Distributed energy storage system and method for voltage regulation priority of power distribution network area

Abstract

The invention provides a distributed energy storage system and method for voltage regulation priority of a power distribution network zone area, relates to the technical field of power systems and automation thereof, and solves the technical problem that the prior art cannot adaptively determine the control priority according to the real-time voltage risk state and cannot drive global optimization decision and cooperative execution based on the control priority. The system comprises a multi-source data acquisition module used for acquiring multi-source data; wherein the multi-source data comprises voltage data; the voltage regulation grade evaluation module is used for carrying out risk evaluation on the voltage data based on a multi-stage voltage threshold to obtain a voltage regulation priority; the optimization model construction module is used for constructing an energy storage multi-objective optimization model based on the voltage regulation priority and the multi-source data; the voltage regulation strategy solving module is used for solving the energy storage multi-objective optimization model through an optimization algorithm to obtain an optimal voltage regulation strategy; and the energy storage control execution module is used for executing the optimal voltage regulation strategy. The method is used in the voltage regulation process of the power distribution network area.

Description

A distributed energy storage system and method prioritizing voltage regulation in distribution network areas Technical Field

[0001] This application relates to the field of power systems and their automation technology, and in particular to a distributed energy storage system and method for prioritizing voltage regulation in distribution network areas. Background Technology

[0002] Currently, in the field of voltage regulation in distribution network areas, existing technologies mainly rely on traditional centralized reactive power compensation devices or on-load tap-changing transformers for extensive regulation. These methods are slow to respond and struggle to cope with the rapid, bidirectional voltage fluctuations caused by the integration of distributed resources such as photovoltaics and electric vehicles. Furthermore, while methods utilizing distributed energy storage for local voltage support have emerged, most solutions either focus solely on maintaining acceptable voltage, lacking coordination with other objectives such as economical operation of energy storage and equipment lifespan; or employ fixed control strategies that fail to dynamically adjust the priority of control objectives based on the real-time risk status of the power grid. This results in insufficient regulation during voltage emergencies or excessive operation sacrificing economic efficiency during periods of stable voltage. Simultaneously, existing systems often malfunction under abnormal conditions such as communication interruptions, lacking a final line of defense for voltage safety. Therefore, how to construct a distributed energy storage-coordinated voltage regulation system that can intelligently sense risks, dynamically balance multiple objectives, and reliably operate under any conditions has become a pressing technical challenge for the industry. Summary of the Invention

[0003] This application provides a distributed energy storage system and method for prioritizing voltage regulation in distribution network areas, which solves the technical problem that existing technologies cannot adaptively determine control priorities based on real-time voltage risk status and thereby drive global optimization decisions and collaborative execution.

[0004] To achieve the above objectives, this application adopts the following technical solution: Firstly, it provides a distributed energy storage system with priority given to voltage regulation in distribution network areas, comprising a multi-source data acquisition module, a voltage regulation level assessment module, an optimization model construction module, a voltage regulation strategy solving module, and an energy storage control execution module; the multi-source data acquisition module is used to acquire multi-source data, including voltage data; the voltage regulation level assessment module is used to perform risk assessment on the voltage data based on multi-level voltage thresholds to obtain voltage regulation priorities; the optimization model construction module is used to construct a multi-objective optimization model for energy storage based on the voltage regulation priorities and multi-source data; the voltage regulation strategy solving module is used to solve the multi-objective optimization model for energy storage using an optimization algorithm to obtain the optimal voltage regulation strategy; and the energy storage control execution module is used to execute the optimal voltage regulation strategy.

[0005] Based on the above technical solution, the distributed energy storage system with voltage regulation priority in the distribution network provided in this application transforms the originally decentralized and passive energy storage control into an active and intelligent regulation mode based on real-time risk perception and multi-objective collaborative optimization. Through modular collaboration, the system first accurately identifies the voltage risk level and dynamically determines the voltage regulation priority. Then, it uses this as the core to drive the construction and solution of an optimization model that integrates multiple dimensions such as voltage quality and economic operation, and finally generates and executes the globally optimal voltage regulation strategy. This effectively solves the pain points of traditional solutions, such as single voltage regulation target, delayed response, and lack of collaboration among energy storage units. It significantly improves the voltage qualification rate and power supply reliability, while ensuring the economic efficiency of energy storage resource utilization and the overall robustness of system operation.

