Multi-stage cooperative regulation and control system for low-voltage distributed photovoltaic access power distribution network

Abstract

The invention discloses a multi-stage coordinated regulation and control system of a low-voltage distributed photovoltaic access power distribution network, which effectively solves the problem of power distribution in the existing system by implementing the multi-stage coordinated regulation and control system and integrating an efficient priority control module and a dynamic priority adjustment algorithm, and improves the power distribution efficiency through the optimization. The system can automatically adjust power resources when power supply is insufficient, it is ensured that key areas such as medical institutions and rescue centers preferentially obtain sufficient power supply when needed, in addition, a manual intervention interface of the system allows operators to manually adjust the priority according to the actual situation, and the system is convenient to use. And on the whole, not only are the operation efficiency and reliability of the power grid improved, but also the support for key services is strengthened, and the stable operation of social infrastructures under emergency conditions is ensured, so that the overall safety and functional efficiency of the society are greatly enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of coordinated control systems, and more specifically, to a multi-level coordinated control system for low-voltage distributed photovoltaic access to a power distribution network. Background Art

[0002] In modern power systems, low-voltage distributed photovoltaic access technology is becoming increasingly mature, which can directly convert solar energy into electrical energy and provide energy to users through distribution networks. This technology supports the widespread use of renewable energy and is of great significance to reducing carbon emissions and improving energy efficiency.

[0003] However, the existing low-voltage distributed photovoltaic system still has deficiencies in power distribution management, especially the lack of an effective priority determination mechanism. During peak power demand periods or special circumstances (such as increased demand for emergency services), the system fails to prioritize the power supply needs of key areas and important facilities, which results in the risk of power shortages for important services such as hospitals and police stations at critical moments, affecting their normal operations and service provision.

[0004] Therefore, in response to the above technical problems, it is necessary to provide a multi-level coordinated control system for low-voltage distributed photovoltaic access to the distribution network. Summary of the invention

[0005] The object of the present invention is to provide a multi-level coordinated control system for low-voltage distributed photovoltaic access to a distribution network to solve the above-mentioned problems.

[0006] In order to achieve the above purpose, the technical solution provided by an embodiment of the present invention is as follows:

[0007] A multi-level coordinated control system for low-voltage distributed photovoltaic access to a distribution network, comprising the following modules:

[0008] Photovoltaic power generation module: used to convert solar energy into electrical energy and make preliminary voltage and current adjustments to meet subsequent power transmission and distribution needs;

[0009] Distribution network access module: used to safely connect the power generated by the photovoltaic power generation module to the existing low-voltage distribution network and perform necessary power quality control;

[0010] Multi-level control module: used to coordinate the operation of photovoltaic power generation modules and distribution network access modules as a whole, and optimize the distribution and use of electric energy. This module includes a local controller and a central controller.

[0011] Local controller: configured in each photovoltaic power generation unit, responsible for collecting real-time data of the photovoltaic unit and executing basic output adjustment commands;

[0012] Central controller: It is configured in the distribution network management center to receive data from local controllers and issue high-level control instructions based on the needs and status of the distribution network;

[0013] Communication network module: used to realize data exchange and command transmission between local controller and central controller, ensure the real-time and accuracy of information, and support encrypted communication to protect data security;

[0014] Priority Control Module: It is used to manage and optimize the allocation of power resources, especially during peak demand periods or sudden high demand in specific areas, to ensure that power supply to important areas or key facilities is given the highest priority.

[0015] As a further improvement of the present invention, the priority control module includes a dynamic priority adjustment algorithm based on real-time grid load data L (t) , Photovoltaic power generation data G i (t) and weather forecast data W(t) are used to dynamically adjust the power distribution priority to optimize power usage and minimize power outages. The priority scoring formula is:

[0016] P i (t) = α × L (t) +β×G i (t)+γ×W(t);

[0017] Among them, α, β and γ are optimization parameters.

