A new energy power plant output power intelligent deployment method and system

Abstract

This application relates to the field of new energy technology and discloses an intelligent power allocation method for new energy power plants. The method includes establishing a power output and energy storage capacity model for the new energy power plant; decomposing the entire power allocation problem into multiple sub-problems using distributed computing technology; solving each sub-problem based on a particle swarm optimization algorithm; updating particles according to the velocity and position update formulas of the particle swarm optimization algorithm; summarizing the local optimization results of each computing node; and generating control commands for each power generation and energy storage device of the new energy power plant according to the globally optimal allocation scheme. This invention maximizes the sum of output power gain and energy storage capacity gain through the particle swarm optimization algorithm, effectively improving the energy utilization efficiency of new energy power plants. The distributed computing architecture not only improves computational efficiency but also enhances the stability and reliability of the system.

Description

Technical Field

[0001] This invention relates to the field of new energy technology, specifically to a method and system for intelligent power allocation of new energy power plants. Background Technology

[0002] With the increasing global demand for renewable energy, the construction and development of new energy power plants have attracted more and more attention. Solar and wind power, as two major renewable energy sources, have the advantages of being clean, pollution-free, and renewable. However, their power generation is greatly affected by environmental factors and is intermittent and unstable. In order to improve the reliability and stability of new energy power plants, energy storage devices are usually required to store excess energy and release it when needed.

[0003] However, existing methods for allocating output power in new energy power plants often suffer from the following problems: lack of intelligence. Traditional power allocation methods are usually based on fixed rules and experience, and cannot adaptively adjust according to real-time environmental changes and equipment status, which can easily lead to energy waste and unstable supply, reducing system efficiency and reliability; low computational efficiency. For large-scale new energy power plants, power allocation problems are usually very complex and require a lot of calculation and optimization. Traditional centralized calculation methods often cannot meet real-time requirements, resulting in low computational efficiency and affecting system response speed and performance. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for intelligent power allocation of new energy power plants, which solves the problems of low reliability and efficiency caused by the lack of intelligence in power allocation in existing technologies.

[0005] To achieve the above objectives, the present invention provides a method and system for intelligent power distribution of new energy power plants, comprising the following steps:

[0006] S1. Establish a power output and energy storage capacity model for new energy power plants, including but not limited to the power output function of solar power generation equipment:

[0007] P s =η s ×A×I, where η s Where A is the solar cell efficiency, A is the panel area, and I is the light intensity;

[0008] Power output function of wind power generation equipment:

[0009] P w =0.5×ρ×A rotor ×C p ×V 3 Where ρ is the air density, Arotor C represents the area swept by the wind turbine. p V is the power coefficient, and V is the wind speed;

[0010] Energy storage capacity function of energy storage device:

[0011] E s (t)=E s (0)+∫0 t (P charge -P discharge Let dt represent the energy storage capacity E at any time t. s (t) and initial energy storage capacity E s (0) and the charging power P during the period from the initial time 0 to time t. charge and discharge power P discharge The relationship between them;

[0012] S2. The entire power allocation problem is decomposed into multiple sub-problems using distributed computing technology. Each sub-problem is assigned to a different computing node for processing. Each computing node collects real-time data from new energy power generation equipment and energy storage equipment in the corresponding region.

[0013] S3. Solve each subproblem based on the particle swarm optimization algorithm. In the particle swarm optimization algorithm, the position of the particle is defined as the power output value of the new energy power generation equipment and the charging and discharging power value of the energy storage equipment. The quality of the particles is evaluated according to the fitness function, which aims to maximize the sum of output power benefit and energy storage capacity benefit.

[0014] S4. Each computing node updates the particles according to the velocity update formula and position update formula of the particle swarm optimization algorithm. The velocity update formula is:

[0015] v ij (t+1)=w×v ij (t)+c1×r1×(pbest ij -x ij (t))+c2×r2×(gbest j -x ij The position update formula for (t) is:

[0016] x ij (t+1)=x ij (t)+v ij (t+1)

[0017] Where i represents the particle number, j represents the dimension, w is the inertia weight, c1 and c2 are learning factors, r1 and c2 are random numbers, pbest is the individual optimal position, and gbest is the global optimal position.

[0018] S5. Each computing node summarizes its local optimization results and transmits them to the central node through the communication network for comprehensive evaluation, thereby obtaining the globally optimal power output and energy storage capacity allocation scheme.

[0019] S6. Based on the global optimal allocation scheme, generate control commands for each power generation and energy storage device of the new energy power plant to achieve intelligent allocation of output power and energy storage capacity to maximize variable benefits.

