Coordination optimization method, device, equipment and medium

Abstract

The embodiment of the invention provides a coordination optimization method and device, equipment and a medium. The method comprises the following steps: firstly, processing a pre-constructed economic dispatching model of the multi-type energy storage device according to charge state data and dead time of the multi-type energy storage device to obtain a target function and a constraint condition for optimizing a power output strategy of the multi-type energy storage device; then, solving the target function by using an improved particle swarm algorithm, and determining a power output strategy of each energy storage device under the condition that the total cost of the multi-type energy storage devices is minimum; and finally, according to the power output strategy of each energy storage device, performing coordinated configuration on each energy storage device. In this way, the operation safety of the park microgrid can be effectively improved.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a coordinated optimization method, device, equipment, and medium. Background Art

[0002] As a typical regional integrated energy system, campus microgrids effectively promote cascaded energy utilization and the complementary operation of multiple energy sources, playing a significant role in improving the economic and environmental benefits of campus microgrid operations. However, due to the random and intermittent output of photovoltaic and wind power generation in campus microgrids, their output is closely related to environmental factors such as light intensity, wind speed, wind direction, and temperature. This makes it difficult for microgrids to flexibly respond to wind and solar power output, reducing the economic efficiency of campus microgrid operations. Therefore, to improve the economic efficiency of microgrid operation, how to coordinate and optimize energy storage in campus microgrids has become an urgent issue.

[0003] In the existing technology, the energy storage coordination optimization method for the park microgrid usually adopts the method of adding an energy storage system to the park microgrid to smooth the uncertain fluctuations of photovoltaic and wind power generation output, thereby improving the economic efficiency of the park microgrid operation.

[0004] However, the energy storage coordination optimization methods in existing technologies are relatively simple and it is difficult to ensure the reliability and stability during the operation of the park microgrid. Summary of the Invention

[0005] The embodiments of the present application provide a coordination optimization method, device, equipment and medium to solve the problem that the energy storage coordination optimization method in the prior art is relatively simple and difficult to ensure reliability and stability during the operation of the park microgrid.

[0006] In a first aspect, an embodiment of the present application provides a coordination optimization method, comprising:

[0007] Based on the state of charge data and dead time of multiple types of energy storage devices, a pre-established economic dispatch model for multiple types of energy storage devices is processed to obtain an objective function and constraints for optimizing the power output strategy of the multiple types of energy storage devices; the dead time is the time when the state of charge of the energy storage device reaches a preset limit and cannot operate; the economic dispatch model for multiple types of energy storage devices is a total cost calculation model established in advance based on the operating status and operating life of the multiple types of energy storage devices;

[0008] The objective function is solved using an improved particle swarm algorithm to determine the power output strategy of each energy storage device when the total cost of multiple types of energy storage devices is minimized; the improved particle swarm algorithm includes a population initialization strategy using a chaotic operator and an improved inertia weight function;

[0009] The energy storage devices are coordinated and configured according to the power output strategies of the energy storage devices.

[0010] In a possible implementation, the constraint conditions include: microgrid power balance constraint and energy storage system operating state constraint.

[0011] In a possible implementation, solving the objective function using an improved particle swarm optimization algorithm includes:

[0012] Initializing the population in the particle swarm algorithm using a chaos operator to obtain a first particle swarm; each particle in the first particle swarm represents a power output strategy of multiple types of energy storage devices;

[0013] Calculating the fitness value of each particle in the first particle swarm according to the objective function;

[0014] Iteratively updating each particle in the first particle swarm according to the fitness value to obtain a second particle swarm;

[0015] According to the second particle swarm, a power output strategy for each energy storage device is determined when the total cost of multiple types of energy storage devices is minimized.

[0016] In a possible implementation, the population initialization strategy of the chaotic operator includes:

[0017]

[0018] in, is the global optimal particle; is the chaotic search step, are the particles in the initial population.

[0019] In a possible implementation, iteratively updating each particle in the first particle swarm according to the fitness value to obtain a second particle swarm includes:

[0020] The optimization ability of the first particle swarm is improved by improving the inertia weight function. The specific formula is as follows:

[0021]

[0022] Among them, ω max 、ω min are the maximum and minimum values of ω; a is the distribution coefficient; N is the total number of iterations, and k is the current number of iterations.

