provides a method and apparatus for configuring energy storage in isolated microgrids that bundles wind, solar, energy storage, and natural gas. By bundling wind, solar, energy storage, and natural gas, the system uses the turbine generators within the energy management nodes of the isolated microgrid system as equivalent generators for system regulation. This avoids the need to update the energy management system of the deep-sea isolated microgrid. At the same time, based on the operating constraints of the energy storage equipment, an optimization problem with energy storage configuration cost as the objective function is solved, thereby reducing the energy storage configuration cost of offshore isolated microgrids. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0031] Figure 1 This is a flowchart illustrating the energy storage configuration method for islanded microgrids bundled with wind, solar, energy storage, and natural gas, provided by the present invention.

[0032] Figure 2 This is a structural diagram of the energy management system of an isolated microgrid in the energy storage configuration method for bundled wind, solar, energy storage and gas provided by the present invention.

[0033] Figure 3 This is an islanded microgrid architecture diagram from an experimental example of the wind, solar, energy storage, and gas energy storage configuration method for islanded microgrids bundled by the present invention.

[0034] Figure 4 This is an experimental result diagram of the energy storage configuration method for islanded microgrids bundled with wind, solar, energy storage and gas provided by the present invention.

[0035] Figure 5 This is a schematic diagram of the structure of the islanded microgrid energy storage configuration device for bundling wind, solar, energy storage and gas provided by the present invention.

[0036] Figure 6 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0038] The following is combined with Figure 1-4 This invention describes a method for configuring energy storage in islanded microgrids that bundles wind, solar, and gas storage. For example... Figure 1 As shown, the energy storage configuration method for islanded microgrids bundled with wind, solar, energy storage, and natural gas provided by this invention includes the following steps:

[0039] S110. Construct energy storage configuration constraints, which are used to constrain the rated power and rated energy of the configured energy storage devices;

[0040] S120. Solve the objective function based on the constraints to obtain the energy storage configuration result of the islanded microgrid. The energy storage configuration result includes the rated power and rated energy of the energy storage equipment. The objective function is determined based on the cost corresponding to the energy storage configuration result.

[0041] The method provided by this invention is applied to an islanded microgrid, wherein the islanded microgrid includes multiple energy management nodes, each energy management node corresponds to a turbine generator, and the photovoltaic power generation equipment, wind power generation equipment and energy storage equipment in the islanded microgrid are connected to the AC grid connection point of the turbine generator in the energy management node through a converter.

[0042] The present invention provides a method for configuring energy storage in isolated microgrids that bundles wind, solar, energy storage, and natural gas. By bundling wind, solar, energy storage, and natural gas, the method uses the turbine generators within the energy management nodes of the isolated microgrid system as equivalent generators for system regulation. This avoids the need to update the energy management system of the deep-sea isolated microgrid. At the same time, it solves an optimization problem with energy storage configuration cost as the objective function based on the operating constraints of the energy storage equipment, thereby reducing the energy storage configuration cost of offshore isolated microgrids.

[0043] Specifically, in the energy management system of a deep-sea island microgrid, turbine generators and important load equipment can all serve as individual energy management nodes for status monitoring and power regulation by the deep-sea island microgrid control center. Some offshore island microgrids have been in operation for several years or even decades, and their energy management system hardware and software deployments are relatively stable. Integrating wind power, photovoltaics, and energy storage as new energy management nodes into the deep-sea island microgrid requires updating the existing energy management system, which presents numerous challenges such as data integration and migration, system compatibility assurance, and increases economic costs. However, the method provided by this invention adopts a bundled wind-solar-storage-gas operation mode, treating wind turbines, photovoltaics, energy storage, and turbine generators within the existing energy management nodes as equivalent generators for coordinated regulation. This does not change the number of energy management nodes in the deep-sea island microgrid, thus avoiding the need to update the energy management system. It achieves an offshore wind power, photovoltaic, and energy storage grid connection scheme without changing the existing operation mode, and is compatible with the existing energy management system of the deep-sea island microgrid.