[0006] In conjunction with the first aspect above, in one possible implementation, the risk assessment of voltage data based on multi-level voltage thresholds includes: constructing multi-level voltage thresholds; wherein the multi-level voltage thresholds include a safety threshold, a warning threshold, an action threshold, and an emergency threshold; calculating a comprehensive deviation index characterizing the overall voltage status of the transformer area based on voltage data; matching the comprehensive deviation index with the multi-level voltage thresholds within threshold ranges to obtain a voltage risk level; and mapping and matching the voltage risk level based on a preset voltage regulation priority mapping table to obtain a voltage regulation priority; wherein the voltage regulation priority includes a priority coefficient.

[0007] In conjunction with the first aspect above, in one possible implementation, the method for obtaining the priority coefficient includes: obtaining available capacity data and short-term load forecast data of the energy storage system; matching the voltage risk level with a preset benchmark coefficient quantization table to obtain a benchmark coefficient; calculating resource availability based on the available capacity data of the energy storage system; calculating the voltage risk trend based on the short-term load forecast data; and dynamically adjusting the benchmark coefficient according to the resource availability and voltage risk trend to obtain the priority coefficient.

[0008] In conjunction with the first aspect mentioned above, in one possible implementation, the energy storage multi-objective optimization model includes a voltage quality optimization objective, an economic operation optimization objective, and an energy storage lifetime optimization objective, and adjusts the weight ratio of the voltage quality optimization objective through a priority coefficient; wherein, the voltage quality optimization objective is used to minimize the overall voltage deviation of the distribution area; the economic operation optimization objective is used to minimize network loss costs and energy storage operation costs; and the energy storage lifetime optimization objective is used to maximize the lifespan of the energy storage device by adjusting the charging and discharging depth and power fluctuation of the energy storage device.

[0009] In conjunction with the first aspect above, in one possible implementation, the step of solving the multi-objective optimization model of energy storage through an optimization algorithm includes: solving the voltage quality optimization objective through heuristic rules to obtain an initial feasible solution set; based on the initial feasible solution set, solving the economic operation optimization objective and the energy storage lifetime optimization objective through gradient descent to obtain a globally approximate optimal solution; and marking the globally approximate optimal solution as the optimal voltage regulation strategy.

[0010] In conjunction with the first aspect above, in one possible implementation, the energy storage control execution module includes an instruction distribution unit, a local control unit, and an autonomous switching unit; the instruction distribution unit is used to distribute the power instruction in the optimal voltage regulation strategy to the local control unit of each distributed energy storage device; the local control unit is built into each energy storage device and is used to receive and execute the power instruction and monitor the local access point voltage in real time; the autonomous switching unit is used to automatically execute droop control based on local voltage measurement when a communication anomaly or partial failure is detected.

[0011] In conjunction with the first aspect mentioned above, one possible implementation also includes a dynamic correction module; wherein the dynamic correction module includes: acquiring actual voltage regulation data; comparing and calculating the actual voltage regulation data with the preset expected regulation effect to obtain the strategy execution deviation; and dynamically correcting the benchmark coefficient quantization table and the energy storage multi-objective optimization model based on the strategy execution deviation through an online learning algorithm.

[0012] In conjunction with the first aspect above, in one possible implementation, the calculation of voltage risk trend based on short-term load forecast data includes: performing trajectory simulation based on short-term load forecast data to obtain voltage change trajectory; calculating the duration, depth integral, and rate of change of voltage over-limit based on the voltage change trajectory; and weighting and normalizing the duration, depth integral, and rate of change to obtain voltage risk trend.

[0013] In conjunction with the first aspect above, in one possible implementation, the method for acquiring the multi-source data includes: acquiring first voltage data at the output side of the transformer in the distribution substation; acquiring second voltage data at key nodes and end-users in the substation; acquiring operating status data of each distributed energy storage device; wherein the operating status data includes state of charge, available power margin, and device health; and performing spatiotemporal alignment and formatting processing on the first voltage data, second voltage data, and operating status data to obtain multi-source data.

[0014] Secondly, a distributed energy storage method prioritizing voltage regulation in distribution network areas is provided, comprising: acquiring multi-source data; wherein the multi-source data includes voltage data; performing risk assessment on the voltage data based on multi-level voltage thresholds to obtain voltage regulation priorities; constructing a multi-objective optimization model for energy storage based on the voltage regulation priorities and multi-source data; solving the multi-objective optimization model for energy storage using an optimization algorithm to obtain the optimal voltage regulation strategy; and executing the optimal voltage regulation strategy.