[0018] As a further improvement of the present invention, the dynamic priority adjustment algorithm is further based on the priority strategy S set by the user. i , the strategy specifies the priority order of different users or regions in power distribution to ensure that critical infrastructure and high-demand users can get priority power supply when there is a power shortage. The priority scoring formula is expanded to:

[0019] P i (t) = α × L (t) +β×G i (t)+γ×W(t)+δ×S i ;

[0020] Where δ is the adjustment coefficient of the priority strategy.

[0021] As a further improvement of the present invention, the system includes a priority conflict resolution mechanism, which is used to automatically determine the allocation of power resources according to a preset conflict resolution rule when multiple regions or users simultaneously meet the preset high priority requirements. The conflict resolution rule is:

[0022] U i (t) = min(ξ × H iC(t));

[0023] Among them, U i (t) is the actual power supply of region i at time t, H i is the historical electricity consumption data of region i, C(t) is the total available electricity at time t, and ξ is the adjustment factor.

[0024] As a further improvement of the present invention, the priority conflict resolution mechanism is further refined, including considering historical electricity consumption data, the continuity requirements of key services and the contract priorities between users. By dynamically analyzing historical consumption patterns and contract obligations, the priority is automatically adjusted to optimize power distribution and ensure the continuous operation of key services when power supply is insufficient.

[0025] As a further improvement of the present invention, the priority control module works in coordination with the central controller to automatically reduce the power supply priority of non-critical areas when it is predicted that the total load of the power grid is about to exceed the production capacity, so as to maintain the stable operation of the power grid.

[0026] As a further improvement of the present invention, the collaborative control system includes a manual intervention interface, allowing operators to manually adjust priority settings or directly resolve priority conflicts when necessary, and the interface provides an intuitive user interface.

[0027] As a further improvement of the present invention, the manual intervention interface includes a graphical user interface, which provides real-time data visualization and can display grid status, photovoltaic power generation and load demand information to help operators make quick and accurate decisions.

[0028] As a further improvement of the present invention, the graphical user interface further provides the function of reviewing historical data and simulating priority adjustment results. By simulating the potential impact of different priority strategies on grid load and stability, operators are allowed to optimize strategy implementation and improve the efficiency and effectiveness of grid management.

[0029] As a further improvement of the present invention, the communication network module uses a high-security protocol to encrypt all transmitted data, including priority control commands and real-time power grid status information, to prevent the data from being accessed or tampered with by unauthorized persons.

[0030] Compared with the prior art, the advantages of the present invention are:

[0031] This solution effectively solves the power distribution problem in the existing system by implementing a multi-level collaborative control system, integrating an efficient priority control module and a dynamic priority adjustment algorithm. Through this optimization, the system can automatically adjust power resources when the power supply is tight, ensuring that key areas such as medical institutions and rescue centers have priority to obtain sufficient power supply when needed. In addition, the system's manual intervention interface allows operators to manually adjust priorities according to actual conditions, further improving the grid's response flexibility and emergency handling capabilities. Overall, this solution not only improves the grid's operating efficiency and reliability, but also strengthens support for critical services, ensuring the stable operation of social infrastructure in emergencies, thereby greatly enhancing the overall safety and functional efficiency of society. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a schematic diagram of the multi-level coordinated control system of the present invention; DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention; it is obvious that the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments, and all other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making creative work are within the scope of protection of the present invention.

[0034] Example:

[0035] See also Figure 1 , a multi-level coordinated control system for low-voltage distributed photovoltaic access to distribution network, including the following modules:

[0036] Photovoltaic power generation module: used to convert solar energy into electrical energy and make preliminary voltage and current adjustments to meet subsequent power transmission and distribution needs;

[0037] Distribution network access module: used to safely connect the power generated by the photovoltaic power generation module to the existing low-voltage distribution network and perform necessary power quality control;

[0038] Multi-level control module: used to coordinate the operation of photovoltaic power generation modules and distribution network access modules as a whole, and optimize the distribution and use of electric energy. This module includes a local controller and a central controller.