[0044] Taking the turbine generator at energy management node i as an example, the wind-solar-storage-gas bundled operation mode in the method provided by this invention is as follows: Figure 2 As shown, for a specific turbine generator management node, photovoltaic, wind turbine, and energy storage systems are connected to the AC grid connection point of the turbine generator via converters. The energy management system treats the equipment at this grid connection point as an equivalent generator for management. The wind, solar, energy storage, and gas storage devices inside the equivalent generator track the target power setting at this node through adjustment processing. It can be seen that the bundled wind, solar, energy storage, and gas storage operation mode in the method provided by this invention does not change the original energy management node configuration and energy management process, thus largely preserving the original operation mode of the offshore island microgrid.

[0045] Energy storage devices play a crucial role in mitigating wind and solar power fluctuations in bundled wind, solar, and gas storage control models. However, energy storage operation is constrained by energy storage limitations and power operation limits. Higher rated energy and power of the energy storage device enhance its regulation capabilities, facilitating the equivalent generator's tracking of the target power setpoint at the energy management node. However, this also increases the investment cost of energy storage. The method provided in this invention solves an optimization problem with energy storage configuration cost as the objective function, based on the established operational constraints of the energy storage device, thereby reducing the energy storage configuration cost.

[0046] Taking a deep-sea island microgrid dominated by turbine generators as an example, energy storage is divided into power-type energy storage (such as supercapacitors) and energy-type energy storage (such as lithium-ion batteries). In order to plan the energy storage configuration, the parameters collected include the load status of the deep-sea island microgrid, topology, irradiance and wind speed in the nearby sea area, as well as the physical parameters of equipment such as energy storage, photovoltaic, and wind turbines.

[0047] When determining an energy storage planning scheme, the objective function is determined based on the cost corresponding to the energy storage configuration result, as follows:

[0048] (1)

[0049] In the formula: For energy storage investment costs; and These are the rated power and rated energy of the energy storage, respectively, and serve as the decision variables for the optimization problem; and These are the investment costs for the rated power and rated energy of the energy storage unit, respectively.

[0050] In solving the optimization problem where the optimization objective is to minimize the investment cost of energy storage, it is necessary to consider the actual operational constraints of the energy storage configuration. In the method provided by this invention, constraints are constructed for the energy storage configuration to constrain the rated power and rated energy of the configured energy storage devices.

[0051] In one possible implementation, the constraints include a first constraint and a second constraint. The first constraint ensures that the rated power and rated energy of the configured energy storage device do not exceed limits. The second constraint ensures that the energy-to-power ratio of the configured energy storage device is within a specified range.

[0052] Specifically, the rated power of energy storage and rated energy Each has an upper limit and That is, the constraints shown in formulas (2) to (3) need to be met. The energy-to-power ratio of energy storage is also subject to certain limitations, namely, the constraints shown in formula (4) need to be met.

[0053] (2)

[0054] (3)

[0055] (4)

[0056] In the formula: and These are the upper and lower limits of the energy-to-power ratio of energy storage, respectively.

[0057] Since energy storage devices need to provide power deviation suppression in scenarios of wind and solar power generation fluctuations, in one possible implementation of the method provided by the present invention, the constraints also include a third constraint, which is used to constrain the power deviation of the energy management node configured with energy storage to not exceed the integration limit in the wind and solar power generation fluctuation test scenario.

[0058] Specifically, under the corresponding wind power and photovoltaic fluctuation test scenarios, the equivalent generator power deviation should not exceed the allowable limit, which can be expressed by the following formula:

[0059] (5)

[0060] In the formula: The root mean square error between the equivalent generator power and the target power setpoint of the energy management node; This represents the output power of the equivalent generator at the i-th power measurement point during the response process. n is the reference value for the target tracking power of the energy management node; n is the number of power measurement points. for The upper limit.

