TWI748650B - Build system, method and computer program product thereof by using particle swarm optimization (pso) method for capacity distribution of renewable energies and energy storage systems of a micro-grid - Google Patents

Abstract

A build system, method and computer program product thereof by using Particle Swarm Optimization (PSO) method for capacity distribution of renewable energies and energy storage systems of a micro-grid are provided. The build system comprise: a user interface, an environmental parameter module, and a particle swarm algorithm module. The user interface configured to allow a user to input a basic parameter and a historical data. The environmental parameter module configured to receive the basic parameter and the historical data and convert them into a particle swarm parameter. The particle swarm algorithm module configured to according the particle swarm parameter with a particle swarm iterative method. When the number of the particle swarm iterative method is match a set value, an optimal solution is obtained and displayed on the user interface. The renewable energies and energy storage system with a lowest generation cost or highest capacity distribution is therefore obtained.

Description

Construction system, method and computer program product of microgrid renewable energy and energy storage ratio based on particle swarm algorithm

The present invention relates to a micro-grid construction system, in particular to a micro-grid renewable energy and energy storage configuration construction system, method and computer program product based on particle swarm algorithm.

With the development and progress of science and technology, the phenomenon of global warming has become more and more serious. Therefore, all countries in the world are actively developing renewable energy, and the power generation of renewable energy is limited by the environment and climate, which has intermittent and uncertain problems. When the proportion of renewable energy in the region gradually increases, once the impact of the environment and climate is bound to affect the stability of the operation of the power system.

In order to increase the penetration rate of renewable energy and increase the safety of system operation, the development of microgrid technology has become a necessary task. However, the microgrid integrates distributed energy and loads in the region, and achieves the balance and stability of the regional system through the key technology of the microgrid, and can be disconnected from the external power system and operate independently if necessary. Therefore, the microgrid is regarded as As a means to increase the proportion of renewable energy in the region, countries are currently actively developing key microgrid technologies and establishing microgrid demonstration areas.

In the conventional method of determining the capacity of a microgrid device, the number of solar panels that can be installed is determined according to the user's maximum load usage and load usage habits, or according to the existing buildable area and system wire diameter capacity. The maximum power generation of solar panels and the maximum use of loads determine the capacity of the energy storage system or diesel generator. The disadvantage of this method is that when operating independently, considering the actual maximum load and the loading of the motor load, the capacity of the diesel generator will be built too large.

Therefore, how to take into account the conditions of economy and stability at the same time, and consider the characteristics of local load power consumption, and find the most suitable building capacity for various power generation devices, has become a problem to be solved in the industry.

The present invention provides a system, method and computer program product for building a microgrid renewable energy and energy storage ratio based on a particle swarm algorithm. The particle swarm algorithm is used to search and calculate in an iterative manner, and gradually converge to the best In order to improve the method of determining the device capacity of the microgrid, the situation that the device capacity of various generators is built too large.

In one embodiment, the present invention proposes a system for building a microgrid renewable energy and energy storage ratio based on a particle swarm algorithm, which includes: a user interface that provides a user to input a basic parameter and a historical data ; An environmental parameter module that receives the basic parameter and the historical data and converts it into a particle swarm parameter; and a particle swarm algorithm module that performs a particle swarm iterative operation based on the particle swarm parameter, when the particle swarm When the number of iterative operations meets a set value, a detailed parameter of the best solution is generated and displayed on the user interface.

In another embodiment, the present invention proposes a method for constructing a ratio of renewable energy to energy storage in a microgrid based on a particle swarm algorithm. The method includes the following steps: introducing a basic parameter and a historical data; transforming the basic parameter The parameter and the historical data are a particle swarm parameter; a particle swarm iterative operation is performed based on the particle swarm parameter and the historical data; and when the number of the particle swarm iterative operation matches a set value, a maximum Detailed parameters of the best solution are displayed on a user interface.

In another embodiment, the present invention proposes a computer program product, which uses a computer to read the computer program product and execute a method for constructing a microgrid renewable energy and energy storage ratio based on a particle swarm algorithm.

Based on the above, in the system, method, and computer program product of the microgrid renewable energy and energy storage ratio construction based on the particle swarm algorithm of the present invention, the collection of renewable energy power generation and load power consumption data is used to consider the energy storage battery , Energy storage converter capacity and standing generator sets and other constraints, the particle swarm algorithm can calculate the energy device capacity ratio of the microgrid, and then complete the construction plan with the lowest power generation cost or the highest proportion of renewable energy.

