A dynamic scheduling method and system for multiple energy storage cabinets

Abstract

The present invention discloses a method and system for dynamic scheduling of multiple energy storage cabinets, comprising: obtaining a registration request from each energy storage cabinet, assigning a unique identifier to each energy storage cabinet, and then establishing a UDP communication connection with each energy storage cabinet; obtaining operating status data periodically sent by each energy storage cabinet; calculating real-time load demand based on a power grid electricity price signal, generating a scheduling instruction based on the operating status data and the real-time load demand, and sending the scheduling instruction to the corresponding energy storage cabinet via a communication connection. The present invention uses UDP to establish a connection with each energy storage cabinet, realizing the application of the UDP protocol in the master-slave architecture of the energy storage system, significantly reducing communication delays, meeting the requirements of dynamic scheduling in seconds, enabling the energy storage system master control terminal to obtain the operating status data of each energy storage cabinet in real time, and quickly generating corresponding scheduling instructions based on the real-time load demand and electricity price signal, thereby realizing efficient coordinated control and dynamic energy distribution of multiple energy storage cabinets and improving the response speed and scheduling flexibility of the energy storage system.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage systems, and in particular to a dynamic scheduling method and system for multiple energy storage cabinets. Background Art

[0002] With the large-scale integration of distributed renewable energy, energy storage systems must possess high scalability, dynamic response capabilities, and coordinated control capabilities. Traditional single-unit energy management system (EMS) architectures suffer from communication delays and insufficient scheduling flexibility when managing multiple energy storage cabinets. They also struggle to achieve independent control of battery clusters and global energy optimization. Furthermore, existing ring network communication topology designs for energy storage cabinets have yet to effectively address multi-node real-time data synchronization and redundant fault tolerance.

[0003] Traditional energy storage systems employ a master-slave EMS architecture that typically uses the TCP / IP protocol for data exchange. However, due to the handshake confirmation mechanism, communication latency is high (typically ≥100ms), making it difficult to meet the demands of dynamic scheduling within seconds. Furthermore, the existing TCP-based retransmission mechanism is prone to data congestion, resulting in significant errors in the coordinated control of multiple energy storage cabinets. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a dynamic scheduling method and system for multiple energy storage cabinets to achieve efficient coordinated control and dynamic energy distribution of multiple energy storage cabinets.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A method for dynamic scheduling of multiple energy storage cabinets, comprising the steps of:

[0007] Obtain a registration request from each energy storage cabinet, assign a unique identifier to each energy storage cabinet, and then establish a UDP communication connection with each energy storage cabinet; obtain operating status data periodically sent by each energy storage cabinet; calculate real-time load demand based on the obtained grid electricity price signal, generate a scheduling instruction based on the operating status data and the real-time load demand, and send the scheduling instruction to the corresponding energy storage cabinet via the communication connection.

[0008] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0009] A multi-energy storage cabinet dynamic scheduling system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the multi-energy storage cabinet dynamic scheduling method are implemented.

[0010] The beneficial effects of the present invention are: providing a dynamic scheduling method and system for multiple energy storage cabinets, by establishing a connection with each energy storage cabinet using the UDP communication protocol, realizing the application of the UDP protocol in the master-slave architecture of the energy storage system, significantly reducing communication delay, meeting the requirements of second-level dynamic scheduling, enabling the energy storage system master control unit to obtain the operating status data of each energy storage cabinet in real time, and quickly generating corresponding scheduling instructions based on real-time load demand and electricity price signals, thereby realizing efficient coordinated control and dynamic energy distribution of multiple energy storage cabinets, and improving the response speed and scheduling flexibility of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 This is an overall flow chart of a dynamic scheduling method for multiple energy storage cabinets according to an embodiment of the present invention;

[0012] Figure 2 A diagram of the master-slave architecture of the energy storage system used in an embodiment of the present invention;

[0013] Figure 3 Schematic diagram of the structure of a dynamic scheduling system for multiple energy storage cabinets according to an embodiment of the present invention.

