Hierarchical energy scheduling method, device and product of shared energy storage power system

Abstract

The application discloses a layered energy scheduling method, device and product of a shared energy storage power system, relates to the field of power scheduling, and comprises the following steps: a signal emission source emits scheduling instructions to a plurality of communication devices; a communication index of the communication layer is calculated, the communication quality of the communication layer is determined according to the communication index, and the transmission power of the signal emission source is optimized with the maximum communication quality as the target. The application can improve the communication quality by continuously optimizing the transmission power of the signal emission source, and can make the energy sharing scheduling effect in the user layer tend to be more ideal.

Description

Technical Field

[0001] This application relates to the field of power dispatching, and in particular to a hierarchical energy dispatching method, equipment and product for a shared energy storage power system. Background Technology

[0002] With the development of information and communication technologies, traditional power grids have gradually evolved into cyber-physical intelligent power systems. Against this backdrop, achieving efficient energy utilization and sharing the high cost of energy storage through shared energy storage has become a mainstream research focus. Achieving complete shared energy dispatch requires the combined efforts of the physical layer, communication layer, and dispatch layer. However, most studies assume that shared energy storage power systems are implemented under the premise of 100% communication reliability, without considering the impact of communication on the shared energy storage power system, thus their accuracy in practical applications is not entirely complete. The "shared" nature of shared energy storage power systems determines that their dispatch process is a dynamic collaborative process across space, across entities, and with multiple objectives. It requires real-time perception of the overall state, coordinated interaction among multiple entities, and the transmission of dynamic commands. Therefore, shared energy storage power systems rely more heavily on communication than ordinary power systems; that is, communication factors cannot be ignored in shared energy storage dispatch. Communication is crucial for shared energy storage power systems; however, existing technologies typically do not consider communication factors in shared energy storage dispatch, leading to a significant gap between actual dispatch performance and ideal dispatch performance. Summary of the Invention

[0003] The purpose of this application is to provide a hierarchical energy dispatching method, device, and product for a shared energy storage power system, which can make the energy sharing dispatching effect in the user layer more likely to be ideal.

[0004] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a hierarchical energy dispatching method for a shared energy storage power system. The shared energy storage power system includes a user layer, a communication layer, and a dispatching layer. The user layer includes multiple user units, each of which is equipped with an energy storage device. The communication layer includes multiple communication devices, which are used to receive and execute dispatching instructions to realize energy sharing dispatching among multiple energy storage devices. The hierarchical energy scheduling method is executed by the scheduling layer and includes: The scheduling command is transmitted to multiple communication devices via a signal transmission source; The communication index of the communication layer is calculated based on the transmission power of the signal transmitter, and the communication quality of the communication layer is determined according to the communication index. The transmission power of the signal transmitter is optimized with the goal of maximizing the communication quality.

[0005] In a second aspect, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the hierarchical energy dispatch method for the shared energy storage power system described above.

[0006] Thirdly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the hierarchical energy dispatch method for the shared energy storage power system described above.

[0007] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a hierarchical energy dispatching method, device, and product for a shared energy storage power system. The dispatching layer issues dispatching commands to communication devices, enabling these devices to execute the commands and achieve energy sharing and dispatching among multiple energy storage devices. Simultaneously, the dispatching layer continuously optimizes the transmission power of the signal source to improve communication quality, ensuring that dispatching commands are transmitted to each communication device in a timely and accurate manner. However, in existing technologies, the impact of communication quality on the transmission of dispatching commands is often neglected. Therefore, this application improves communication quality by continuously optimizing the transmission power of the signal source, making the energy sharing and dispatching effect at the user layer more closely resemble the ideal outcome. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a flowchart illustrating a hierarchical energy dispatching method for a shared energy storage power system according to an embodiment of this application; Figure 2 This is a schematic diagram illustrating the mechanism of a hierarchical energy dispatch method for a shared energy storage power system in one embodiment of this application; Figure 3 This is a schematic diagram of the architecture of a shared energy storage power system according to one embodiment of this application. Detailed Implementation

[0010] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0011] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0012] like Figure 1 As shown, this application provides a hierarchical energy dispatch method for a shared energy storage power system. The shared energy storage power system includes a user layer, a communication layer, and a dispatch layer. The user layer includes multiple user units, each equipped with an energy storage device. The communication layer includes multiple communication devices used to receive and execute dispatch instructions to achieve energy sharing and dispatch among the multiple energy storage devices. The dispatch layer includes a computer device, which executes the hierarchical energy dispatch method. Specifically, it can be executed by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is described using a server as an example, including steps 110 to 120.

[0013] Step 110: Send scheduling commands to multiple communication devices through a signal transmission source.