[0020] Preferably, when establishing the power model, the effects of the power generation equipment efficiency curve, environmental factors, and equipment aging factors on power output are fully considered.

[0021] Preferably, in distributed computing technology, data is exchanged between computing nodes through a high-speed data bus to ensure the real-time performance and accuracy of the data.

[0022] Preferably, in the particle swarm optimization algorithm, the dimension of the particles is dynamically adjusted according to the characteristics of different new energy power generation equipment and energy storage equipment.

[0023] Preferably, a new energy power plant output power intelligent dispatching system includes a distributed data acquisition unit for collecting operating data of various power generation equipment and energy storage equipment in the new energy power plant;

[0024] A distributed computing unit, comprising multiple computing nodes, is used to solve the power tuning subproblem based on the particle swarm optimization algorithm.

[0025] The central control unit is used to summarize the optimization results of each computing node and perform a comprehensive evaluation to obtain the globally optimal allocation plan.

[0026] The control command generation and execution unit is used to generate control commands based on the global optimal allocation scheme and control each device.

[0027] Preferably, the sensors in the distributed data acquisition unit include, but are not limited to, solar radiation sensors, wind speed sensors, power sensors, and energy storage capacity sensors.

[0028] Preferably, each computing node of the distributed computing unit uses a high-performance processor to improve computing efficiency.

[0029] Preferably, the central control unit and the distributed computing unit employ an efficient communication protocol to ensure the stability and reliability of data transmission.

[0030] Preferably, the control command generation and execution unit is connected to the equipment of the new energy power plant through an industrial control network.

[0031] Preferably, the system also includes a data storage unit for storing the collected data, intermediate results during the calculation process, and global optimal allocation scheme information.

[0032] This invention provides a method and system for intelligent power output allocation in new energy power plants. It offers the following advantages:

[0033] 1. This invention can accurately allocate power to solar and wind power generation equipment and energy storage equipment based on real-time environmental conditions and equipment status. Through particle swarm optimization algorithm, it maximizes the sum of output power gain and energy storage capacity gain, effectively improving the energy utilization efficiency of new energy power plants.

[0034] 2. This invention adopts a distributed computing architecture, which decomposes the power allocation problem into multiple sub-problems and distributes them to different computing nodes for processing. The distributed architecture not only improves computing efficiency, but also enhances the stability and reliability of the system. Even if a computing node fails, other nodes can still continue to work, ensuring the normal operation of the system. Attached Figure Description

[0035] Figure 1 This is a flowchart of the method of the present invention;

[0036] Figure 2 This is a system block diagram of the present invention. Detailed Implementation

[0037] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] Example:

[0039] Please see the appendix Figure 1-2 This invention provides a method and system for intelligent power allocation of a new energy power plant. The method was tested in a new energy power plant that includes solar power generation, wind power generation, and energy storage equipment. In the solar power generation area, solar panels with an area of ​​A = 1000㎡ were installed, and their solar cell efficiency η... s =0.2, in the wind power generation area, the wind turbine swept area A is set. rotor A wind turbine with a capacity of 500 square meters has an air density ρ of 1.2 kg / m³. 3 Power coefficient C p Based on the standard characteristic curve of the wind turbine at different wind speeds, the initial energy storage capacity E of the lithium battery pack used in the energy storage device is determined.s (0) = 1000 kWh. This includes the following steps:

[0040] S1. Establish a power output and energy storage capacity model for new energy power plants, including but not limited to the power output function of solar power generation equipment:

[0041] P s =η s ×A×I, where η s Let A be the solar cell efficiency, A be the panel area, and I be the irradiance. For solar power generation equipment, the irradiance I is collected in real time by a high-precision solar radiation sensor. At a certain moment, the collected irradiance I = 800 W / m². 2 According to the power output function P s =η s The solar power generation P is calculated by multiplying A by I. s =0.2×1000×800=160000W;

[0042] Power output function of wind power generation equipment:

[0043] P w =0.5×ρ×A rotor ×C p ×V 3 Where ρ is the air density, A rotor C represents the area swept by the wind turbine. p Let V be the power coefficient and V be the wind speed. For wind power generation equipment, the wind speed V is monitored in real time by a wind speed sensor. At another moment, the wind speed V = 10 m / s. Let C be the wind speed at this moment. p =0.4, according to the power output function P w =0.5×ρ×A rotor ×C p ×V 3 Calculate the wind power generation P w = 0.5 × 1.2 × 500 × 0.4 × 10 3 =120000W;

[0044] The charging and discharging power of the energy storage device is collected by the corresponding power sensor and expressed through the energy storage capacity function E. s (t)=E s (0)+∫0 t (P charge -P discharge )dt calculates the change in energy storage capacity;

[0045] S2. The power allocation problem is decomposed into three sub-problems: solar power generation power optimization sub-problem, wind power generation power optimization sub-problem, and energy storage device charging and discharging power optimization sub-problem. The three computing nodes of the distributed computing unit collect data from the corresponding regions. For example, the computing node that processes the solar power generation problem collects solar radiation sensor data, solar panel area and efficiency data.

