Feeder-Load-Storage Active Power Balance Method and Device Based on Dynamic Control Interval

Abstract

This invention provides a method and device for active power balancing of feeder source-load and energy storage based on dynamic control intervals, relating to the field of low-voltage distribution network operation control technology. This method, through dynamic control intervals and hierarchical optimization mechanisms, dynamically adjusts the charging and discharging intervals based on real-time data and trend predictions, aiming to minimize feeder switching power and balance SOC, prioritizing the use of local resources. This approach not only improves feeder balancing capabilities but also flexibly addresses uncertainties, reduces cross-regional transmission demands, and enables cross-regional coordination to be initiated only when necessary, reducing losses and improving energy storage utilization.

Description

Technical Field

[0001] This invention relates to the field of power distribution network operation control technology, and in particular to a method and apparatus for active power balancing of feeder source-load-storage based on dynamic control intervals. Background Technology

[0002] With the large-scale integration of distributed photovoltaic, energy storage, and electric vehicles, distribution network feeders face the dual challenges of power volatility and uncertainty in the source-load-storage system. Traditional power balancing methods rely on fixed dispatch strategies or single-time-scale optimization, which are difficult to cope with intraday real-time fluctuations and have low efficiency in cross-regional coordination, leading to accumulated power deviations, increased line overload risks, and uneven utilization of energy storage resources. Summary of the Invention

[0003] In view of the above problems, the present invention provides a feeder source-load-storage active power balancing method and device based on dynamic control interval, so as to solve the problem that the existing technology cannot dynamically adjust the regulation range of energy storage and electric vehicles, which leads to the easy occurrence of power imbalance in the feeder when the load, photovoltaic and electric vehicle power fluctuate.

[0004] In a first aspect, embodiments of the present invention provide a feeder-source-load-storage active power balance method based on dynamic control intervals, comprising:

[0005] (1) Multiple power supply areas connected by feeders form the basic unit of a regional power network. Based on the real-time operation data of distributed energy storage, electric vehicles and distributed photovoltaics in each area, a multi-level coordination control mechanism is established between the day-ahead and intraday. The multi-level coordination control mechanism is used to realize the dynamic power balance of regional source, load and storage.

[0006] (2) During the day-ahead control phase, based on the load trend curve of the basic unit, the distributed photovoltaic power output forecast and the minimum access expectation of electric vehicles, and with the goal of satisfying the daily clearing constraints of power balance and energy storage state of charge (SOC), the base SOC curve of the distribution area and the power allocation base are generated.

[0007] (3) During the intraday real-time control phase, the charging and discharging power ranges of distributed energy storage and electric vehicles in each transformer area are dynamically adjusted. Specifically, this includes: calculating the real-time power deviation of each transformer area based on the real-time collected distributed photovoltaic output, load power and energy storage SOC data, combined with the benchmark SOC curve and the predicted power change trend; dynamically setting the upper and lower limits of charging and discharging power of energy storage and electric vehicles according to the real-time power deviation to form a dynamic control range; prioritizing the use of local energy storage and electric vehicle resources for adjustment with the optimization objectives of minimizing the total switching power of the feeder, balancing the energy storage SOC of the transformer area and meeting the line capacity constraints.

[0008] (4) When local resources are insufficient, power interaction between stations is carried out based on the power allocation benchmark. Through the improved optimization algorithm, a penalty term is introduced in the power interaction between stations to suppress frequent cross-station transmission. The weight coefficient is dynamically adjusted according to the multi-layer coordination control mechanism and photovoltaic power output fluctuation to ensure the overall power balance of the feeder and the safe operation of the equipment.

[0009] In one possible implementation, the expression for the line capacity constraint is:

[0010] ,in, Let be the interactive power of station k at time t, with the output being positive and the reception being negative; This refers to the dynamic transmission limit of the line in area k.

[0011] In one possible implementation, the objective function for minimizing the total switching power of the feeder is:

[0012] ;

[0013] The objective function for the SOC of the balanced distribution area energy storage is:

[0014] ,in, Let be the state of charge of the i-th energy storage at time t. This is the average SOC of all energy storage systems.

[0015] In one possible implementation, the method for generating the dynamic control interval includes:

[0016] According to the real-time power deviation of the transformer area To determine whether the local adjustment capacity meets the balance requirements;

[0017] like If this occurs, cross-regional coordination will be initiated, and dynamic control intervals between regions will be generated based on preset constraints and the upper and lower limits of the charging and discharging power of electric vehicles.

[0018] in, For threshold coefficient, The reference power for the transformer area is given by the following constraints: .

[0019] In one possible implementation, the constraint condition further includes a charge / discharge power constraint, which is:

[0020] ,

[0021] ,

[0022] in, , , Different dynamic adjustment coefficients are used, which are adjusted in real time according to the power change trend; This is the upper limit of the energy storage discharge power. The upper limit of charging power for electric vehicles, Charging power for electric vehicles, This refers to the energy storage discharge power.

[0023] In one possible implementation, the improved optimization algorithm is a particle swarm optimization algorithm that introduces a power interaction penalty term, with the fitness function being:

[0024] ,

[0025] in, and This is a dynamic weighting coefficient, which is increased during peak photovoltaic output periods. To suppress frequent interactions.

[0026] In one possible implementation, the method for establishing the baseline SOC curve is as follows:

[0027] ,in, Let τ be the total charging and discharging power of the energy storage in the distribution area. Let the total energy storage capacity be [value], and satisfy the following:

[0028] and ;

[0029] in, , To establish a safe operating range for energy storage devices and prevent overcharging / over-discharging; The SOC value at the beginning of each day; This is the end time of the day.