[0014] Step 120: Calculate the communication index of the communication layer based on the transmission power of the signal transmitter, determine the communication quality of the communication layer based on the communication index, and optimize the transmission power of the signal transmitter with the goal of maximizing the communication quality.

[0015] It should be noted that the above two steps are parallel. On one hand, the scheduling layer sends scheduling commands to the communication devices, enabling the communication devices to execute these commands to achieve energy sharing and scheduling among multiple energy storage devices. Considering that communication quality at the communication layer can cause delays and data loss in the transmission of scheduling commands, leading to inaccurate or non-execution of these commands by the communication devices, the scheduling layer also continuously optimizes the transmission power of the signal source to improve communication quality, thereby ensuring that scheduling commands are transmitted to each communication device in a timely and accurate manner. However, in existing technologies, the impact of communication quality on the transmission of scheduling commands is often overlooked. Therefore, this application improves communication quality by continuously optimizing the transmission power of the signal source, making the energy sharing and scheduling effect at the user layer closer to the ideal outcome.

[0016] It should be noted that the communication quality of the communication layer is related to the transmission power of the signal source. Once the relationship between the two is determined, a mature objective optimization algorithm from existing technologies can be used to perform the relevant optimization work. Specifically, the transmission power of the signal source is used as the decision variable in the objective optimization algorithm, while the communication quality of the communication layer is used as the objective function value. The decision variable is adjusted to maximize the objective function value.

[0017] In one embodiment of this application, the communication metric is communication reliability, which characterizes communication quality. Generally speaking, higher communication reliability indicates higher communication quality, and lower communication reliability indicates lower communication quality.

[0018] Specifically, the calculation of communication indicators of the communication layer based on the transmission power of the signal source includes steps 210 to 250.

[0019] Step 210: Calculate the base noise power of the communication layer and construct a noise power enhancement model. The power enhancement model is used to enhance the base noise power to different degrees at different times. Determine the enhanced noise power for the current time period based on the noise power enhancement model. The enhanced noise power is the enhanced base noise power.

[0020] For example, the basic noise power The calculation formula is: in, This represents the Boltzmann constant (1.3803 × 10⁻⁶). -23 J / K), This indicates the ambient temperature of the communication layer (for example, 290K, approximately 16.85℃). This represents the noise bandwidth of the communication layer. This formula is used to quantify the fundamental noise level of the channel in the communication layer, which directly determines the sensitivity of the communication layer.

[0021] For example, the noise power enhancement model is as follows: in, Indicates time period Increased noise power This indicates the timeframe is between 6 PM and 10 PM. In this example, the noise power during the evening peak hours (6 PM to 10 PM) is doubled to simulate the increased congestion and interference during peak hours in a real communication system. The increased noise during peak hours simulates a decrease in signal-to-noise ratio and an increase in bit error rate. It should be noted that the specific time of the peak period and the noise amplification factor can be adjusted according to actual conditions.

[0022] Step 220: Construct a path loss model for the communication layer. The path loss model is used to calculate the signal loss at different communication devices.

[0023] The path loss model is as follows: in, This indicates the actual distance between the communication equipment and the signal transmission source. Indicates the reference distance. Indicates actual distance The resulting signal loss Indicates reference distance The resulting signal loss This represents a random disturbance factor used to simulate environmental disturbances.

[0024] The path loss model characterizes the degree of signal attenuation with distance, reflecting the effects of obstacles and multipath effects in the real environment. For each communication device in the communication layer, the signal loss at its location can be calculated using the path loss model described above.

[0025] Step 230: Construct a signal transmission model for the communication layer. The signal transmission model is used to calculate the receiving power of different communication devices for scheduling commands based on the path loss model and the transmission power of the scheduling commands.

[0026] The signal transmission model is as follows: in, This indicates the transmission power of the signal source. This indicates the actual distance from the signal source. The receiving power of the communication equipment.

[0027] For example, the initial value of the transmission power of the signal transmitting source can be... Using the signal transmission model described above, the received power of each communication device can be calculated. After obtaining the received power of the communication devices, a unit conversion can be performed to facilitate subsequent signal-to-noise ratio calculations. The conversion formula is: .

[0028] Step 240: Calculate the signal-to-noise ratio (SNR) of the communication layer for the current time period based on the signal transmission model and the enhanced noise power for the current time period. For example, this specifically includes: The received power of each communication device is calculated based on the signal transmission model. The average received power is then calculated based on the received power of each communication device. The ratio of the average received power to the enhanced noise power of the current time period is used as the signal-to-noise ratio of the communication layer in the current time period.

[0029] Signal-to-noise ratio The calculation formula is: in, Indicates average received power. This indicates an increase in noise power.

