CN117236152A - Twin simulation method and system for new energy power grid - Google Patents
Twin simulation method and system for new energy power grid Download PDFInfo
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Abstract
The invention provides a twin simulation method and a system of a new energy power grid, which relate to the technical field of power grid simulation and comprise the following steps: acquiring photovoltaic cell parameters, determining a circuit characteristic equation according to the photovoltaic cell parameters and combining a photovoltaic cell model, selecting a topological structure according to the circuit characteristic equation, and combining circuit elements and circuit element states to generate a photovoltaic energy model; acquiring power grid state information, generating a power grid model according to the power grid state information by combining a photovoltaic energy model, embedding the power grid model into a pre-generated three-dimensional virtual model, and combining a virtual reality platform to obtain a power simulation model; according to the electric power simulation model, generating initial particles and random particles at random, calculating the fitness of the initial particles and updating the position information of the initial particles to obtain secondary particles, determining the optimal solution of the electric power simulation model according to the secondary particles and the random particles, marking the optimal solution as the optimal particles, and dynamically updating the electric power simulation model according to the optimal particles to obtain a twin simulation result.
Description
Technical Field
The invention relates to the technical field of power grid simulation, in particular to a twin simulation method and system of a new energy power grid.
Background
Along with the rapid increase of the installed capacity of the new energy of the power grid and the continuous increase of the grid-connected power generation of the new energy, the proportion of the power generation output of the new energy to the total load of the power grid is also continuously increased. Because of the uncertainty characteristic of the new energy power generation output, whether the traditional automatic power generation control system can cope with the increase of the new energy output and the expansion of the fluctuation becomes an important problem affecting the safe and stable operation of the power grid.
In the prior art, CN115563873a discloses a digital twin simulation system and method for a power network, wherein the system comprises: the sensing module is used for collecting real-time monitoring data of physical entities in the whole domain of the power grid; the modeling module is used for constructing a digital twin model according to real-time monitoring data by combining model driving modeling and data driving modeling; the simulation module is used for inputting the real-time monitoring data and the historical monitoring data into the digital twin model, and performing simulation analysis on the real-time monitoring data and the historical monitoring data by adopting a machine learning algorithm to obtain predicted running state data of the physical entity; the regulation and control module is used for comparing the predicted operation state data of the physical entity with the preset standard operation state data, generating an operation control strategy of the power network according to the comparison result, and carrying out operation control management on the power network by the operation control strategy.
CN115657493a provides a digital twin simulation test system for a power grid dispatching platform, which is constructed based on a full-service simulation test platform and a network security test verification platform, wherein the full-service simulation test platform is used for twin out one or more sets of power grid dispatching control test systems and power spot market test systems which are consistent with the power grid dispatching control systems and the power spot market systems on the premise of not influencing the operation of the power grid dispatching systems; the network security test verification platform is used for twining one or more sets of network security test systems of the power monitoring system, which are consistent with the power monitoring system, on the premise of not affecting the operation of the power grid dispatching system.
In summary, although the twin simulation of the power grid can be realized in the prior art, the corresponding design is not made for the new energy system, so that a scheme is needed to perform the twin simulation on the new energy power grid according to the actual situation of the existing new energy power grid.
Disclosure of Invention
The embodiment of the invention provides a twin simulation method and a twin simulation system for a new energy power grid, which are used for realizing twin simulation of the new energy power grid so as to evaluate the actual performance of the new energy power grid.
In a first aspect of the embodiment of the present invention, a twin simulation method for a new energy power grid is provided, including:
acquiring photovoltaic cell parameters, determining a circuit characteristic equation according to the photovoltaic cell parameters and a preset photovoltaic cell model, selecting a topological structure according to the circuit characteristic equation, and generating a photovoltaic energy model according to the topological structure and the states of a circuit element and the circuit element;
acquiring power grid state information, generating a power grid model by combining the photovoltaic energy model according to the power grid state information, embedding the power grid model into a pre-generated three-dimensional virtual model, and combining a virtual reality platform to obtain a power simulation model;
according to the electric power simulation model, initial particles and random particles are randomly generated, according to the initial particles, a preset model optimization algorithm is combined, fitness of the initial particles is calculated, position information of the initial particles is updated, secondary particles are obtained, according to the secondary particles and the random particles, an optimal solution of the electric power simulation model is determined through the model optimization algorithm and is recorded as optimized particles, the electric power simulation model is dynamically updated according to the optimized particles, and a twin simulation result is obtained, wherein the model optimization algorithm is constructed based on a simulated annealing algorithm.
