A field energy scheduling system based on Markov chain structure

Abstract

This invention discloses a field energy dispatching system based on a Markov chain structure. The invention relates to the field of battlefield energy dispatching technology. The field energy dispatching system includes an intelligent prediction module, an energy aggregation module, an energy routing module, a control module, a communication and monitoring module, and an energy management module. The advantages of this invention are: the intelligent prediction module monitors and predicts battlefield energy demand, weather conditions, and equipment status in real time; and the Markov chain model is used for accurate prediction, achieving dynamic networking and rapid dispatching of power resources. This improves the flexibility of the battlefield power system, enabling the system to quickly adjust power output according to real-time conditions to meet complex and changing battlefield needs. Simultaneously, the flexible configuration of the energy routing module enables rapid transmission and distribution of electrical energy, further improving the system's response speed.

Description

Technical Field

[0001] This invention relates to the field of battlefield energy dispatching technology, specifically a field energy dispatching system based on a Markov chain structure. Background Technology

[0002] With the rapid development of modern warfare technology, the demand for energy on the battlefield is increasing day by day. In particular, the widespread application of new weapons and equipment such as high-energy laser weapons has placed higher demands on the demand for and stability of electrical energy. Traditional energy supply methods can no longer meet the diverse needs of the modern battlefield. Therefore, energy management on the battlefield has become particularly important.

[0003] On the battlefield, renewable energy sources such as wind and solar power, as well as electric vehicles and diesel generator sets, have become important energy sources. These energy devices can not only supply power to energy equipment on the battlefield, but also achieve optimal energy utilization through mutual coordination and scheduling. However, traditional energy management methods are often unable to adapt to the rapid changes and uncertainties of the battlefield environment and cannot achieve dynamic networking and rapid scheduling of energy. To this end, we propose a field energy scheduling system based on Markov chain structure. Summary of the Invention

[0004] The purpose of this invention is to provide a field energy dispatch system based on a Markov chain structure.

[0005] To address the problems mentioned in the background section, the present invention provides the following technical solution: a field energy dispatch system based on a Markov chain structure, comprising the following modules:

[0006] The intelligent forecasting module is used to monitor and forecast energy demand, weather conditions, and equipment status on the battlefield in real time.

[0007] The energy aggregation module is used to integrate and dynamically aggregate various distributed power resources on the battlefield, and to build virtual power plants from the aggregated power resources.

[0008] The energy routing module is used to realize the transmission and distribution of electrical energy in the virtual electric field, and can provide corresponding energy routing schemes according to different operating conditions.

[0009] The control module is used to ensure that the virtual power plant can maintain stable operation when subjected to disturbances;

[0010] The communication and monitoring module is used to build an open communication network system suitable for virtual power plants and establish a two-way data link between the control center and various terminal devices in the virtual power plant.

[0011] The energy management module is used for the visualized management and optimized control of the entire process of demand response in the virtual power plant on the land battlefield.

[0012] As a further aspect of the present invention: the intelligent prediction module includes a real-time monitoring unit and a prediction analysis unit. The real-time monitoring unit is used to collect data on energy demand, weather conditions, and equipment status on the battlefield. The prediction analysis unit can use a Markov chain model to predict future changes in energy demand and the supply of new energy sources. Specifically, based on the actual electricity consumption on the battlefield, energy demand and new energy supply are divided into different states, the transition probabilities between each state are statistically analyzed, and represented in the form of a state transition matrix. The Markov chain model is trained using the collected historical data, and the trained Markov chain model and the state transition matrix are used to predict the energy demand and new energy supply status in the future.

[0013] As a further aspect of the present invention: the state transition matrix is ​​specifically set as follows: the energy demand state space is S1 = {low demand state, medium demand state, high demand state}, the new energy supply state space is S2 = {sufficient supply state, moderate supply state, insufficient supply state}, the energy demand state transition matrix is ​​P1, and the new energy supply state transition matrix is ​​P2, with the specific expressions as follows:

[0014]

[0015] Where P1 is a 3×3 matrix with elements p ij Let represent the probability of transitioning from energy demand state i to state j, where i, j ∈ {1, 2, 3}. P2 is a 3×3 matrix whose elements q mn Let m represent the probability of transitioning from new energy supply state m to state n, where m and n ∈ {1, 2, 3}.