[0030] In one possible implementation, the intraday real-time control phase further includes:

[0031] Based on the predicted power deviation of the transformer area at the current moment and the predicted power deviation at the next moment Determine the target adjustment scenario, wherein the target adjustment scenario includes one of the following:

[0032] If there is excess power for two consecutive moments, the energy storage will be called up for charging, and the electric vehicle will be guided to charge simultaneously.

[0033] Current surplus, future shortage, dynamically reserve energy storage SOC margin;

[0034] In the face of current shortages and future surpluses, we will utilize energy storage for charging and guide the orderly charging of electric vehicles.

[0035] When power is insufficient for two consecutive moments, the energy storage discharge and electric vehicle discharge are maximized.

[0036] In one possible implementation, the allocation rule for the charging and discharging power range is as follows:

[0037] In scenario a), the upper limit of energy storage discharge power is The upper limit of electric vehicle charging power is ;

[0038] In scenario b), the upper limit of energy storage discharge power is The charging power of electric vehicles will be dynamically adjusted according to the predicted shortfall.

[0039] In scenario c), the lower limit of energy storage charging power is The lower limit of electric vehicle charging power is ;

[0040] In scenario d), both the energy storage discharge power and the electric vehicle charging power are allocated according to their maximum capacity.

[0041] Secondly, embodiments of the present invention provide a feeder-load-storage active power balancing device based on a dynamic control interval, comprising:

[0042] The mechanism establishment module is used to form the basic unit of a regional power network by connecting multiple power supply areas through feeders. Based on the real-time operation data of distributed energy storage, electric vehicles and distributed photovoltaics in each area, a multi-level coordination control mechanism is established between the day-ahead and intraday. The multi-level coordination control mechanism is used to realize the dynamic power balance of regional source, load and storage.

[0043] The day-ahead control module is used to generate the base SOC curve and power allocation benchmark for the distribution area based on the load trend curve of the basic unit, the distributed photovoltaic output forecast and the minimum access expectation of electric vehicles, with the goal of meeting the daily clearing constraints of power balance and energy storage state of charge (SOC).

[0044] The intraday control module is used to dynamically adjust the charging and discharging power range of distributed energy storage and electric vehicles in each transformer area during the intraday real-time control phase. Specifically, it includes: calculating the real-time power deviation of each transformer area based on real-time collected distributed photovoltaic output, load power, and energy storage SOC data, combined with the benchmark SOC curve and the predicted power change trend; dynamically setting the upper and lower limits of charging and discharging power of energy storage and electric vehicles according to the real-time power deviation to form a dynamic control range; and prioritizing the use of local energy storage and electric vehicle resources for adjustment with the optimization objectives of minimizing the total feeder switching power, balancing the energy storage SOC of the transformer area, and meeting the line capacity constraints.

[0045] The optimization module is used to perform power interaction between transformer substations based on the power allocation benchmark when local resources are insufficient. Through an improved optimization algorithm, a penalty term is introduced into the power interaction between transformer substations to suppress frequent cross-regional transmission. The weighting coefficient is dynamically adjusted according to the multi-layer coordination control mechanism and photovoltaic output fluctuations to ensure the overall power balance of the feeder and the safe operation of the equipment.

[0046] By implementing the method provided by this invention, the charging and discharging range is dynamically adjusted based on real-time data and trend prediction through dynamic control intervals and hierarchical optimization mechanisms. The goal is to minimize feeder switching power and balance SOC, prioritizing the use of local resources. This approach not only improves feeder balancing capabilities but also flexibly addresses uncertainties, reduces cross-regional transmission demands, and enables cross-regional coordination to be initiated only when necessary, thereby reducing losses and improving energy storage utilization.

[0047] Meanwhile, during the feeder balancing process, the day-ahead control will be optimized to intraday control with reserved adjustment margin, and the improved algorithm will be combined to suppress frequent interactions, ensuring feeder safety and economy. Attached Figure Description

[0048] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0049] Figure 1 A flowchart illustrating a feeder-load-storage active power balance method based on dynamic control intervals, provided in an embodiment of the present invention;

[0050] Figure 2 A schematic diagram of the structure of a feeder source-load-storage active power balancing device based on dynamic control interval provided in an embodiment of the present invention;

[0051] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] In related technologies, power distribution network feeder systems have long relied on fixed dispatch strategies and single-time-scale optimization methods for power balance management. Traditional methods, based on historical load data, formulate static dispatch plans, which cannot adapt to the random fluctuations in distributed photovoltaic (PV) output, the spatiotemporal differences in electric vehicle charging demand, and the dynamic response characteristics of energy storage. With the increasing integration of distributed energy resources, fixed dispatch modes lead to the accumulation of real-time power deviations within the day. Cross-regional regulation relies on manual experience and judgment, resulting in low coordination efficiency and a high risk of line overload and energy storage SOC imbalance. For example, in scenarios where PV output suddenly drops at midday while electric vehicles are charging intensively, traditional methods struggle to adjust energy storage charging and discharging strategies in a timely manner, often requiring emergency calls to the upper-level grid for support, increasing system operating costs.

[0054] To address the aforementioned issues, in-depth analysis of the source-load-storage fluctuation characteristics and the bottlenecks in transformer area coordination revealed three core flaws in the current implementation scheme:

[0055] First, the day-ahead control and intraday control are disconnected, making it impossible to achieve dynamic connection across time scales;

[0056] Second, the local resource adjustment capacity has not been fully utilized, and cross-regional interactions lack priority control.

[0057] Third, the optimization model did not consider the impact of photovoltaic fluctuations on weight allocation.

