Distributed photovoltaic power station access power distribution network control device

Abstract

The invention discloses a distributed photovoltaic power station access power distribution network control device, which comprises a data acquisition and transmission module, a central control unit, an island protection monitoring module, a grid-connected power management module, a cloud server and a client, the distributed photovoltaic power station core controller communicates with the electrical quantity acquisition and transmission unit and the island protection monitoring unit through RS485, and the distributed photovoltaic power station core controller communicates with the grid-connected power control unit through any one of RS485, Ethernet, optical fiber or wireless; according to the invention, remote access and control are carried out through the client, the power loss electric quantity cost of the power distribution network is reduced through accurate power management and optimal scheduling, the operation efficiency of the whole power system is improved, and the operation and maintenance efficiency and the operation convenience of accessing the distributed photovoltaic power station to the power distribution network are greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a control device for accessing a distributed photovoltaic power station to a distribution network. Background Art

[0002] As the global demand for renewable energy continues to grow, solar energy, as a clean and sustainable form of energy, is increasingly accounting for an increasing share of electricity supply. Distributed photovoltaic power stations have gradually become an important part of promoting energy transformation in many countries and regions due to their flexible deployment and efficient utilization. However, the large-scale access of distributed photovoltaic power stations has brought new challenges and opportunities to traditional distribution networks.

[0003] At present, there are still the following deficiencies in the application of distributed photovoltaic power stations connected to the distribution network: 1. When the main grid is out of power, if the distributed photovoltaic power station continues to supply power to the local grid, an "island" phenomenon may occur. The island phenomenon may not only cause safety risks to maintenance personnel, but also affect the normal operation of grid equipment. Nowadays, the lack of a key technology that can effectively detect and prevent the island phenomenon is the key to the access of distributed photovoltaic power stations to the distribution network; 2. Accurately predicting photovoltaic power generation is crucial for optimizing grid dispatch and improving energy utilization. Since photovoltaic power generation is affected by multiple factors such as light intensity and temperature, its changing trend is difficult to accurately grasp. Traditional power prediction methods often have a large error range and cannot meet the needs of modern distribution networks for high-precision prediction. In addition, how to dynamically adjust the output power of photovoltaic power stations based on prediction results to adapt to changes in grid load is also an urgent problem to be solved; 3. In order to effectively monitor and manage distributed photovoltaic power stations, it is necessary to collect electrical parameters (such as voltage, current, power, etc.) and meteorological data of each node in real time and transmit them to the central control system for analysis and processing. The existing data collection and transmission technology still has deficiencies in data accuracy, transmission efficiency and security, which limits the overall performance of the system; Therefore, there is an urgent need for a distributed photovoltaic power station access distribution network control device that can overcome the above-mentioned shortcomings. Summary of the invention

[0004] In order to overcome the shortcomings of the background technology, the present invention discloses a distributed photovoltaic power station access to a distribution network control device. The present invention uses remote access and control by a client, which not only reduces the network loss and electricity cost of the distribution network through precise power management and optimized scheduling, and improves the operating efficiency of the entire power system, but also greatly improves the operation and maintenance efficiency and operational convenience of the distributed photovoltaic power station access to the distribution network.

[0005] To achieve the above object, the present invention adopts the following technical solution: A distributed photovoltaic power station access distribution network control device, including a data acquisition and transmission module, a central control unit, an island protection monitoring module, a grid-connected power management module, a cloud server and a client; Data acquisition and transmission module, used to monitor and collect electrical parameters of each photovoltaic node in real time, and send the data to the cloud server; Central control unit, including: The core controller of the distributed photovoltaic power station is used to process the data collected from each node and execute the optimization algorithm to determine the best grid connection point and capacity configuration plan; The electrical quantity collection and transmission unit is used to receive the data from the data collection and transmission module, and transmit it to the core controller of the distributed photovoltaic power station after preliminary processing; Island protection monitoring unit, used to detect whether the system has island phenomenon and take corresponding protection measures; The grid-connected power control unit predicts the changing trend of photovoltaic power generation and formulates a reasonable power generation plan based on the seasonal typical daily power generation curve and historical meteorological data; The island protection monitoring module is integrated in the central control unit and is responsible for managing the working logic of the island protection monitoring unit; The grid-connected power management module is integrated in the central control unit and is responsible for managing the working logic of the grid-connected power control unit; A cloud server is used to receive photovoltaic power generation information of multiple photovoltaic nodes and perform simulation calculation on the photovoltaic power generation information using a grid-connected power management module; The client is used to receive and display the optimal configuration solution.