In order to make the present invention more obvious and understandable, the following specific examples are given in conjunction with the accompanying drawings to describe in detail as follows.

The specific implementation of the present invention will be further described below in conjunction with the accompanying drawings and embodiments. The following embodiments are only used to illustrate the technical solutions of the present invention more clearly, and cannot be used to limit the protection scope of the present invention.

Please refer to FIG. 1. FIG. 1 is a block diagram of a system for constructing a microgrid renewable energy and energy storage ratio of the present invention. The construction system 400 for the ratio of renewable energy and energy storage in the microgrid is mainly composed of a user interface 100, an environmental parameter module 200, and a particle swarm algorithm module 300. In fact, the construction system 400 of the microgrid renewable energy and energy storage ratio can be realized by a computer device with data calculation, storage, display, and human-machine interface functions.

The user interface 100 can provide the user to input at least one basic parameter and at least one historical data. Preferably, the user interface 100 is implemented by a graphical interface, so as to provide a concise and clear way for the user to operate, and to clearly present the calculation result generated by the particle swarm algorithm module 300 in a chart. The historical data may be the hourly sunshine value, sunshine hour value, wind speed value and/or load consumption value of the field where the microgrid is located.

The environmental parameter module 200 is connected to the user interface 100 for data transmission. The environmental parameter module 200 can receive the basic parameters and historical data, and convert them into particle swarm parameters. In fact, the environmental parameter module 200 can be implemented by compiling the received data into an Excel table file, and then directly reading the Excel table file through a computer program for calculation.

The particle swarm algorithm module 300 is respectively connected to the user interface 100 and the environmental parameter module 200 for data transmission. The particle swarm algorithm module 300 may be composed of a particle parameter initialization unit 310, a parameter calculation unit 320, a parameter comparison unit 330, and a particle swarm iterative operation unit 340. It should be noted that the particle swarm algorithm module 300 in the embodiment of the present invention is implemented by using a particle swarm optimization (PSO) algorithm.

In the calculation process of the particle swarm algorithm module 300, each individual particle can remember the best position it currently finds, which is called the particle best fitness value (pbest). In addition, each individual particle can remember the best position found by all particles in the group, which is called the best fitness value of the group (gbest). In fact, the particle swarm algorithm module 300 can be implemented by a procedural programming language, for example, C language or similar programming languages.

The particle parameter initialization unit 310 can perform random initialization operations between the upper and lower boundary values of a region on the particle swarm parameters after the environmental parameter module 200 has been converted. In other words, first let the particle swarm be randomly distributed in a specific range to obtain the initial value corresponding to each particle.

The parameter calculation unit 320 is connected to the particle parameter initialization unit 310 for data transmission. The parameter calculation unit 320 calculates and outputs an average unit power generation cost value according to the particle swarm parameters and historical data after random initialization by the particle parameter initialization unit 310.

In the embodiment of the present invention, the particle swarm parameters may be, for example, a stroke of particle position value, a stroke of particle velocity value, a stroke of position parameter upper limit value, a stroke of position parameter limit lower limit, a stroke of velocity parameter upper limit value, Data such as the best historical position value of a number of parameters and the best historical position value of a group of parameters. The types of the particle swarm parameters can correspond to a group of solar panels, a group of fans, a group of energy storage converters, and a group of energy storage batteries. The particle position value may correspond to a set of solar panel building capacity values, a set of wind turbine building capacity values, a set of energy storage battery building capacity values, and a set of energy storage converter building capacity values. The particle velocity value may correspond to a set of solar panel building capacity changes, a set of wind turbine building capacity changes, a set of energy storage battery build capacity changes, and a set of energy storage converter build capacity changes. .

The parameter comparison unit 330 is connected to the parameter calculation unit 320 for data transmission. The parameter comparison unit 330 is used to sequentially compare each average unit power generation cost value, so as to generate a minimum average unit power generation cost value after the current comparison operation.

The particle swarm iterative computing unit 340 is connected to the user interface 100, the environmental parameter module 200, the parameter calculation unit 320, and the parameter comparison unit 330 for data transmission, respectively. The particle swarm iterative operation unit 340 successively performs the particle swarm iterative operation according to the minimum value generated by the parameter comparison unit 330, and generates the best solution detailed parameter when the number of the particle swarm iterative operation meets a set value. The detailed parameter of the optimal solution may be a unit power generation cost value and/or a renewable energy percentage value. It should be noted that the set value of the number of times can be a system default value or determined by user input. Preferably, the particle swarm iterative computing unit 340 displays the detailed parameters of the optimal solution on the user interface 100 in graphs, patterns, charts, and/or numbers.