[0030] Step 250: Determine the dynamic packet loss rate of the communication layer based on the signal-to-noise ratio, and determine the communication reliability based on the dynamic packet loss rate and the failure rate of the communication equipment.

[0031] For example, determining the dynamic packet loss rate of the communication layer based on the signal-to-noise ratio specifically includes steps 251 and 252.

[0032] Step 251, construct the segmented bit error rate model: in, Indicates bit error rate, This represents the ratio of energy per bit to noise power spectral density. Indicates the signal-to-noise ratio. Indicates channel bandwidth. Indicates the data transmission rate.

[0033] Step 252: Determine the bit error rate of the communication layer based on the signal-to-noise ratio and segmented bit error rate model, and calculate the dynamic packet loss rate of the communication layer based on the bit error rate. in, Indicates the length of the dynamic data packet. This indicates the dynamic packet loss rate.

[0034] Communication reliability is determined based on dynamic packet loss rate and communication equipment failure rate, specifically including: The failure rate of communication equipment follows a Bernoulli distribution: in, This represents the failure rate of communication device i at time t. k This represents the failure rate coefficient of communication equipment; Calculate communication reliability: in, This indicates the communication reliability of the communication layer at time t. N Indicates the number of communication devices.

[0035] The above is an example of the calculation process for communication reliability.

[0036] In one embodiment of this application, multiple user units are clustered into several user communities. The communication device is a smart meter, and each user unit is equipped with one smart meter. The communication layer also includes several aggregation switches and one core switch. Each user community is equipped with one aggregation switch (usually located at the center of the user community to minimize transmission distance). The core switch is used to receive scheduling instructions and forward them to each aggregation switch. Each aggregation switch is used to forward scheduling instructions to each smart meter in the corresponding user community. The smart meters forward the scheduling instructions to the corresponding energy storage devices. The energy storage devices are configured to perform energy transfer operations with other energy storage devices according to the scheduling instructions. Here, a smart meter refers to a meter that has the ability to read the energy data of the energy storage device, control the energy storage device to charge or discharge, and has communication functions.

[0037] In this embodiment, user units within a certain range belong to different user communities. Each user community has a central aggregation switch responsible for the initial energy scheduling within that community. The entire system is configured with a core switch covering the entire community's communication network, enabling cross-community scheduling of redundant energy. The communication equipment consists of user smart meters, aggregation switches, and core switches. In this scenario, electricity flows unidirectionally, allowing only the purchase of electricity from the grid without transmitting it back to the grid. A model of household loads, energy storage devices, and photovoltaic (PV) equipment is built within the residence. The PV power generated within the residence is initially for self-consumption. If there is redundant energy, it is stored in local energy storage devices. These energy storage devices are connected to the aggregation switch via a control switch, enabling communication and energy interaction with the energy management system. The aggregation switch connects to the energy storage boxes and uploads data to the core switch, which then performs global data exchange.

[0038] For the clustering of user units, energy dispatching areas can be configured first through k-means clustering. In order to achieve efficient and full dispatching of energy in the entire system, it is necessary to cluster user units and divide them into a certain number of user communities. Overall dispatching is carried out on a user community basis. Considering factors such as the inability of household users to connect to energy storage devices beyond a certain distance and the limited coverage of WiFi communication, communities need to be set up according to physical limitations, i.e., geographical location.

[0039] For example, the clustering process includes the following steps: Step 1: For a given dataset (where one data point represents the location data of a user unit), randomly select... k 10 data points were used as the initial cluster centers. : in, Indicates the first k Cluster centers.

[0040] Step 2: Calculate the Euclidean distance from non-cluster centers to cluster centers, and assign the corresponding data points to cluster centers. In the corresponding clusters: in, Indicates the first i Data points that are not cluster centers d This represents the Euclidean distance between two data points.

[0041] Step 3: Take the average value of all data points in each cluster and use it as the new cluster center, as shown in the following formula: in, Indicates the latest number i Cluster center This indicates the number of data points within a cluster.

[0042] Repeat steps 2 and 3 until convergence is achieved, yielding the final clustering result. The final clustering result is the clustering result of user units, with each cluster of user units constituting a user community.