[0039] Local controller: configured in each photovoltaic power generation unit, responsible for collecting real-time data of the photovoltaic unit and executing basic output adjustment commands;

[0040] Central controller: It is configured in the distribution network management center to receive data from local controllers and issue high-level control instructions based on the needs and status of the distribution network;

[0041] Communication network module: used to realize data exchange and command transmission between local controller and central controller, ensure the real-time and accuracy of information, and support encrypted communication to protect data security;

[0042] Priority Control Module: It is used to manage and optimize the allocation of power resources, especially during peak demand periods or sudden high demand in specific areas, to ensure that power supply to important areas or key facilities is given the highest priority.

[0043] Among them, by implementing a multi-level collaborative control system, integrating an efficient priority control module and a dynamic priority adjustment algorithm, the power distribution problem in the existing system is effectively solved. Through this optimization, the system can automatically adjust power resources when the power supply is tight, ensuring that key areas such as medical institutions and rescue centers have priority to obtain sufficient power supply when needed. In addition, the system's manual intervention interface allows operators to manually adjust priorities according to actual conditions, further improving the grid's response flexibility and emergency handling capabilities. Overall, this solution not only improves the grid's operating efficiency and reliability, but also strengthens support for critical services, ensuring the stable operation of social infrastructure in emergencies, thereby greatly enhancing the overall safety and functional efficiency of society.

[0044] The priority control module includes a dynamic priority adjustment algorithm based on real-time grid load data L (t) , Photovoltaic power generation data G i (t) and weather forecast data W(t) are used to dynamically adjust the power distribution priority to optimize power usage and minimize power outages. The priority scoring formula is:

[0045] P i (t) = α × L (t) +β×G i (t)+γ×W(t);

[0046] Among them, α, β and γ are optimization parameters.

[0047] Among them, the power grid load data L (t) Real-time monitoring of the load of the entire power grid, including the current power consumption, reflects the power demand of the entire power grid, photovoltaic power generation data G i (t) Monitoring the amount of electricity generated by each photovoltaic power generation unit can reflect the power supply capacity of the distributed photovoltaic system, while the weather forecast data W(t) uses weather forecast data to predict the potential impact of weather changes on photovoltaic power generation, such as sunlight intensity, cloud cover, etc.

[0048] Calculate the priority score P through the algorithm i(t), the score consists of three parts: the impact of grid load (regulated by parameter α), the contribution of photovoltaic power generation (regulated by parameter β), and the estimated impact of weather forecast on power generation (regulated by parameter γ). Based on the calculation results, the system dynamically adjusts the power supply priority of each region or user to give priority to meeting the power demand of high-scoring regions.

[0049] The specific data for optimizing the parameters of α, β, and γ can be obtained through historical data analysis. For example, by analyzing the relationship between grid load and photovoltaic power generation, and how weather conditions affect photovoltaic output, the relative importance of α, β, and γ can be estimated. Using statistical or machine learning methods, such as regression analysis, the optimal estimates of these parameters can be extracted from the data.

[0050] The allocation of power resources is optimized based on real-time data and forecast models, thereby improving energy efficiency. For example, when high photovoltaic power generation is predicted, reliance on traditional power sources can be reduced and more renewable energy can be used. At the same time, by dynamically adjusting the allocation of power resources, the system can prevent power outages caused by excessive load or insufficient power generation. When it is predicted that the grid load is approaching its peak, the system can adjust in advance to reserve sufficient power supply for key areas or facilities.

[0051] The dynamic priority adjustment algorithm is further based on the priority strategy S set by the user. i , the strategy specifies the priority order of different users or regions in power distribution to ensure that critical infrastructure and high-demand users can get priority power supply when there is a power shortage. The priority scoring formula is expanded to:

[0052] P i (t) = α × L (t) +β×G i (t)+γ×W(t)+δ×S i ;

[0053] Where δ is the adjustment coefficient of the priority strategy.

[0054] Among them, α, β and γ control the impact of total grid load, PV power generation and weather forecast on priority score, respectively, while δ is the factor that adjusts the user-set priority strategy S i The weight in the overall priority score is the priority set by users or areas based on their importance and urgency of electricity demand. For example, hospitals, emergency services, industrial areas, etc. may be given higher priority.

[0055] The system uses real-time data to dynamically adjust power distribution, ensuring that the needs of critical infrastructure and high-demand users are prioritized when power resources are limited.