In an alternative embodiment of the present invention,
obtaining photovoltaic cell parameters, determining a circuit characteristic equation according to the photovoltaic cell parameters and a preset photovoltaic cell model, selecting a topological structure according to the circuit characteristic equation, and generating a photovoltaic energy model according to the topological structure and the states of a circuit element and the circuit element, wherein the generating the photovoltaic energy model comprises:
acquiring voltage, current and power of a photovoltaic cell, recording the voltage, current and power as photovoltaic cell parameters, adding the photovoltaic cell parameters into the preset photovoltaic cell model, and establishing a circuit characteristic equation of the photovoltaic cell by considering the influence of environmental factors on the photovoltaic cell parameters;
according to the circuit characteristic equation, a topological structure is selected by considering the maximum current and the maximum voltage in the circuit, and according to the topological structure, a photovoltaic energy model is established by combining the circuit elements contained in the circuit and the states of the circuit elements, namely the parameters and the performances of the circuit elements and the switching states.
In an alternative embodiment of the present invention,
the photovoltaic cell parameters are added into the preset photovoltaic cell model, the influence of environmental factors on the photovoltaic cell parameters is considered, and a circuit characteristic equation of the photovoltaic cell is established as shown in the following formula:
；
wherein,Irepresenting the output current of the photovoltaic cell,I ph the photo-generated current is represented by the formula,I 0 representing the saturation current of the current and,qrepresenting the charge of the element(s),Vis the output voltage of the photovoltaic cell,R s representing the series resistance of the resistor in series,nthe ideal factor is represented by a factor of interest,krepresenting the boltzmann constant of the sample,R sh representing the parallel resistance of the resistor,Tthe temperature is indicated as a function of the temperature,I 2 representing voltageVA lower current.
In an alternative embodiment of the present invention,
generating a power grid model according to the power grid state information by combining the photovoltaic energy model, embedding the power grid model into a pre-generated three-dimensional virtual model, and combining a virtual reality platform to obtain a power simulation model, wherein the power simulation model comprises the following steps of:
acquiring stability information and grid line information of a grid system, recording the stability information and the grid line information as grid state information, and calculating to obtain a grid model by combining the photovoltaic energy model according to the grid state information and considering environmental factors and interference generated during equipment operation, wherein the grid line information comprises a generator, a transformer substation, a power distribution network and loss generated by a connecting line in a grid;
embedding parameters and equations contained in the power grid model into a three-dimensional virtual model which is built in advance through three-dimensional modeling software to obtain an initial three-dimensional model, and applying a calculation result of the initial three-dimensional model to the virtual reality platform through a program interface to obtain the power simulation model.
In an alternative embodiment of the present invention,
the stability information of the power grid system is shown in the following formula:
；
wherein,CSIthe integrated stability index is indicated as such,w 1 representing the dynamic stability weight of the object,nthe number of discrete time steps is indicated,p i representation ofiThe frequency stability of the time of day,p 0 a frequency stability reference is indicated and a frequency stability reference is indicated,w 2 representing the weight of the transient stability,mrepresenting the total number of transient events,jrepresent the firstjA number of transient events are carried out,z j representing the stability of the transient state,z 0 representing a transient stability reference.
In an alternative embodiment of the present invention,
according to the power simulation model, generating initial particles and random particles at random, according to the initial particles, combining a preset model optimization algorithm, calculating fitness of the initial particles and updating position information of the initial particles to obtain secondary particles, according to the secondary particles and the random particles, determining an optimal solution of the power simulation model through the model optimization algorithm, marking the optimal solution as optimized particles, and dynamically updating the power simulation model according to the optimized particles, wherein the obtaining a twin simulation result comprises:
randomly generating initial particles and random particles, wherein each particle represents a solution of an objective function, and the objective solution of the objective function is to minimize the power generation cost and maximize the energy utilization rate at the same time;
according to the initial particles, calculating fitness values of the initial particles, for each initial particle, if the fitness value at the current moment is larger than the fitness value at the previous moment, traversing the current individual optimal position of the initial particle by taking the current position as an individual optimal position, taking the individual optimal position of the initial particle with the highest fitness value as a global optimal position, updating the position information of the initial particle according to the individual optimal position and the global optimal position and a pre-introduced learning factor to obtain primary particles, calculating a distance difference value between the primary particles and the global optimal position, and taking the particle with the smallest distance difference value as the secondary particle;
according to the secondary particles and the random particles, the random particles and the secondary particles are used as solutions of the objective function, a random function value corresponding to the random particles and a secondary function value corresponding to the secondary particles are calculated, the probability of being accepted of the random function value is calculated according to the random function value and the secondary function value, if the probability of being accepted of the random function value is larger than 0.5, the random particles are used as the optimal solutions of the electric power simulation model, otherwise, the secondary particles are used as the optimal solutions of the electric power simulation model and are marked as optimized particles;
and optimizing the electric power simulation model according to the optimized particles, and simulating by using the updated electric power simulation model to obtain a twin simulation result.
In an alternative embodiment of the present invention,
and calculating the accepted probability of the random function value according to the random function value and the secondary function value, wherein the accepted probability is shown in the following formula:
wherein,μrepresenting the probability of being accepted for the random function value,x 2 the value of the secondary function is represented,x 0 represents the value of the random function,krepresenting the boltzmann constant,Tindicating temperature.