[0015] This application provides a distributed energy storage system and method with priority for voltage regulation in distribution network areas. First, by introducing a dynamic risk assessment and priority mapping mechanism based on multi-level voltage thresholds, the system achieves a leap from "fixed strategy" to "risk self-adaptation," ensuring that when the risk of voltage exceeding limits increases, control resources can be tilted towards voltage quality targets without disturbance, fundamentally guaranteeing power supply safety. Second, by creatively embedding dynamic priority coefficients as core adjustment factors into a multi-objective optimization model, the system achieves online and refined trade-offs among voltage quality, economic operation, and energy storage life protection, enabling the system to cope with emergency voltage events while pursuing comprehensive optimal benefits under normal conditions. Finally, by designing a hybrid execution architecture of "centralized optimization-local autonomy" and integrating online learning and strategy self-correction capabilities, the system possesses high robustness and long-term adaptability, ensuring the continuous and reliable operation of voltage regulation functions under various complex and time-varying scenarios, effectively improving the distribution network's carrying capacity for high-proportion distributed energy and its intelligent management level.

[0016] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments. Attached Figure Description

[0017] Figure 1 is a system architecture diagram of a distributed energy storage system with priority for voltage regulation in a distribution network area provided in an embodiment of this application; Figure 2 is a flowchart of a distributed energy storage system with priority for voltage regulation in a distribution network area provided in an embodiment of this application; Figure 3 is a flowchart of another distributed energy storage system with priority for voltage regulation in a distribution network area provided in an embodiment of this application; Figure 4 is a flowchart of a distributed energy storage method with priority for voltage regulation in a distribution network area provided in an embodiment of this application. Detailed Implementation

[0018] In the description of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. The "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" means one or more, and "multiple" means two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.

[0019] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0020] To address the technical problem of existing technologies failing to adaptively determine control priorities based on real-time voltage risk status and thus drive global optimization decisions and collaborative execution, this application provides a distributed energy storage system 100 with priority voltage regulation in distribution network areas, as shown in Figure 1. This system includes a multi-source data acquisition module 10, a voltage regulation level assessment module 20, an optimization model construction module 30, a voltage regulation strategy solving module 40, and an energy storage control execution module 50. The multi-source data acquisition module 10 acquires multi-source data, including voltage data. The voltage regulation level assessment module 20 performs risk assessment on the voltage data based on multi-level voltage thresholds to obtain voltage regulation priorities. The optimization model construction module 30 constructs a multi-objective optimization model for energy storage based on the voltage regulation priorities and multi-source data. The voltage regulation strategy solving module 40 solves the multi-objective optimization model for energy storage using an optimization algorithm to obtain the optimal voltage regulation strategy. The energy storage control execution module 50 executes the optimal voltage regulation strategy.

[0021] Based on this, the technical problem of existing technologies being unable to adaptively determine control priorities based on real-time voltage risk status, and thereby drive global optimization decisions and collaborative execution, is solved.

[0022] This application provides a distributed energy storage system 100 with priority for voltage regulation in distribution network areas, including: a multi-source data acquisition module 10 for acquiring multi-source data.

[0023] The multi-source data includes voltage data.

[0024] In some implementations, the acquisition of multi-source data includes: acquiring first voltage data at the output side of the transformer in the distribution substation; acquiring second voltage data at key nodes and end users in the substation; acquiring operating status data of each distributed energy storage device; wherein the operating status data includes state of charge, available power margin, and device health; and performing spatiotemporal alignment and formatting processing on the first voltage data, second voltage data, and operating status data to obtain multi-source data.

[0025] It should be noted that the key nodes in the transformer area are nodes with high voltage sensitivity and strong voltage vulnerability. The method for determining high voltage sensitivity is: sequentially simulating the injection of unit reactive power and screening out nodes that cause a reduction in the sum of squares of voltage deviations across the entire network greater than the deviation threshold; the method for determining strong voltage vulnerability is: screening out nodes whose voltage is below the vulnerability threshold through power flow calculation.

[0026] For example, the system utilizes voltage transformers installed on the output side of the transformer in the distribution substation to collect first voltage data in real time at a millisecond sampling frequency, with a measurement accuracy better than 0.5 volts. Simultaneously, by deploying intelligent monitoring terminals at 5 key nodes and 10 representative end-user sides pre-determined based on power flow calculations and sensitivity analysis, second voltage data is collected synchronously. These terminals support Ethernet and wireless dual-mode communication to ensure real-time data transmission. Each distributed energy storage device reports operational status data, including state of charge, current maximum chargeable and dischargeable power, and device health based on internal resistance and capacity decay assessment, at second-level cycles through its built-in battery management system. All data is uniformly stamped with high-precision BeiDou satellite time stamps and synchronized by time using a linear interpolation algorithm by the edge computing gateway. It is then encapsulated according to a predefined IoT data format, ultimately forming a multi-source data packet that is spatiotemporally consistent and dimensionally rich, directly serving subsequent risk assessment and optimization calculations.