[0006] Furthermore, the data acquisition and transmission module includes a plurality of data acquisition devices, each of which includes: Electrical quantity collection unit, used for real-time monitoring and collection of electrical parameters such as voltage, current, active power, reactive power, etc. of photovoltaic nodes; A / D converter, used to convert the collected analog signal into a digital signal; The first wireless communication unit is used to send the converted digital signal to the cloud server in a wireless manner.

[0007] Furthermore, the distributed photovoltaic power station core controller communicates with the electrical quantity collection and transmission unit and the island protection monitoring unit via RS485, and the distributed photovoltaic power station core controller communicates with the grid-connected power control unit via RS485, Ethernet, optical fiber or wireless.

[0008] Furthermore, the island protection monitoring module detects the island phenomenon by combining the active island detection method and the passive island detection method, which specifically includes the following steps: a. Initialize the system; Configure the voltage, frequency, and phase thresholds for the passive islanding detection method, and configure the frequency offset and disturbance period for the active islanding detection method; b. Real-time monitoring; Use the passive island detection method to monitor the changes in grid voltage, frequency and phase in real time. If an abnormality is detected, the active island detection method is immediately triggered; c. Active detection; Inject specific disturbance signals into the power grid, monitor the power grid response, and determine whether there is an islanding phenomenon; d. Decision-making and protection; If the passive islanding detection method and the active islanding detection method both confirm the islanding phenomenon, the islanding protection monitoring unit is controlled to immediately perform the protection operation.

[0009] Furthermore, the grid-connected power management module predicts the changing trend of photovoltaic power generation based on the power flow calculation simulator and formulates a reasonable power generation plan, which specifically includes the following steps: a. Data collection and preprocessing; Obtain historical data of each photovoltaic node from the cloud server and pre-process the data; b. Establish mathematical model; The node voltage and current equations are established according to the topological structure of the distribution network. The linearized power flow equation is usually used to approximate them: in, is the node-branch association matrix, is the diagonal matrix composed of the conductance of each branch, and are the real and imaginary parts of the node voltage vector respectively; Power balance equation to ensure the steady-state operation constraints of the distribution network: in, and are the active and reactive power injected from the main grid into the distribution network, and They are respectively the active and reactive outputs injected into the distribution network by the distributed photovoltaic power station. and Respectively represent the active load and reactive load of the node; c. Photovoltaic output modeling; The uncertain variable set, considering the randomness and volatility of photovoltaic output, uses a box-type uncertainty set to represent the uncertain variables: in, For the Photovoltaic power station on a typical day in the season The expected value of output during a period, For the Photovoltaic power station on a typical day in the season Fluctuation value of output during a period of time; d. Power flow simulation calculation; In the uncertain variable In the fluctuation set, find the uncertain variable change scenario that maximizes the annual network loss electricity cost of the distribution network, and define this scenario as an extreme scenario to maximize the annual network loss electricity cost of the distribution network. , its objective function is: in, It is The number of days in a year corresponding to the typical daily photovoltaic output scenario in each season, It is the cost of power loss in the distribution network corresponding to this scenario; e. Application of optimization algorithms; Through the Benders decomposition method, the Min-Max two-level optimization model is decomposed into two parts: the upper main problem and the lower sub-problem. The sub-problem is to find the uncertain variables that maximize the annual operation network loss electricity cost of the distribution network. The main problem is aimed at extreme scenarios, solving the photovoltaic power station grid-connected node and capacity selection scheme that minimizes the system's annual construction investment and total operating costs. By alternately iterating the upper main problem and the lower sub-problems, the optimal solution is gradually approached, and the best configuration scheme that minimizes the total system cost and meets the safety operation restrictions is obtained.

[0010] Furthermore, the client includes: a first data processor for initializing the system, setting parameters, and communicating with other components; The second data processor is used to process the simulation structure and other data received from the cloud server.

[0011] Further, the second data processor comprises: A data receiving and analyzing unit, used for receiving and analyzing simulation results; A result display unit, used to display the simulation structure; A user interaction unit, used to provide parameter adjustment and remote control functions; The feedback mechanism unit is used to collect feedback from operators and feed it back to the cloud server or central control unit.