[0056] Choosing the value of the adjustment coefficient δ is a key step in achieving an effective power distribution strategy and plays a vital role in the dynamic priority adjustment algorithm. If the goal is to ensure that critical infrastructure such as hospitals and emergency services receive power first when power is insufficient, the value of δ may be set relatively high to ensure that these services are given significantly higher priority than other non-critical services.

[0057] By analyzing historical data, patterns and trends in electricity demand for different users or regions can be identified. Based on this data, statistical methods or machine learning techniques can be used to estimate an initial δ value so that the electricity distribution during the historical period matches the user priority strategy.

[0058] By adjusting the power distribution priority in real time, the system can quickly respond to changes in grid load and fluctuations in photovoltaic power generation, thereby more effectively managing power resources and reducing the risk of power outages. At the same time, the priority strategy set by the user ensures that when there is a power shortage, key services such as hospitals and fire protection will not be interrupted due to insufficient power, thereby maintaining the stability and safety of social operations. The system optimizes resource allocation and improves the efficiency and effectiveness of energy use by comprehensively considering grid load, photovoltaic power generation performance, weather conditions and user priorities.

[0059] The system includes a priority conflict resolution mechanism, which is used to automatically determine the allocation of power resources according to the preset conflict resolution rules when multiple areas or users meet the preset high priority requirements at the same time. The conflict resolution rules are:

[0060] U i (t) = min(ξ × H i C(t));

[0061] Among them, U i (t) is the actual power supply of region i at time t, H i is the historical electricity consumption data of region i, C(t) is the total available electricity at time t, and ξ is the adjustment factor.

[0062] Among them, U i (t) At any given time point t, the amount of electricity that region i can receive is determined by the conflict resolution rule, which is a dynamic allocation of region i based on historical consumption data and current grid available power. The historical power consumption data of each region H i Used to assess the typical electricity demand in the area, it helps the system understand the electricity usage patterns of different areas under normal conditions, so that past consumption behavior can be taken into account when allocating resources.

[0063] At any time t, the total available power of the grid C(t) represents the maximum power resource that the current system can allocate. This total amount depends on the total power generation capacity of the grid and the current power demand. The adjustment factor ξ is a preset coefficient used to adjust the historical consumption data H i The power distribution can be adjusted according to different strategies and needs to reflect policy goals or priority strategies in a specific period of time.

[0064] Solution Rule U i (t) = min(ξ × H i C(t)) ensures that the power supply of each region or user does not exceed its adjusted demand based on history and the current power supply capacity of the system, which means that if ξ×H i If C(t) is less than or equal to C(t), then region i will receive electricity based on its adjusted historical demand. If C(t) is smaller, the supply to this region will be limited by the available power, even though its historical demand may be higher.

[0065] By taking historical consumption into account, the fairness of electricity distribution is ensured, so that high-consumption areas can obtain sufficient power supply when resources are sufficient, and reasonable restrictions are imposed when resources are tight. At the same time, the system optimizes the use of limited resources, reduces waste, and improves the overall efficiency of the power grid by dynamically adjusting the power supply in different areas. When faced with fluctuations in electricity demand or changes in power generation, this adjustment mechanism increases the adaptability and resilience of the power grid, ensuring stable operation under various circumstances.

[0066] The priority conflict resolution mechanism is further refined to include consideration of historical electricity consumption data, the continuity requirements of key services, and contractual priorities between users. By dynamically analyzing historical consumption patterns and contractual obligations, priorities are automatically adjusted to optimize electricity distribution and ensure the continued operation of key services when electricity supply is insufficient.

[0067] Among them, for historical electricity consumption data, the system analyzes the historical electricity consumption patterns of each user or region and identifies high-demand and low-demand periods. These data help predict future electricity demand and serve as an important factor in determining priorities;

[0068] In response to the continuous demand for critical services, specific users such as hospitals, fire stations, police stations and other facilities that provide critical public services are given priority in power distribution. The system ensures that these services can continue to receive the necessary power supply when there is a power shortage based on the continuous operation needs of these services;

[0069] Regarding contractual priorities between users, contracts between users may specify specific priorities in power allocation. These contractual priorities are encoded in the system and considered in the decision-making process to ensure compliance with contract terms.