In a second aspect of the embodiment of the present invention, a twin simulation system for a new energy grid is provided, including:
the first unit is used for acquiring photovoltaic cell parameters, determining a circuit characteristic equation according to the photovoltaic cell parameters and a preset photovoltaic cell model, selecting a topological structure according to the circuit characteristic equation, and generating a photovoltaic energy model according to the topological structure and the states of a circuit element and the circuit element;
the second unit is used for acquiring power grid state information, generating a power grid model by combining the photovoltaic energy model according to the power grid state information, embedding the power grid model into a pre-generated three-dimensional virtual model, and combining a virtual reality platform to obtain a power simulation model;
and the third unit is used for randomly generating initial particles and random particles according to the electric power simulation model, calculating the fitness of the initial particles and updating the position information of the initial particles according to the initial particles by combining a preset model optimization algorithm to obtain secondary particles, determining the optimal solution of the electric power simulation model according to the secondary particles and the random particles by using the model optimization algorithm, recording the optimal solution as optimized particles, dynamically updating the electric power simulation model according to the optimized particles to obtain a twin simulation result, wherein the model optimization algorithm is constructed based on a simulated annealing algorithm.
In a third aspect of an embodiment of the present invention,
there is provided an electronic device including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to invoke the instructions stored in the memory to perform the method described previously.
In a fourth aspect of an embodiment of the present invention,
there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method as described above.
According to the invention, through acquiring actual photovoltaic cell parameters and combining with the photovoltaic cell model, the electrical characteristics of the photovoltaic cell can be accurately simulated, the photovoltaic cell behavior under different conditions can be better understood and predicted, key information is provided for planning and management of a new energy power grid, the power grid model is embedded into a pre-generated three-dimensional virtual model, a visual mode is provided for understanding the complexity of the power system and the operation under different operation conditions, and a twin simulation result is obtained by considering actual power grid state information and an optimized power simulation model, which means that the virtual simulation result is closer to the performance and response of the actual power system, and the planning and management of the power system can be better facilitated.
Drawings
FIG. 1 is a flow chart of a twin simulation method of a new energy grid according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a twin simulation system of a new energy grid according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The technical scheme of the invention is described in detail below by specific examples. The following embodiments may be combined with each other, and some embodiments may not be repeated for the same or similar concepts or processes.
Fig. 1 is a flow chart of a twin simulation method of a new energy power grid according to an embodiment of the present invention, as shown in fig. 1, the method includes:
s1, acquiring photovoltaic cell parameters, determining a circuit characteristic equation according to the photovoltaic cell parameters and a preset photovoltaic cell model, selecting a topological structure according to the circuit characteristic equation, and generating a photovoltaic energy model according to the topological structure and the states of circuit elements and circuit elements;
the photovoltaic cell is a semiconductor device for converting sunlight into electric energy, the photovoltaic cell parameters are specifically voltage, current and power, the photovoltaic cell parameters comprise open-circuit voltage, short-circuit current, maximum power voltage, maximum power current, energy conversion efficiency and the like, the photovoltaic cell parameters in the scheme are rated output voltage, rated input current and maximum power of the photovoltaic cell, the circuit characteristic equation is a mathematical equation describing circuit behaviors and is constructed based on kirchhoff's law and ohm's law and is used for analyzing the relation among voltage, current and resistance in a circuit, the topological structure of the circuit refers to the connection mode and arrangement mode among elements in the circuit, the circuit elements are basic components forming the circuit and are simply understood to be power sources and electric appliances, and the state of the circuit elements refers to the voltage and current values of the circuit elements at specific moments.
In an alternative embodiment of the present invention,
obtaining photovoltaic cell parameters, determining a circuit characteristic equation according to the photovoltaic cell parameters and a preset photovoltaic cell model, selecting a topological structure according to the circuit characteristic equation, and generating a photovoltaic energy model according to the topological structure and the states of a circuit element and the circuit element, wherein the generating of the photovoltaic energy model comprises:
acquiring voltage, current and power of a photovoltaic cell, recording the voltage, current and power as photovoltaic cell parameters, adding the photovoltaic cell parameters into the preset photovoltaic cell model, and establishing a circuit characteristic equation of the photovoltaic cell by considering the influence of environmental factors on the photovoltaic cell parameters;
according to the circuit characteristic equation, a topological structure is selected by considering the maximum current and the maximum voltage in the circuit, and according to the topological structure, a photovoltaic energy model is established by combining the circuit elements contained in the circuit and the states of the circuit elements, namely the parameters and the performances of the circuit elements and the switching states.
The photovoltaic energy model is a mathematical model that is used to describe and predict the performance and behavior of photovoltaic cells or solar energy systems.