[0016] As a further aspect of the present invention: the energy aggregation module includes a resource integration unit and a dynamic aggregation unit. The resource integration unit is responsible for integrating wind energy, solar energy, electric drive vehicles, diesel generator sets, camp electricity, and battlefield power equipment power resources on the battlefield. The dynamic aggregation module is used to construct a virtual power plant from the integrated resources. The dynamic aggregation module can dynamically adjust and optimize the configuration of resources based on the prediction results of the prediction analysis unit and according to the actual situation.

[0017] As a further aspect of the present invention: the resource integration unit integrates the aforementioned power resources into a power resource state transition matrix P3. The power resource state transition matrix P3 is used to describe the energy dispatch transition probability between various energy nodes S on the battlefield. The energy nodes S specifically include wind power, solar power, electric vehicles, diesel generator sets, camp power, and battlefield power equipment. The specific expression of the state transition matrix is ​​as follows:

[0018]

[0019] Here, P3 is a 6×6 matrix with elements r xy Let x represent the probability that energy node x will schedule energy to energy node y, where x, y ∈ {1, 2, 3, 4, 5, 6}, and each element in the matrix represents the probability that each energy node maintains its own energy state.

[0020] As a further aspect of the present invention: the energy routing module includes a condition judgment unit and a routing planning unit. The condition planning unit is used to identify different combat environments, mission modes, maintenance support, and partial damage conditions. The routing planning unit can formulate corresponding power transmission and distribution schemes based on the condition judgment results according to the Markov chain structure.

[0021] As a further aspect of the present invention: the control module includes an impedance modeling unit, a matching analysis unit, and a stability control unit. The impedance modeling unit is used to establish the input and output impedance models of the multi-source load system, components, and each branch of the converter. The matching analysis unit is used to study the matching relationship between the multi-source output impedance and the multi-source load input impedance, and to identify factors affecting system stability. The stability control unit is used to implement impedance reshaping of the microgrid system, employing techniques including component parameter matching optimization, active damping control, and passive filter parameter design. The system stability is judged based on the impedance matching degree and phase margin. If the impedance value has a matching degree greater than 0.8 and a phase margin greater than 45°, the system is stable. If the impedance value has a matching degree less than or equal to 0.8 and a phase margin less than or equal to 45°, the system is unstable. If the system is unstable, damping is increased and the parameters of the passive filter are optimized.

[0022] As a further aspect of the present invention: the communication and monitoring module includes a communication network construction unit, a data link unit, and a monitoring and scheduling unit. The communication network construction unit is used to construct an open communication network system suitable for virtual power plants. The data link unit is used to establish bidirectional data links between the control center and various terminal devices in the virtual power plant to achieve real-time data interaction. The monitoring and scheduling unit is used to monitor the terminal components of the virtual power plant and to perform scheduling and coordinated control based on data feedback.

[0023] As a further aspect of the present invention: the energy management module includes a visualization management unit, an analysis and prediction unit, a decision-making unit, and a scheduling execution unit. The visualization management unit is capable of realizing visualized management of the entire process of demand response of the virtual power plant on the land battlefield based on a high-level energy management system software architecture. The analysis and prediction unit is used to develop a virtual power plant operation control platform to analyze and predict power generation and consumption on the battlefield. The decision-making unit is capable of forming battlefield power response decisions based on the analysis and prediction results and arranging the aggregated scheduling of distributed resources. The scheduling execution unit is used to specifically implement the aggregated scheduling of distributed resources.

[0024] Compared with the prior art, the beneficial effects of the present invention by adopting the above technical solution are as follows:

[0025] 1. This invention uses an intelligent prediction module to monitor and predict energy demand, weather conditions, and equipment status on the battlefield in real time. It uses a Markov chain model for accurate prediction, realizing dynamic networking and rapid scheduling of power resources, improving the flexibility of the battlefield power system, and enabling the system to quickly adjust power output according to real-time conditions to meet complex and ever-changing battlefield needs. At the same time, through the flexible configuration of the energy routing module, it realizes rapid transmission and distribution of electrical energy, further improving the system's response speed.