[0043] This implementation case presents a specific communication architecture. The architecture consists of three layers: the first layer, the terminal layer, comprises smart meters configured in each user unit; the second layer, the community layer, comprises aggregation switches configured in each user community; and the third layer, the system layer, comprises core switches. This three-layer architecture serves as the information exchange and connection between the physical layer and the scheduling layer. Before generating scheduling commands, the scheduling layer needs to complete data acquisition: the smart meters of each user unit synchronously collect the photovoltaic power generation output data and the remaining power data of the energy storage devices for the corresponding user. Photovoltaic and energy storage data within the same community are aggregated to the community's aggregation switch, which then forwards the data to the core switch. The core switch ultimately transmits all data to the scheduling layer. The scheduling commands include the discharge or charging amount of each energy storage device (forming a sub-scheduling command, which can be assigned a corresponding energy storage device ID for each sub-scheduling command). First, the scheduling layer centrally sends scheduling commands to the core switch. The core switch then breaks down these commands and sends them to the aggregation switches of the corresponding user communities. The aggregation switches then forward each sub-scheduling command to the smart meters of the corresponding user units. The smart meters control the corresponding energy storage devices to perform charging or discharging operations, ultimately achieving energy transfer between different energy storage devices. Simultaneously, to generate scheduling commands, the scheduling layer needs to collect energy storage data (such as remaining power) from each energy storage device. This can be done by first collecting energy storage data from each smart meter, then aggregating the energy storage data from the same user community to the corresponding aggregation switch, and finally forwarding all energy storage data to the core switch. The core switch then sends the total energy storage data to the scheduling layer.

[0044] In the aforementioned communication architecture, smart meters need to consider the diversity of communication methods. Smart meter ports can flexibly access fiber optics, industrial Ethernet, PLC carriers, and Ethernet signals converted via wireless CPE, solving the problems of inflexible wired deployment and standardized wireless access. Aggregation switches integrate multi-path access traffic and, based on DiffServ's QoS mechanism, allocate high priority to critical business data, effectively suppressing latency jitter in wireless transmission. Core switches coordinate bandwidth allocation between massive data acquisition and real-time control services, while supporting basic security mechanisms such as VLANs and ACLs to achieve logical isolation between different business areas, enhancing overall communication security. The communication layer couples physical energy flow with upper-layer decision-making information, supporting real-time data transmission and control command issuance for energy sharing across multiple communities.

[0045] The scheduling process prioritizes the user's own energy consumption and then follows the hierarchical sharing rules. The specific execution steps are as follows: Each user unit's photovoltaic power generation output first supplies its own electricity load; if the photovoltaic output exceeds its own load demand, the remaining electricity is preferentially stored in the unit's energy storage device; if the photovoltaic output is insufficient to meet its own load demand, the electricity from the unit's energy storage device is preferentially used to supplement the energy, thus achieving energy self-balancing at its own level.

[0046] If, after users consume their own photovoltaic and energy storage, there is still an overall supply-demand gap in the community, that is, some users have excess energy storage while others have insufficient energy storage, the dispatch layer generates a corresponding sub-dispatch instruction for the community. This instruction includes the energy storage device number and the specific charging and discharging amount. After the instruction is split by the core switch, it is distributed to the aggregation switch of the community. The aggregation switch then sends the sub-dispatch instructions one by one to the smart meters of the corresponding user units. The smart meters control the associated energy storage devices to perform charging and discharging operations to complete the energy self-balancing within the community.

[0047] After the sharing within the community is completed, if there is still a supply and demand gap in the community, and the scheduling layer detects that the communication reliability between the community and the neighboring communities has reached a preset threshold, the scheduling layer generates a sub-scheduling instruction covering the neighboring communities; the instruction is distributed to the aggregation switch of the corresponding community through the core switch, and then forwarded by each aggregation switch to the smart meter of the relevant user unit to perform charging and discharging operations.

[0048] If there is still an unfilled supply and demand gap in the system after cross-community boundary sharing, and the global cross-community communication reliability reaches a preset threshold, then global cross-community sharing is triggered: the scheduling layer generates sub-scheduling instructions covering all relevant communities in the region, which are distributed to the aggregation switches of each target community through the core switch, and finally the corresponding energy storage device in the target community completes the charging and discharging, realizing the energy sharing scheduling of the entire system.

[0049] Therefore, the dispatch instructions are generated based on a three-tiered energy sharing mechanism, which includes, in descending order of priority, intra-community sharing, cross-community sharing, and global community sharing. Intra-community sharing refers to the transfer of electricity within the same user community; cross-community sharing refers to the transfer of electricity between adjacent user communities; and global community sharing refers to the transfer of electricity between non-adjacent user communities. Only the remaining electricity after each user community has completed intra-community sharing is allowed to be transferred to adjacent user communities in the form of cross-community sharing; and only the remaining electricity after each user community has completed cross-community sharing is allowed to be transferred to non-adjacent user communities in the form of global community sharing. Specifically, the three-tiered energy sharing mechanism can be described as follows: priority is given to meeting the user's own consumption; if there is any remaining energy, it is shared with other users within the same community (intra-community sharing); and if users at the community boundary still have remaining energy after meeting their own needs and completing intra-community sharing, it is shared with users in adjacent communities (cross-community sharing).

