Dual-mode collaborative control method for charging pile in transformer area based on dynamic switching of load state
By using intelligent fusion terminals to collect real-time data and make multi-mode adaptive decisions, charging resources are dynamically adjusted, which solves the problems of load response lag and insufficient resource coordination in the distribution network when electric vehicles are connected, and realizes the improvement of load optimization and new energy consumption.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA THREE GORGES UNIV
- Filing Date
- 2026-02-04
- Publication Date
- 2026-06-02
AI Technical Summary
When electric vehicles are connected to the grid on a large scale, the existing distribution network in the transformer area suffers from load response lag, insufficient coordination of multiple resources, difficulty in guaranteeing users' rigid demand, and the existing electricity pricing strategy fails to effectively address emergency charging needs.
By deploying intelligent fusion terminals to collect data in real time and dynamically switching between three operating modes, combined with multi-objective optimization functions, the charging piles, energy storage systems, and V2G vehicles are coordinated to achieve real-time adjustment of load status and resource optimization.
It has improved the real-time charging performance and grid security of the distribution area, reduced the load rate to ≤85%, increased the renewable energy consumption rate by more than 20%, and optimized the user charging experience.
Smart Images
Figure CN122126121A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of smart grid and electric vehicle charging control technology, specifically to a dual-mode collaborative control technology for substation charging piles based on dynamic load state switching, which is applicable to dynamic load optimization management of distribution substations. Background Technology
[0002] With the large-scale integration of electric vehicles into the power distribution network, the centralization of charging behavior has led to two major contradictions in distribution areas: the risk of transformer overload is aggravated during peak load periods and the insufficient absorption of renewable energy during off-peak periods. Existing solutions have significant bottlenecks. For example, patent CN118578927A relies on fixed heavy / light load periods and cannot respond to sudden load fluctuations in real time, resulting in a lag in strategy. Although patent CN 119182117A establishes a multi-objective optimization function including minimizing load variance, minimizing peak-to-valley difference, and maximizing renewable energy utilization, it does not integrate adjustable resources such as V2G and distribution area energy storage, and cannot achieve global resource coordinated scheduling. Meanwhile, existing electricity pricing strategies have failed to address users' urgent charging needs, such as the lack of a guarantee mechanism when a vehicle's battery level is less than 10% and the vehicle needs to be used within one hour.
[0003] To address the aforementioned technical bottlenecks, the applicant proposes a dual-mode collaborative control method for charging piles in the distribution area based on dynamic load state switching. Summary of the Invention
[0004] The purpose of this invention is to solve the technical problems of dynamic load response lag, insufficient multi-resource coordination, and difficulty in guaranteeing users' rigid demand in the existing distribution network technology for large-scale electric vehicle access. The invention proposes a dual-mode collaborative control method for charging piles in the distribution area based on dynamic load state switching, so as to significantly improve the real-time performance of charging in the distribution area and the security of the power grid.
[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for coordinated control of charging piles in a transformer substation with dynamic load status switching includes the following steps: Step 1: Through the intelligent converged terminal deployed in the distribution transformer area, collect in real time the low-voltage side operation parameters of the transformer area, the operation parameters of the charging pile cluster, user charging demand data, and dynamic electricity price information; Step 2: Based on the real-time collected data on distribution area load and charging demand, combined with dynamic electricity price information, activate and execute three different operating modes; Step 3: Under the selected operating mode, invoke the corresponding control strategy and construct a multi-objective dynamic optimization function that considers grid operation risk, economic operating cost and user charging satisfaction, and generate collaborative control instructions for charging pile clusters, energy storage systems and V2G vehicles. Step 4: Send the control command to the charging pile cluster and related equipment, and calculate the execution deviation rate based on the actual execution results for closed-loop adjustment in subsequent cycles.