[0014] Description of labels:

[0015] 1. A dynamic scheduling system for multiple energy storage cabinets; 2. Memory; 3. Processor. DETAILED DESCRIPTION

[0016] To illustrate the technical content, achieved objectives and effects of the present invention in detail, the following description is given in conjunction with the embodiments and accompanying drawings.

[0017] Please refer to Figure 1 and Figure 2 , a multi-energy storage cabinet dynamic scheduling method, comprising the steps of:

[0018] Obtain a registration request from each energy storage cabinet, assign a unique identifier to each energy storage cabinet, and then establish a UDP communication connection with each energy storage cabinet; obtain operating status data periodically sent by each energy storage cabinet; calculate real-time load demand based on the obtained grid electricity price signal, generate a scheduling instruction based on the operating status data and the real-time load demand, and send the scheduling instruction to the corresponding energy storage cabinet via the communication connection.

[0019] As can be seen from the foregoing description, the beneficial effects of the present invention are as follows: by adopting the UDP communication protocol to establish a connection with each energy storage cabinet, the application of the UDP protocol in the master-slave architecture of the energy storage system is realized, which significantly reduces communication latency and meets the requirements of dynamic scheduling in seconds. This enables the energy storage system master control end to obtain the operating status data of each energy storage cabinet in real time and quickly generate corresponding scheduling instructions based on real-time load demand and electricity price signals, thereby achieving efficient coordinated control and dynamic energy distribution of multiple energy storage cabinets, and improving the response speed and scheduling flexibility of the energy storage system.

[0020] Furthermore, after allocating a unique identifier to each energy storage cabinet, the method further includes:

[0021] The topology information of each energy storage cabinet is recorded, where the topology information includes the IP address of each energy storage cabinet and the assigned unique identifier.

[0022] As can be seen from the above description, by recording the IP address and unique identifier of the energy storage cabinet, the main control unit can accurately manage the topology of multiple energy storage cabinets, facilitating subsequent dynamic scheduling, maintenance, and troubleshooting.

[0023] Furthermore, the acquiring of the operating status data periodically sent by each of the energy storage cabinets includes:

[0024] The operating status data packaged and sent by each of the energy storage cabinets through the communication connection is obtained, where the operating status data is in a UDP data packet format.

[0025] As can be seen from the above description, the use of UDP data packet format to transmit operation status data reduces UDP protocol overhead and communication delay. At the same time, the lightweight data packet design optimizes network bandwidth utilization and ensures the efficiency of real-time data interaction. It is particularly suitable for high-frequency periodic data reporting scenarios.

[0026] Furthermore, after obtaining the operating status data periodically sent by each energy storage cabinet, the method further includes:

[0027] A redundancy check and time stamp mechanism is used to filter duplicate data and timed-out data in the running status data, and the duplicate data and timed-out data are discarded.

[0028] From the above description, it can be seen that through the redundant check and timestamp mechanism, duplicate or timed-out invalid data can be effectively filtered, thus avoiding the generation of data redundancy and erroneous instructions, improving data reliability and system stability, and reducing the processing burden of the main control unit.

[0029] Furthermore, the operating status data includes the battery SOC, battery temperature, battery charge and discharge voltage, battery charge and discharge current, and PCS real-time power of each energy storage cabinet.

[0030] As can be seen from the above description, the operating status data covers key parameters such as battery SOC, temperature, voltage, current, and PCS power, providing the main control unit with comprehensive energy storage cabinet status information. This makes the dispatch instructions generated based on this data more accurate, enabling the optimization of charging and discharging strategies, extending battery life, and improving the overall efficiency of the energy storage system.