[0058] Based on this, the present invention proposes to construct a multi-layered coordinated control architecture, which realizes the priority use of local resources and the on-demand complementarity across distribution areas through hierarchical optimization of the day-ahead baseline curve and intraday dynamic range; at the same time, it designs a penalty function and a weight adaptive mechanism to balance the requirements of line safety and economy.

[0059] See Figure 1 This diagram shows the overall flowchart of the feeder balancing method. The entire control process is divided into two stages:

[0060] Phase 1: To meet the power balance within the power supply area and the daily clearing constraint of distributed energy storage SOC, based on the load trend curve and the trend of distributed photovoltaic power generation, a power balance within the power supply area is established to fully absorb new energy. The minimum expected electric vehicle access is used as the calculation basis in this control.

[0061] The second phase involves addressing the uncertainties in load power and electric vehicle access within the operating power supply area during the day. With the goal of maximizing the SOC control margin of distributed energy storage, the upper and lower limits for regulation of distributed energy storage and electric vehicles are dynamically established based on real-time distributed energy storage charging and discharging power, real-time output of distributed photovoltaic power, and real-time load power, combined with the trends of distributed photovoltaic power output and load power. This forms a dynamic control range to cope with uncertain load and electric vehicle power changes.

[0062] like Figure 1 The process shown may include:

[0063] S110. Multiple power supply areas connected by feeders form the basic unit of a regional power network. Based on the real-time operation data of distributed energy storage, electric vehicles and distributed photovoltaics in each area, a multi-level coordination and control mechanism is established for day-ahead and intraday operation.

[0064] The multi-layered coordinated control mechanism is used to achieve regional-level dynamic power balance of source, load and storage. Specifically, it refers to a two-layer optimization framework that integrates day-ahead control and intraday control. It can be implemented by combining rolling optimization and model predictive control to coordinate resource allocation and power balance at different time scales.

[0065] S120. During the day-ahead control phase, based on the load trend curve of the basic unit, the distributed photovoltaic output forecast, and the minimum access expectation of electric vehicles, and with the goal of meeting the daily clearing constraints of power balance and energy storage state of charge (SOC), the base SOC curve of the distribution area and the power allocation base are generated.

[0066] It should be noted that the method for establishing the benchmark SOC curve is as follows:

[0067] ,in, Let τ be the total charging and discharging power of the energy storage in the distribution area. Let the total energy storage capacity be [value], and satisfy the following:

[0068] and ;

[0069] in, , To establish a safe operating range for energy storage devices (e.g., 20%~80%), and to prevent overcharging / over-discharging; The SOC value at the beginning of each day (e.g., 00:00); This refers to the end time of each day (e.g., 24:00).

[0070] The power allocation benchmark is specifically calculated based on load forecasting and photovoltaic output forecasting, determining the benchmark power P for the distribution area. base (t), where the reference power is used for deviation determination in real-time control during the day.

[0071] P base (t) = P load,forecast (t)-P pv,forecast (t)-P ev,min (t), P load,forecast (t) represents the load trend curve, P pv,forecast (t) represents the photovoltaic power output prediction curve, P ev,min (t) represents the minimum access expectation for electric vehicles.

[0072] S130. During the intraday real-time control phase, the charging and discharging power ranges of distributed energy storage and electric vehicles in each transformer area are dynamically adjusted. Specifically, this includes: calculating the real-time power deviation of each transformer area based on real-time collected distributed photovoltaic output, load power, and energy storage SOC data, combined with the baseline SOC curve and predicted power change trends; dynamically setting the upper and lower limits of charging and discharging power for energy storage and electric vehicles according to the real-time power deviation to form a dynamic control range; prioritizing the use of local energy storage and electric vehicle resources for adjustment with the optimization objectives of minimizing the total feeder switching power, balancing the energy storage SOC of the transformer area, and meeting line capacity constraints.

[0073] Specifically, during the day-ahead control phase, a baseline SOC curve is generated based on load trends, photovoltaic (PV) forecasts, and the minimum expected connection for electric vehicles, reserving an operational margin for intraday real-time adjustments. Once the intraday phase begins, PV output, load power, and energy storage SOC data are collected in real time. Power deviations are calculated by integrating short-term forecast trends, and upper and lower limits for charging and discharging power are dynamically set. When local energy storage and electric vehicle adjustment capabilities are insufficient, a cross-regional power interaction mechanism is activated. An improved algorithm dynamically allocates interaction power weights, prioritizing the use of redundant resources from neighboring regions. The coordinated execution of day-ahead planning and intraday control ensures regional dynamic balance while meeting daily SOC constraints.

[0074] Compared to existing technologies, traditional methods employ static scheduling on a single time scale, and cross-regional coordination relies on manual experience, easily leading to accumulated power deviations and energy storage SOC imbalance. This solution achieves dynamic interval adjustment through a multi-time-scale optimization framework, combining a local resource priority allocation strategy with a cross-regional penalty mechanism, significantly reducing the total feeder switching power and line overload risk. For example, in scenarios with sudden drops in photovoltaic output, traditional methods require frequent cross-regional power dispatch, while this solution prioritizes local energy storage discharge through dynamic interval adjustment, reducing the frequency of cross-regional transmission.

[0075] In this embodiment, the expression for the line capacity constraint is:

[0076] ,in, Let be the interactive power of station k at time t, with the output being positive and the reception being negative; This refers to the dynamic transmission limit of the line in area k.

[0077] The objective function for minimizing the total switching power of the feeder is:

[0078] ;

[0079] The objective function for the SOC of the balanced distribution area energy storage is:

[0080] ,in, Let be the state of charge of the i-th energy storage at time t. This is the average SOC of all energy storage systems.