[0006] In step 2, the three different operating modes are as follows: Mode 1) Activate "Grid Priority Mode" when the load in the distribution area is detected to be approaching or exceeding the safe capacity; The following actions are performed in this mode: (1) Based on the dynamic assessment results of the charging emergency coefficient, suspend non-critical charging processes and prioritize grid security; (2) Allocate basic guaranteed power to charging requests that meet the emergency exemption conditions to ensure that the charging needs of critical equipment are met; (3) Collaborate with energy storage systems to dynamically adjust energy storage discharge to support grid load and balance grid demand; (4) Activate the reverse power support of vehicles with vehicle-to-grid (V2G) function to further alleviate grid pressure; Mode 2) When the load in the distribution area is within the safe capacity threshold range, the "User Priority Mode" is activated; in this mode, the following is executed: (1) A conflict arbitration algorithm based on multi-objective optimization is used to coordinate the power requests of the charging pile cluster; (2) Combine the real-time electricity price curve and user demand constraints to generate the optimal charging scheme for the group, so as to maximize the user charging experience while avoiding excessive grid load; (3) Push guidance information to user terminals to promote off-peak charging behavior, achieve load balance and grid stability; Mode 3) Activates "Off-Peak Load Mode" when a transformer area is detected to be in a reverse overload state; The following actions are performed in this mode: (1) Remove the charging power limit on the charging pile cluster and allow it to charge at the rated maximum power; (2) Dynamically regulate the energy storage system to perform reverse peak-shaving charging in order to absorb excess electrical energy.
[0007] The intelligent fusion terminal (SCU) deployed in the distribution substation collects load status data, charging pile operating parameters, and user charging behavior data in real time. The intelligent fusion terminal, as the core node for data sensing and aggregation on the substation side, establishes communication connections with the low-voltage side monitoring and control equipment, the charging pile cluster control unit, and the user's mobile terminal application. It uses power line carrier communication (PLC) to achieve bidirectional data transmission, thereby completing the synchronous collection and centralized aggregation of substation operating status information, charging pile cluster operating information, and user demand information. This provides a data foundation for the dynamic determination of the operating mode in step 2 and the generation of the collaborative control strategy in step 3.
[0008] The data collected by the intelligent fusion terminal in step 1 includes at least the following three categories, which are used to support the determination of the load status of the distribution area, the switching of the operating mode, and the collaborative control decision-making: (1) Operating parameters of the low-voltage side of the transformer substation, including: effective value of three-phase voltage Va, Vb, Vc RMS value of three-phase current Ia, Ib, Ic Three-phase active power Pa, Pb, Pc Three-phase reactive power Qa, Qb, Qc and total apparent power S ; The aforementioned low-voltage side operating parameters of the distribution transformer area are used to characterize the real-time load level, power flow status, and key power quality indicators of the distribution transformer area power grid, providing a basis for judgment for the identification of the load status of the distribution transformer area and the dynamic switching of the operating mode in step 2. (2) Charging pile cluster operation parameters, including: real-time output power of each charging pile Pch,i Charging connection status indicator Scon,i Maximum output power Pmax_out,i And the maximum power absorption capacity that supports vehicle-to-grid operation. Pmax_in, i ,in i This refers to the number of charging stations; The charging pile cluster operating parameters are used to characterize the real-time working status, service capacity boundary and flexible adjustment potential of the charging pile cluster, and provide constraints for building a collaborative control model of the charging pile cluster, energy storage system and V2G vehicle in step 3. (3) User charging behavior and vehicle status data, including: scheduled charging start time Tstart,i The deadline for scheduled charging Tend,i Target state of charge SOCtgt,i and the vehicle's real-time state of charge. SOCact,i(t) ; The user charging behavior and vehicle status data are used to characterize user charging demand preferences and vehicle energy status, providing a decision-making basis for determining the charging request priority in the operating mode in step 2 and constructing the multi-objective dynamic optimization function in step 3.