[0031] Furthermore, the real-time load demand is calculated based on the acquired grid electricity price signal, and the dispatch instruction is generated based on the operating status data and the real-time load demand, specifically:

[0032] Collect the real-time power of the AC meter on the current grid side, combine the real-time power and the current grid electricity price signal to determine the peak and valley periods of the current grid, and obtain the real-time load demand of the grid-side transformer under the conditions where the grid is not overcharged or the energy storage system is not discharged in reverse according to the peak and valley periods of the current grid;

[0033] A scheduling instruction with different priorities is generated for each of the energy storage cabinets according to the real-time load demand and the operating status data.

[0034] As can be seen from the above description, by combining the real-time power of the grid and peak and valley electricity price signals, the charging and discharging strategies of the energy storage cabinets are dynamically adjusted. This maximizes the economic benefits of valley-time charging and peak-time discharging, without overloading the transformer or preventing reverse current in the system. This achieves global energy optimization and grid load balancing.

[0035] Furthermore, the sending of the scheduling instruction to the corresponding energy storage cabinet via the communication connection is specifically as follows:

[0036] Distribute the scheduling instructions to the corresponding energy storage cabinets for execution according to priority, and receive execution confirmation packets returned by each energy storage cabinet;

[0037] It is determined according to the unique identifier carried in the execution confirmation packet whether there is an energy storage cabinet that has not returned the execution confirmation packet, and a retransmission mechanism is triggered for the energy storage cabinet that has not returned the execution confirmation packet.

[0038] As can be seen from the above description, the priority distribution and confirmation packet feedback mechanism ensures the reliable transmission of scheduling instructions. At the same time, a retransmission mechanism is introduced to actively retransmit unconfirmed instructions a limited number of times, balancing the unreliability of the UDP protocol with the energy storage system's high reliability requirements for key scheduling instructions.

[0039] Furthermore, before sending the scheduling instruction to the corresponding energy storage cabinet through the communication connection, the method further includes:

[0040] The scheduling instruction is packaged into a binary data packet of UDP data.

[0041] From the above description, it can be seen that packaging scheduling instructions into UDP binary data packets reduces the complexity and delay of data transmission. UDP binary data packets, as a standardized data format, can facilitate rapid parsing and execution between master-slave architectures, further improving the real-time performance and response speed of the system.

[0042] Furthermore, the step of obtaining the operating status data periodically sent by each energy storage cabinet further includes:

[0043] When the operating status data of the energy storage cabinet is not obtained for a preset period of time, the energy storage cabinet is marked as being in an offline state, and the dispatching instruction is stopped from being issued to the energy storage cabinet in the offline state.

[0044] As can be seen from the above description, through the periodic operation status data monitoring mechanism and offline marking mechanism, faulty or disconnected energy storage cabinets are promptly discovered and the scheduling strategy is dynamically adjusted, which enhances the system's fault tolerance and self-healing capabilities and ensures stable operation even when some energy storage cabinet nodes fail.

[0045] Please refer to Figure 3 A multi-energy storage cabinet dynamic scheduling system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned multi-energy storage cabinet dynamic scheduling method are implemented.

[0046] The present invention provides a multi-energy storage cabinet dynamic scheduling method and system, which is applicable to independent control of battery clusters and global energy dynamic scheduling scenarios under a master-slave architecture of an energy storage system, and is described below with reference to specific embodiments.

[0047] Please refer to Figure 1 and Figure 2 , embodiment 1 of the present invention is:

[0048] A dynamic scheduling method for multiple energy storage cabinets adopts a master-slave architecture of energy storage system. Figure 2 As shown, in this embodiment, the master control unit (i.e., the central server of the energy storage system) serves as the control end, responsible for generating global scheduling strategies, status monitoring, and issuing instructions. Each energy storage cabinet serves as a slave control unit, executing instructions issued by the master control unit and providing feedback on local operating status (such as SOC, temperature, voltage, etc.). A master-slave architecture is constructed, and a UDP communication module is deployed in the master control unit to establish a UDP communication connection with each slave control unit (each energy storage cabinet) to fully utilize the connectionless and low-latency (usually <10ms) characteristics of UDP. The dynamic scheduling steps of each energy storage cabinet are as follows: Figure 1 As shown:

[0049] S1. Obtain registration requests from each energy storage cabinet, assign a unique identifier to each energy storage cabinet, and establish a UDP communication connection with each energy storage cabinet. Each energy storage cabinet can request registration from the main control unit via UDP broadcast.