[0023] In one possible implementation, the objective function for optimizing the power output strategy of multiple types of energy storage devices is:

[0024]

[0025] in, is the investment cost coefficient of the battery; is the investment cost coefficient of supercapacitor; is the unit price of the battery; is the unit price of supercapacitor; is the initial capacity of the energy storage device; is the rated charge and discharge times of the energy storage device; is the remaining capacity of the battery during period t; is the actual charge and discharge times of the energy storage device.

[0026] In a possible implementation manner, the microgrid power balance constraint includes:

[0027]

[0028] in, is the power value input to the grid at time t; is the output of the photovoltaic power station at time t; is the wind power output at time t; is the energy storage discharge power at time t; The energy storage charging time at time t; is the load power at time t; is the node voltage at time t; is the conductivity; is the electrical admittance; is the phase angle; are the reactive outputs of wind power and photovoltaic power stations at time t; is the reactive value of the load at time t;

[0029] The energy storage system operating state constraints include:

[0030]

[0031] Among them, E BESS (t) is the capacity of the battery at time t; and are the minimum and maximum charging powers allowed by the energy storage device at time t; and are the minimum and maximum discharge powers allowed by the energy storage device at time t respectively.

[0032] In a second aspect, an embodiment of the present application provides a coordinated optimization device, comprising:

[0033] A processing module is configured to process a pre-established economic dispatch model for multiple types of energy storage devices based on the state of charge data and dead time of the multiple types of energy storage devices to obtain an objective function and constraints for optimizing the power output strategy of the multiple types of energy storage devices; the dead time is the time during which the state of charge of the energy storage device reaches a preset limit and cannot operate; the economic dispatch model for multiple types of energy storage devices is a total cost calculation model pre-established based on the operating status and operating life of the multiple types of energy storage devices;

[0034] A determination module is used to solve the objective function using an improved particle swarm algorithm to determine the power output strategy of each energy storage device when the total cost of multiple types of energy storage devices is minimized; the improved particle swarm algorithm includes a population initialization strategy using a chaotic operator and an improved inertia weight function;

[0035] The configuration module is used to coordinate the configuration of each energy storage device according to the power output strategy of each energy storage device.

[0036] In a possible implementation, the constraint conditions include: microgrid power balance constraint and energy storage system operating state constraint.

[0037] In a possible implementation, the determining module is specifically configured to:

[0038] Initializing the population in the particle swarm algorithm using a chaos operator to obtain a first particle swarm; each particle in the first particle swarm represents a power output strategy of multiple types of energy storage devices;

[0039] Calculating the fitness value of each particle in the first particle swarm according to the objective function;

[0040] Iteratively updating each particle in the first particle swarm according to the fitness value to obtain a second particle swarm;

[0041] According to the second particle swarm, a power output strategy for each energy storage device is determined when the total cost of multiple types of energy storage devices is minimized.

[0042] In a possible implementation, the population initialization strategy of the chaotic operator includes:

[0043]

[0044] in, is the global optimal particle; is the chaotic search step, are the particles in the initial population.

[0045] In a possible implementation, the determining module is specifically configured to:

[0046] The optimization ability of the first particle swarm is improved by improving the inertia weight function. The specific formula is as follows:

[0047]

[0048] Among them, ω max 、ω min are the maximum and minimum values of ω; a is the distribution coefficient; N is the total number of iterations, and k is the current number of iterations.

[0049] In one possible implementation, the objective function for optimizing the power output strategy of multiple types of energy storage devices is:

[0050]

[0051] in, is the investment cost coefficient of the battery; is the investment cost coefficient of supercapacitor; is the unit price of the battery; is the unit price of supercapacitor; is the initial capacity of the energy storage device; is the rated charge and discharge times of the energy storage device; is the remaining capacity of the battery during period t; is the actual charge and discharge times of the energy storage device.