[0081] Furthermore, the generation of this dynamic control range refers to the dynamic adjustment of the upper and lower limits of the charging and discharging power of energy storage and electric vehicles based on real-time power deviation and predicted trends. Specifically, it can be achieved using a range rolling correction algorithm to ensure that the adjustment capability dynamically matches the demand.

[0082] The method for generating the dynamic control interval includes determining whether the local regulation capacity meets the balance requirements based on the real-time power deviation of the transformer area. If not, cross-transformer area coordination is initiated, and dynamic control intervals between transformer areas are generated based on preset constraints.

[0083] Specifically, the method for generating the dynamic control interval includes:

[0084] According to the real-time power deviation of the transformer area To determine whether the local adjustment capacity meets the balance requirements;

[0085] like If this occurs, cross-regional coordination will be initiated, and dynamic control intervals between regions will be generated based on preset constraints and the upper and lower limits of the charging and discharging power of electric vehicles.

[0086] in, For threshold coefficient, The reference power for the transformer area is given by the following constraints: .

[0087] It should be noted that, This is the baseline power value generated by the transformer substation during the day-ahead control phase, used for determining deviations in intraday real-time control. Its calculation is based on load forecasting, photovoltaic output forecasting, and the minimum charging demand for electric vehicles. The specific formula is as follows:

[0088]

[0089] in, The load forecast (unit: kW) for the transformer area at time t is based on historical load data (such as data from the same period in the past 30 days) and meteorological factors (temperature, humidity), and is predicted using a time series model (ARIMA or LSTM).

[0090] The predicted value of distributed photovoltaic power output (unit: kW) for the transformer area at time t is specifically predicted by combining weather forecasts (irradiance, cloud cover) and the efficiency curve of photovoltaic modules, using physical models or machine learning algorithms (such as support vector machines).

[0091] The minimum charging demand (in kW) for electric vehicles in the power distribution area at time t is estimated using Monte Carlo simulation or a probabilistic model based on user charging habits (such as the probability of charging on weekdays / holidays), the number of electric vehicles in operation, and battery capacity.

[0092] In another feasible embodiment, the constraint condition further includes a charge / discharge power constraint, wherein the charge / discharge power constraint is:

[0093] ,

[0094] ,

[0095] in, , , Different dynamic adjustment coefficients are used, which are adjusted in real time according to the power change trend; This is the upper limit of the energy storage discharge power. The upper limit of charging power for electric vehicles, Charging power for electric vehicles, This refers to the energy storage discharge power.

[0096] Furthermore, the intraday real-time control phase also includes: determining the target adjustment scenario based on the predicted power deviation of the transformer area at the current moment and the predicted power deviation at the next moment. The target adjustment scenario includes one of the following: if there is excess power for two consecutive moments, prioritize the discharge of energy storage and limit the charging of electric vehicles; if there is excess power at present and insufficient power at the next moment, dynamically reserve the SOC margin of energy storage; if there is insufficient power at present and excess power at the next moment, prioritize charging energy storage and guide electric vehicles to charge during off-peak hours; if there is insufficient power for two consecutive moments, maximize the discharge of energy storage and reduce the load of electric vehicle charging.

[0097] Specifically, based on the predicted power deviation of the transformer area at the current moment. and the predicted power deviation at the next moment Determine the target adjustment scenario, wherein the target adjustment scenario includes one of the following:

[0098] If there is excess power for two consecutive moments, the system will call upon energy storage charging and simultaneously guide electric vehicle charging. The excess power will be actively consumed through energy storage charging, reducing or suspending the charging power of electric vehicles (reducing load demand) to avoid exacerbating the power excess.

[0099] Current surplus, future shortage, dynamically reserve energy storage SOC margin;

[0100] In the face of current shortages and future surpluses, we will utilize energy storage for charging and guide the orderly charging of electric vehicles.

[0101] When power is insufficient for two consecutive moments, the energy storage discharge and electric vehicle discharge are maximized.

[0102] The allocation rule for the charging and discharging power range is as follows:

[0103] In scenario a), the upper limit of energy storage discharge power is The upper limit of electric vehicle charging power is ;

[0104] In scenario b), the upper limit of energy storage discharge power is The charging power of electric vehicles will be dynamically adjusted according to the predicted shortfall.

[0105] In scenario c), the lower limit of energy storage charging power is The lower limit of electric vehicle charging power is ;

[0106] In scenario d), both the energy storage discharge power and the electric vehicle charging power are allocated according to their maximum capacity.

[0107] In other words, this paper proposes a rule for dynamically adjusting the allocation of energy storage and electric vehicle charging / discharging power ranges under different power fluctuation scenarios. Specifically, in scenarios where there is excess power for two consecutive moments, the upper limit of energy storage discharge power and the upper limit of electric vehicle charging power are set to specific values; in scenarios where there is current excess power and insufficient power in the next moment, the upper limit of energy storage discharge power is set, and the electric vehicle charging power is dynamically adjusted according to the predicted gap; in scenarios where there is current insufficient power and excess power in the next moment, the lower limit of energy storage charging power and the lower limit of electric vehicle charging power are set; in scenarios where there is insufficient power for two consecutive moments, both energy storage discharge power and electric vehicle charging power are allocated according to maximum capacity.

[0108] In this embodiment, the charging and discharging power range refers to the power range within which energy storage devices and electric vehicles are allowed to charge and discharge during the real-time control phase. Specifically, this can be achieved by calculating upper and lower limits based on real-time power deviation and predicted trends of the distribution area, thus constraining device output to balance supply and demand. Dynamic adjustment refers to real-time correction of the charging and discharging range based on predicted power change trends. This can be achieved using a rolling optimization algorithm combined with a scenario recognition strategy to adapt to adjustment needs under different fluctuation modes. The classification criteria for scenarios a) to d) are the combination of power deviation directions between the current and next time moments. This can be achieved using a discrete state partitioning method based on time series prediction to capture cross-time-period fluctuation characteristics.