Collecting voltage-current characteristic data of a photovoltaic cell, including current-voltage curves under different illumination intensities, collecting environmental factor data such as solar radiation, temperature, shadow and the like, establishing a circuit model of the photovoltaic cell by using an ideal diode model according to the collected current-voltage curve data, combining the model of the photovoltaic cell with other elements in the circuit, namely combining a photovoltaic cell model equation and characteristic equations of other elements, and establishing a characteristic equation of the whole photovoltaic circuit;
the maximum current and the maximum voltage of the photovoltaic cell under different conditions are determined through the constructed circuit characteristic equation, an appropriate circuit topological structure is selected according to actual demands and constraint conditions in the circuit, the characteristic equation of the photovoltaic cell and other circuit elements is integrated into the whole photovoltaic energy system according to the selected topological structure, the states of the circuit elements including parameters, performance and switching states of the elements are considered to simulate the operation of the photovoltaic energy system, a photovoltaic energy model is obtained, and the photovoltaic energy system is simulated and verified through a circuit simulation tool such as SPICE by utilizing the established photovoltaic energy model, so that the photovoltaic energy model is ensured to be in a normal working state.
In this embodiment, an accurate model based on actual measured values can be created by acquiring voltage, current and power data of an actual photovoltaic cell, a more real and reliable model is provided, the behavior of the photovoltaic cell can be better reflected, a circuit characteristic equation is established to allow the performance of the photovoltaic cell to be evaluated under different circuit configurations, the optimal working point is facilitated to be selected, the voltage and current are prevented from exceeding a safety range, the reliability and efficiency of the circuit are ensured, a proper circuit topological structure is selected according to the circuit characteristic equation and requirements, the circuit is allowed to be designed according to specific application requirements, and the energy of the photovoltaic cell can be ensured to be utilized to the greatest extent.
In an alternative embodiment, the adding the photovoltaic cell parameters to the preset photovoltaic cell model, taking into consideration the influence of environmental factors on the photovoltaic cell parameters, and establishing a circuit characteristic equation of the photovoltaic cell as shown in the following formula:
；
wherein,Irepresenting the output current of the photovoltaic cell,I ph the photo-generated current is represented by the formula,I 0 representing the saturation current of the current and,qrepresenting the charge of the element(s),Vis the output voltage of the photovoltaic cell,R s representing the series resistance of the resistor in series,nthe ideal factor is represented by a factor of interest,krepresenting the boltzmann constant of the sample,R sh representing the parallel resistance of the resistor,Tthe temperature is indicated as a function of the temperature,I 2 representing voltageVA lower current.
The photo-generated current refers to that when light irradiates a semiconductor material, photon energy excites electron-hole pairs in the semiconductor, so that current flows, the magnitude of the photo-generated current depends on factors such as light intensity, absorption rate of the material, service life of the electron-hole pairs of the semiconductor, and the like, higher light intensity and higher absorption rate generally lead to larger photo-generated current, the ideal factor is one of parameters for describing efficiency and performance of the solar cell, non-ideal performance of the solar cell, namely performance difference between the solar cell and an ideal photocell is measured, and the Boltzmann constant is one physical constant in natural science, and is generally used for describing thermal motion and energy distribution of electrons in semiconductor physics, particularly in Fermi-Dirac distribution, so as to understand the behavior of electrons in the semiconductor material.
In the function, the influence of temperature on the parameters of the photovoltaic cell can be considered to simulate the performance of the cell under different temperature conditions, each parameter (such as photo-generated current, saturated current, series resistance, ideal factor and the like) in the equation is adjustable, the parameterization is allowed according to specific model of the photovoltaic cell and environmental conditions, the photovoltaic system can be better adapted to different cells and application scenes, the photovoltaic system can be better designed by knowing the characteristics of the cells, the proper inverter, series resistance, parallel resistance and other elements are selected to maximize energy collection and improve system efficiency, and in sum, the function can more accurately and adjustably simulate and predict the performance of the photovoltaic cell and consider the influence of environmental factors, so that the solar cell system can be better designed and optimized.
S2, acquiring power grid state information, combining the photovoltaic energy model according to the power grid state information to generate a power grid model, embedding the power grid model into a pre-generated three-dimensional virtual model, and combining a virtual reality platform to obtain a power simulation model;
the grid model is a mathematical and graphical representation describing the power system, including power production, transmission, distribution, and consumption. It may be a static or dynamic model for analyzing the operation, reliability and safety of the power grid, the three-dimensional virtual model is a tool for visualizing the power system, and is usually based on Computer Aided Design (CAD) technology, which presents elements such as power equipment, transmission lines, substations, etc. in three dimensions in order to better understand the structure and layout of the power grid, and the virtual reality platform combines the power system model with virtual reality technology, so that a user can explore the power system in an interactive manner. A user may interact with the power system through virtual reality head mounted display (VR), a handle, etc., and the power simulation model is a tool for simulating the operation of the power system, and is used for analyzing dynamic behaviors of the power system, including load change, fault recovery, stability, and voltage regulation, in consideration of various factors in the power system, such as load, power generation, power transmission, and control strategies.