[0061] From formula (5), we can see that, In fact, it is a decision variable for energy storage configuration in deep-sea island microgrids. and The strongly nonlinear function cannot be processed using current commercial solvers. However, the particle swarm optimization algorithm has advantages such as simple principle, few parameters, and low information dependence, and can handle nonlinear problems. Therefore, in one possible implementation of the method provided in this invention, the particle swarm optimization algorithm is selected to solve the energy storage planning problem of a deep-sea island microgrid. That is, the objective function is solved based on constraints, including:

[0062] The objective function is solved under constraints using the particle swarm optimization algorithm.

[0063] In the particle swarm optimization algorithm, the position of each particle corresponds to a set of rated power and rated energy values ​​for energy storage, and the fitness value of each particle is determined based on the objective function.

[0064] In the particle swarm optimization algorithm, the position coordinate vector of each particle is the energy storage configuration decision variable. and The particle fitness is a set of values ​​that correspond to the objective function. The particle swarm optimization algorithm iteratively updates the position and velocity of each particle, gradually approaching the optimal solution through continuous searching.

[0065] Furthermore, the energy storage power ratio constraint shown in formula (4) and the equivalent generator power deviation constraint shown in formula (5) involve multivariate coupling relationships. In order to improve the solution efficiency of the particle swarm optimization algorithm, the method provided in this invention solves the objective function under the constraint conditions using the particle swarm optimization algorithm, including:

[0066] A penalty function is generated based on the second and third constraints. The objective function is then rewritten based on the penalty function to obtain the augmented objective function. This augmented objective function is then used as the fitness function in the particle swarm optimization algorithm.

[0067] Under the first constraint, the particle position and velocity are updated based on the fitness function.

[0068] The penalty function method is used to add the energy storage power ratio constraint shown in equation (4) and the equivalent generator power deviation constraint shown in equation (5) to the original objective function (1) of energy storage investment cost, forming an augmented objective function. As shown below:

[0069] (6)

[0070] (7)

[0071] (8)

[0072] (9)

[0073] In the formula: , and These represent the penalty functions corresponding to the equivalent generator power deviation constraint, the upper limit of the energy storage power ratio constraint, and the lower limit of the energy storage power ratio constraint, respectively. , and They are respectively , and The penalty coefficient takes the value of a relatively large positive number; , and They are penalty functions , and The power parameter takes the value of a positive number greater than 1; This represents the minimum value between x and y. Formulas (7) to (9) show that when the energy storage configuration variable... , When the value is within the range of constraints (4) and (5), the penalty function takes the value of 0. When the limit is exceeded, the corresponding penalty function becomes a positive number, and the more severe the limit, the larger the value of the penalty function. Thus, the energy storage planning model of the offshore island microgrid is transformed into:

[0074] (10)

[0075] Furthermore, the problem shown in equation (10) can be solved using an adaptive particle swarm optimization algorithm, and the update formulas for the position and velocity of each particle are as follows:

[0076] (11)

[0077] In the formula: and Let be the velocity and position of the i-th particle in the k-th iteration, respectively; The inertia factor at the k-th iteration of the particle swarm; and These are the neighborhood factor and the global factor of the particle, respectively. and represents the random parameters for the k-th iteration of the particle swarm, with values ​​ranging from [0,1]. and These are the optimal position within the current neighborhood of the i-th particle after the k-th iteration and the global optimal position, respectively. The current neighborhood refers to the other nearest neighbors to the i-th particle. n k A collection of particles.

[0078] The adaptive behavior of the particle swarm optimization algorithm is that the parameters are adjusted accordingly after each iteration based on the iteration results, as shown below:

[0079] (12)

[0080] (13)

[0081] (14)

[0082] In the formula: The number of particles; , represents the initial neighborhood size, where The selected neighborhood size factor, Represents the largest integer not greater than x; This represents the optimal augmented objective function value after the k-th iteration. In particular, This represents the optimal augmented objective function value in the initial particle swarm. and These are the maximum and minimum limits of the inertia factor, respectively. If the k-th iteration does not improve the augmented objective function, it is said that iterative viscosity has occurred. In the minimum optimization problem, iterative viscosity manifests as... ; For an iterative viscous counter, where ; and The selected stage-specific iterative viscosity parameter.