[0050] After assigning a unique identifier to each energy storage cabinet, the following steps are also included:

[0051] Record the topology information of each energy storage cabinet, where the topology information includes the IP address and assigned unique identifier of each energy storage cabinet.

[0052] That is, the master control unit accurately manages the topology of multiple energy storage cabinets by recording the IP addresses and unique identifiers of the energy storage cabinets (slave control units), ensuring that the energy storage system under the master-slave architecture can support plug-and-play and dynamic expansion of energy storage cabinets. While ensuring the accuracy of communication and subsequent command issuance, it also enhances the system's scalability and fault tolerance, facilitating maintenance and troubleshooting.

[0053] S2. Obtain the operating status data sent periodically (for example, every second or every 500ms) by each energy storage cabinet, where Figure 2 As shown, the operating status data includes but is not limited to the battery SOC, battery temperature, battery charge and discharge voltage, battery charge and discharge current, and PCS real-time power of each energy storage cabinet.

[0054] That is, the operating status data covers key parameters such as battery SOC, temperature, voltage, current and PCS power, providing the main control unit with comprehensive energy storage cabinet status information, making the dispatch instructions generated based on this data more accurate, able to optimize charging and discharging strategies, extend battery life and improve the overall efficiency of the energy storage system.

[0055] In this embodiment, the operating status data is in the format of a UDP data packet and is packaged and sent by each energy storage cabinet via a UDP communication connection so as to be acquired.

[0056] That is, the UDP data packet format is used to transmit the operation status data, which reduces the UDP protocol overhead and communication delay. At the same time, the lightweight data packet design optimizes the network bandwidth utilization and ensures the efficiency of real-time data interaction. It is particularly suitable for high-frequency periodic data reporting scenarios. The UDP data packet format can be designed to include a sequence number, checksum, instruction type, and a CRC checksum at the end. In this embodiment, the UDP protocol content reference is shown in Table 1 below:

[0057] Table 1: UDP protocol content

[0058]

[0059] S3. Calculate the real-time load demand based on the acquired grid electricity price signal, generate a dispatch instruction based on the real-time load demand and operating status data, and send the dispatch instruction to the corresponding energy storage cabinet through the UDP communication connection.

[0060] That is, in this embodiment, by using the UDP communication protocol to establish a connection with each energy storage cabinet, the UDP protocol is applied in the master-slave architecture of the energy storage system, which significantly reduces communication latency and meets the requirements of dynamic scheduling in seconds. This enables the energy storage system master control end to obtain the operating status data of each energy storage cabinet in real time and quickly generate corresponding scheduling instructions based on real-time load demand and electricity price signals. This achieves efficient coordinated control and dynamic energy distribution of multiple energy storage cabinets, improving the response speed and scheduling flexibility of the energy storage system.

[0061] In addition, in this embodiment, step S2 also includes:

[0062] The operation status data periodically sent by each energy storage cabinet is obtained, including:

[0063] If the operating status data of the energy storage cabinet is not obtained within a preset time (which can be set to 6 seconds), the energy storage cabinet is marked as offline and the dispatching instructions to the offline energy storage cabinet are stopped.

[0064] That is, periodic operating status data is also used for heartbeat monitoring. Combined with the offline marking mechanism, this ensures timely detection of faulty or disconnected energy storage cabinets and dynamic adjustment of scheduling strategies. This enhances the system's fault tolerance and self-healing capabilities, ensuring stable operation even when some energy storage cabinet nodes fail. It is worth noting that when scheduling instructions are stopped for an offline energy storage cabinet, the operating status data of that energy storage cabinet will still be periodically obtained. When the operating status data of an energy storage cabinet marked as offline is recovered, scheduling instructions will be subsequently restarted to ensure that the temporarily failed energy storage cabinet resumes operation.