[0027] In this embodiment, by integrating multi-point precise voltage and real-time energy storage status, the completeness and timeliness of the system's perception of the global voltage situation of the transformer area are significantly improved. This lays a solid and reliable data foundation for subsequent accurate dynamic risk assessment and coordinated voltage regulation decision-making, effectively overcoming the problems of regulation deviation and response delay caused by incomplete and lagging data in traditional methods.

[0028] The voltage regulation level assessment module 20 is used to perform risk assessment on voltage data based on multi-level voltage thresholds to obtain voltage regulation priority.

[0029] In some implementations, the risk assessment of voltage data based on multi-level voltage thresholds, as shown in Figure 2, includes: constructing multi-level voltage thresholds; wherein the multi-level voltage thresholds include a safety threshold, a warning threshold, an action threshold, and an emergency threshold; calculating a comprehensive deviation index characterizing the overall voltage status of the transformer area based on voltage data; matching the comprehensive deviation index with the multi-level voltage thresholds within threshold ranges to obtain a voltage risk level; and mapping and matching the voltage risk level based on a preset voltage regulation priority mapping table to obtain a voltage regulation priority; wherein the voltage regulation priority includes a priority coefficient.

[0030] It should be noted that the voltage risk levels include at least the following: Safety level: the voltage at all monitoring points is within the preset safety range of the rated value; Warning level: the voltage at at least one monitoring point deviates from the safety range, but does not exceed the qualified limit; Action level: the voltage at at least one monitoring point exceeds the qualified limit, but does not reach the equipment safety limit; Emergency level: the voltage at at least one monitoring point reaches or exceeds the equipment safety limit; the voltage regulation priority is positively correlated with the voltage risk level, and the emergency level corresponds to the highest voltage regulation priority.

[0031] For example, the system presets a safety threshold of ±5 volts, a warning threshold of ±7 volts, an action threshold of ±10 volts, and an emergency threshold of ±15 volts. Based on real-time voltage data obtained from 10 key monitoring points, the root mean square deviation is calculated as a comprehensive deviation index. By comparing the calculated deviation of 5.8 volts with the preset threshold range, it is determined that it falls between the safety threshold and the warning threshold, and thus is matched as a warning risk level. Subsequently, according to the preset mapping relationship, the warning priority, including the priority coefficient, is finally output.

[0032] In this embodiment, by using quantified multi-level thresholds and clear mapping rules, a rapid, objective, and consistent digital assessment of grid voltage risk is achieved. This enables the system to accurately perceive the degree of risk and output a corresponding urgent control signal, providing clear and reliable risk quantification input for subsequent optimization modules, thereby ensuring the accuracy and timeliness of control decisions.

[0033] In some implementations, the method for obtaining the priority coefficient, as shown in Figure 3, includes: obtaining available capacity data and short-term load forecast data of the energy storage system; matching the voltage risk level with a preset benchmark coefficient quantization table to obtain the benchmark coefficient; calculating resource availability based on the available capacity data of the energy storage system; calculating the voltage risk trend based on the short-term load forecast data; and dynamically adjusting the benchmark coefficient according to the resource availability and voltage risk trend to obtain the priority coefficient.

[0034] For example, the system first obtains the total available capacity data of the energy storage system reported by the battery management system, which is 80 kWh, and reads the short-term load forecast data for the next 30 minutes, showing that the power will increase by 50 kW; then, it matches the voltage risk level that has been determined to be a warning level with the preset quantification table to obtain a baseline coefficient of 0.7; next, it calculates the resource availability as 0.8 by dividing the current available capacity by the rated total capacity of 100 kWh; at the same time, it simulates future voltage changes based on the load forecast data and calculates the risk trend factor as 1.2; finally, according to the predefined adjustment algorithm, it uses the resource availability to fine-tune the baseline coefficient downward and uses the risk trend factor to correct upward, and outputs the final real-time priority coefficient of 0.75 after calculation.

[0035] In this embodiment, by integrating real-time resource status with forward-looking risk prediction, the priority coefficient can sensitively and reasonably reflect the urgency of the current voltage regulation demand of the system and the matching relationship of available resources, thereby guiding the subsequent optimization model to make an accurate balance between ensuring voltage safety and saving energy storage resources, effectively avoiding the problems of over-regulation or under-response caused by traditional fixed coefficients or decisions based solely on the current state.

[0036] In some implementations, the calculation of voltage risk trend based on short-term load forecast data includes: performing trajectory simulation based on short-term load forecast data to obtain voltage change trajectory; calculating the duration, depth integral, and rate of change of voltage over-limit based on the voltage change trajectory; and weighting and normalizing the duration, depth integral, and rate of change to obtain voltage risk trend.