[0050] Considering the impact of communication quality on the transmission of dispatch commands, especially the tendency for errors to accumulate over long distances, the preferred approach is as follows: when the communication quality at the communication layer is below a first threshold, only intra-community sharing is allowed; when the communication quality at the communication layer is above the first threshold but below a second threshold, only intra-community and cross-community sharing are allowed; when the communication quality at the communication layer is above the second threshold, intra-community sharing, cross-community sharing, and global community sharing are allowed simultaneously. This determines the power sharing scheme based on communication quality. When communication quality is high, dispatch commands are almost unaffected, so all three sharing modes can be activated simultaneously. When communication quality is moderate, in the long-distance global community sharing mode, dispatch commands need to be sent to two distant user communities simultaneously, which can easily lead to command errors; therefore, the global community sharing mode is disabled. If communication quality further deteriorates, the cross-community sharing mode will also be disabled for the same reason.

[0051] Specifically, the scheduling layer generates scheduling instructions by solving an objective function. The objective function aims to minimize the grid's electricity purchase cost while maximizing shared electricity within, across, and globally through shared incentives. The objective function is: in, For shared electricity within the community, To enable cross-community power sharing, For the purpose of sharing electricity across the entire community, The incentive coefficient is shared within the community. To share incentive coefficients across communities, For globally shared incentive coefficients, for, The total number of user units, This refers to the amount of electricity purchased by user unit i from the power grid during time period t. For communication failure costs, Penalize users with low communication reliability by imposing costs.

[0052] The objective function has shared quantity hierarchical constraints, spatiotemporal coupling constraints, and basic physical constraints.

[0053] The shared quantity hierarchy constraint is: in, For user unit i, the community-shared electricity consumption during time period t. For user unit i, the power consumption is shared across communities within time period t. The global shared power of user unit i within time period t. This refers to the ratio of shared electricity usage within the community. This refers to the proportional constraint coefficient for cross-community electricity sharing. This is the proportional coefficient for globally shared power. Let be the photovoltaic power generation of user unit i during time period t. For user community k The set of user units in the system.

[0054] The above-mentioned shared capacity hierarchical constraints respectively mean that the total shared power within a single community does not exceed the total photovoltaic power generation within the community, the total cross-community shared power of all user communities does not exceed the shared power within the community, and the total global shared power of all user communities does not exceed the total cross-community shared power.

[0055] Meanwhile, cross-community sharing cannot exceed the local sharing capacity of adjacent user units within the same community. Therefore, the spatiotemporal coupling constraint is: in, Let be the cross-community shared electricity consumption of user unit i within time period t, and let be the set of neighboring users of user unit i. User unit j shares electricity within the community during time period t.

[0056] By limiting the scale of cross-regional power sharing through spatiotemporal coupling, we can ensure that the scale of cross-regional power sharing matches the local energy supply capacity and avoid spatial imbalance in energy flow.

[0057] The basic physical constraints include: in, For user unit i, the community-shared electricity consumption during time period t. For user unit i, the power consumption is shared across communities within time period t. The total power of user unit i within time period t must be non-negative. Let be the photovoltaic power generation of user unit i during time period t. The discharge amount of user unit i during time period t. This refers to the amount of electricity purchased by user unit i from the power grid during time period t. The total shared power of user unit i during time period t. The base load power of user unit i during time period t. The uninterrupted load power of user unit i during time period t. The interruptible load power of user unit i during time period t. The heat load power of user unit i during time period t. Let be the net energy output by user unit i to user unit i during time period t. The distance between user unit i and user unit i is the power transmission distance. Let t be the transmission loss of the electrical energy output by user unit i during time period t. Constraint (1) The shared power is non-negative. Constraint (2) Ensure the energy balance of the shared energy storage power system, ensure the closed loop of energy flow at each node, and ensure that the power supply of each user unit is equal to the power consumption. Constraint (3) Ensure that the energy output of each user unit cannot exceed the part of its discharge power minus the transmission loss.

[0058] The above describes the generation of scheduling instructions by the scheduling layer. The scheduling layer can use mixed integer programming (MILP) to jointly optimize the objective function across multiple time periods to obtain the amount of electricity each user unit outputs to another user unit in each time period. For example, in time period 5, user unit 3 needs to output 8 kWh of electricity to user unit 6, and user unit 5 needs to output 10 kWh of electricity to user unit 1.