In an alternative embodiment of the present invention,
generating a power grid model according to the power grid state information by combining the photovoltaic energy model, embedding the power grid model into a pre-generated three-dimensional virtual model, and combining a virtual reality platform to obtain a power simulation model, wherein the power simulation model comprises the following steps of:
acquiring stability information and grid line information of a grid system, recording the stability information and the grid line information as grid state information, and calculating to obtain a grid model by combining the photovoltaic energy model according to the grid state information and considering environmental factors and interference generated during equipment operation, wherein the grid line information comprises a generator, a transformer substation, a power distribution network and loss generated by a connecting line in a grid;
embedding parameters and equations contained in the power grid model into a three-dimensional virtual model which is built in advance through three-dimensional modeling software to obtain an initial three-dimensional model, and applying a calculation result of the initial three-dimensional model to the virtual reality platform through a program interface to obtain the power simulation model.
Collecting power grid line information, including actual running states and performance data of each generator, transformer substation and power distribution network, acquiring power grid line information, including parameters such as line topology, line resistance and reactance, obtaining power grid state information, using the collected power grid state information, taking actual running conditions of equipment into consideration, including environmental factors (such as temperature and humidity), equipment working conditions and possible interference, creating a state model of the power grid, using the power grid line information, taking electrical characteristics, line loss, transmission capacity and stability of the lines, loss, resistance and reactance between the generator, the transformer substation, the power distribution network and a connecting line into consideration, creating a line model of the power grid, and integrating a photovoltaic energy model into the power grid model by combining the performance characteristics and output model of photovoltaic cells with the power grid state information and the line information, thereby obtaining the power grid model.
Illustratively, the mathematical model corresponding to the generator is shown in the following formula,wherein, the method comprises the steps of, wherein,P gen representing the output power of the generator,P m indicating the maximum power that can be generated,η gen indicating the efficiency of the generator,Tthe temperature is indicated as a function of the temperature,Gthe intensity of the light is indicated and,f()representing the input of a function, the mathematical model corresponding to the transformer substation is shown in the following formula,whereinP out Which represents the output power of the device,V out the output voltage is represented by a voltage value,I out which represents the output current of the current transformer,I in representing the input current flow and the current level,Tthe temperature is indicated as a function of the temperature,f tran s()representing transmission characteristics, wherein the mathematical model corresponding to the distribution network is shown in the following formula>WhereinP loss Indicating the loss of the line and,Irepresenting the input current flow and the current level,Rthe resistance value of the unit line is represented,Lthe length of the line is indicated and,Tthe temperature is indicated as a function of the temperature,f tem ()the temperature characteristics of the wire are shown.
Creating a three-dimensional virtual model of a photovoltaic energy system and a power grid by using three-dimensional modeling software, modeling system components such as a photovoltaic cell, an inverter and a power transmission line as three-dimensional objects, embedding the power grid model into a pre-built three-dimensional virtual model in the three-dimensional modeling software to obtain an initial three-dimensional model, integrating the initial three-dimensional model into reality through VR equipment or an AR application program, and realizing simulation of the photovoltaic energy system and the power grid, wherein the final model is an electric power simulation model.
In this embodiment, by considering the environmental factors and the interference of the equipment during working, the power grid model is closer to the actual running situation, so that the twin simulation can more accurately simulate the behavior of the power grid, including different weather conditions and equipment faults, the power grid model is embedded into the virtual reality platform, so that the user can explore the power grid in an interactive manner, the user participation is increased, the running and performance of the power grid can be better understood, real-time simulation and optimization are allowed, and therefore, the user can test different operation strategies in the virtual environment, observe how the performance of the power grid is affected, and the real-time operation and optimization of the power grid are facilitated.
In an alternative embodiment, the stability information of the grid system is represented by the following formula:
；
wherein,CSIthe integrated stability index is indicated as such,w 1 representing the dynamic stability weight of the object,nthe number of discrete time steps is indicated,p i representation ofiThe frequency stability of the time of day,p 0 a frequency stability reference is indicated and a frequency stability reference is indicated,w 2 representing the weight of the transient stability,mrepresenting the total number of transient events,jrepresent the firstjA number of transient events are carried out,z j representing the stability of the transient state,z 0 representing a transient stability reference.
In the function, the comprehensive stability index is a parameter for evaluating the overall stability of the power grid system. It takes into account two main aspects of stability, namely frequency stability, which represents the number of discrete time steps divided in a certain period of time, and transient stability, which is typically used to evaluate the balance between the instantaneous load and the supply of the grid system, for calculating the mean value of the frequency stability, which includes sudden load changes or equipment malfunctions in the power system.
The function helps to evaluate the overall stability of the power grid by integrating the frequency stability and the transient stability of the power grid system, is beneficial to monitoring the performance of the power grid by a power grid operator, timely identifies problems, and takes measures to improve the stability and the reliability of the power grid.