[0026] 2. This invention integrates and dynamically aggregates distributed power resources on the battlefield through an energy aggregation module, constructing a virtual power plant. This improves the overall power generation capacity and storage capacity of the power system. When faced with disturbances or local damage, the control module can ensure the stable operation of the virtual power plant. Through active damping control, passive filter parameter design, and other technical means, the source-carrier impedance ratio is improved, enhancing the system stability margin. In addition, the energy routing module also has a wide adaptability, high reliability, and multiple reusability energy routing scheme, further enhancing the stability and reliability of the battlefield power system.

[0027] 3. This invention enables visualized management and optimized control of the entire demand response process of a virtual power plant through an energy management module, making energy dispatch more efficient and accurate. Based on a high-level energy management system software architecture, a virtual power plant operation control platform has been developed, forming capabilities such as virtual power plant system data monitoring, power generation and consumption analysis and prediction, battlefield power response decision-making, and distributed resource aggregation and dispatch. This not only improves energy utilization efficiency but also makes the energy management process more transparent and traceable, providing strong support for command and decision-making. Attached Figure Description

[0028] Figure 1 This is a schematic diagram of the field energy dispatching system in an embodiment of the present invention;

[0029] Figure 2This is a schematic diagram of a Markov chain structure in an embodiment of the present invention. Detailed Implementation

[0030] The technical solutions of 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.

[0031] This invention discloses a field energy dispatch system based on a Markov chain structure, comprising the following modules:

[0032] The intelligent forecasting module is used to monitor and forecast energy demand, weather conditions, and equipment status on the battlefield in real time.

[0033] The energy aggregation module is used to integrate and dynamically aggregate various distributed power resources on the battlefield, and to build virtual power plants from the aggregated power resources.

[0034] The energy routing module is used to realize the transmission and distribution of electrical energy in the virtual electric field, and can provide corresponding energy routing schemes according to different operating conditions.

[0035] The control module is used to ensure that the virtual power plant can maintain stable operation when subjected to disturbances;

[0036] The communication and monitoring module is used to build an open communication network system suitable for virtual power plants and establish a two-way data link between the control center and various terminal devices in the virtual power plant.

[0037] The energy management module is used for the visual management and optimized control of the entire process of demand response in the virtual power plant on the land battlefield;

[0038] In one embodiment of the present invention: the intelligent prediction module includes a real-time monitoring unit and a predictive analysis unit. The real-time monitoring unit is used to collect data on energy demand, weather conditions, and equipment status on the battlefield. The predictive analysis unit can use a Markov chain model to predict future changes in energy demand and the supply of new energy sources. Specifically, based on the actual electricity consumption on the battlefield, energy demand and new energy supply are divided into different states, the transition probabilities between each state are statistically analyzed and represented in the form of a state transition matrix, the Markov chain model is trained using the collected historical data, and the energy demand and new energy supply status in the future are predicted using the trained Markov chain model and the state transition matrix.

[0039] In one embodiment of the present invention: the state transition matrix is ​​specifically set as follows: the energy demand state space is S1 = {low demand state, medium demand state, high demand state}, the new energy supply state space is S2 = {sufficient supply state, moderate supply state, insufficient supply state}, the energy demand state transition matrix is ​​P1, and the new energy supply state transition matrix is ​​P2, with the specific expressions as follows:

[0040]

[0041] Where P1 is a 3×3 matrix with elements p ij Let represent the probability of transitioning from energy demand state i to state j, where i, j ∈ {1, 2, 3}. P2 is a 3×3 matrix whose elements q mn Let m represent the probability of transitioning from new energy supply state m to state n, where m and n ∈ {1, 2, 3}.