[0070] The system uses an algorithm that combines real-time data (such as current power supply conditions, immediate power demand, etc.) with the three factors mentioned above to dynamically adjust the allocation of power resources, not only based on static settings, but also responding to real-time changes to optimize resource usage.

[0071] The priority control module works in conjunction with the central controller to automatically reduce the power supply priority of non-critical areas when it predicts that the total load of the power grid is about to exceed the production capacity, so as to maintain the stable operation of the power grid.

[0072] The collaborative control system includes a manual intervention interface that allows operators to manually adjust priority settings or directly resolve priority conflicts when necessary. The interface provides an intuitive user interface.

[0073] The manual intervention interface includes a graphical user interface that provides real-time data visualization, which can display grid status, PV power generation and load demand information, helping operators make fast and accurate decisions.

[0074] The graphical user interface further provides the function of reviewing historical data and simulating the results of priority adjustment. By simulating the potential impact of different priority strategies on grid load and stability, operators can optimize strategy implementation and improve the efficiency and effectiveness of grid management.

[0075] When the system predicts that the total load on the power grid is about to exceed the current production capacity, the priority control module automatically reduces the power supply priority of non-critical areas. This is achieved by real-time monitoring of the power grid status, photovoltaic power generation and actual power consumption, ensuring that critical areas such as hospitals and emergency services can continue to operate normally when power is tight.

[0076] The system includes a manual intervention interface that allows operators to manually adjust priority settings or directly resolve priority conflicts in specific situations. The interface is implemented through a graphical user interface, providing an intuitive operation interface that allows operators to quickly understand the grid status and make decisions.

[0077] The graphical user interface is designed to provide real-time data visualization of the power grid, including key information such as grid status, PV power generation, load demand, etc. In addition, the interface allows operators to review historical data and simulate the results of priority adjustments, which helps operators understand the possible impact of different priority strategies and optimize strategy implementation accordingly.

[0078] By automatically adjusting the power supply priority of non-critical areas, the system can effectively manage the grid load and avoid grid failures caused by overload, thereby maintaining the stable operation of the grid. At the same time, the design of the manual intervention interface and graphical user interface enables operators to quickly respond to grid changes and make necessary adjustments in a timely manner, enhancing the system's ability to handle emergencies.

[0079] The communication network module uses a high-security protocol to encrypt all transmitted data, including priority control commands and real-time grid status information, to prevent unauthorized access or tampering of the data.

[0080] Among them, the communication network module adopts advanced encryption protocols, such as SSL / TLS, AES or RSA, to encrypt all transmitted data, and uses complex algorithms to ensure the security of data during transmission, ensuring that only the recipient holding the correct key can decrypt and access the data.

[0081] In addition to encryption, the system may also use data integrity verification mechanisms, such as digital signatures or hash functions, to ensure that the data has not been tampered with during transmission, and any illegal modifications to the encrypted data will be detected when the data reaches the recipient.

[0082] Using high-security protocol encryption can prevent sensitive data such as power grid operation commands and real-time status information from being accessed or stolen without authorization, protect the system from external attacks, and improve system reliability by ensuring the security and integrity of data transmission. Power grid operators can trust system data for effective power grid management and decision-making.

[0083] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0084] In addition, it should be understood that although the present specification is described according to embodiments, not every embodiment contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation methods that those skilled in the art can understand.

Claims

1. A multi-level coordinated control system for low-voltage distributed photovoltaic access to a distribution network, characterized in that: Includes the following modules: Photovoltaic power generation module: used to convert solar energy into electrical energy and make preliminary voltage and current adjustments to meet subsequent power transmission and distribution needs; Distribution network access module: used to safely connect the power generated by the photovoltaic power generation module to the existing low-voltage distribution network and perform necessary power quality control; Multi-level control module: used to coordinate the operation of photovoltaic power generation modules and distribution network access modules as a whole, and optimize the distribution and use of electric energy. This module includes a local controller and a central controller. Local controller: configured in each photovoltaic power generation unit, responsible for collecting real-time data of the photovoltaic unit and executing basic output adjustment commands; Central controller: It is configured in the distribution network management center to receive data from local controllers and issue high-level control instructions based on the needs and status of the distribution network; Communication network module: used to realize data exchange and command transmission between local controller and central controller, ensure the real-time and accuracy of information, and support encrypted communication to protect data security; Priority Control Module: It is used to manage and optimize the allocation of power resources, especially during peak demand periods or sudden high demand in specific areas, to ensure that power supply to important areas or key facilities is given the highest priority.