[0052] In a possible implementation manner, the microgrid power balance constraint includes:

[0053]

[0054] in, is the power value input to the grid at time t; is the output of the photovoltaic power station at time t; is the wind power output at time t; is the energy storage discharge power at time t; The energy storage charging time at time t; is the load power at time t; is the node voltage at time t; is the conductivity; is the electrical admittance; is the phase angle; are the reactive outputs of wind power and photovoltaic power stations at time t; is the reactive value of the load at time t;

[0055] The energy storage system operating state constraints include:

[0056]

[0057] Among them, E BESS (t) is the capacity of the battery at time t; and are the minimum and maximum charging powers allowed by the energy storage device at time t; and are the minimum and maximum discharge powers allowed by the energy storage device at time t respectively.

[0058] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a memory, a processor;

[0059] The memory stores computer-executable instructions;

[0060] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0061] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementation methods of the first aspect.

[0062] The coordinated optimization method, apparatus, device, and medium provided in the embodiments of the present application first process a pre-built economic dispatch model for multiple types of energy storage devices based on the state of charge data and dead time of multiple types of energy storage devices to obtain an objective function and constraints for optimizing the power output strategy of the multiple types of energy storage devices. By considering the state of charge of each energy storage device and the operating characteristics of the energy storage system, the rationality of the configuration of each energy storage device can be ensured, and the frequent charging and discharging of the energy storage devices, which may lead to a decrease in the life of the equipment, can be avoided. Then, an improved particle swarm algorithm is used to solve the objective function to determine the power output strategy of each energy storage device when the total cost of the multiple types of energy storage devices is minimized. Finally, the energy storage devices are coordinated and configured based on their power output strategies. In this way, the power output of different types of energy storage devices can be better coordinated, energy utilization and economic benefits can be improved, and the uncertain fluctuations of photovoltaic output and load in the microgrid can be smoothed, thereby improving the safety of microgrid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0064] Figure 1 Schematic diagram of the process of the coordination optimization method provided in the embodiment of this application Figure 1 ;

[0065] Figure 2 Schematic diagram of the process of the coordination optimization method provided in the embodiment of this application Figure 2 ;

[0066] Figure 3 A schematic diagram of the structure of the coordinated optimization device provided in an embodiment of the present application;

[0067] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0068] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0069] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0070] As a typical regional integrated energy system, campus microgrids effectively promote cascaded energy utilization and the complementary operation of multiple energy sources, playing a significant role in improving the economic and environmental benefits of campus microgrid operations. However, due to the random and intermittent output of photovoltaic and wind power generation in campus microgrids, their output is closely related to environmental factors such as light intensity, wind speed, wind direction, and temperature. This makes it difficult for microgrids to flexibly respond to wind and solar power output, reducing the economic efficiency of campus microgrid operations. Therefore, to improve the economic efficiency of microgrid operation, how to coordinate and optimize energy storage in campus microgrids has become an urgent issue.

[0071] In the existing technology, the energy storage coordination optimization method for the park microgrid usually adopts the method of adding an energy storage system to the park microgrid to smooth the uncertain fluctuations of photovoltaic and wind power generation output, thereby improving the economic efficiency of the park microgrid operation.

[0072] However, the energy storage coordination optimization methods in existing technologies are relatively simple and it is difficult to ensure the reliability and stability during the operation of the park microgrid.

[0073] Based on this, this application proposes a coordinated optimization method. The main drawback of existing microgrid energy storage configuration technology is its over-reliance on a single energy storage device (such as a battery) for energy regulation, ignoring the differentiated power and energy requirements at different time scales. Single energy storage technology is limited by its own characteristics. For example, although batteries have a high energy density, their power response speed, cycle life, and frequent charge and discharge losses restrict their ability to regulate high-frequency power fluctuations. This single configuration results in the system being unable to fully cope with the wide-time-scale fluctuations in wind and solar power output and load demand, resulting in limited new energy absorption capacity, low energy storage equipment utilization, and insufficient operating economy. In this context, a multi-type energy storage coordinated optimization method can fully leverage the complementary advantages of various types of energy storage in terms of response speed, storage capacity, and cost characteristics. Therefore, by utilizing multiple types of storage devices, it is possible to achieve refined scheduling of energy storage resources while ensuring system security.

[0074] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0075] Figure 1 Schematic diagram of the process of the coordination optimization method provided in the embodiment of this application Figure 1 ;like Figure 1 As shown, the method includes:

[0076] S101. Process a pre-built economic dispatch model for multiple types of energy storage devices based on state of charge data and dead time of multiple types of energy storage devices to obtain an objective function and constraints for optimizing a power output strategy for the multiple types of energy storage devices.