S3, according to the electric power simulation model, generating initial particles and random particles at random, according to the initial particles, combining a preset model optimization algorithm, calculating fitness of the initial particles, updating position information of the initial particles to obtain secondary particles, according to the secondary particles and the random particles, determining an optimal solution of the electric power simulation model through the model optimization algorithm, marking the optimal solution as optimized particles, and dynamically updating the electric power simulation model according to the optimized particles to obtain a twin simulation result, wherein the model optimization algorithm is constructed based on a simulated annealing algorithm.
The model optimization algorithm is a technology for improving the performance of a mathematical model or a machine learning model, and is used for adjusting parameters, feature selection, super-parameter adjustment and the like of the model so as to enable the model to be more accurate and efficient.
In an alternative embodiment of the present invention,
according to the power simulation model, generating initial particles and random particles at random, according to the initial particles, combining a preset model optimization algorithm, calculating fitness of the initial particles and updating position information of the initial particles to obtain secondary particles, according to the secondary particles and the random particles, determining an optimal solution of the power simulation model through the model optimization algorithm, marking the optimal solution as optimized particles, and dynamically updating the power simulation model according to the optimized particles, wherein the obtaining a twin simulation result comprises:
randomly generating initial particles and random particles, wherein each particle represents a solution of an objective function, and the objective solution of the objective function is to minimize the power generation cost and maximize the energy utilization rate at the same time;
according to the initial particles, calculating fitness values of the initial particles, for each initial particle, if the fitness value at the current moment is larger than the fitness value at the previous moment, traversing the current individual optimal position of the initial particle by taking the current position as an individual optimal position, taking the individual optimal position of the initial particle with the highest fitness value as a global optimal position, updating the position information of the initial particle according to the individual optimal position and the global optimal position and a pre-introduced learning factor to obtain primary particles, calculating a distance difference value between the primary particles and the global optimal position, and taking the particle with the smallest distance difference value as the secondary particle;
according to the secondary particles and the random particles, the random particles and the secondary particles are used as solutions of the objective function, a random function value corresponding to the random particles and a secondary function value corresponding to the secondary particles are calculated, the probability of being accepted of the random function value is calculated according to the random function value and the secondary function value, if the probability of being accepted of the random function value is larger than 0.5, the random particles are used as the optimal solutions of the electric power simulation model, otherwise, the secondary particles are used as the optimal solutions of the electric power simulation model and are marked as optimized particles;
and optimizing the electric power simulation model according to the optimized particles, and simulating by using the updated electric power simulation model to obtain a twin simulation result.
Randomly generating a group of initial particles and a group of random particles, wherein each particle represents a solution of an objective function, the solutions comprise power generation cost and energy utilization rate variables, for each initial particle, calculating an adaptability value of the initial particle, the adaptability value is a value of the objective function, the objective function is to minimize the power generation cost and maximize the energy utilization rate, for each initial particle, if the adaptability value at the current moment is larger than the adaptability value at the last moment, taking the current position as an individual optimal position, traversing the individual optimal positions of all initial particles, finding the initial particle with the highest adaptability value, taking the individual optimal position as a global optimal position, using a pre-introduced learning factor, adjusting the position of the particle according to the individual optimal position and the global optimal position, updating the position information of each initial particle, calculating a distance difference value between each primary particle and the global optimal position, and the fact that the distance difference value is an Euclidean distance is required, comparing the distance difference value of all primary particles, and selecting the particle with the smallest distance difference value as a secondary particle.
For the secondary particles, calculating a corresponding secondary function value, for the random particles, calculating a corresponding random function value, wherein the random function value also corresponds to an objective function, namely the power generation cost and the energy utilization rate, calculating the acceptance probability of the random function value by using Boltzmann distribution according to the random function value and the secondary function value, if the acceptance probability of the random function value is more than 0.5, namely the random function value is more likely to be the minimum value, taking the random particles as the optimal solution of the power simulation model, representing the model to accept a new solution as the optimal solution, otherwise, taking the secondary particles as the optimal solution of the power simulation model, representing the optimal solution before the model continues to be used, and taking the compared optimal solution as the optimal particle;
updating the power simulation model by using the selected optimized particles as parameter values, including updating various components, parameters or other related settings of the power grid model, performing power simulation by using the updated power simulation model, recording various power grid performance indexes such as frequency stability, transient stability, power generation cost, energy utilization rate and the like in the simulation process, and generating a twin simulation result based on the simulation performed by the optimized power simulation model.
In this embodiment, by randomly generating initial particles, the optimization algorithm can find the optimal operation condition of the power system, so as to minimize the power generation cost and maximize the energy utilization rate at the same time, which is helpful for improving the economical efficiency and sustainability of the power system, the particle swarm optimization in the algorithm has self-adaptability, the position of the particles can be adjusted according to different operation conditions and system changes, i.e. the system can adapt to the continuously changing environment and requirements, and by maintaining the global optimal position, the algorithm ensures that the optimal solution in the whole search space is found, which is helpful for avoiding local minimum and improving the system performance. In summary, the embodiment provides an effective optimization method for the new energy power grid so as to improve the economy and the sustainability of the power system, and by combining an optimization algorithm and a simulation technology, the behavior of the power system can be better understood, and the performance of the power system can be improved.