[0065] The second embodiment of the present invention is:

[0066] A method for dynamic scheduling of multiple energy storage cabinets, based on the above embodiment 1, in this embodiment, after step S2, further includes:

[0067] Redundancy check and time stamp mechanisms are used to filter duplicate and timed data in the running status data, and duplicate and timed data are discarded.

[0068] That is, in this embodiment, through the redundant check and timestamp mechanism, duplicate or timed-out invalid data is effectively filtered, thus avoiding the generation of data redundancy and erroneous instructions, improving data reliability and system stability, and reducing the processing burden of the main control unit.

[0069] At the same time, in this embodiment, in step S3, the real-time load demand is calculated according to the electricity price signal, and the scheduling instruction is generated according to the operating status data and the operating status data, specifically:

[0070] The real-time power of the AC meter on the current grid side is collected, and the peak and valley periods of the current grid are determined by combining the real-time power and the current grid electricity price signal. Based on the peak and valley periods of the current grid, the real-time load demand of the grid-side transformer is obtained when the grid is not overloaded during charging or the energy storage system is not discharged in reverse.

[0071] For example, if the current grid electricity price signal indicates that it is off-peak time, the total charging power from the grid to each energy storage cabinet must ensure that the transformer is not overloaded. In other words, the charging power allowed by the main control unit = transformer overload protection threshold - grid-side AC meter real-time power + energy storage gateway meter power. If the current grid electricity price signal indicates that it is peak time, the discharge of each energy storage cabinet must ensure that there is no reverse flow to the grid. In other words, the discharge power allowed by the main control unit = (grid-side AC meter real-time power - energy storage gateway meter power) × load power. The load power can be set based on the grid factor (default 0.85). Whether the transformer is overloaded can be determined by whether the grid-side AC meter power is greater than the transformer's rated capacity (i.e., the overload protection threshold). Whether the discharge generates reverse flow (i.e., whether it is discharging to the grid) can be determined by whether the grid-side AC meter power is less than 0.

[0072] The master control unit then generates scheduling instructions of different priorities for each energy storage cabinet based on real-time load demand and operating status data. For example, the corresponding proportional distribution of maximum chargeable and discharge power is calculated: for example, the charging weight coefficient is calculated as the maximum chargeable power of the slave control unit / SUM (maximum chargeable power of the slave control unit), and the charging power of the slave control unit is issued as the dispatched charging power × weight coefficient. Subsequent scheduling instructions can be executed by the PCS of each energy storage cabinet.

[0073] That is, by combining the real-time power of the power grid and the peak and valley electricity price signals, the charging and discharging strategies of the energy storage cabinet are dynamically adjusted. This maximizes the economic efficiency of valley charging and peak discharging without overloading the transformer or reverse current in the system, thus achieving global energy optimization and grid load balance.

[0074] Then, in step S3, the scheduling instruction is sent to the corresponding energy storage cabinet via the UDP communication connection, specifically:

[0075] Distribute the scheduling instructions to the corresponding energy storage cabinets for execution according to priority, and receive the execution confirmation packets returned by each energy storage cabinet.

[0076] Based on the unique identifier carried in the execution confirmation packet, it is determined whether there are any energy storage cabinets that have not returned the execution confirmation packet, and the retransmission mechanism is triggered for the energy storage cabinets that have not returned the execution confirmation packet. The number of triggers of the retransmission mechanism can be set according to specific needs, so that multiple transmissions can be executed to avoid packet loss.

[0077] That is, through the priority distribution and confirmation packet feedback mechanism, the reliable transmission of scheduling instructions is ensured. At the same time, a retransmission mechanism is introduced to actively retransmit unconfirmed instructions a limited number of times, balancing the unreliability of the UDP protocol and the energy storage system's high reliability requirements for key scheduling instructions.