[0083] Formula (12) can be intuitively understood as the fact that when iterative viscosity occurs, the neighborhood size of each particle will expand by a certain increment, but the maximum will not exceed Formula (13) can be intuitively understood as follows: in the initial stage of iterative viscosity, the inertia factor will be increased within the limit to search for energy storage capacity configuration schemes for offshore island microgrids in a larger space. After continuous iterative viscosity, the inertia factor will be reduced within the limit to accelerate the convergence speed of energy storage optimization configuration solution.

[0084] To verify the effectiveness of the method provided by this invention, a microgrid on an isolated offshore island was used as the research object. The system structure and equipment parameters of this research object are as follows: Figure 3 As shown, the energy storage, 50kW photovoltaic, and 4MW wind turbine are connected to the same DC operating point via DC / DC and AC / DC converters, respectively, and then share a DC / AC converter to be connected to the offshore island microgrid AC network. The wind power, photovoltaic, energy storage, and a 5MW gas turbine generator from platform 2-2 are bundled together to form an equivalent generator.

[0085] The system structure and the proposed wind-solar-storage-gas bundling control strategy were modeled in Matlab / Simulink. To verify the effectiveness of the energy storage configuration method provided by this invention, the method was compared with existing capacity calculation methods based on energy storage charge and discharge throughput. A scenario with a large step drop in wind speed was selected, using the equivalent generator power deviation limit. Taking 0.112 PU as an example, four energy storage configuration strategies were compared as shown in Table 1. The capacity optimization results and corresponding investment costs under different energy storage configuration strategies are as follows: Figure 4 As shown.

[0086] Table 1

[0087]

[0088] The above results show that the investment cost of configuration strategy 2 is reduced by 72.6% compared to configuration strategy 1, and the investment cost of configuration strategy 4 is reduced by 72.3% compared to configuration strategy 3. This indicates that, regardless of whether lithium-ion batteries or supercapacitors are configured, the method provided by this invention can significantly reduce energy storage investment costs compared to existing methods, thus verifying the effectiveness of the method provided by this invention in different energy storage technologies. This is because capacity calculation methods based on energy storage charge / discharge throughput use the peak charge / discharge power during the test period as the design benchmark for rated power, resulting in configuration schemes that are typically conservative. The method provided by this invention, however, continuously iterates and searches for energy storage capacity configuration schemes within the power tracking error constraint range, thereby obtaining more economical energy storage configuration schemes while meeting the equivalent generator control objectives.

[0089] The following describes the energy storage configuration device for islanded microgrids bundled with wind, solar, energy storage, and natural gas provided by this invention. The energy storage configuration device for islanded microgrids bundled with wind, solar, energy storage, and natural gas described below can be referred to in correspondence with the energy storage configuration method for islanded microgrids bundled with wind, solar, energy storage, and natural gas described above. For example... Figure 5 As shown, the energy storage configuration device for islanded microgrids bundled with wind, solar, energy storage, and natural gas provided by this invention includes the following modules:

[0090] Constraint module 510 is used to construct energy storage configuration constraints, which constrain the rated power and rated energy of the configured energy storage devices.

[0091] The solver module 520 is used to solve the optimization problem based on the constraints and obtain the energy storage configuration results of the islanded microgrid. The energy storage configuration results include the rated power and rated energy of the energy storage devices. The objective function of the optimization problem is determined based on the cost corresponding to the energy storage configuration results.

[0092] Figure 6An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 6 As shown, the electronic device may include: a processor 610, a communication interface 620, a memory 630, and a communication bus 640. The processor 610, communication interface 620, and memory 630 communicate with each other via the communication bus 640. The processor 610 can call logic instructions in the memory 630 to execute a method for configuring energy storage in an islanded microgrid bundled with wind, solar, and gas storage. This method includes: constructing energy storage configuration constraints, which constrain the rated power and rated energy of the configured energy storage devices; solving an optimization problem based on the constraints to obtain the energy storage configuration result of the islanded microgrid, where the energy storage configuration result includes the values ​​of the rated power and rated energy of the energy storage devices; and determining the objective function of the optimization problem based on the cost corresponding to the energy storage configuration result. The islanded microgrid includes multiple energy management nodes, each corresponding to a turbine generator. The photovoltaic power generation devices, wind power generation devices, and energy storage devices in the islanded microgrid are connected to the AC grid connection point of the turbine generator in the energy management node via converters.