[0037] For example, based on short-term load forecast data for the next 30 minutes, power flow calculation simulation is performed using the backward substitution method to obtain the voltage change trajectory of each key node at seven time points in the next seven time points. The simulated voltage value of one key node will gradually decrease from the current 215.4 volts to 209.1 volts. Subsequently, the trajectory data is analyzed to calculate that the duration of the voltage below the lower limit is 15 minutes, the limit depth integral value is 8.6 volts per minute, and the average voltage change rate is 0.12 volts per minute. Finally, the system assigns preset weight coefficients of 0.4, 0.4, and 0.2 to these three indicators, respectively, performs weighted summation to obtain the original risk value, and converts it into a final voltage risk trend value of 0.64, ranging from 0 to 1, through a set normalization benchmark.

[0038] In this embodiment of the application, the calculation process transforms the abstract prediction data into a precise and quantifiable forward-looking risk indicator, enabling the system to perceive the potential severity and urgency of voltage deterioration in advance. This provides a key future perspective for adjusting the priority of the current voltage regulation strategy, effectively enhancing the foresight and initiative of control decisions and avoiding the problems of insufficient regulation margin or over-preparation caused by the traditional method of responding with lag based only on the current state.

[0039] The optimization model building module 30 is used to build a multi-objective optimization model for energy storage based on voltage regulation priority and multi-source data.

[0040] In some implementations, the multi-objective optimization model for energy storage includes voltage quality optimization objective, economic operation optimization objective, and energy storage lifetime optimization objective, and the weight ratio of the voltage quality optimization objective is adjusted by a priority coefficient; wherein, the voltage quality optimization objective is used to minimize the overall voltage deviation of the distribution area; the economic operation optimization objective is used to minimize network loss cost and energy storage operation cost; and the energy storage lifetime optimization objective is used to maximize the lifespan of the energy storage device by adjusting the charging and discharging depth and power fluctuation of the energy storage device.

[0041] For example, the model uses six consecutive time points within a future 15-minute scheduling cycle as the optimization span. Its objective function consists of three core parts: first, the voltage quality objective, which aims to minimize the sum of squares of the voltage deviations from the rated value of 220 volts at 10 key monitoring points; second, the economic operation objective, which aims to minimize the approximately 3.5 kWh network loss cost calculated according to the transformer area line loss formula and the energy storage charging and discharging cost calculated based on real-time electricity prices; and third, the energy storage life optimization objective, which aims to minimize battery life loss caused by charging and discharging depth and power fluctuations by introducing an equivalent aging model based on the Rainflow counting method. In this solution, the system sets the real-time priority coefficient of 0.75 as the weight of the voltage quality objective item, and automatically adjusts the combined weight of the economic operation and life objectives to 0.25. Then, a constrained nonlinear programming solver is used to solve the model, and finally outputs a set of power command sequences for each energy storage device that optimizes the weighted overall objective.

[0042] In this embodiment, by constructing an optimization model that integrates multi-dimensional demands and using dynamic priority coefficients to allocate decision weights among objectives in real time, the system achieves synergistic optimization of economic operating costs and long-term health of energy storage assets while ensuring the core task of voltage regulation. This significantly improves overall operational efficiency and equipment sustainability while solving voltage problems.

[0043] The voltage regulation strategy solution module 40 is used to solve the multi-objective optimization model of energy storage through optimization algorithms to obtain the optimal voltage regulation strategy.

[0044] In some implementations, solving the multi-objective optimization model of energy storage through optimization algorithms includes: solving the voltage quality optimization objective through heuristic rules to obtain an initial feasible solution set; based on the initial feasible solution set, solving the economic operation optimization objective and the energy storage lifetime optimization objective through gradient descent to obtain a globally approximate optimal solution; and marking the globally approximate optimal solution as the optimal voltage regulation strategy.