[0109] Specifically, when two consecutive moments of power surplus are detected, the upper limit of energy storage discharge power can be set as a percentage of the rated capacity, such as 70%-80%, and the upper limit of electric vehicle charging power can be set as 50%-60% of the rated power of the charging pile. By limiting the maximum output of both, excessive absorption leading to subsequent shortages is avoided. When there is current surplus but insufficient capacity is predicted for the next moment, the upper limit of energy storage discharge is compressed to a lower range, such as 30%-40% of the rated capacity. At the same time, the electric vehicle charging power is adjusted proportionally according to the predicted shortage value for the next moment. For example, for every 10kW increase in the shortage, the charging power is reduced by 5kW to reserve energy storage SOC to cope with future demand. When there is current shortage but surplus is predicted for the next moment, the lower limit of energy storage charging is set as the minimum power required to maintain SOC above the safety threshold, such as 20%-30% of the rated capacity. The lower limit of electric vehicle charging is set as the minimum power to meet basic charging needs, such as 30%-40% of the rated power, to ensure energy storage reserve capacity while guiding load shifting. When there is a continuous shortage, the energy storage discharge power is allowed to reach 90%-100% of the rated capacity, and the electric vehicle charging power is limited to the minimum acceptable level, such as 10%-20% of the rated power, to alleviate the shortage by maximizing the synergistic effect of discharge and load reduction.

[0110] Compared to existing technologies, traditional methods typically employ fixed charge / discharge intervals or single timescale optimization, failing to differentiate adjustment needs under varying fluctuation scenarios. For instance, in power state transition scenarios, existing technologies may continuously draw upon energy storage for discharge, leading to insufficient capacity to handle subsequent power shortages. This solution, however, maintains SOC margin through dynamic interval adjustment. In scenarios with persistent power shortages, existing technologies may prioritize load reduction while neglecting the maximization of energy storage utilization. This solution, on the other hand, improves adjustment efficiency through a collaborative allocation strategy. Furthermore, existing technologies lack fine-grained, tiered control over electric vehicle charging power, while this solution ensures basic charging needs are met through a lower limit setting.

[0111] Through the above technical solutions, this application resolves the coordination contradiction between real-time power balance demand and cross-time period power fluctuation prediction. In continuous surplus scenarios, it avoids the decline in subsequent regulation capacity caused by excessive discharge of energy storage; in state transition scenarios, it achieves a balance between current regulation and future demand; in reverse fluctuation scenarios, it maintains energy storage reserve capacity and guides load transfer through lower limit setting; and in continuous deficit scenarios, it alleviates the power gap by maximizing resource utilization. This reduces the overload risk and resource waste under a single strategy and improves the accuracy of coordinated regulation in multi-timescale fluctuation scenarios.

[0112] In another feasible implementation, the above implementation steps are specifically implemented through a two-level control architecture at the district level and the regional level:

[0113] The district-level control is responsible for the real-time power allocation of local energy storage, electric vehicles, and photovoltaics;

[0114] Regional control coordinates the interactive power of each transformer area, optimizes line transmission efficiency and energy storage SOC balance, and ensures that the feeder as a whole operates within a dynamic safety range.

[0115] It should be noted that the regulation of local energy storage and electric vehicle resources refers to the charging and discharging power capacity that can be called upon by distributed energy storage and electric vehicles within the distribution area. Specifically, it can be calculated by superimposing the remaining charging and discharging power margin of energy storage with the adjustable range of electric vehicle charging load, and is used to evaluate the autonomous balancing capability of the distribution area.

[0116] Achieving regional coordinated balance through power interaction between power distribution stations refers to a power interaction and adjustment mechanism between multiple power supply stations. Specifically, this can be achieved by generating cross-regional transmission power commands through regional optimization algorithms, which is used to expand the adjustment resource pool. The preset constraints include line capacity limitations, energy storage state-of-charge balance, and cross-regional transmission loss thresholds. These can be achieved by jointly setting real-time monitoring data of line current carrying capacity and energy storage state-of-charge difference thresholds to ensure that power interaction remains within safe boundaries.

[0117] In practical applications, the specific steps for prioritizing the use of local energy storage and electric vehicle resources for regulation include:

[0118] (1) Calculate the real-time power deviation of the transformer area:

[0119] ;in, Distributed photovoltaic power generation in real time; This represents the real-time charging and discharging power of energy storage; discharging is positive, and charging is negative. For load power; Power for charging electric vehicles;

[0120] (2) If Local resources should be adjusted in the following order first:

[0121] a) Adjust the energy storage charging and discharging power to within the dynamic control range;

[0122] b) Adjust the charging power of the electric vehicle to ensure that its deviation from the previous day's reference value does not exceed a preset proportion;

[0123] (3) If a power deviation still exists after local adjustment, cross-regional coordination will be triggered.

[0124] Understandably, the condition for determining insufficient local resources is as follows:

[0125] ;in, The power deviation threshold coefficient is 0 < α < 1; This is the reference power for the transformer area; The threshold for SOC balance; This represents the real-time state of charge of the i-th energy storage unit. This is the baseline SOC curve value.

[0126] The following constraints must be met when coordinating across different distribution areas:

[0127] The power exchanged between individual transformer substations shall not exceed the dynamic transmission limit of the line. ;

[0128] in, Let be the interactive power of station k at time t, with the output being positive and the reception being negative; This is the dynamic limit of the line capacity for transformer area k.