[0046] S3. Solve each subproblem based on the particle swarm optimization algorithm. In the particle swarm optimization algorithm, the position of the particle is defined as the power output value of the new energy power generation equipment and the charging and discharging power value of the energy storage equipment. The quality of the particles is evaluated according to the fitness function, which aims to maximize the sum of output power benefit and energy storage capacity benefit.

[0047] S4. Each computing node updates the particles according to the velocity update formula and position update formula of the particle swarm optimization algorithm. The velocity update formula is:

[0048] v ij (t+1)=w×v ij (t)+c1×r1×(pbest ij -x ij (t))+c2×r2×(gbest j -x ij The position update formula for (t) is:

[0049] x ij (t+1)=x ij (t)+v ij (t+1)

[0050] Where i represents the particle number, j represents the dimension, w is the inertia weight, c1 and c2 are learning factors, r1 and c2 are random numbers, pbest is the individual optimal position, and gbest is the global optimal position; v ij (t) represents the velocity of particle i in dimension j during the t-th iteration, indicating the particle's inertia. Inertia causes the particle to maintain a certain "impact" in its original direction of motion. If w is large, the particle is more likely to maintain its original velocity direction and continue exploring; if w is small, the particle is more easily affected by other factors and changes its velocity direction. c1×r1×(pbest) ij -x ij (t) reflects the particle's cognitive ability, c1 determines the degree to which the particle learns from its own experience, r1 is a random number between [0,1] used to increase the randomness of the algorithm, and pbest ij It is the optimal position of particle i in dimension j, x ij(t) represents the particle's current position in dimension j during the i-th iteration. This part causes the particle to tend towards the optimal position it has previously found, guiding the particle to search within a local range; c2×r2×(gbest) j -x ij (t) represents the particle's ability to share social information, c2 determines the degree to which the particle learns from the group's optimal experience, and gbest j It is the global optimal position of the entire particle swarm in dimension j. This term makes the particles tend to fly towards the optimal position found by the entire swarm, allowing the particles to explore better regions on a global scale.

[0051] In the computation node that handles the subproblem of solar power generation optimization, the position of a particle is defined as the power output value of the solar power generation device and the charging and discharging power of the energy storage device related to solar energy. The particle dimension j is set to 2, and the particle number i ranges from 1 to 50. The position and velocity of the particle are initialized, the inertia weight w = 0.7, and the learning factors c1 = c2 = 1.4. In each iteration, the particle is updated according to the velocity update formula and the position update formula.

[0052] For the sub-problems of wind power generation and energy storage equipment, the particle swarm optimization algorithm is also applied according to the characteristics of the equipment, and the particle dimension is dynamically adjusted according to the characteristics of the equipment. For example, in the wind power generation problem, as the range of wind speed changes increases, the particle dimension is appropriately increased to search for the optimal solution more accurately.

[0053] S5. Each computing node transmits its local optimization results to the central control unit via the fiber optic bus. The central control unit receives the optimization results of the solar power generation problem, the wind power generation problem, and the energy storage equipment sub-problem. Taking into account factors such as grid load demand and equipment stability, the central control unit evaluates and obtains the globally optimal power output and energy storage capacity allocation scheme.

[0054] S6. Based on the global optimal allocation scheme, control commands are generated for each power generation and energy storage device of the new energy power plant to achieve intelligent allocation of output power and energy storage capacity to maximize variable benefits. After executing these control commands, it is observed that the output power and energy storage capacity of the new energy power plant can be intelligently adjusted according to the actual situation to maximize variable benefits.

[0055] Throughout the implementation process, the solar radiation sensor, wind speed sensor, power sensor, and energy storage capacity sensor in the distributed data acquisition unit collect data accurately and in real time. The high-performance processor of the distributed computing unit, which uses an Intel Core i9 processor, performs calculations efficiently. The central control unit and the distributed computing unit transmit data stably and reliably through a high-efficiency communication protocol based on TCP / IP. The control command generation and execution unit is connected to the new energy power plant equipment through an industrial Ethernet. The data storage unit stores all relevant data completely for subsequent analysis and system optimization.

[0056] The above embodiments verify that the intelligent power output allocation method and system of the new energy power plant of the present invention can effectively realize the optimized configuration of power output and energy storage capacity, and improve the operating efficiency and economic benefits of the new energy power plant.