[0077] Among them, the dead time is the time when the charge state of the energy storage device reaches the preset limit and cannot work; the economic dispatch model of multiple types of energy storage devices is a total cost calculation model established in advance based on the operating status and operating life of multiple types of energy storage devices; the state of charge is a characterization quantity used to describe the real-time status of energy storage, which is used to reveal the remaining power after the energy storage element is used.

[0078] Specifically, the specific formula of the state of charge can be expressed as:

[0079]

[0080] Wherein, x represents the energy storage type. For example, when x is a, it represents a battery energy storage device, and when x is b, it represents a supercapacitor energy storage device. 、 represent the remaining capacity of the x energy storage device at time t and t-1 respectively; and is the discharge power and charging power of device x at time t, in MW; is the self-discharge rate; is the charge and discharge efficiency; is the regulating factor; is the minimum value corresponding to the remaining capacity value of the x energy storage device; is the maximum value corresponding to the remaining capacity value of the x energy storage device.

[0081] Furthermore, since long periods of dead time can affect the lifespan of energy storage devices, calculating dead time allows scheduling strategies to avoid these unavailable periods, rationally arrange for other energy storage devices to provide support, and improve the availability of energy storage devices. Therefore, the specific formula for dead time is:

[0082]

[0083] Among them, T d T is the duration of the dead zone; s is the unit time step, such as minutes, hours, etc.; N is the total number of time steps; is the state of charge of the energy storage device x at time t;

[0084] is the minimum value corresponding to the remaining capacity value of the x energy storage device; is the maximum value corresponding to the remaining capacity value of the x energy storage device; represents the union, indicating that if any of the conditions is met, the current moment is included in the dead zone time; f is the function used to determine whether the SOC is in the dead zone, for example:

[0085] When the SOC is too low (less than ), Output 1, otherwise output 0;

[0086] When the SOC is too high (greater than ), Output 1 if yes, otherwise output 0.

[0087] The formula for the number of charge and discharge times of the energy storage device can be specifically expressed as:

[0088]

[0089]

[0090] in, and represents the actual output power of energy storage equipment x at time t+1 and t; represents the number of power switching times of the energy storage device, that is, the number of times the power direction changes; N is the total number of time steps; g(x) is the function used to determine whether the power output direction changes, for example: when the power changes from positive to negative (charging → discharging) or from negative to positive (discharging → charging), If the power direction does not change (such as continuous charging or continuous discharging), the output value of g(x) is 1. If it is greater than 0, the output value of g(x) is 0.

[0091] It is understandable that overcharging and over-discharging the battery will accelerate the degradation of battery life. By constraining the SOC operating range and calculating the dead time and the number of charge and discharge times of the energy storage device, the device can be prevented from being in an extreme state for a long time, thereby improving the operating life of the energy storage device and the stability of the microgrid operation.

[0092] In one achievable approach, the objective function for optimizing the power output strategy of multiple types of energy storage devices is:

[0093]

[0094] in, is the investment cost coefficient of the battery; is the investment cost coefficient of supercapacitor; is the unit price of the battery; is the unit price of supercapacitor; is the initial capacity of the energy storage device; is the rated charge and discharge times of the energy storage device; is the remaining capacity of the battery during period t; is the actual charge and discharge times of the energy storage device.

[0095] It should be understood that by considering the energy storage advantages of different energy storage devices, for example, batteries have high energy density and are suitable for long-term energy storage, but the number of charge and discharge times is limited, and frequent switching will accelerate aging; while supercapacitors have fast charge and discharge speeds, but low energy density; making these two work together can reasonably allocate power output, more efficiently absorb new energy such as wind energy and photovoltaics, reduce power fluctuations, and improve the power balancing capability of microgrids.

[0096] It should be noted that the constraints are the microgrid power balance constraints and the energy storage system operating status constraints.