[0078] In addition, in this embodiment, before sending the scheduling instruction to the corresponding energy storage cabinet via the UDP communication connection in step S3, the following steps are further included:

[0079] Pack scheduling instructions into UDP data binary packets.

[0080] The scheduling instructions are packaged into UDP binary data packets, which reduces the complexity and delay of data transmission. As a standardized data format, UDP binary data packets can facilitate rapid parsing and execution between the master-slave architecture, further improving the real-time performance and response speed of the system.

[0081] Please refer to Figure 3 , the third embodiment of the present invention is:

[0082] A multi-energy storage cabinet dynamic scheduling system 1 includes a memory 2, a processor 3, and a computer program stored in the memory 2 and executable on the processor 3. When the processor 3 executes the computer program, the steps of the multi-energy storage cabinet dynamic scheduling method described in the first or second embodiment are implemented.

[0083] In summary, the present invention provides a method and system for dynamic scheduling of multiple energy storage cabinets. By using the UDP communication protocol to establish connections with each energy storage cabinet, the UDP protocol is applied in the master-slave architecture of the energy storage system. This significantly reduces communication latency and meets the requirements of dynamic scheduling in seconds. The master control end of the energy storage system can obtain the operating status data of each energy storage cabinet in real time and quickly generate corresponding scheduling instructions based on real-time load demand and electricity price signals. This achieves efficient coordinated control and dynamic energy distribution of multiple energy storage cabinets, improving the response speed and scheduling flexibility of the energy storage system.

[0084] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's description and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A dynamic scheduling method for multiple energy storage cabinets, characterized in that: Including steps: Obtaining a registration request from each energy storage cabinet, assigning a unique identifier to each energy storage cabinet, and then establishing a UDP communication connection with each energy storage cabinet; recording topology information of each energy storage cabinet, wherein the topology information includes the IP address of each energy storage cabinet and the assigned unique identifier; Obtaining the operating status data periodically sent by each energy storage cabinet: obtaining the operating status data packaged and sent by each energy storage cabinet through the communication connection, wherein the operating status data is in UDP data packet format; using a redundancy check and timestamp mechanism to filter duplicate data and timed-out data in the operating status data, and discarding the duplicate data and timed-out data; when the operating status data of the energy storage cabinet is not obtained for a preset time period, marking the energy storage cabinet as offline, and stopping issuing scheduling instructions to the energy storage cabinet in the offline state; Calculating real-time load demand based on the acquired grid electricity price signal, and generating scheduling instructions based on the operating status data and the real-time load demand: collecting the real-time power of the current grid-side AC meter, combining the real-time power and the current grid electricity price signal to determine the peak and valley periods of the current grid; obtaining the real-time load demand of the grid-side transformer under the conditions where the grid is not overloaded during charging or the energy storage system is not reversely discharged based on the current peak and valley periods; and generating scheduling instructions of different priorities for each energy storage cabinet based on the real-time load demand and the operating status data; Packing the scheduling instruction into a binary data packet of UDP data, and sending the scheduling instruction to the corresponding energy storage cabinet through the communication connection; distributing the scheduling instruction to the corresponding energy storage cabinet for execution according to priority, and receiving execution confirmation packets returned by each energy storage cabinet, determining whether there is an energy storage cabinet that has not returned an execution confirmation packet based on the unique identifier carried in the execution confirmation packet, and triggering a retransmission mechanism for the energy storage cabinet that has not returned an execution confirmation packet; The operating status data includes the battery SOC, battery temperature, battery charge and discharge voltage, battery charge and discharge current, and PCS real-time power of each energy storage cabinet.

2. A multi-energy storage cabinet dynamic scheduling system, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the method for dynamic scheduling of multiple energy storage cabinets according to claim 1 are implemented.

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