[0059] To assess the impact of communication on shared energy storage power systems, communication failure costs and energy imbalance penalties are introduced to scan system benefits under different communication reliability levels. Communication failure costs are calculated using the Load Loss Value (VoLL), which quantifies costs associated with power supply interruptions. For residential users, the VoLL ranges from a few euros to tens of euros per kWh. The impact of communication on various indicators of the shared energy storage power system is quantified using Pearson correlation coefficient analysis to assess the linear correlation between communication parameters and system indicators, thereby optimizing system design and parameter configuration. The calculation formula is as follows: Where r represents the Pearson correlation coefficient, r=1 indicates perfect positive correlation, r=-1 indicates perfect negative correlation, and r=0 indicates no correlation. and Let x and y represent the i-th observation values ​​of variables x and y, respectively. In this invention, variable x can represent communication reliability, shared power of the assembly, energy storage utilization rate, shared power within the community, shared power across communities, and energy saving. Variable y can represent communication reliability, shared power of the assembly, energy storage utilization rate, shared power within the community, shared power across communities, and energy saving. and Let x and y represent the arithmetic mean of variables x and y, respectively.

[0060] High communication reliability ensures the correct transmission of shared information and is conducive to the system sharing energy to reduce the cost of purchasing electricity from the grid. However, the improvement of communication reliability is usually accompanied by an increase in hardware costs and energy consumption. Therefore, it is necessary to find the Pareto optimal solution of "reliability-cost" to provide parameter basis for the economic operation of the system.

[0061] In summary, the hierarchical energy dispatch method for the shared energy storage power system in this embodiment has the following technical effects: First, a hierarchical energy dispatch architecture is proposed for a shared energy storage power system that integrates the physical network (power system, including photovoltaic power generation equipment, energy storage equipment, and smart meters) and the information system (communication system, including data and dispatch information transmitted by switches). This architecture constructs a community-based distributed energy dispatch network that integrates the physical network and information system. By leveraging a community wireless communication network (communication layer) composed of switches at various levels, reliable transmission of energy sharing information within and between communities is achieved. This ensures communication stability while effectively improving energy utilization and optimizing system operating costs. Based on a cost-optimal hierarchical energy sharing mechanism, energy sharing is executed sequentially using three hierarchical scheduling modes: intra-community sharing, cross-community sharing for boundary users, and global community sharing. The specific strategies are as follows: Intra-community sharing is executed on a community-by-community basis, relying on short-range communication within the community to complete local energy supply and demand matching. At this stage, the communication domain is limited, data interaction is locally concentrated, and the communication transmission load is significantly reduced. Cross-community sharing for boundary users is only initiated when there is a supply-demand imbalance within the community. This process must meet a preset threshold for inter-community communication reliability, transmitting scheduling instructions only for supply-demand gaps between adjacent users, avoiding data redundancy in full cross-community communication. Global community sharing is triggered only when cross-community sharing cannot absorb the system's supply-demand gap. This hierarchical scheduling mode, through a demand-driven, progressive resource matching logic, uses local communication to carry most energy interaction needs, reducing the frequency and data volume of long-distance communication, thereby alleviating communication congestion during energy interaction and avoiding the reduced energy interaction efficiency caused by communication constraints in traditional scheduling. This contributes to improving scheduling efficiency in large-scale energy sharing scenarios. This study innovatively quantifies the impact of communication indicators on the effectiveness of shared energy storage, and uses Pearson correlation analysis to assess the correlation between communication and shared energy storage power systems. It clearly presents the interaction between communication and shared energy storage power systems, providing an analytical basis for the subsequent synergistic optimization of communication and shared energy storage power systems.

[0062] Figure 1This embodiment describes the communication-energy coupling mechanism of the hierarchical energy dispatch architecture for a shared energy storage system that integrates physical networks and information systems. When the communication environment deteriorates, the signal-to-noise ratio decreases, leading to increased bit error rate and packet loss rate, thus reducing communication reliability. This decreased reliability further causes errors or delays in control commands and distortions in information transmission, resulting in deviations in variables such as local sharing, cross-community sharing, and global sharing in energy sharing decisions. Ultimately, this leads to a decline in the efficiency of the energy system's objective function, creating a negative feedback loop. However, by employing an optimization strategy that adjusts transmission power to improve communication quality and suppress interference, the energy sharing strategy can be optimized, and precise dispatch control can be achieved. This ultimately improves the overall efficiency of the shared energy storage power system, completing a positive cycle of communication optimization, energy strategy optimization, and system efficiency improvement. Figure 1 The study reveals the core logic of communication-energy synergistic optimization. There is a deep interactive coupling relationship between the communication system and the shared energy storage power system. The degradation of communication performance will have a negative constraint on the shared energy storage power system, while communication optimization strategies can promote the efficient operation of the shared energy storage power system.