[0129] Furthermore, achieving regional-level coordinated balance through power interaction between distribution stations is essentially cross-distribution power allocation, and the allocation method is as follows:

[0130] (1) The power demand of the receiving power area j is shared proportionally by the output power area k:

[0131] ,in, To meet the power shortage in demand area j, The state of charge of the energy storage in output station area k;

[0132] (2) Update the SOC of each energy storage area after allocation:

[0133] In the formula, The actual charging and discharging power of the i-th energy storage device. For its rated capacity, To control the time interval.

[0134] In this embodiment, when utilizing local energy storage and electric vehicle resources for regulation, a charge / discharge rate constraint must be met. This charge / discharge rate constraint specifically includes:

[0135] (1) Limitation of energy storage charging and discharging power:

[0136] ,in, , Reserve a 10% margin to cope with volatility;

[0137] (2) Electric vehicle charging power limit:

[0138] Retain a 20% margin to ensure charging safety.

[0139] In this embodiment, the cross-regional coordination adopts the following optimization model:

[0140] ,

[0141] in, The SOC equilibrium weight coefficient, This represents the average SOC of all energy storage systems.

[0142] S140. When local resources are insufficient, power interaction between transformer substations is carried out based on the power allocation benchmark. Through an improved optimization algorithm, a penalty term is introduced into the power interaction between transformer substations to suppress frequent cross-regional transmission. The weighting coefficient is dynamically adjusted according to the multi-layer coordination control mechanism and photovoltaic power output fluctuations to ensure the overall power balance of the feeder and the safe operation of the equipment.

[0143] It should be noted that this improved optimization algorithm refers to adding a cross-regional power interaction penalty term to the objective function. Specifically, it can be implemented by using quadratic programming combined with a dynamic weight adjustment strategy to reduce line transmission loss and equipment overload risk.

[0144] Specifically, the improved optimization algorithm is a particle swarm optimization algorithm that introduces a power interaction penalty term, and its fitness function is:

[0145] ,

[0146] in, and This is a dynamic weighting coefficient, which is increased during peak photovoltaic output periods. To suppress frequent interactions.

[0147] Through the above technical solutions, this application solves the problem of insufficient real-time fluctuation response caused by fixed scheduling strategies in traditional methods. By dynamically adjusting the charging and discharging intervals to match the needs of different scenarios, it effectively suppresses the accumulation of power deviations. By prioritizing the use of local resources and dynamically optimizing cross-regional interactions, it reduces the risk of line overload and improves collaborative efficiency. Based on the dual mechanism of daily SOC constraints and real-time interval adjustments, it achieves balanced utilization of energy storage resources in both time and space dimensions, avoiding local overcharging or over-discharging. By introducing penalty terms and dynamic weighting coefficients, it balances the uncertainty caused by photovoltaic fluctuations, ensuring overall power balance of the feeder and safe operation of the equipment.

[0148] Please see Figure 2 , Figure 2 This is a schematic diagram of a feeder-load-storage active power balancing device based on dynamic control intervals, provided as an embodiment of the present invention. Figure 2 The apparatus shown may include:

[0149] The mechanism establishment module 210 is used to form a basic unit of a regional power network by connecting multiple power supply areas through feeders. Based on the real-time operation data of distributed energy storage, electric vehicles and distributed photovoltaics in each area, a multi-level coordination control mechanism is established between the day-ahead and intraday. The multi-level coordination control mechanism is used to realize the dynamic power balance of regional source, load and storage.

[0150] The day-ahead control module 220 is used to generate a base SOC curve for the distribution area and a power allocation benchmark during the day-ahead control phase, based on the load trend curve of the basic unit, the distributed photovoltaic output forecast, and the minimum access expectation of electric vehicles, with the goal of meeting the daily clearing constraints of power balance and energy storage state of charge (SOC).

[0151] The intraday control module 230 is used to dynamically adjust the charging and discharging power range of distributed energy storage and electric vehicles in each transformer area during the intraday real-time control phase. Specifically, it includes: calculating the real-time power deviation of each transformer area based on real-time collected distributed photovoltaic output, load power, and energy storage SOC data, combined with the benchmark SOC curve and the predicted power change trend; dynamically setting the upper and lower limits of charging and discharging power of energy storage and electric vehicles according to the power deviation to form a dynamic control range; and prioritizing the use of local energy storage and electric vehicle resources for adjustment with the optimization objectives of minimizing the total feeder switching power, balancing the energy storage SOC of the transformer area, and meeting the line capacity constraints.

[0152] The optimization module 240 is used to perform power interaction between transformer substations based on the power allocation benchmark when local resources are insufficient. It introduces a penalty term in the power interaction between transformer substations through an improved optimization algorithm to suppress frequent cross-transformer transmission. The weighting coefficient is dynamically adjusted according to the multi-layer coordination control mechanism and photovoltaic output fluctuations to ensure the overall power balance of the feeder and the safe operation of the equipment.

[0153] In one possible implementation, the expression for the line capacity constraint is:

[0154] ,in, Let be the interactive power of station k at time t, with the output being positive and the reception being negative; This refers to the dynamic transmission limit of the line in area k.

[0155] In one possible implementation, the objective function for minimizing the total switching power of the feeder is:

[0156] ;

[0157] The objective function for the SOC of the balanced distribution area energy storage is:

[0158] ,in, Let be the state of charge of the i-th energy storage at time t. This is the average SOC of all energy storage systems.

[0159] In one possible implementation, the method for generating the dynamic control interval includes:

[0160] According to the real-time power deviation of the transformer area To determine whether the local adjustment capacity meets the balance requirements;

[0161] like If this occurs, cross-regional coordination will be initiated, and dynamic control intervals between regions will be generated based on preset constraints and the upper and lower limits of the charging and discharging power of electric vehicles.