[0097] In one possible implementation, the microgrid power balance constraints include:

[0098]

[0099] in, is the power value input to the grid at time t; is the output of the photovoltaic power station at time t; is the wind power output at time t; is the energy storage discharge power at time t; The energy storage charging time at time t; is the load power at time t; is the node voltage at time t; is conductivity; is the electrical admittance; is the phase angle; are the reactive outputs of wind power and photovoltaic power stations at time t; is the reactive value of the load at time t.

[0100] It should be understood that establishing a power balance constraint within the microgrid ensures power balance within the microgrid, ensuring that the sum of photovoltaic, wind power, energy storage, and grid input power always equals the sum of load power consumption and line power flow. This helps maintain stable operation of the microgrid system and avoids problems such as frequency fluctuations and voltage deviations caused by power imbalance. Furthermore, this constraint optimizes reactive power scheduling, improves the microgrid's power quality, reduces power losses, and increases the efficiency of renewable energy utilization.

[0101] Energy storage system operating state constraints, including:

[0102]

[0103] Among them, E BESS (t) is the capacity of the battery at time t; and are the minimum and maximum charging powers allowed by the energy storage device at time t; and are the minimum and maximum discharge powers allowed by the energy storage device at time t respectively.

[0104] It should be understood that energy storage operating state constraints ensure that energy storage equipment operates within a safe range, which can increase the service life of energy storage devices, reduce operating and maintenance costs, and at the same time enhance the ability of energy storage to regulate fluctuations in renewable energy output and optimize the operating economy of microgrids.

[0105] It is understandable that through the synergistic effect of the two constraints, multiple types of energy storage devices can effectively respond to load demand, smooth out fluctuations in renewable energy, optimize the economic operation of microgrids, and improve the safety and flexibility of microgrids.

[0106] S102. Solve the objective function using an improved particle swarm algorithm to determine the power output strategy of each energy storage device when the total cost of multiple types of energy storage devices is minimized.

[0107] Among them, the improved particle swarm optimization algorithm includes a population initialization strategy using a chaotic operator and an improved inertia weight function.

[0108] It should be noted that traditional particle swarm optimization (PSO) algorithms are prone to falling into local optima during the initialization phase, resulting in insufficient global search capabilities. Using a chaotic operator for population initialization leverages the ergodic nature of chaotic sequences to evenly distribute initial particles across the entire solution space, increasing population diversity and enhancing the algorithm's global search capabilities. In standard PSO algorithms, the inertia weight determines the exploratory nature (global search) and exploitative nature (local search) of particle search. This embodiment employs an adaptively changing inertia weight function, which enhances global search capabilities and prevents local optima in the early stages of optimization. It also strengthens local search in the later stages of optimization, accelerating convergence and ultimately improving both optimization accuracy and efficiency.

[0109] It should be understood that by improving the PSO algorithm to solve the objective function, it is possible to optimize the power output strategy of various energy storage devices while ensuring the minimum total cost. This can not only effectively improve the new energy absorption rate and reduce the loss of energy storage equipment, but also optimize the economic efficiency of the microgrid and improve the stability and flexibility of the microgrid operation.

[0110] S103 : Coordinate and configure the energy storage devices according to their power output strategies.

[0111] The coordinated optimization method, apparatus, device, and medium provided in the embodiments of the present application first process a pre-built economic dispatch model for multiple types of energy storage devices based on the state of charge data and dead time of multiple types of energy storage devices to obtain an objective function and constraints for optimizing the power output strategy of the multiple types of energy storage devices. By considering the state of charge of each energy storage device and the operating characteristics of the energy storage system, the rationality of the configuration of each energy storage device can be ensured, and the frequent charging and discharging of the energy storage devices, which may lead to a decrease in the life of the equipment, can be avoided. Then, an improved particle swarm algorithm is used to solve the objective function to determine the power output strategy of each energy storage device when the total cost of the multiple types of energy storage devices is minimized. Finally, the energy storage devices are coordinated and configured based on their power output strategies. In this way, the power output of different types of energy storage devices can be better coordinated, energy utilization and economic benefits can be improved, and the uncertain fluctuations of photovoltaic output and load in the microgrid can be smoothed, thereby improving the safety of microgrid operation.