[0045] For example, a pre-defined heuristic rule is first invoked. This rule generates an initial power command sequence within 5 milliseconds based on the real-time calculated ranking of voltage sensitivity at each node and the state of charge (SOC) of the four energy storage devices (total capacity 200 kWh). The core logic of this rule is to prioritize discharging the No. 1 energy storage device, which has the lowest voltage node sensitivity ranking and a current SOC above 60%, within its maximum 30 kW discharge capacity. Simultaneously, priority is given to charging the No. 3 energy storage device, which has the highest voltage node sensitivity and a SOC below 40%. The resulting command set constitutes the initial feasible solution set that satisfies the voltage quality constraints. Subsequently, based on this, the gradient descent method is initiated to perform a deep joint optimization of the economic operation objective (i.e., the real-time electricity price cost of 0.8 yuan per kWh and the network loss cost) and the energy storage lifetime objective (based on an equivalent cyclic aging model). After 50 iterations, the algorithm converges and outputs a new power command sequence. Compared to the initial solution set, this sequence globally optimized the charging and discharging power and time distribution of the four energy storage devices while strictly maintaining the voltage deviation of all nodes to no more than 2.1 volts. Finally, the complete scheme, which includes a total of 12 refined power commands at 5-minute intervals for the next 15 minutes, was marked as the optimal voltage regulation strategy for the current cycle and encapsulated and output to the execution module.

[0046] In this embodiment, by solving the problem in two stages, the speed of heuristic rules and the accuracy of gradient optimization algorithms are effectively integrated while strictly ensuring the core hard constraint of voltage regulation. This approach simultaneously takes into account real-time response speed and global economic optimization capability in complex multi-objective decision-making, significantly improving the overall quality of the strategy.

[0047] The energy storage control execution module 50 is used to execute the optimal voltage regulation strategy.

[0048] In some implementations, the energy storage control execution module includes an instruction distribution unit, a local control unit, and an autonomous switching unit. The instruction distribution unit is used to send the power instruction in the optimal voltage regulation strategy to the local control unit of each distributed energy storage device. The local control unit is built into each energy storage device and is used to receive and execute the power instruction and monitor the local access point voltage in real time. The autonomous switching unit is used to automatically execute droop control based on local voltage measurement when a communication anomaly or partial failure is detected.

[0049] For example, the instruction distribution unit, via a 5G private network, synchronously distributes the optimal voltage regulation strategy, which includes 84 specific power instructions for four energy storage devices over the next 15 minutes, at the top of each hour. The local control unit of each energy storage device monitors its access point voltage in real time with a period of 10 milliseconds. When it receives an instruction requiring a discharge of 30 kilowatts in the next minute, it completes the power closed-loop adjustment within 50 milliseconds and feeds back the actual execution result along with the local voltage data of 215.3 volts. When the autonomous switching unit detects that the communication with a certain energy storage device has been continuously interrupted for more than 3 sampling cycles, it immediately triggers the switching logic and controls the device to automatically enter the local control mode according to the preset voltage-power droop coefficient. In this mode, the device will operate autonomously according to its local measured voltage value, adjusting the power by 5 kilowatts per volt deviation. The entire process is smooth and takes about 2 seconds.

[0050] In the embodiments of this application, the clear division of labor and intelligent fault response mechanism ensure the accurate and reliable execution of the optimization strategy, and can still maintain basic but effective voltage support based on local information in extreme cases such as communication failures, thereby significantly enhancing the adaptability and survivability of the entire voltage regulation system in complex field environments.

[0051] In some implementations, a dynamic correction module is also included; wherein the dynamic correction module includes: acquiring actual voltage regulation data; comparing and calculating the actual voltage regulation data with the preset expected regulation effect to obtain the strategy execution deviation; and dynamically correcting the benchmark coefficient quantization table and the energy storage multi-objective optimization model based on the strategy execution deviation through an online learning algorithm.

[0052] For example, the module uses a 24-hour learning cycle to collect 1440 sets of actual voltage regulation data recorded by 10 key monitoring points within that cycle. This data is compared with the expected regulation effect corresponding to the optimized model, and the root mean square error of the average regulation deviation is calculated to be 1.8 volts. Subsequently, this deviation data is input into an online learning stochastic gradient descent algorithm. The algorithm automatically adjusts the model parameters based on the historical deviation sequence. After calculation, the coefficient corresponding to the warning risk level in the baseline coefficient quantization table is finely adjusted from 0.9 to 0.87, and the corresponding element of the voltage sensitivity matrix in the optimized model is simultaneously updated by 0.05. After seven consecutive cycles of iterative learning, the average regulation deviation of the system strategy has steadily decreased to 1.2 volts.

[0053] In the embodiments of this application, the mechanism enables the system to continuously and automatically optimize its core decision parameters and models based on objective feedback from historical execution results, thereby continuously improving the matching accuracy between the control strategy and the dynamic characteristics of the real power grid. This achieves a leap from a fixed strategy to an intelligent system with autonomous evolution capabilities, significantly enhancing the adaptability and stability of long-term operation and control quality.