[0162] in, For threshold coefficient, The reference power for the transformer area is given by the following constraints: .

[0163] In one possible implementation, the constraint condition further includes a charge / discharge power constraint, which is:

[0164] ,

[0165] ,

[0166] in, , , Different dynamic adjustment coefficients are used, which are adjusted in real time according to the power change trend; This is the upper limit of the energy storage discharge power. The upper limit of charging power for electric vehicles, Charging power for electric vehicles, This refers to the energy storage discharge power.

[0167] In one possible implementation, the improved optimization algorithm is a particle swarm optimization algorithm that introduces a power interaction penalty term, with the fitness function being:

[0168] ,

[0169] in, and This is a dynamic weighting coefficient, which is increased during peak photovoltaic output periods. To suppress frequent interactions.

[0170] In one possible implementation, the method for establishing the baseline SOC curve is as follows:

[0171] ,in, Let τ be the total charging and discharging power of the energy storage in the distribution area. Let the total energy storage capacity be [value], and satisfy the following:

[0172] and ;

[0173] in, , To establish a safe operating range for energy storage devices and prevent overcharging / over-discharging; The SOC value at the beginning of each day; This is the end time of the day.

[0174] In one possible implementation, the intraday control module 230 is further configured to:

[0175] Based on the predicted power deviation of the transformer area at the current moment and the predicted power deviation at the next moment Determine the target adjustment scenario, wherein the target adjustment scenario includes one of the following:

[0176] If there is excess power for two consecutive moments, the energy storage will be called up for charging, and the electric vehicle will be guided to charge simultaneously.

[0177] Current surplus, future shortage, dynamically reserve energy storage SOC margin;

[0178] In the face of current shortages and future surpluses, we will utilize energy storage for charging and guide the orderly charging of electric vehicles.

[0179] When power is insufficient for two consecutive moments, the energy storage discharge and electric vehicle discharge are maximized.

[0180] In one possible implementation, the allocation rule for the charging and discharging power range is as follows:

[0181] In scenario a), the upper limit of energy storage discharge power is The upper limit of electric vehicle charging power is ;

[0182] In scenario b), the upper limit of energy storage discharge power is The charging power of electric vehicles will be dynamically adjusted according to the predicted shortfall.

[0183] In scenario c), the lower limit of energy storage charging power is The lower limit of electric vehicle charging power is ;

[0184] In scenario d), both the energy storage discharge power and the electric vehicle charging power are allocated according to their maximum capacity.

[0185] This embodiment also provides an electronic device, including a processor and a memory. The memory stores computer-executable instructions that can be executed by the processor. The processor executes the computer-executable instructions to implement the above-described feeder source-load-storage active power balancing method based on dynamic control intervals. This electronic device can be a server or a terminal device.

[0186] See Figure 3 As shown, the electronic device includes a processor 100 and a memory 101. The memory 101 stores computer-executable instructions that can be executed by the processor 100. The processor 100 executes the computer-executable instructions to implement the above-described feeder source-load-storage active power balance method based on dynamic control intervals.

[0187] Furthermore, Figure 3 The electronic device shown also includes a bus 102 and a communication interface 103, with the processor 100, the communication interface 103 and the memory 101 connected via the bus 102.

[0188] The processor in the aforementioned electronic device can execute computer-executable instructions to implement the steps in the above-mentioned feeder source-load-storage active power balance method based on dynamic control intervals.

[0189] This embodiment also provides a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions cause the processor to implement the above-mentioned feeder source-load-storage active power balance method based on dynamic control interval.

[0190] The computer-executable instructions stored in the aforementioned computer-readable storage medium can be executed to implement the steps in the aforementioned feeder-load-storage active power balance method based on dynamic control intervals.

[0191] This embodiment also provides a computer program product, including program code. The instructions included in the program code can be used to execute the methods in the preceding method embodiments. For specific implementation details, please refer to the method embodiments, which will not be repeated here.

[0192] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and apparatus described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0193] Furthermore, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0194] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0195] Finally, it should be noted that the above embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A feeder-based active power balance method for source-load-storage based on dynamic control intervals, characterized in that, The method includes: (1) Multiple power supply areas connected by feeders form the basic unit of a regional power network. Based on the real-time operation data of distributed energy storage, electric vehicles and distributed photovoltaics in each area, a multi-level coordination control mechanism is established between the day-ahead and intraday. The multi-level coordination control mechanism is used to realize the dynamic power balance of regional source, load and storage. (2) During the day-ahead control phase, based on the load trend curve of the basic unit, the distributed photovoltaic power output prediction and the minimum access expectation of electric vehicles, and with the goal of satisfying the daily clearing constraints of power balance and energy storage state of charge (SOC), the base SOC curve of the distribution area and the power allocation base are generated. (3) During the intraday real-time control phase, the charging and discharging power ranges of distributed energy storage and electric vehicles in each transformer area are dynamically adjusted. Specifically, this includes: calculating the real-time power deviation of each transformer area based on the real-time collected distributed photovoltaic output, load power and energy storage SOC data, combined with the benchmark SOC curve and the predicted power change trend; dynamically setting the upper and lower limits of charging and discharging power of energy storage and electric vehicles according to the real-time power deviation to form a dynamic control range; prioritizing the use of local energy storage and electric vehicle resources for adjustment with the optimization objectives of minimizing the total switching power of the feeder, balancing the energy storage SOC of the transformer area and meeting the line capacity constraints. (4) When local resources are insufficient, power interaction between stations is carried out based on the power allocation benchmark. An improved optimization algorithm is used to introduce a penalty term in the power interaction between stations to suppress frequent cross-station transmission. The weight coefficient is dynamically adjusted according to the multi-layer coordination control mechanism and photovoltaic power output fluctuation to ensure the overall power balance of the feeder and the safe operation of the equipment. The expression for the line capacity constraint is: ,in, Let be the interactive power of station k at time t, with the output being positive and the reception being negative; The dynamic transmission limit for line in transformer area k; The improved optimization algorithm is a particle swarm optimization algorithm that introduces a power interaction penalty term, and its fitness function is: ， in, and This is a dynamic weighting coefficient, which is increased during peak photovoltaic output periods. To suppress frequent interactions.