[0112] Figure 2 Schematic diagram of the process of the coordination optimization method provided in the embodiment of this application Figure 2 ;like Figure 2 As shown, this embodiment Figure 1 Based on the embodiment, the specific process of the particle swarm algorithm is described in detail. The method includes:

[0113] S201. Initialize the population in the particle swarm algorithm using a chaos operator to obtain a first particle swarm.

[0114] Each particle in the first particle group represents a power output strategy of multiple types of energy storage devices.

[0115] In one possible implementation, the population initialization strategy of the chaos operator includes:

[0116]

[0117] in, is the global optimal particle; is the chaotic search step, are the particles in the initial population.

[0118] It should be understood that by entering the chaotic search step, the particles of the random initial population are smoothly adjusted, avoiding the initial population from being concentrated in a local area, improving the global search capability of the algorithm, and helping to find the optimal energy storage scheduling solution more quickly.

[0119] For example, if there are 5 particles in the initial population, namely: [10, -5, 20, -15, 5], is 60, and the chaotic search step is 0.1. Then, through the population initialization strategy of the chaotic operator, the first particle swarm calculated is: [61, 59.5, 62, 58.5, 60.5]. It can be seen that since the particles have been adjusted around the current optimal solution during initialization, the algorithm has a certain optimization direction in the initial stage, which reduces the possibility of invalid search and accelerates the convergence speed.

[0120] S202: Calculate the fitness value of each particle in the first particle swarm according to the objective function.

[0121] It should be understood that by calculating the fitness value, the quality of each particle (i.e., the energy storage power allocation scheme) can be quantified, providing a basis for evaluation for the next iterative update, so that the optimization process can move towards the optimal solution.

[0122] S203 , iteratively updating each particle in the first particle swarm according to the fitness value to obtain a second particle swarm.

[0123] In one feasible approach, the optimization ability of the first particle swarm is improved by improving the inertia weight function. The specific formula is as follows:

[0124]

[0125] Among them, ω max 、ω minare the maximum and minimum values of ω; a is the distribution coefficient; N is the total number of iterations, and k is the current number of iterations.

[0126] It should be understood that through the improved inertia weight function, the inertia weight in the early stage of the algorithm is large, which is more conducive to global search and avoids local optimal traps; the inertia weight in the later stage of the algorithm is smaller, which is conducive to local fine search and improves convergence accuracy. Compared with the fixed inertia weight, this adaptive inertia weight function can be flexibly adjusted in different search stages to improve the optimization ability.

[0127] It should be noted that during the iterative update process, each particle adjusts its position according to its fitness value, approaching the current global optimal solution, until the number of iterations meets the preset iteration threshold, and then the corresponding population is output, that is, the second particle population. Specifically, the speed and position of each particle are updated according to the following formula:

[0128]

[0129] Where ω is the inertia weight; k is the current iteration number; c1 and c2 are learning factors; r1 and r2 are random sequences that are uniformly distributed and independent of each other between (0, 1); 、 is the position and velocity of the i-th particle at time k; pbest is the historical optimal solution of the particle; gbest is the global optimal solution in the current iteration process.

[0130] S204 : Determine, based on the second particle swarm, a power output strategy for each energy storage device when the total cost of multiple types of energy storage devices is minimized.

[0131] As you can see, through multiple iterations, we arrive at the globally optimal particle, which then determines the optimal power output for each energy storage device (such as batteries and supercapacitors) to minimize the total cost. This approach can reduce the cost of using multiple types of energy storage devices, such as batteries and supercapacitors, while ensuring a stable power supply.

[0132] Figure 3 A schematic diagram of the structure of the coordination optimization device provided in the embodiment of the present application; Figure 3 As shown, the device includes:

[0133] Processing module 301 is configured to process a pre-established economic dispatch model for multiple types of energy storage devices based on the state of charge data and dead time of the multiple types of energy storage devices to obtain an objective function and constraints for optimizing the power output strategy of the multiple types of energy storage devices. Dead time is the time during which the state of charge of an energy storage device reaches a preset limit and becomes inoperable. The economic dispatch model for multiple types of energy storage devices is a total cost calculation model pre-established based on the operating status and service life of the multiple types of energy storage devices.