[0057] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent power distribution of a new energy power plant, characterized in that, Includes the following steps: S1. Establish a power output and energy storage capacity model for new energy power plants, including but not limited to the power output function of solar power generation equipment: P s =η s ×A×I, where η s Where A is the solar cell efficiency, A is the panel area, and I is the light intensity; Power output function of wind power generation equipment: P w =0.5×ρ×A rotor ×C p ×V 3 Where ρ is the air density, A rotor C represents the area swept by the wind turbine. p V is the power coefficient, and V is the wind speed; Energy storage capacity function of energy storage device: E s (t)=E s (0)+∫0 t (P charge -P discharge Let dt represent the energy storage capacity E at any time t. s (t) and initial energy storage capacity E s (0) and the charging power P during the period from the initial time 0 to time t. charge and discharge power P discharge The relationship between them; S2. The entire power allocation problem is decomposed into multiple sub-problems using distributed computing technology. Each sub-problem is assigned to a different computing node for processing. Each computing node collects real-time data from new energy power generation equipment and energy storage equipment in the corresponding region. S3. Solve each subproblem based on the particle swarm optimization algorithm. In the particle swarm optimization algorithm, the position of the particle is defined as the power output value of the new energy power generation equipment and the charging and discharging power value of the energy storage equipment. The quality of the particles is evaluated according to the fitness function, which aims to maximize the sum of output power benefit and energy storage capacity benefit. S4. Each computing node updates the particles according to the velocity update formula and position update formula of the particle swarm optimization algorithm. The velocity update formula is: v ij (t+1)=w×v ij (t)+c1×r1×(pbest ij -x ij (t))+c2×r2×(gbest j -x ij (t)) The position update formula is: x ij (t+1)=x ij (t)+v ij (t+1) Where i represents the particle number, j represents the dimension, w is the inertia weight, c1 and c2 are learning factors, r1 and c2 are random numbers, pbest is the individual optimal position, and gbest is the global optimal position. S5. Each computing node summarizes its local optimization results and transmits them to the central node through the communication network for comprehensive evaluation, thereby obtaining the globally optimal power output and energy storage capacity allocation scheme. S6. Based on the global optimal allocation scheme, control commands are generated for each power generation and energy storage device of the new energy power plant to achieve intelligent allocation of output power and energy storage capacity to maximize variable benefits.

2. The intelligent power distribution method for a new energy power plant according to claim 1, characterized in that, When establishing the power model, the effects of the power generation equipment efficiency curve, environmental factors, and equipment aging factors on power output should be fully considered.

3. The intelligent power distribution method for a new energy power plant according to claim 1, characterized in that, In distributed computing technology, computing nodes exchange data through a high-speed data bus to ensure the real-time performance and accuracy of the data.

4. The intelligent power distribution method for a new energy power plant according to claim 1, characterized in that, In particle swarm optimization algorithms, the dimensions of particles are dynamically adjusted according to the characteristics of different new energy power generation and energy storage devices.

5. A smart power distribution system for a new energy power plant, using the smart power distribution method for a new energy power plant as described in any one of claims 1-4, characterized in that, include Distributed data acquisition unit is used to collect operating data of various power generation equipment and energy storage equipment in new energy power plants; A distributed computing unit, comprising multiple computing nodes, is used to solve the power tuning subproblem based on the particle swarm optimization algorithm. The central control unit is used to summarize the optimization results of each computing node and perform a comprehensive evaluation to obtain the globally optimal allocation plan. The control command generation and execution unit is used to generate control commands based on the global optimal allocation scheme and control each device.

6. The intelligent power distribution method and system for a new energy power plant according to claim 5, characterized in that, The sensors in the distributed data acquisition unit include, but are not limited to, solar radiation sensors, wind speed sensors, power sensors, and energy storage capacity sensors.

7. The intelligent power distribution system for a new energy power plant according to claim 5, characterized in that, Each computing node in the distributed computing unit employs a high-performance processor to improve computing efficiency.

8. The intelligent power distribution system for a new energy power plant according to claim 5, characterized in that, The central control unit and the distributed computing unit employ an efficient communication protocol to ensure the stability and reliability of data transmission.

9. The intelligent power distribution system for a new energy power plant according to claim 5, characterized in that, The control command generation and execution unit is connected to the equipment of the new energy power plant through an industrial control network.

10. The intelligent power distribution system for a new energy power plant according to claim 5, characterized in that, The system also includes a data storage unit for storing the collected data, intermediate results during the calculation process, and information on the global optimal allocation scheme.

Images