[0093] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0094] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the energy storage configuration method for islanded microgrids bundled with wind, solar, energy storage, and gas provided by the above methods. The method includes: constructing energy storage configuration constraints, which are used to constrain the rated power and rated energy of the configured energy storage devices; solving an optimization problem based on the constraints to obtain the energy storage configuration result of the islanded microgrid, which includes the values ​​of the rated power and rated energy of the energy storage devices; and determining the objective function of the optimization problem based on the cost corresponding to the energy storage configuration result. The islanded microgrid includes multiple energy management nodes, each energy management node corresponding to a turbine generator. The photovoltaic power generation devices, wind power generation devices, and energy storage devices in the islanded microgrid are connected to the AC grid connection point of the turbine generator in the energy management node through a converter.

[0095] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the energy storage configuration method for islanded microgrids bundled with wind, solar, energy storage, and gas provided by the above methods. The method includes: constructing energy storage configuration constraints, which constrain the rated power and rated energy of the configured energy storage devices; solving an optimization problem based on the constraints to obtain the energy storage configuration result of the islanded microgrid, the energy storage configuration result including the values ​​of the rated power and rated energy of the energy storage devices; the objective function of the optimization problem being determined based on the cost corresponding to the energy storage configuration result; the islanded microgrid includes multiple energy management nodes, each energy management node corresponding to a turbine generator; the photovoltaic power generation devices, wind power generation devices, and energy storage devices in the islanded microgrid are connected to the AC grid connection point of the turbine generator in the energy management node through a converter.

[0096] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0097] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for configuring energy storage in isolated microgrids that bundle wind, solar, energy storage, and natural gas, characterized in that, The isolated microgrid includes multiple energy management nodes, each of which corresponds to a turbine generator. The photovoltaic power generation equipment, wind power generation equipment, and energy storage equipment in the isolated microgrid are connected to the AC grid connection point of the turbine generator in the energy management node through converters. The method includes: Establish energy storage configuration constraints, which are used to constrain the rated power and rated energy of the configured energy storage devices; The optimization problem is solved based on the constraints to obtain the energy storage configuration result of the islanded microgrid. The energy storage configuration result includes the rated power and rated energy of the energy storage devices. The objective function of the optimization problem is determined based on the cost corresponding to the energy storage configuration result. The constraints include a first constraint and a second constraint. The first constraint condition is used to ensure that the rated power and rated energy of the configured energy storage device do not exceed the limit value; The second constraint is used to ensure that the energy-to-power ratio of the configured energy storage device is within a limited range; The constraints also include a third constraint, which ensures that the power deviation of the energy management node equipped with energy storage does not exceed the allowable limit under wind power and photovoltaic fluctuation testing scenarios; the third constraint is: ； in, The root mean square error between the power of the energy management node and the target power setting value of the energy management node; The output power of the energy management node at the i-th power measurement point during the response process; The reference value for the target tracking power of the energy management node; n is the number of power measurement points; for The upper limit; Solving the objective function based on the constraints includes: The objective function is solved under the constraints using a particle swarm optimization algorithm. In the particle swarm optimization algorithm, the position of each particle corresponds to a set of values ​​for rated power and rated energy of energy storage, and the fitness value of each particle is determined based on the objective function. Solving the objective function under the constraints using the particle swarm optimization algorithm includes: A penalty function is generated based on the second and third constraints. The objective function is then rewritten based on the penalty function to obtain an augmented objective function. This augmented objective function is then used as the fitness function in the particle swarm optimization algorithm. Under the first constraint, the particle position and velocity are updated based on the fitness function; The augmented objective function is: ； ； ； ； In the formula: For energy storage investment costs, , and These represent the penalty functions corresponding to the equivalent generator power deviation constraint, the upper limit of the energy storage power ratio constraint, and the lower limit of the energy storage power ratio constraint, respectively. , and They are respectively , and The penalty coefficient; , and They are penalty functions , and The power parameter takes the value of a positive number greater than 1; This represents the minimum value between x and y. Rated power of energy storage The upper limit, Rated energy for energy storage The upper limit, and These are the upper and lower limits of the energy-to-power ratio of energy storage, when the energy storage configuration variables... , When the value is within the range of the second and third constraints, the penalty function takes the value of 0. When the limit is exceeded, the corresponding penalty function becomes a positive number, and the more severe the limit is exceeded, the larger the value of the penalty function becomes.