[0054] Based on the above technical solutions, this application provides a distributed energy storage system with priority voltage regulation in distribution network areas. First, by introducing a dynamic risk assessment and priority mapping mechanism based on multi-level voltage thresholds, the system achieves a leap from "fixed strategy" to "risk self-adaptation," ensuring that when the risk of voltage exceeding limits increases, control resources can be tilted towards voltage quality targets without disturbance, fundamentally guaranteeing power supply safety. Second, by creatively embedding dynamic priority coefficients as core adjustment factors into a multi-objective optimization model, the system achieves online and refined trade-offs among voltage quality, economic operation, and energy storage life protection, enabling the system to cope with emergency voltage events while pursuing comprehensive optimal benefits under normal conditions. Finally, by designing a hybrid execution architecture of "centralized optimization-local autonomy" and integrating online learning and strategy self-correction capabilities, the system possesses high robustness and long-term adaptability, ensuring the continuous and reliable operation of voltage regulation functions under various complex and time-varying scenarios, effectively improving the distribution network's carrying capacity for high-proportion distributed energy and its intelligent management level.

[0055] In one possible implementation, this application embodiment also provides a distributed energy storage method prioritizing voltage regulation in distribution network areas, as shown in Figure 4, comprising: acquiring multi-source data; wherein the multi-source data includes voltage data; performing risk assessment on the voltage data based on multi-level voltage thresholds to obtain voltage regulation priority; constructing a multi-objective optimization model for energy storage based on the voltage regulation priority and multi-source data; solving the multi-objective optimization model for energy storage using an optimization algorithm to obtain the optimal voltage regulation strategy; and executing the optimal voltage regulation strategy.

[0056] For example, firstly, sensors deployed on the transformer outlet side and at 10 key nodes synchronously collect real-time voltage data at millisecond-level frequencies, and align it spatiotemporally with the state-of-charge data reported by four energy storage devices. Then, based on preset voltage thresholds of ±5V, ±7V, and ±10V, the current comprehensive voltage deviation is calculated to be 3.8V, classifying it as a level two risk and mapping a dynamic priority coefficient of 0.9. Next, using this coefficient as the core adjustment factor, a multi-objective optimization model is constructed that integrates minimizing voltage deviation, minimizing network losses and operating costs, and minimizing battery life loss. Within a 15-minute scheduling cycle, the model first... A heuristic rule based on voltage sensitivity generates a feasible initial solution within 5 milliseconds, and then uses gradient descent to perform 50 iterations of optimization to finally find the power command sequence with the optimal total cost. This strategy is distributed to each energy storage unit via a 5G private network for execution. The local controller performs closed-loop control with a 10-millisecond cycle. If communication is interrupted for more than 3 cycles, it automatically switches to a droop control mode that adjusts by 5 kilowatts for every 1 volt deviation of the local voltage, thereby ensuring continuous and reliable voltage support. At the same time, based on the deviation between 1,440 sets of actual adjustment data and expected results each day, the system dynamically optimizes the model parameters and priority mapping relationship through an online learning algorithm to achieve continuous self-evolution of the strategy.

[0057] Based on the above technical solutions, through a closed-loop design of the entire chain of perception, decision-making, execution, and learning, the unified approach of dynamic quantification of voltage regulation priority with risk, multi-objective collaborative optimization, and robust operation under abnormal conditions has been achieved, significantly improving the intelligence level and comprehensive benefits of voltage management in distribution areas.

[0058] In implementation, each step of the method provided in this embodiment can be completed by integrated logic circuits in the processor or by instructions in software form. The steps of the method disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or being executed by a combination of hardware and software modules in the processor.

[0059] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).

[0060] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0061] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of this application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and modifications of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and modifications.

Claims

1. A distributed energy storage system with priority for voltage regulation in distribution network areas, characterized in that, The system includes a multi-source data acquisition module, a voltage regulation level assessment module, an optimization model construction module, a voltage regulation strategy solution module, and an energy storage control execution module. The multi-source data acquisition module acquires multi-source data, including voltage data. The voltage regulation level assessment module performs risk assessment on the voltage data based on multi-level voltage thresholds to obtain voltage regulation priorities. The optimization model construction module constructs a multi-objective optimization model for energy storage based on the voltage regulation priorities and multi-source data. The voltage regulation strategy solution module solves the multi-objective optimization model for energy storage using an optimization algorithm to obtain the optimal voltage regulation strategy. The energy storage control execution module executes the optimal voltage regulation strategy.