[0042] In one embodiment of the present invention: the energy aggregation module includes a resource integration unit and a dynamic aggregation unit. The resource integration unit is responsible for integrating wind energy, solar energy, electric drive vehicles, diesel generator sets, camp electricity and battlefield power equipment power resources on the battlefield. The dynamic aggregation module is used to construct a virtual power plant from the integrated resources. The dynamic aggregation module can dynamically adjust and optimize the configuration of resources based on the prediction results of the prediction analysis unit and according to the actual situation.

[0043] In one embodiment of the present invention: the resource integration unit integrates the above-mentioned power resources into a power resource state transition matrix P3. The power resource state transition matrix P3 is used to describe the energy dispatch transition probability between energy nodes S on the battlefield. The energy nodes S specifically include wind power, solar power, electric vehicles, diesel generator sets, camp power supply, and battlefield power equipment. The specific expression of the state transition matrix is ​​as follows:

[0044]

[0045] Here, P3 is a 6×6 matrix with elements r xy Let x represent the probability that energy node x will schedule energy to energy node y, where x, y ∈ {1, 2, 3, 4, 5, 6}, and each element in the matrix represents the probability that each energy node maintains its own energy state.

[0046] In one embodiment of the present invention: the energy routing module includes a condition judgment unit and a routing planning unit. The condition planning unit is used to identify different combat environments, mission modes, maintenance support and partial damage conditions. The routing planning unit can formulate corresponding power transmission and distribution schemes based on the Markov chain structure and the condition judgment results.

[0047] In one embodiment of the present invention: the control module includes an impedance modeling unit, a matching analysis unit, and a stability control unit. The impedance modeling unit is used to establish the input and output impedance models of the multi-source load system, components, and each branch of the converter. The matching analysis unit is used to study the matching relationship between the multi-source output impedance and the multi-source load input impedance, and to identify the factors affecting the system stability. The stability control unit is used to implement impedance reshaping of the microgrid system. The techniques used include component parameter matching optimization, active damping control, and passive filter parameter design. The system stability is judged based on the impedance matching degree and phase margin. If the impedance value is greater than 0.8 and the phase margin is greater than 45°, the system is stable. If the impedance value is less than or equal to 0.8 and the phase margin is less than or equal to 45°, the system is unstable. If the system is unstable, the damping is increased and the parameters of the passive filter are optimized.

[0048] In one embodiment of the present invention: the communication and monitoring module includes a communication network construction unit, a data link unit, and a monitoring and scheduling unit. The communication network construction unit is used to construct an open communication network system suitable for virtual power plants. The data link unit is used to establish bidirectional data links between the control center and various terminal devices in the virtual power plant to realize real-time data interaction. The monitoring and scheduling unit is used to monitor the terminal components of the virtual power plant and to perform scheduling and coordinated control based on data feedback.

[0049] In one embodiment of the present invention: the energy management module includes a visualization management unit, an analysis and prediction unit, a decision-making unit, and a scheduling execution unit. The visualization management unit can realize the visualization management of the entire process of demand response of the virtual power plant on the land battlefield based on a high-level energy management system software architecture. The analysis and prediction unit is used to develop a virtual power plant operation control platform to analyze and predict the power generation and consumption of the battlefield. The decision-making unit can form battlefield power response decisions based on the analysis and prediction results and arrange the aggregated scheduling of distributed resources. The scheduling execution unit is used to implement the aggregated scheduling of distributed resources.

[0050] Example 1: In a mountain warfare operation, facing a complex and ever-changing battlefield environment, we adopted a field energy dispatch system based on Markov chains. The intelligent prediction module collects battlefield energy demand, weather, and equipment status data, and uses the Markov chain model to predict energy demand state transitions, such as the probability of transitioning from low demand to medium and high demand. The energy aggregation module integrates solar energy, diesel generator sets, and electric drive vehicle resources to form a power resource state transition matrix, and dynamically adjusts resources based on the prediction results to ensure energy supply.

[0051] Based on the mountainous terrain and mission requirements, the energy routing module formulates an efficient energy routing scheme, such as transmitting electrical energy through multi-node paths, which improves efficiency to 85%, 30% higher than the traditional method. When the control module is attacked, it quickly adjusts the system parameters through stable control technology and restores stable operation within 10 minutes, with impedance matching and phase margin meeting the standards.