[0012] Compared with the prior art, the present invention has the following beneficial effects: the present invention can not only combine active and passive island detection methods to ensure that the island phenomenon can be effectively detected in the distribution network under various circumstances, and take corresponding protective measures to avoid potential risks to maintenance personnel and equipment, but also monitor the voltage, frequency and other key parameters of the power grid in real time to ensure that the system operates within a safe range and prevent equipment damage caused by excessively high or low voltage; The present invention uses remote access and control by the client, which not only reduces the network loss and electricity cost of the distribution network through precise power management and optimized scheduling, but also improves the operating efficiency of the entire power system, and also greatly improves the operation and maintenance efficiency and operational convenience of the distributed photovoltaic power station connected to the distribution network, thereby promoting the sustainable development of the distributed photovoltaic power station connected to the distribution network. DETAILED DESCRIPTION

[0013] The technical solution of the present invention will be described below in conjunction with the embodiments of the present invention.

[0014] The present invention provides a technical solution: a distributed photovoltaic power station access distribution network control device, including a data acquisition and transmission module, a central control unit, an island protection monitoring module, a grid-connected power management module, a cloud server and a client, the distributed photovoltaic power station core controller communicates with the electrical quantity acquisition and transmission unit and the island protection monitoring unit via RS485, and the distributed photovoltaic power station core controller communicates with the grid-connected power control unit via any one of RS485, Ethernet, optical fiber or wireless; The data acquisition and transmission module is used to monitor and collect the electrical parameters of each photovoltaic node in real time and send the data to the cloud server. Specifically, the data acquisition and transmission module includes multiple data acquisition devices, each of which includes: Electrical quantity collection unit, used for real-time monitoring and collection of electrical parameters such as voltage, current, active power, reactive power, etc. of photovoltaic nodes; A / D converter, used to convert the collected analog signal into a digital signal; A first wireless communication unit, used to send the converted digital signal to a cloud server wirelessly; Central control unit, including: The core controller of the distributed photovoltaic power station is used to process the data collected from each node and execute the optimization algorithm to determine the best grid connection point and capacity configuration plan; The electrical quantity collection and transmission unit is used to receive the data from the data collection and transmission module, and transmit it to the core controller of the distributed photovoltaic power station after preliminary processing; Island protection monitoring unit, used to detect whether the system has island phenomenon and take corresponding protection measures; The grid-connected power control unit predicts the changing trend of photovoltaic power generation and formulates a reasonable power generation plan based on the seasonal typical daily power generation curve and historical meteorological data; The island protection monitoring module is integrated in the central control unit and is responsible for managing the working logic of the island protection monitoring unit. Specifically, the island protection monitoring module detects the island phenomenon by combining the active island detection method and the passive island detection method, which specifically includes the following steps: a. Initialize the system; Configure the voltage, frequency, and phase thresholds for the passive islanding detection method, and configure the frequency offset and disturbance period for the active islanding detection method; b. Real-time monitoring; Use the passive island detection method to monitor the changes in grid voltage, frequency and phase in real time. If an abnormality is detected, the active island detection method is immediately triggered; c. Active detection; Inject specific disturbance signals into the power grid, monitor the power grid response, and determine whether there is an islanding phenomenon; d. Decision-making and protection; If both the passive islanding detection method and the active islanding detection method confirm the islanding phenomenon, the islanding protection monitoring unit is controlled to immediately perform the protection operation; The grid-connected power management module is integrated in the central control unit and is responsible for managing the working logic of the grid-connected power control unit. Specifically, the grid-connected power management module predicts the changing trend of photovoltaic power generation based on the power flow calculation simulator and formulates a reasonable power generation plan, which specifically includes the following steps: a. Data collection and preprocessing; Obtain historical data of each photovoltaic node from the cloud server and pre-process the data; b. Establish mathematical model; The node voltage and current equations are established according to the topological structure of the distribution network. The linearized power flow equation is usually used to approximate them: in, is the node-branch association matrix, is the diagonal matrix composed of the conductance of each branch, and are the real and imaginary parts of the node voltage vector respectively; Power balance equation to ensure the steady-state operation constraints of the distribution network: in, and are the active and reactive power injected from the main grid into the distribution network, and They are respectively the active and reactive outputs injected into the distribution network by the distributed photovoltaic power station. and Respectively represent the active load and reactive load of the node; c. Photovoltaic output modeling; The uncertain variable set, considering the randomness and volatility of photovoltaic output, uses a box-type uncertainty set to represent the uncertain variables: in, For the Photovoltaic power station on a typical day in the season The expected value of output during a period, For the Photovoltaic power station on a typical day in the season Fluctuation value of output during a period of time; d. Power flow simulation calculation; In the uncertain variable In the fluctuation set, find the uncertain variable change scenario that maximizes the annual network loss electricity cost of the distribution network, and define this scenario as an extreme scenario to maximize the annual network loss electricity cost of the distribution network. , its objective function is: in, It is The number of days in a year corresponding to the typical daily photovoltaic output scenario in each season, It is the cost of power loss in the distribution network corresponding to this scenario; e. Application of optimization algorithms; Through the Benders decomposition method, the Min-Max two-level optimization model is decomposed into two parts: the upper main problem and the lower sub-problem. The sub-problem is to find the uncertain variables that maximize the annual operation network loss electricity cost of the distribution network. The main problem is to solve the grid-connected nodes and capacity selection scheme of the photovoltaic power station that minimizes the annual construction investment and total operating cost of the system for extreme scenarios. By alternating the upper main problem and the lower sub-problems, the optimal solution is gradually approached to obtain the best configuration scheme that minimizes the total system cost and meets the safety operation restriction requirements. A cloud server is used to receive photovoltaic power generation information of multiple photovoltaic nodes and perform simulation calculation on the photovoltaic power generation information using a grid-connected power management module; The client is used to receive and display the optimal configuration solution, including: a first data processor for initializing the system, setting parameters, and communicating with other components; The second data processor is used to process the simulation structure and other data received from the cloud server.