[0063] Figure 2 This is a framework diagram of the energy dispatching frame of the shared energy storage system integrating physical networks and information systems involved in this embodiment. It can be seen that users within a certain range belong to different communities. At the center of each community is a convergence switch responsible for the initial energy dispatching within that community. The entire system is configured with a core switch covering the entire community's communication network, enabling cross-community dispatching of redundant energy. The communication equipment consists of user smart meters, convergence switches, and core switches. In this scenario, electricity flows unidirectionally, only allowing the purchase of electricity from the grid without transmitting it back to the grid. A model of household loads, energy storage devices, and photovoltaic equipment is built within the residence. The photovoltaic power generated within the residence is initially for self-consumption. If there is redundant energy, it is stored in local energy storage. The energy storage is connected to the convergence switch via a control switch, enabling communication and energy interaction with the EMS system. The convergence switch connects to the energy storage box and uploads data to the core switch, which performs global data exchange.

[0064] The user layer consists of multiple user units, integrating loads, power generation, and energy storage devices. The distributed energy and energy storage devices aggregated by the community microgrid together form a physical power distribution network. Local power generation networks are built through household photovoltaic systems to enhance energy self-sufficiency. A certain amount of energy storage is configured to support the storage and release of energy on the user side. Based on this architecture, loads are classified and prioritized. Provincial loads have the highest priority, ensuring basic electricity needs for users; uninterruptible loads have relatively high priority, supporting the continuity of critical business operations; thermal loads have flexible priority, absorbing excess photovoltaic power and appropriately reducing power during shortages; interruptible loads have relatively low priority, serving as a flexible control buffer, absorbing excess power and reducing power during shortages to achieve efficient energy matching.

[0065] The communication layer serves as the hub connecting the physical layer and the scheduling layer. It is managed by a Layer 3 switch. Information flows from the user-side access layer switch to the community-level aggregation switch. The aggregation switch integrates critical data from the area and uploads it to the system's core switch. Under this design, the access layer switches must consider the diversity of communication methods. Switch ports can flexibly access fiber optics, industrial Ethernet, PLC carriers, and Ethernet signals converted via wireless CPE, addressing the inflexibility of wired deployments and the standardization of wireless access. The aggregation layer switch integrates multiple access traffic streams and, based on DiffServ's QoS mechanism, allocates high priority to critical business data, effectively suppressing latency jitter in wireless transmission. The core switch coordinates bandwidth allocation between massive data acquisition and real-time control services, while supporting basic security mechanisms such as VLANs and ACLs to achieve logical isolation between different business areas, enhancing overall communication security. The communication layer couples physical energy flow with upper-layer decision-making information, supporting real-time data transmission and control command issuance for energy sharing across multiple communities. The basic communication architecture is as follows: noise power is quantified using parameters such as the Boltzmann constant, and the received signal strength is determined by combining the path loss model and signal transmission formula; signal quality is evaluated, considering noise enhancement during peak hours, and the signal-to-noise ratio, bit error rate, and packet loss rate are calculated; Bernoulli distribution is introduced to simulate equipment failure, and the system reliability is evaluated by comprehensively considering the packet loss rate; when reliability is insufficient, the charging and discharging operations of non-critical users are frozen, and communication reliability is adjusted for low-reliability users during evening peak hours, increasing failure costs.

[0066] The optimized dispatch center conducts global control of energy sharing, determining how system operators should execute optimal energy dispatch operations. This layer focuses on economic issues and improves resource utilization through a sharing incentive mechanism. The optimized dispatch center integrates user-side and communication layer data to form an energy allocation strategy, and sends instructions to the user side through the communication layer to achieve economic dispatch throughout the entire process.

[0067] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0068] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0069] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0070] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0071] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0072] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0073] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A hierarchical energy dispatch method for a shared energy storage power system, wherein the shared energy storage power system comprises a user layer, a communication layer, and a dispatch layer, characterized in that, The user layer includes multiple user units, each of which is equipped with an energy storage device. The communication layer includes multiple communication devices, which are used to receive and execute scheduling instructions to realize energy sharing scheduling among multiple energy storage devices. The hierarchical energy scheduling method is executed by the scheduling layer and includes: The scheduling command is transmitted to multiple communication devices via a signal transmission source; The communication index of the communication layer is calculated based on the transmission power of the signal transmitter, and the communication quality of the communication layer is determined according to the communication index. The transmission power of the signal transmitter is optimized with the goal of maximizing the communication quality.

2. The hierarchical energy dispatch method for a shared energy storage power system according to claim 1, characterized in that, The communication metric is communication reliability, which is used to characterize the communication quality. The communication metrics of the communication layer are calculated based on the transmission power of the signal source, specifically including: The basic noise power of the communication layer is calculated, and a noise power enhancement model is constructed. The power enhancement model is used to enhance the basic noise power to different degrees at different time periods. The enhanced noise power for the current time period is determined based on the noise power enhancement model. The enhanced noise power is the enhanced basic noise power. Construct a path loss model for the communication layer, which is used to calculate the signal loss at different communication devices; A signal transmission model for the communication layer is constructed, which is used to calculate the receiving power of different communication devices for the scheduling command based on the path loss model and the transmission power of the scheduling command. Calculate the signal-to-noise ratio of the communication layer in the current time period based on the signal transmission model and the enhanced noise power in the current time period; The dynamic packet loss rate of the communication layer is determined based on the signal-to-noise ratio, and the communication reliability is determined based on the dynamic packet loss rate and the failure rate of the communication device.