For example, if the number of particles is set to n, the parameter calculation unit 320 will search for n different ratios during calculation, and obtain n power generation cost calculation results, and then the parameter comparison unit 330 will search for the calculation results. The lowest power generation cost and its corresponding capacity ratio will be calculated and used as a reference for the next adjustment of the ratio.

Please refer to FIG. 1 and FIG. 2 together. FIG. 2 is a flowchart of a method for constructing a microgrid renewable energy and energy storage ratio according to an embodiment of the present invention. In addition, the computer program product of the embodiment of the present invention refers to recording computer-readable program instructions on a computer-readable storage medium (for example, optical disc, random access memory, read-only memory, flash memory, Hard disk or cloud server, etc.), and the computer-readable program instructions can be used to enable computer equipment to implement functions such as a method for constructing a micro-grid renewable energy and energy storage ratio.

First, in step S410, a basic parameter and a historical data are introduced from the user interface 100. In fact, the user interface 100 can be realized by a man-machine interface with graphical, touch and/or non-touch operation functions.

Next, proceed to step S420, where the environmental parameter module 200 converts the basic parameters and historical data into particle swarm parameters.

In step S430, the particle swarm algorithm module 300 performs a particle swarm iterative calculation based on the particle swarm parameters and historical data converted by the environmental parameter module 200. The particle swarm algorithm module 300 can change the particle position value (for example, solar panel building capacity value, wind turbine building capacity value, energy storage battery building capacity value and energy storage converter building capacity value) to be regarded as An objective function, and calculate the minimum value of the current average unit power generation cost value. In addition, if the currently calculated average unit power generation cost value is less than the result of the previous calculation, it is confirmed that it tends to the minimum value of the average unit power generation cost value.

In step S440, the particle swarm algorithm module 300 confirms whether the number of particle swarm iteration operations meets a set number of times? The set number of times can be a system default value or determined by user input.

When the number of iterative operations in the particle swarm meets the set value, step S450 is performed. When the number of iterative operations in the particle swarm has not reached the set value, return to step S430. For example, in each iteration of the particle swarm algorithm module 300, the movement of the particle is affected by the particle's best fitness value (pbest) that it has searched so far, and other particles searched so far. Therefore, the best fitness value (gbest) of the group is the best fitness value (nbest) in this iteration result.

In step S450, the particle swarm algorithm module 300 generates a detailed parameter of the best solution, and displays it on the user interface 100 to provide the user with an evaluation of the construction solution with the lowest power generation cost or the highest proportion of renewable energy.

The following example illustrates the actual operation of the particle swarm algorithm module 300. After the first calculation of five randomly distributed particles is completed, the iterative operation will start. The 4 variables in the particles (corresponding to the solar panel building capacity value, wind turbine building capacity value, energy storage battery building capacity value and energy storage converter building capacity value) will refer to themselves during the search and solution process The best experience of the group and the best experience of the group are used to adjust the search direction of each variable until after multiple iterations (for example, 5) are completed, the calculation result of each time will be obtained.

Then, the particle swarm algorithm module 300 will automatically find the capacity of the renewable energy device at the lowest power generation cost from the calculation results, and find the highest proportion of renewable energy in all the results as a reference, so that the user can understand Which type of renewable energy setting in this field is more economical, and under what configuration can get the highest proportion of renewable energy power generation.

In summary, in the construction system of the microgrid renewable energy and energy storage ratio based on the particle swarm algorithm of the present invention, all particles are used to search for the best fitness value within a specific range, and in the search process The best position in the reference group and its own historical experience, adjust the speed and direction of the flight in the next iteration, and repeat the iteration until the conditions for the final end of the calculation are met, so as to produce the best solution detailed parameters, and then provide The construction plan with the lowest power generation cost or the highest proportion of renewable energy.

Although the present invention has been disclosed in the above embodiments, it is not intended to limit the present invention. Anyone with ordinary knowledge in the relevant technical field can make some changes and modifications without departing from the spirit and scope of the present invention. The scope of protection of the present invention shall be subject to those defined by the attached patent scope.

100: User interface

200: Environmental parameter module

300: Particle Swarm Algorithm Module

310: Particle parameter initialization unit

320: Parameter calculation unit

330: Parameter comparison unit

340: Particle Swarm Iterative Operation Unit

400: Construction system of microgrid renewable energy and energy storage ratio

S410~S450: steps

Fig. 1 is a block diagram of the system for constructing the ratio of renewable energy and energy storage in the microgrid of the present invention; and

Figure 2 is a flow chart of the method for constructing the ratio of renewable energy and energy storage in the microgrid of the present invention.