[0134] Determination module 302 is used to solve the objective function using an improved particle swarm algorithm to determine the power output strategy of each energy storage device when the total cost of multiple types of energy storage devices is minimized; the improved particle swarm algorithm includes a population initialization strategy using a chaotic operator and an improved inertia weight function;

[0135] The configuration module 303 is used to coordinate and configure each energy storage device according to the power output strategy of each energy storage device.

[0136] In a possible implementation, the constraints include: microgrid power balance constraints and energy storage system operating state constraints.

[0137] In a possible implementation, the determination module 302 is specifically configured to:

[0138] A chaos operator is used to initialize the population in the particle swarm algorithm to obtain a first particle swarm; each particle in the first particle swarm represents a power output strategy of multiple types of energy storage devices;

[0139] According to the objective function, the fitness value of each particle in the first particle swarm is calculated;

[0140] Iteratively update each particle in the first particle swarm according to the fitness value to obtain a second particle swarm;

[0141] According to the second particle swarm, the power output strategy of each energy storage device is determined when the total cost of multiple types of energy storage devices is minimized.

[0142] In one possible implementation, the population initialization strategy of the chaos operator includes:

[0143]

[0144] in, is the global optimal particle; is the chaotic search step, are the particles in the initial population.

[0145] In a possible implementation, the determination module 302 is specifically configured to:

[0146] The optimization ability of the first particle swarm is improved by improving the inertia weight function. The specific formula is as follows:

[0147]

[0148] Among them, ω max 、ω min are the maximum and minimum values of ω; a is the distribution coefficient; N is the total number of iterations, and k is the current number of iterations.

[0149] In one possible implementation, the objective function for optimizing the power output strategy of multiple types of energy storage devices is:

[0150]

[0151] in, is the investment cost coefficient of the battery; is the investment cost coefficient of supercapacitor; is the unit price of the battery; is the unit price of supercapacitor; is the initial capacity of the energy storage device; is the rated charge and discharge times of the energy storage device; is the remaining capacity of the battery during period t; is the actual charge and discharge times of the energy storage device.

[0152] In one possible implementation, the microgrid power balance constraint includes:

[0153]

[0154] in, is the power value input to the grid at time t; is the output of the photovoltaic power station at time t; is the wind power output at time t; is the energy storage discharge power at time t; The energy storage charging time at time t; is the load power at time t; is the node voltage at time t; is conductivity; is the electrical admittance; is the phase angle; are the reactive outputs of wind power and photovoltaic power stations at time t; is the reactive value of the load at time t.

[0155] Energy storage system operating state constraints, including:

[0156]

[0157] Among them, E BESS (t) is the capacity of the battery at time t; and are the minimum and maximum charging powers allowed by the energy storage device at time t; and are the minimum and maximum discharge powers allowed by the energy storage device at time t respectively.

[0158] The coordination optimization device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effects are similar, and are not described in detail in this embodiment.

[0159] Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present application. Figure 4 As shown, the electronic device 40 provided in this embodiment includes: at least one processor 401 and a memory 402. Optionally, the device 40 further includes a communication component 403. The processor 401, the memory 402 and the communication component 403 are connected via a bus 404.

[0160] In a specific implementation process, at least one processor 401 executes the computer-executable instructions stored in the memory 402, so that the at least one processor 401 performs the above method.

[0161] The specific implementation process of the processor 401 can be found in the above method embodiment. Its implementation principle and technical effects are similar and will not be repeated here in this embodiment.

[0162] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASICs), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly executed by a hardware processor or by a combination of hardware and software modules within the processor.

[0163] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage.

[0164] A bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be categorized as address buses, data buses, and control buses. For ease of illustration, the buses in the drawings of this application are not limited to just one bus or just one type of bus.

[0165] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0166] The readable storage medium may be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium may be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0167] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be an integral part of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0168] The division of units is merely a logical functional division; actual implementations may employ alternative divisions, such as combining or integrating multiple units or components into another system, or omitting or disabling certain features. Furthermore, any direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units, either through an interface, electrical, mechanical, or other means.

[0169] Units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0170] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0171] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion 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 for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0172] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0173] Finally, it should be noted that those skilled in the art will readily identify other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art not disclosed herein. The present invention is not limited to the precise structure described above and illustrated in the accompanying drawings, and various modifications and variations may be made without departing from the scope thereof. The scope of the present invention is limited solely by the appended claims.