[0015] Further, the second data processor comprises: A data receiving and analyzing unit, used for receiving and analyzing simulation results; A result display unit, used to display the simulation structure; A user interaction unit, used to provide parameter adjustment and remote control functions; The feedback mechanism unit is used to collect feedback from operators and feed it back to the cloud server or central control unit.

[0016] Embodiment,Assume that we have a 33-node distribution network system, all photovoltaic power stations adopt constant power factor control, and the minimum power factor allowed is 0.9. For each season (spring, summer, autumn, and winter), we select a typical day output scenario of a photovoltaic power station. For example, in a typical day scenario in winter, the photovoltaic output is low, which easily causes the voltage of each node in the distribution network to be too low. Therefore, we focus on analyzing the voltage distribution of nodes 18 and 33 in a typical day scenario in winter; Initial simulation: In deterministic optimization, PV fluctuations are not considered, PV output in the four seasons is equal to the expected value, and the single-layer optimization model is directly solved; Robust optimization: Assuming that the PV output fluctuation range is ±30% of the expected value, the Benders decomposition method is used to solve the two-layer robust optimization model, find extreme scenarios and adjust the PV power station grid connection point and capacity configuration plan; Through the above process, the changing trend of photovoltaic power generation can be effectively predicted, and a reasonable power generation plan can be formulated to ensure the safe and stable operation of the distribution network. At the same time, the economy and reliability of the system can be guaranteed within the fluctuation range of photovoltaic output.

[0017] The parts of the present invention that are not described in detail are prior art. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention.

Claims

1. A distributed photovoltaic power station access distribution network control device, characterized in that: It includes data acquisition and transmission module, central control unit, island protection monitoring module, grid-connected power management module, cloud server and client; Data acquisition and transmission module, used to monitor and collect electrical parameters of each photovoltaic node in real time, and send the data to the cloud server; Central control unit, including: The core controller of the distributed photovoltaic power station is used to process the data collected from each node and execute the optimization algorithm to determine the best grid connection point and capacity configuration plan; The electrical quantity collection and transmission unit is used to receive the data from the data collection and transmission module, and transmit it to the core controller of the distributed photovoltaic power station after preliminary processing; Island protection monitoring unit, used to detect whether the system has island phenomenon and take corresponding protection measures; The grid-connected power control unit predicts the changing trend of photovoltaic power generation and formulates a reasonable power generation plan based on the seasonal typical daily power generation curve and historical meteorological data; The island protection monitoring module is integrated in the central control unit and is responsible for managing the working logic of the island protection monitoring unit; The grid-connected power management module is integrated in the central control unit and is responsible for managing the working logic of the grid-connected power control unit; A cloud server is used to receive photovoltaic power generation information of multiple photovoltaic nodes and perform simulation calculation on the photovoltaic power generation information using a grid-connected power management module; The client is used to receive and display the optimal configuration solution.

2. A distributed photovoltaic power station access distribution network control device according to claim 1, characterized in that: The data acquisition and transmission module includes multiple data acquisition devices.