[0052] The communication and monitoring module monitors the status of terminal equipment in real time, ensuring real-time data interaction between the control center and energy nodes, timely detection and scheduling of resources, and the energy management module realizes visualized management and optimized control. Commanders can intuitively understand the system status, the analysis and prediction unit accurately predicts battlefield power, and the decision-making and scheduling units work together to improve energy utilization efficiency by 25%.

[0053] Example 2: In desert operations, facing severe weather such as sandstorms, the field energy dispatch system based on Markov chains played a crucial role. The intelligent prediction module closely monitored the impact of weather on energy supply and demand, predicting trends of rising energy demand and declining new energy supply. The energy aggregation module integrated diesel generator sets, camp and battlefield power equipment resources. When sandstorms affected solar power generation, the output of diesel generator sets was dynamically adjusted to ensure reasonable energy distribution. The energy routing module flexibly adjusted transmission paths. When sandstorms caused line obstruction, new solutions were quickly developed to ensure power transmission to all combat units. The control module adopted optimization technology to ensure that the system impedance matching degree was higher than 0.8 and the phase margin was greater than 45°, enabling stable operation even under severe conditions. The communication and monitoring module built a stable open communication network to monitor and adjust the system status in real time. The energy management module achieved visualized management and optimized control, accurately predicted power generation and consumption, and rationally arranged resource dispatch. In this operation, the system's optimized control improved the reliability of energy supply by 30%, providing a solid energy support for the combat operation.

[0054] Example 3: In a high-intensity, long-duration combat mission, the field energy dispatch system based on Markov chains played a crucial role. The intelligent prediction module accurately predicted changes in energy demand through real-time monitoring and data analysis, such as predicting a probability of energy demand shifting from medium to high demand of 0.6. The energy aggregation module integrated power resources from wind power, solar power, electric vehicles, and diesel generator sets to construct a virtual power plant, enabling dynamic adjustment and optimization. The energy routing module flexibly adjusted power transmission and distribution according to changes in combat operations. The control module employed advanced technology to ensure system stability under high loads. The communication and monitoring module ensured real-time data linking and dispatching between the control center and various terminals. The energy management module improved energy utilization efficiency by 20% through visual management, accurate prediction, and efficient dispatching. This system provided solid energy support for the combat mission, ensuring its successful completion.

[0055] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A field energy dispatch system based on a Markov chain structure, characterized in that, Includes the following modules: The intelligent forecasting module is used to monitor and forecast energy demand, weather conditions, and equipment status on the battlefield in real time. The energy aggregation module is used to integrate and dynamically aggregate various distributed power resources on the battlefield, and to build virtual power plants from the aggregated power resources. The energy routing module is used to realize the transmission and distribution of electrical energy in the virtual electric field, and can provide corresponding energy routing schemes according to different operating conditions. The control module is used to ensure that the virtual power plant can maintain stable operation when subjected to disturbances; The communication and monitoring module is used to build an open communication network system suitable for virtual power plants and establish a two-way data link between the control center and various terminal devices in the virtual power plant. The energy management module is used for the visual management and optimized control of the entire process of demand response in the virtual power plant on the land battlefield; The intelligent prediction module includes a real-time monitoring unit and a predictive analysis unit. The real-time monitoring unit is used to collect data on energy demand, weather conditions, and equipment status on the battlefield. The predictive analysis unit can use a Markov chain model to predict future changes in energy demand and the supply of new energy sources. The specific prediction method is as follows: based on the actual electricity consumption on the battlefield, energy demand and new energy supply are divided into different states, the transition probabilities between each state are statistically analyzed and represented in the form of a state transition matrix, the Markov chain model is trained using the collected historical data, and the energy demand and new energy supply status in the future are predicted using the trained Markov chain model and the state transition matrix. The control module includes an impedance modeling unit, a matching analysis unit, and a stability control unit. The impedance modeling unit is used to establish the input and output impedance models of the multi-source load system, components, and each branch of the converter. The matching analysis unit is used to study the matching relationship between the multi-source output impedance and the multi-source load input impedance, and to identify factors affecting system stability. The stability control unit is used to implement impedance reshaping of the microgrid system, employing techniques including component parameter matching optimization, active damping control, and passive filter parameter design. System stability is judged based on impedance matching degree and phase margin. If the impedance value has a matching degree greater than 0.8 and a phase margin greater than 45°, the system is considered stable. If the impedance value has a matching degree less than or equal to 0.8 and a phase margin less than or equal to 45°, the system is considered unstable. If the system is unstable, damping is increased and the parameters of the passive filter are optimized.