2. The method according to claim 1, characterized in that, The objective function for minimizing the total switching power of the feeder is: ； The objective function for the SOC of the balanced distribution area energy storage is: ,in, Let i be the state of charge of the i-th energy storage at time t. This is the average SOC of all energy storage systems.

3. The method according to claim 2, characterized in that, The method for generating the dynamic control interval includes: According to the real-time power deviation of the transformer area To determine whether the local adjustment capacity meets the balance requirements; like If this occurs, cross-regional coordination will be initiated, and dynamic control intervals between regions will be generated based on preset constraints and the upper and lower limits of the charging and discharging power of electric vehicles. in, For threshold coefficient, As the reference power for the transformer area, the preset constraints include: .

4. The method according to claim 3, characterized in that, The preset constraints also include charging and discharging power constraints, which are as follows: ， ， in, , , Different dynamic adjustment coefficients are used, which are adjusted in real time according to the power change trend; This is the upper limit of the energy storage discharge power. The upper limit of charging power for electric vehicles, Charging power for electric vehicles, This refers to the energy storage discharge power.

5. The method according to claim 1, characterized in that, The method for establishing the benchmark SOC curve is as follows: ， in, The baseline SOC curve value, To control the time interval, Let τ be the total charging and discharging power of the energy storage in the distribution area. Let the total energy storage capacity be [value], and satisfy the following: and ; in, , To establish a safe operating range for energy storage devices and prevent overcharging / over-discharging; The SOC value at the beginning of each day; This is the end time of the day.

6. The method according to claim 1, characterized in that, Step (3) further includes: Based on the predicted power deviation of the transformer area at the current moment and the predicted power deviation at the next moment Determine the target adjustment scenario, wherein the target adjustment scenario includes one of the following: Scenario a) Power is excessive for two consecutive moments, so energy storage is called up for charging, and electric vehicles are guided to charge at the same time; Scenario b) Current surplus, future shortage, dynamically reserve energy storage SOC margin; Scenario c) If there is a current shortage but a surplus in the next moment, call upon energy storage for charging and guide electric vehicles to charge in an orderly manner; Scenario d) Power is insufficient for two consecutive moments, maximizing energy storage discharge and electric vehicle discharge.

7. The method according to claim 6, characterized in that, The allocation rule for the charging and discharging power range is as follows: In scenario a), the upper limit of energy storage discharge power is The upper limit of electric vehicle charging power is ; In scenario b), the upper limit of energy storage discharge power is The charging power of electric vehicles will be dynamically adjusted according to the predicted shortfall. In scenario c), the lower limit of energy storage charging power is The lower limit of electric vehicle charging power is ; In scenario d), both the energy storage discharge power and the electric vehicle charging power are allocated according to their maximum capacity.

8. A feeder-load-storage active power balancing device based on dynamic control intervals, characterized in that, The device includes: The mechanism establishment module is used to form the basic unit of a regional power network by connecting multiple power supply areas through feeders. Based on the real-time operation data of distributed energy storage, electric vehicles and distributed photovoltaics in each area, a multi-level coordination control mechanism is established between the day-ahead and intraday. The multi-level coordination control mechanism is used to realize the dynamic power balance of regional source, load and storage. The day-ahead control module is used to generate the substation reference SOC curve and power allocation reference based on the load trend curve of the basic unit, the distributed photovoltaic power output prediction and the minimum access expectation of electric vehicles, with the goal of meeting the daily clearing constraints of power balance and energy storage state of charge (SOC). The intraday control module is used to dynamically adjust the charging and discharging power range of distributed energy storage and electric vehicles in each transformer area during the intraday real-time control phase. Specifically, it includes: calculating the real-time power deviation of each transformer area based on real-time collected distributed photovoltaic output, load power, and energy storage SOC data, combined with the benchmark SOC curve and the predicted power change trend; dynamically setting the upper and lower limits of charging and discharging power of energy storage and electric vehicles according to the real-time power deviation to form a dynamic control range; and prioritizing the use of local energy storage and electric vehicle resources for adjustment with the optimization objectives of minimizing the total feeder switching power, balancing the energy storage SOC of the transformer area, and meeting the line capacity constraints. The optimization module is used to perform power interaction between transformer stations based on the power allocation benchmark when local resources are insufficient. Through an improved optimization algorithm, a penalty term is introduced into the power interaction between transformer stations to suppress frequent cross-transformer station transmission. The weighting coefficient is dynamically adjusted according to the multi-layer coordination control mechanism and photovoltaic power output fluctuations to ensure the overall power balance of the feeder and the safe operation of the equipment. The expression for the line capacity constraint is: ,in, Let be the interactive power of station k at time t, with the output being positive and the reception being negative; The dynamic transmission limit for line in transformer area k; The improved optimization algorithm is a particle swarm optimization algorithm that introduces a power interaction penalty term, and its fitness function is: ， in, and This is a dynamic weighting coefficient, which is increased during peak photovoltaic output periods. To suppress frequent interactions.

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