3. A distributed photovoltaic power station access distribution network control device according to claim 2, characterized in that: The data acquisition device includes: Electrical quantity collection unit, used for real-time monitoring and collection of electrical parameters such as voltage, current, active power, reactive power, etc. of photovoltaic nodes; A / D converter, used to convert the collected analog signal into a digital signal; The first wireless communication unit is used to send the converted digital signal to the cloud server in a wireless manner.

4. A distributed photovoltaic power station access distribution network control device according to claim 1, characterized in that: The core controller of the distributed photovoltaic power station communicates with the electrical quantity collection and transmission unit and the island protection monitoring unit via RS485, and the core controller of the distributed photovoltaic power station communicates with the grid-connected power control unit via RS485, Ethernet, optical fiber or wireless.

5. A distributed photovoltaic power station access distribution network control device according to claim 1, characterized in that: The island protection monitoring module detects island phenomena by combining active island detection method and passive island detection method, which specifically includes the following steps: a. Initialize the system; Configure the voltage, frequency, and phase thresholds for the passive islanding detection method, and configure the frequency offset and disturbance period for the active islanding detection method; b. Real-time monitoring; Use the passive island detection method to monitor the changes in grid voltage, frequency and phase in real time. If an abnormality is detected, the active island detection method is immediately triggered; c. Active detection; Inject specific disturbance signals into the power grid, monitor the power grid response, and determine whether there is an islanding phenomenon; d. Decision-making and protection; If the passive islanding detection method and the active islanding detection method both confirm the islanding phenomenon, the islanding protection monitoring unit is controlled to immediately perform the protection operation.

6. A distributed photovoltaic power station access distribution network control device according to claim 1, characterized in that: The grid-connected power management module predicts the changing trend of photovoltaic power generation based on the power flow calculation simulator and formulates a reasonable power generation plan, which specifically includes the following steps: a. Data collection and preprocessing; Obtain historical data of each photovoltaic node from the cloud server and pre-process the data; b. Establish mathematical model; The node voltage and current equations are established according to the topological structure of the distribution network. The linearized power flow equation is usually used to approximate them: in, is the node-branch association matrix, is the diagonal matrix composed of the conductance of each branch, and are the real and imaginary parts of the node voltage vector respectively; Power balance equation to ensure the steady-state operation constraints of the distribution network: in, and are the active and reactive power injected from the main grid into the distribution network, and They are respectively the active and reactive outputs injected into the distribution network by the distributed photovoltaic power station. and Respectively represent the active load and reactive load of the node; c. Photovoltaic output modeling; The uncertain variable set, considering the randomness and volatility of photovoltaic output, uses a box-type uncertainty set to represent the uncertain variables: in, For the Photovoltaic power station on a typical day in the season The expected value of output during a period, For the Photovoltaic power station on a typical day in the season Fluctuation value of output during a period of time; d. Power flow simulation calculation; In the uncertain variable In the fluctuation set, find the uncertain variable change scenario that maximizes the annual network loss electricity cost of the distribution network, and define this scenario as an extreme scenario to maximize the annual network loss electricity cost of the distribution network. , and its objective function is: in, It is The number of days in a year corresponding to the typical daily photovoltaic output scenario in each season, It is the cost of power loss in the distribution network corresponding to this scenario; e. Application of optimization algorithms; Through the Benders decomposition method, the Min-Max two-level optimization model is decomposed into two parts: the upper main problem and the lower sub-problem. The sub-problem is to find the uncertain variables that maximize the annual operation network loss electricity cost of the distribution network. The main problem is aimed at extreme scenarios, solving the photovoltaic power station grid-connected node and capacity selection scheme that minimizes the system's annual construction investment and total operating costs. By alternately iterating the upper main problem and the lower sub-problems, the optimal solution is gradually approached, and the best configuration scheme that minimizes the total system cost and meets the safety operation restrictions is obtained.

7. A distributed photovoltaic power station access distribution network control device according to claim 1, characterized in that: The client includes: a first data processor for initializing the system, setting parameters, and communicating with other components; The second data processor is used to process the simulation structure and other data received from the cloud server.

8. A distributed photovoltaic power station access distribution network control device according to claim 7, characterized in that: The second data processor comprises: A data receiving and analyzing unit, used for receiving and analyzing simulation results; A result display unit, used to display the simulation structure; A user interaction unit, used to provide parameter adjustment and remote control functions; The feedback mechanism unit is used to collect feedback from operators and feed it back to the cloud server or central control unit.