2. The distributed energy storage system with priority for voltage regulation in distribution network areas according to claim 1, characterized in that, The risk assessment of voltage data based on multi-level voltage thresholds includes: constructing multi-level voltage thresholds; wherein the multi-level voltage thresholds include a safety threshold, a warning threshold, an action threshold, and an emergency threshold; calculating a comprehensive deviation index characterizing the overall voltage status of the transformer area based on voltage data; matching the comprehensive deviation index with the multi-level voltage thresholds within threshold ranges to obtain a voltage risk level; and mapping and matching the voltage risk level based on a preset voltage regulation priority mapping table to obtain a voltage regulation priority; wherein the voltage regulation priority includes a priority coefficient.

3. A distributed energy storage system with priority for voltage regulation in a distribution network area according to claim 2, characterized in that, The method for obtaining the priority coefficient includes: obtaining available capacity data and short-term load forecast data of the energy storage system; matching the voltage risk level with a preset benchmark coefficient quantization table to obtain the benchmark coefficient; calculating the resource availability based on the available capacity data of the energy storage system; calculating the voltage risk trend based on the short-term load forecast data; and dynamically adjusting the benchmark coefficient according to the resource availability and voltage risk trend to obtain the priority coefficient.

4. A distributed energy storage system with priority for voltage regulation in a distribution network area according to claim 3, characterized in that, The multi-objective optimization model for energy storage includes voltage quality optimization objective, economic operation optimization objective, and energy storage lifespan optimization objective, and adjusts the weight ratio of voltage quality optimization objective through priority coefficients; wherein, the voltage quality optimization objective is used to minimize the overall voltage deviation of the distribution area; the economic operation optimization objective is used to minimize network loss cost and energy storage operation cost; and the energy storage lifespan optimization objective is used to maximize the lifespan of energy storage equipment by adjusting the charging and discharging depth and power fluctuation of energy storage equipment.

5. A distributed energy storage system with priority for voltage regulation in a distribution network area according to claim 4, characterized in that, The step of solving the multi-objective optimization model of energy storage using optimization algorithms includes: solving the voltage quality optimization objective using heuristic rules to obtain an initial feasible solution set; based on the initial feasible solution set, solving the economic operation optimization objective and the energy storage lifetime optimization objective using gradient descent method to obtain a globally approximate optimal solution; and marking the globally approximate optimal solution as the optimal voltage regulation strategy.

6. A distributed energy storage system with priority for voltage regulation in a distribution network area according to claim 1, characterized in that, The energy storage control execution module includes an instruction distribution unit, a local control unit, and an autonomous switching unit. The instruction distribution unit is used to send the power instruction in the optimal voltage regulation strategy to the local control unit of each distributed energy storage device. The local control unit is built into each energy storage device and is used to receive and execute the power instruction and monitor the local access point voltage in real time. The autonomous switching unit is used to automatically execute droop control based on local voltage measurement when a communication anomaly or partial failure is detected.

7. A distributed energy storage system with priority for voltage regulation in a distribution network area according to claim 3, characterized in that, It also includes a dynamic correction module; wherein the dynamic correction module includes: acquiring actual voltage regulation data; comparing and calculating the actual voltage regulation data with the preset expected regulation effect to obtain the strategy execution deviation; and dynamically correcting the benchmark coefficient quantization table and the energy storage multi-objective optimization model based on the strategy execution deviation through an online learning algorithm.

8. A distributed energy storage system with priority for voltage regulation in a distribution network area according to claim 3, characterized in that, The voltage risk trend calculated based on short-term load forecast data includes: performing trajectory simulation based on short-term load forecast data to obtain the voltage change trajectory; calculating the duration, depth integral, and rate of change of voltage over-limit based on the voltage change trajectory; and weighting and normalizing the duration, depth integral, and rate of change to obtain the voltage risk trend.

9. A distributed energy storage system with priority for voltage regulation in a distribution network area according to claim 1, characterized in that, The method for acquiring the multi-source data includes: acquiring the first voltage data at the output side of the transformer in the distribution substation; acquiring the second voltage data at the key nodes and end users in the substation; acquiring the operating status data of each distributed energy storage device; wherein the operating status data includes the state of charge, available power margin, and device health; and performing spatiotemporal alignment and formatting processing on the first voltage data, the second voltage data, and the operating status data to obtain the multi-source data.

10. A distributed energy storage method prioritizing voltage regulation in distribution network areas, applied to a distributed energy storage system prioritizing voltage regulation in distribution network areas as described in any one of claims 1-9, characterized in that, include: Acquire multi-source data, including voltage data; perform risk assessment on the voltage data based on multi-level voltage thresholds to obtain voltage regulation priorities; construct a multi-objective optimization model for energy storage based on the voltage regulation priorities and multi-source data; solve the multi-objective optimization model for energy storage using an optimization algorithm to obtain the optimal voltage regulation strategy; and execute the optimal voltage regulation strategy.

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