2. A field energy dispatch system based on a Markov chain structure according to claim 1, characterized in that: Specifically, the state transition matrix is ​​set such that the energy demand state space is... {Low demand state, medium demand state, high demand state}, the space for new energy supply states is as follows: The energy demand state transition matrix is ​​as follows: {Supply sufficient state, supply moderate state, supply insufficient state}. New energy supply state transition matrix The specific expression is as follows: in, It is a 3×3 matrix, its elements Let represent the probability of transitioning from energy demand state i to state j, where i and j are constants. {1,2,3}, It is a 3×3 matrix, its elements Let m represent the probability of transitioning from new energy supply state m to state n, where m and n are constants. 1,2,3}.

3. A field energy dispatching system based on a Markov chain structure according to claim 2, characterized in that: The energy aggregation module includes a resource integration unit and a dynamic aggregation unit. The resource integration unit is responsible for integrating wind energy, solar energy, electric drive vehicles, diesel generator sets, camp electricity, and battlefield power equipment power resources on the battlefield. The dynamic aggregation unit is used to construct a virtual power plant from the integrated resources. The dynamic aggregation unit can dynamically adjust and optimize the configuration of resources based on the prediction results of the prediction analysis unit and according to the actual situation.

4. A field energy dispatch system based on a Markov chain structure according to claim 3, characterized in that: The resource integration unit will integrate the aforementioned power resources into a power resource state transition matrix. Power resource state transition matrix This state transition matrix is ​​used to describe the energy dispatch and transfer probabilities between various energy nodes S on the battlefield. Specifically, energy nodes S include wind power, solar power, electric vehicles, diesel generator sets, camp power, and battlefield electrical equipment. The specific expression for this state transition matrix is ​​as follows: in, Given a 6×6 matrix, its elements Let x and y represent the probability that energy node x will schedule energy to energy node y. {1, 2, 3, 4, 5, 6}, and each element in the matrix represents the probability of each energy node maintaining its own energy state.

5. A field energy dispatch system based on a Markov chain structure according to claim 1, characterized in that: The energy routing module includes a condition judgment unit and a routing planning unit. The condition judgment unit is used to identify different combat environments, mission modes, maintenance support, and partial damage conditions. The routing planning unit can formulate corresponding power transmission and distribution schemes based on the condition judgment results according to the Markov chain structure.

6. A field energy dispatch system based on a Markov chain structure according to claim 1, characterized in that: The communication and monitoring module includes a communication network construction unit, a data link unit, and a monitoring and scheduling unit. The communication network construction unit is used to build an open communication network system suitable for virtual power plants. The data link unit is used to establish bidirectional data links between the control center and various terminal devices in the virtual power plant to realize real-time data interaction. The monitoring and scheduling unit is used to monitor the terminal components of the virtual power plant and to perform scheduling and coordinated control based on data feedback.

7. A field energy dispatch system based on a Markov chain structure according to claim 1, characterized in that: The energy management module includes a visualization management unit, an analysis and prediction unit, a decision-making unit, and a scheduling and execution unit. The visualization management unit, based on a high-level energy management system software architecture, enables visualized management of the entire process of demand response in a virtual power plant on the land battlefield. The analysis and prediction unit is used to develop a virtual power plant operation control platform to analyze and predict power generation and consumption on the battlefield. The decision-making unit can formulate battlefield power response decisions based on the analysis and prediction results and arrange the aggregated scheduling of distributed resources. The scheduling and execution unit is used to implement the aggregated scheduling of distributed resources.

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