In an alternative embodiment, the calculating the probability of acceptance of the random function value based on the random function value and the secondary function value is as follows:
wherein,μrepresenting the probability of being accepted for the random function value,x 2 the value of the secondary function is represented,x 0 represents the value of the random function,krepresenting the boltzmann constant,Tindicating temperature.
The function has self-adaptability, and depends on the current solution and the secondary function value, namely the probability calculation can be adjusted according to the nature of the problem and the current solution space, so that the algorithm is more targeted, the function can help the algorithm to overcome the problem of local minimum value by adjusting the temperature, and in conclusion, the function is used for controlling the direction and the amplitude of random search so as to better explore the solution space and find the optimal operation condition. By reasonably adjusting the temperature and parameters, the algorithm can be made to perform well under different problems and situations.
Fig. 2 is a schematic structural diagram of a twin simulation system of a new energy grid according to an embodiment of the present invention, as shown in fig. 2, where the system includes:
the first unit is used for acquiring photovoltaic cell parameters, determining a circuit characteristic equation according to the photovoltaic cell parameters and a preset photovoltaic cell model, selecting a topological structure according to the circuit characteristic equation, and generating a photovoltaic energy model according to the topological structure and the states of a circuit element and the circuit element;
the second unit is used for acquiring power grid state information, generating a power grid model by combining the photovoltaic energy model according to the power grid state information, embedding the power grid model into a pre-generated three-dimensional virtual model, and combining a virtual reality platform to obtain a power simulation model;
and the third unit is used for randomly generating initial particles and random particles according to the electric power simulation model, calculating the fitness of the initial particles and updating the position information of the initial particles according to the initial particles by combining a preset model optimization algorithm to obtain secondary particles, determining the optimal solution of the electric power simulation model according to the secondary particles and the random particles by using the model optimization algorithm, recording the optimal solution as optimized particles, dynamically updating the electric power simulation model according to the optimized particles to obtain a twin simulation result, wherein the model optimization algorithm is constructed based on a simulated annealing algorithm.
The present invention may be a method, apparatus, system, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for performing various aspects of the present invention.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (10)
1. The twin simulation method of the new energy power grid is characterized by comprising the following steps of:
acquiring photovoltaic cell parameters, determining a circuit characteristic equation according to the photovoltaic cell parameters and a preset photovoltaic cell model, selecting a topological structure according to the circuit characteristic equation, and generating a photovoltaic energy model according to the topological structure and the states of circuit elements and circuit elements;
acquiring power grid state information, generating a power grid model by combining the photovoltaic energy model according to the power grid state information, embedding the power grid model into a pre-generated three-dimensional virtual model, and combining a virtual reality platform to obtain a power simulation model;
according to the electric power simulation model, initial particles and random particles are randomly generated, according to the initial particles, a preset model optimization algorithm is combined, fitness of the initial particles is calculated, position information of the initial particles is updated, secondary particles are obtained, according to the secondary particles and the random particles, an optimal solution of the electric power simulation model is determined through the model optimization algorithm and is recorded as optimized particles, the electric power simulation model is dynamically updated according to the optimized particles, and a twin simulation result is obtained, wherein the model optimization algorithm is constructed based on a simulated annealing algorithm.
2. The method of claim 1, wherein obtaining photovoltaic cell parameters, determining a circuit characterization equation based on the photovoltaic cell parameters in combination with a preset photovoltaic cell model, selecting a topology based on the circuit characterization equation, and combining circuit elements and circuit element states based on the topology, generating a photovoltaic energy model comprises:
acquiring voltage, current and power of a photovoltaic cell, recording the voltage, current and power as photovoltaic cell parameters, adding the photovoltaic cell parameters into the preset photovoltaic cell model, and establishing a circuit characteristic equation of the photovoltaic cell by considering the influence of environmental factors on the photovoltaic cell parameters;
according to the circuit characteristic equation, a topological structure is selected by considering the maximum current and the maximum voltage in the circuit, and according to the topological structure, a photovoltaic energy model is established by combining the circuit elements and the states of the circuit elements, namely the parameters and the performances of the circuit elements and the switching states.
3. The method according to claim 2, wherein the adding the photovoltaic cell parameters to the predetermined photovoltaic cell model and taking into account the influence of environmental factors on the photovoltaic cell parameters, establishes a circuit characteristic equation of the photovoltaic cell as follows:
；
wherein,Irepresenting the output current of the photovoltaic cell,I ph the photo-generated current is represented by the formula,I 0 representing the saturation current of the current and,qrepresenting the charge of the element(s),Vis the output voltage of the photovoltaic cell,R s representing the series resistance of the resistor in series,nthe ideal factor is represented by a factor of interest,krepresenting the boltzmann constant of the sample,R sh representing the parallel resistance of the resistor,Tthe temperature is indicated as a function of the temperature,I 2 representing voltageVA lower current.