Claims

1. A coordination optimization method, characterized in that: include: Based on the state of charge data and dead time of multiple types of energy storage devices, a pre-established economic dispatch model for multiple types of energy storage devices is processed to obtain an objective function and constraints for optimizing the power output strategy of the multiple types of energy storage devices; the dead time is the time when the state of charge of the energy storage device reaches a preset limit and cannot operate; the economic dispatch model for multiple types of energy storage devices is a total cost calculation model established in advance based on the operating status and operating life of the multiple types of energy storage devices; The objective function is solved using an improved particle swarm algorithm to determine the power output strategy of each energy storage device when the total cost of multiple types of energy storage devices is minimized; the improved particle swarm algorithm includes a population initialization strategy using a chaotic operator and an improved inertia weight function; The energy storage devices are coordinated and configured according to the power output strategies of the energy storage devices.

2. The method according to claim 1, characterized in that The constraints include: microgrid power balance constraints and energy storage system operating status constraints.

3. The method according to claim 1, characterized in that The method of solving the objective function by using an improved particle swarm algorithm includes: Initializing the population in the particle swarm algorithm using a chaos operator to obtain a first particle swarm; each particle in the first particle swarm represents a power output strategy of multiple types of energy storage devices; Calculating the fitness value of each particle in the first particle swarm according to the objective function; Iteratively updating each particle in the first particle swarm according to the fitness value to obtain a second particle swarm; According to the second particle swarm, a power output strategy for each energy storage device is determined when the total cost of multiple types of energy storage devices is minimized.

4. The method according to any one of claims 1 to 3, characterized in that The population initialization strategy of the chaotic operator includes: in, is the global optimal particle; is the chaotic search step, are the particles in the initial population.

5. The method according to any one of claims 1 to 3, characterized in that Iteratively updating each particle in the first particle swarm according to the fitness value to obtain a second particle swarm, including: The optimization ability of the first particle swarm is improved by improving the inertia weight function. The specific formula is as follows: Among them, ω max 、ω min are the maximum and minimum values of ω; a is the distribution coefficient; N is the total number of iterations, and k is the current number of iterations.

6. The method according to claim 1, characterized in that The objective function for optimizing the power output strategy of multiple types of energy storage devices is: in, is the investment cost coefficient of the battery; is the investment cost coefficient of supercapacitor; is the unit price of the battery; is the unit price of supercapacitor; is the initial capacity of the energy storage device; is the rated charge and discharge times of the energy storage device; is the remaining capacity of the battery during period t; is the actual charge and discharge times of the energy storage device.

7. The method according to claim 1 or 2, characterized in that The microgrid power balance constraints include: in, is the power value input to the grid at time t; is the output of the photovoltaic power station at time t; is the wind power output at time t; is the energy storage discharge power at time t; The energy storage charging time at time t; is the load power at time t; is the node voltage at time t; is conductivity; is the electrical admittance; is the phase angle; are the reactive outputs of wind power and photovoltaic power stations at time t; is the reactive value of the load at time t; The energy storage system operating state constraints include: Among them, E BESS (t) is the capacity of the battery at time t; and are the minimum and maximum charging powers allowed by the energy storage device at time t; and are the minimum and maximum discharge powers allowed by the energy storage device at time t respectively.

8. A coordinated optimization device, characterized in that: include: A processing module is configured to process a pre-established economic dispatch model for multiple types of energy storage devices based on the state of charge data and dead time of the multiple types of energy storage devices to obtain an objective function and constraints for optimizing the power output strategy of the multiple types of energy storage devices; the dead time is the time during which the state of charge of the energy storage device reaches a preset limit and cannot operate; the economic dispatch model for multiple types of energy storage devices is a total cost calculation model pre-established based on the operating status and operating life of the multiple types of energy storage devices; A determination module is used to solve the objective function using an improved particle swarm algorithm to determine the power output strategy of each energy storage device when the total cost of multiple types of energy storage devices is minimized; the improved particle swarm algorithm includes a population initialization strategy using a chaotic operator and an improved inertia weight function; The configuration module is used to coordinate the configuration of each energy storage device according to the power output strategy of each energy storage device.

9. An electronic device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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