100: User interface

200: Environmental parameter module

300: Particle Swarm Algorithm Module

310: Particle parameter initialization unit

320: Parameter calculation unit

330: Parameter comparison unit

340: Particle Swarm Iterative Operation Unit

400: Construction system of microgrid renewable energy and energy storage ratio

Claims (11)

A construction system of microgrid renewable energy and energy storage ratio based on particle swarm algorithm, including: a user interface, providing a user to input a basic parameter and a historical data; an environmental parameter module to receive the basic The parameter and the historical data are converted into a particle swarm parameter; and a particle swarm algorithm module, according to the particle swarm parameter, performs a particle swarm iteration operation, when the number of the particle swarm iteration operation meets a set value At the time, a detailed parameter of the optimal solution is generated and displayed on the user interface. The particle swarm algorithm module includes: a particle parameter initialization unit for the upper and lower boundary values of the particle swarm parameter in a region Perform a random initialization operation; a parameter calculation unit, based on the particle swarm parameters after the random initialization operation and the historical data, calculate and output an average unit power generation cost value; a parameter comparison unit for successively comparing the average unit Generate a minimum value for the power generation cost; and a particle swarm iterative arithmetic unit that successively performs the particle swarm iterative operation according to the minimum value, and when the number of particle swarm iterative operations meets the set value, Output the detailed parameters of the best solution. The construction system of the microgrid renewable energy and energy storage ratio based on the particle swarm algorithm as described in claim 1, wherein the particle swarm parameter includes a particle position value, a particle velocity value, and a position parameter upper limit value , A position parameter limit lower limit value, a speed parameter upper limit value, a number parameter best historical position value and a group parameter best historical position value. The construction system of the microgrid renewable energy and energy storage ratio based on the particle swarm algorithm described in claim 2, wherein the particle position value includes a solar panel construction capacity value, a wind turbine construction capacity value, and a storage capacity value. The built-in capacity value of the energy battery and the built-in capacity value of an energy storage converter. The construction system of microgrid renewable energy and energy storage ratio based on the particle swarm algorithm as described in claim 3, wherein the particle swarm algorithm module changes the solar panel construction capacity value and the wind turbine construction The capacity value, the built-up capacity value of the energy storage battery, and the built-up capacity value of the energy storage converter are used to calculate the minimum value of the average unit power generation cost value and use it as an objective function. The construction system of the microgrid renewable energy and energy storage ratio based on the particle swarm algorithm described in claim 2, wherein the particle velocity value includes a solar panel construction capacity change, a wind turbine construction capacity change, An energy storage battery build capacity change amount and an energy storage converter build capacity change amount. The construction system of microgrid renewable energy and energy storage ratio based on the particle swarm algorithm described in claim 1, wherein the basic parameters include a renewable energy construction cost value, an energy storage energy construction cost value, and a The value of generator construction cost. The establishment system of the microgrid renewable energy and energy storage ratio based on the particle swarm algorithm as described in claim 1, wherein the historical data includes the hourly sunshine value, sunshine hour value, Wind speed value and/or load consumption value. The construction system of microgrid renewable energy and energy storage ratio based on the particle swarm algorithm described in claim 1, wherein the types of the particle swarm parameters include a solar panel, a wind turbine, an energy storage converter and a storage Can battery. As described in claim 1, the microgrid based on particle swarm algorithm construction system of renewable energy and energy storage ratio, wherein the detailed parameters of the optimal solution include a unit power generation cost value and a renewable energy ratio value. A method for constructing the ratio of renewable energy to energy storage in a microgrid based on particle swarm algorithm. The method includes the following steps: Introduce a basic parameter and a historical data; transform the basic parameter and the historical data into a particle swarm parameter; perform a random initialization operation on the particle swarm parameter between the upper and lower boundary values of a region; The particle swarm parameters and the historical data are used to calculate the output-average unit power generation cost value; the average unit power generation cost value is successively compared to produce a minimum value; a particle swarm iterative operation is successively performed according to the minimum value; and when the When the number of particle swarm iteration operations meets a set value, a detailed parameter of the best solution is generated and displayed on a user interface. A computer program product uses a computer to read the computer program product and execute the method for constructing the ratio of renewable energy and energy storage of a microgrid based on a particle swarm algorithm as described in claim 10.

Images