2. According to claim 1, a multi-level coordinated control system for low-voltage distributed photovoltaic access to a distribution network is characterized in that: The priority control module includes a dynamic priority adjustment algorithm based on real-time grid load data L (t) , Photovoltaic power generation data G i (t) and weather forecast data W(t) are used to dynamically adjust the power distribution priority to optimize power usage and minimize power outages. The priority scoring formula is: P i (t)=α×L (t) +β×G i (t)+γ×W(t); Among them, α, β and γ are optimization parameters.

3. According to claim 2, a multi-level coordinated control system for low-voltage distributed photovoltaic access to a distribution network is characterized in that: The dynamic priority adjustment algorithm is further based on the priority strategy S set by the user. i , the strategy specifies the priority order of different users or regions in power distribution to ensure that critical infrastructure and high-demand users can get priority power supply when there is a power shortage. The priority scoring formula is expanded to: P i (t)=α×L (t) +β×G i (t)+γ×W(t)+δ×S i ; Where δ is the adjustment coefficient of the priority strategy.

4. According to claim 1, a multi-level coordinated control system for low-voltage distributed photovoltaic access to a distribution network is characterized in that: The system includes a priority conflict resolution mechanism, which is used to automatically determine the allocation of power resources according to a preset conflict resolution rule when multiple regions or users simultaneously meet the preset high priority requirements. The conflict resolution rule is: U i (t)=min(ξ×H i C(t)); Among them, U i (t) is the actual power supply of region i at time t, H i is the historical electricity consumption data of region i, C(t) is the total available electricity at time t, and ξ is the adjustment factor.

5. A multi-level coordinated control system for low-voltage distributed photovoltaic access to a distribution network according to claim 4, characterized in that: The priority conflict resolution mechanism is further refined to include consideration of historical electricity consumption data, continuity requirements for key services, and contract priorities between users. By dynamically analyzing historical consumption patterns and contract obligations, priorities are automatically adjusted to optimize electricity distribution and ensure the continued operation of key services when electricity supply is insufficient.

6. A multi-level coordinated control system for low-voltage distributed photovoltaic access to a distribution network according to claim 1, characterized in that: The priority control module works in coordination with the central controller to automatically reduce the power supply priority of non-critical areas when it is predicted that the total load of the power grid is about to exceed the production capacity, so as to maintain the stable operation of the power grid.

7. A multi-level coordinated control system for low-voltage distributed photovoltaic access to a distribution network according to claim 1, characterized in that: The collaborative control system includes a manual intervention interface that allows operators to manually adjust priority settings or directly resolve priority conflicts when necessary, and the interface provides an intuitive user interface.

8. A multi-level coordinated control system for low-voltage distributed photovoltaic access to a distribution network according to claim 7, characterized in that: The manual intervention interface includes a graphical user interface, which provides real-time data visualization and can display grid status, photovoltaic power generation and load demand information to help operators make fast and accurate decisions.

9. A multi-level coordinated control system for low-voltage distributed photovoltaic access to a distribution network according to claim 8, characterized in that: The graphical user interface further provides the function of reviewing historical data and simulating priority adjustment results, and allows operators to optimize strategy implementation and improve the efficiency and effectiveness of grid management by simulating the potential impact of different priority strategies on grid load and stability.

10. A multi-level coordinated control system for low-voltage distributed photovoltaic access to a distribution network according to claim 1, characterized in that: The communication network module uses a high-security protocol to encrypt all transmitted data, including priority control commands and real-time grid status information, to prevent unauthorized access or tampering of the data.

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