4. The method of claim 1, wherein generating a grid model in combination with the photovoltaic energy model according to the grid state information, embedding the grid model into a pre-generated three-dimensional virtual model, and combining with a virtual reality platform, the obtaining a power simulation model comprises:
acquiring stability information and grid line information of a grid system, recording the stability information and the grid line information as grid state information, and calculating to obtain a grid model by combining the photovoltaic energy model according to the grid state information and considering environmental factors and interference generated during equipment operation, wherein the grid line information comprises a generator, a transformer substation, a power distribution network and loss generated by a connecting line in a grid;
embedding parameters and equations contained in the power grid model into a three-dimensional virtual model which is built in advance through three-dimensional modeling software to obtain an initial three-dimensional model, and applying a calculation result of the initial three-dimensional model to the virtual reality platform through a program interface to obtain the power simulation model.
5. The method of claim 4, wherein the stability information of the grid system is represented by the formula:
；
wherein,CSIthe integrated stability index is indicated as such,w 1 representing the dynamic stability weight of the object,nthe number of discrete time steps is indicated,p i representation ofiThe frequency stability of the time of day,p 0 a frequency stability reference is indicated and a frequency stability reference is indicated,w 2 representing the weight of the transient stability,mrepresenting the total number of transient events,jrepresent the firstjA number of transient events are carried out,z j representing the stability of the transient state,z 0 representing a transient stability reference.
6. The method according to claim 1, wherein the generating initial particles and random particles randomly according to the power simulation model, calculating fitness of the initial particles according to the initial particles in combination with a preset model optimization algorithm, and updating position information of the initial particles to obtain secondary particles, determining an optimal solution of the power simulation model according to the secondary particles and the random particles through the model optimization algorithm, and recording the optimal solution as optimized particles, and dynamically updating the power simulation model according to the optimized particles, wherein the obtaining twin simulation results comprises:
randomly generating initial particles and random particles, wherein each particle represents a solution of an objective function, and the objective solution of the objective function is to minimize the power generation cost and maximize the energy utilization rate at the same time;
according to the initial particles, calculating fitness values of the initial particles, for each initial particle, if the fitness value at the current moment is larger than the fitness value at the previous moment, traversing the current individual optimal position of the initial particle by taking the current position as an individual optimal position, taking the individual optimal position of the initial particle with the highest fitness value as a global optimal position, updating the position information of the initial particle according to the individual optimal position and the global optimal position and a pre-introduced learning factor to obtain primary particles, calculating a distance difference value between the primary particles and the global optimal position, and taking the particle with the smallest distance difference value as the secondary particle;
according to the secondary particles and the random particles, the random particles and the secondary particles are used as solutions of the objective function, a random function value corresponding to the random particles and a secondary function value corresponding to the secondary particles are calculated, the probability of being accepted of the random function value is calculated according to the random function value and the secondary function value, if the probability of being accepted of the random function value is larger than 0.5, the random particles are used as the optimal solutions of the electric power simulation model, otherwise, the secondary particles are used as the optimal solutions of the electric power simulation model and are marked as optimized particles;
and optimizing the electric power simulation model according to the optimized particles, and simulating by using the updated electric power simulation model to obtain a twin simulation result.
7. The method of claim 6, wherein the calculating the probability of acceptance of the random function value from the random function value and the secondary function value is as follows:
wherein,μrepresenting the probability of being accepted for the random function value,x 2 the value of the secondary function is represented,x 0 represents the value of the random function,krepresenting the boltzmann constant,Tindicating temperature.
8. The twin simulation system of the new energy power grid is characterized by comprising:
the first unit is used for acquiring photovoltaic cell parameters, determining a circuit characteristic equation according to the photovoltaic cell parameters and a preset photovoltaic cell model, selecting a topological structure according to the circuit characteristic equation, and generating a photovoltaic energy model according to the topological structure and the states of a circuit element and the circuit element;
the second unit is used for acquiring power grid state information, generating a power grid model by combining the photovoltaic energy model according to the power grid state information, embedding the power grid model into a pre-generated three-dimensional virtual model, and combining a virtual reality platform to obtain a power simulation model;
and the third unit is used for randomly generating initial particles and random particles according to the electric power simulation model, calculating the fitness of the initial particles and updating the position information of the initial particles according to the initial particles by combining a preset model optimization algorithm to obtain secondary particles, determining the optimal solution of the electric power simulation model according to the secondary particles and the random particles by using the model optimization algorithm, recording the optimal solution as optimized particles, dynamically updating the electric power simulation model according to the optimized particles to obtain a twin simulation result, wherein the model optimization algorithm is constructed based on a simulated annealing algorithm.
9. An electronic device, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to invoke the instructions stored in the memory to perform the method of any of claims 1 to 7.
10. A computer readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the method of any of claims 1 to 7.
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