3. The hierarchical energy dispatch method for a shared energy storage power system according to claim 2, characterized in that, The basic noise power The calculation formula is: in, Represents Boltzmann's constant. This indicates the ambient temperature of the communication layer. This represents the noise bandwidth of the communication layer; The noise power enhancement model is as follows: in, Indicates time period Increased noise power This indicates that it is between 6 PM and 10 PM; The path loss model is as follows: in, This indicates the actual distance between the communication device and the signal transmitting source. Indicates the reference distance. Indicates actual distance The resulting signal loss Indicates reference distance The resulting signal loss Indicates the random disturbance factor; The signal transmission model is as follows: in, This indicates the transmission power of the signal source. This indicates the actual distance to the signal source is... The receiving power of the communication device; The signal-to-noise ratio of the communication layer in the current time period is calculated based on the signal transmission model and the enhanced noise power in the current time period, specifically including: The received power of each of the communication devices is calculated according to the signal transmission model, and the average received power is calculated according to the received power of each of the communication devices. The ratio of the average received power to the enhanced noise power in the current time period is used as the signal-to-noise ratio of the communication layer in the current time period.

4. The hierarchical energy dispatch method for a shared energy storage power system according to claim 2, characterized in that, Determining the dynamic packet loss rate of the communication layer based on the signal-to-noise ratio specifically includes: Constructing a segmented bit error rate model: in, Indicates bit error rate, This represents the ratio of energy per bit to noise power spectral density. This indicates the signal-to-noise ratio. Indicates channel bandwidth. Indicates the data transmission rate; The bit error rate of the communication layer is determined based on the signal-to-noise ratio and the segmented bit error rate model, and the dynamic packet loss rate of the communication layer is calculated based on the bit error rate. in, Indicates the length of the dynamic data packet. Indicates the dynamic packet loss rate; Determining the communication reliability based on the dynamic packet loss rate and the failure rate of the communication equipment specifically includes: The failure rate of the communication device is determined to follow a Bernoulli distribution: in, This represents the failure rate of communication device i at time t. k This represents the failure rate coefficient of communication equipment; Calculate communication reliability: in, This indicates the communication reliability of the communication layer at time t. N Indicates the number of communication devices.

5. The hierarchical energy dispatch method for a shared energy storage power system according to claim 1, characterized in that, The multiple user units are clustered into several user communities; The communication device is a smart meter, and each user unit is equipped with one of the smart meters; The communication layer also includes several aggregation switches and a core switch. Each user community is configured with one aggregation switch. The core switch is used to receive the scheduling instructions and forward them to each aggregation switch. Each aggregation switch is used to forward the scheduling instructions to each smart meter in the corresponding user community. The smart meter forwards the scheduling instructions to the corresponding energy storage device. The energy storage device is configured to perform power transfer operations with other energy storage devices according to the scheduling instructions.

6. The hierarchical energy dispatch method for a shared energy storage power system according to claim 5, characterized in that, The scheduling instructions are generated based on a three-level energy sharing mechanism, which includes intra-community sharing, cross-community sharing, and global community sharing, with priorities ranging from high to low. The intra-community sharing refers to the transfer of power within the same user community, the cross-community sharing refers to the transfer of power between adjacent user communities, and the global community sharing refers to the transfer of power between non-adjacent user communities. Only the remaining electrical energy of each user community after completing the sharing within the community is allowed to be transferred to adjacent user communities in the form of cross-community sharing; Only the remaining electrical energy of each user community after completing the cross-community sharing is allowed to be transferred to non-adjacent user communities in the form of global community sharing.

7. The hierarchical energy dispatch method for a shared energy storage power system according to claim 6, characterized in that, When the communication quality of the communication layer is lower than a first threshold, only intra-community sharing is allowed; when the communication quality of the communication layer is higher than the first threshold but lower than a second threshold, only intra-community sharing and cross-community sharing are allowed; when the communication quality of the communication layer is higher than the second threshold, intra-community sharing, cross-community sharing, and global community sharing are allowed to be executed simultaneously.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the hierarchical energy dispatch method for a shared energy storage power system according to any one of claims 1-7.

9. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the hierarchical energy dispatch method for a shared energy storage power system as described in any one of claims 1-7.

Images