2. A bundled energy storage configuration device for isolated microgrids with wind, solar, energy storage, and natural gas storage, characterized in that, The isolated microgrid includes multiple energy management nodes, each of which corresponds to a turbine generator. The photovoltaic power generation equipment, wind power generation equipment, and energy storage equipment in the isolated microgrid are connected to the AC grid connection point of the turbine generator in the energy management node through converters. The device includes: A constraint module is used to construct energy storage configuration constraints, which constrain the rated power and rated energy of the configured energy storage devices. The solution module is used to solve the optimization problem based on the constraints to obtain the energy storage configuration result of the islanded microgrid. The energy storage configuration result includes the rated power and rated energy of the energy storage devices. The objective function of the optimization problem is determined based on the cost corresponding to the energy storage configuration result. The constraints include a first constraint and a second constraint. The first constraint condition is used to ensure that the rated power and rated energy of the configured energy storage device do not exceed the limit value; The second constraint is used to ensure that the energy-to-power ratio of the configured energy storage device is within a limited range; The constraints also include a third constraint, which ensures that the power deviation of the energy management node equipped with energy storage does not exceed the allowable limit under wind power and photovoltaic fluctuation testing scenarios; the third constraint is: ； in, The root mean square error between the power of the energy management node and the target power setting value of the energy management node; The output power of the energy management node at the i-th power measurement point during the response process; The reference value for the target tracking power of the energy management node; n is the number of power measurement points; for The upper limit; Solving the objective function based on the constraints includes: The objective function is solved under the constraints using a particle swarm optimization algorithm. In the particle swarm optimization algorithm, the position of each particle corresponds to a set of values ​​for rated power and rated energy of energy storage, and the fitness value of each particle is determined based on the objective function. Solving the objective function under the constraints using the particle swarm optimization algorithm includes: A penalty function is generated based on the second and third constraints. The objective function is then rewritten based on the penalty function to obtain an augmented objective function. This augmented objective function is then used as the fitness function in the particle swarm optimization algorithm. Under the first constraint, the particle position and velocity are updated based on the fitness function; The augmented objective function is: ； ； ； ； In the formula: For energy storage investment costs, , and These represent the penalty functions corresponding to the equivalent generator power deviation constraint, the upper limit of the energy storage power ratio constraint, and the lower limit of the energy storage power ratio constraint, respectively. , and They are respectively , and The penalty coefficient; , and They are penalty functions , and The power parameter takes the value of a positive number greater than 1; This represents the minimum value between x and y. Rated power of energy storage The upper limit, Rated energy for energy storage The upper limit, and These are the upper and lower limits of the energy-to-power ratio of energy storage, when the energy storage configuration variables... , When the value is within the range of the second and third constraints, the penalty function takes the value of 0. When the limit is exceeded, the corresponding penalty function becomes a positive number, and the more severe the limit is exceeded, the larger the value of the penalty function becomes.

3. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the energy storage configuration method for islanded microgrids bundled with wind, solar, energy storage and gas as described in claim 1.

4. 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 energy storage configuration method for islanded microgrids bundled with wind, solar, energy storage and gas as described in claim 1.

5. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the energy storage configuration method for islanded microgrids bundled with wind, solar, energy storage and gas as described in claim 1.

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