[0009] In step 2, the dynamic switching of the operating mode is completed based on a real-time comparison between the current load status of the transformer area and a preset load threshold range, including the following determination and calculation process: Real-time load quantification calculation for the transformer substation is performed by using the low-voltage side operating parameters of the substation substation to calculate the load at a given time. t Three-phase total apparent power S total (t) Its expression is the warning threshold setting: (1); (2); In the formula, S total (t) This represents the total apparent power of the three phases. All three phases are meritorious; This refers to the real-time reactive power of the three phases. Let A be the active power of the three phases. The active power of phase B is... The active power of phase C is... The reactive power of phase A is... The reactive power of phase B is... The reactive power of phase C is denoted as C.
[0010] The load threshold interval is constructed as follows: pass Stotal(t) and θB L Based on the relative relationship, the load status of the transformer area is divided into the following three intervals: (3); In the formula, B L For the capacity of the transformer area; θ To set the threshold coefficient, it is set according to the load capacity of the transformer area. θ The value is in {-1, 1}; S total (t) > θB L , θ In the case of (0.8,1), the strategy prioritizes the power grid. S total (t) < θB L , θ ∈[-1,-0.8), at this time the strategy removes all charging restrictions and charges at maximum power; S total (t) When the value is in the range [-0.8, 0.8], the strategy will prioritize the user.
[0011] In the user-priority mode described in step 2, a conflict arbitration model based on multi-objective optimization is constructed to coordinate charging power allocation. Under the premise of satisfying the safety operation constraints of the distribution area, the model synergistically optimizes user charging satisfaction and grid economic benefits, specifically including: Objective function: (4); In the formula, F(x) is the comprehensive optimization objective; This results in a loss of user satisfaction. For peak and valley revenue of the power grid; By minimizing the difference between user satisfaction loss and maximizing peak-valley revenue, a dynamic balance between user-side experience and grid-side economy is achieved, providing a mathematical model basis for multi-objective dynamic optimization in step 3. User satisfaction loss function: (5); In the formula, k The number of vehicles connected to the transformer station; α The weighting factor for the urgency of the user's time; β This is a time decay rate parameter, used to characterize the degree of urgency of charging as the time approaches the deadline; To dynamically increase urgency over time, T end，i The scheduled charging deadline for users ; γ Weighting the importance of the user's initial battery shortage; SOC act,i For the current number i Real-time battery level of the vehicle; SOC tgt,i Set a target battery level for the user; this function is used to quantify the user's waiting cost and the risk of insufficient battery, and provides a basis for prioritizing charging. The power grid peak-valley revenue function is expressed as follows: (6); In the formula, t This refers to the start time of the peak and trough. T This refers to the end time of the peak and valley. C peak Peak-valley electricity pricing; P grid (τ) The exchange power between the distribution area and the upstream power grid at time τ; μ The coefficient for subsidies for the consumption of new energy sources; This represents the power generation capacity of new energy sources; this function is used to characterize the peak shaving and valley filling benefits and new energy consumption benefits obtained through optimized charging scheduling. To ensure that the transformer area does not experience overload, the optimization model satisfies the following power constraints: (7); In the formula, i The first in Taiwan i One charging station; n The number of charging piles in the area; P ch_req，i (t) For the first i Each charging station requests power. P avail (t) The dynamic available active power capacity of the transformer substation;λ safe This is the safety margin factor; this constraint is used to reserve the necessary operating margin for the transformer area and improve the reliability of system operation.
[0012] In step 2, when the distribution substation operation mode switches to grid priority mode, a coordinated control strategy aimed at ensuring the safe operation of the distribution substation is executed. The coordinated control strategy includes at least the following steps: 1) Dynamic calculation and hierarchical control of charging emergency factor: Based on the user charging behavior and vehicle status data, calculate the charging pile emergency factor. i exist t Urgency level at any moment ε i (t) : (8); In the formula, P ch_req,i (t) For the first i The requested charging power of each charging station. η soc Weighting based on the urgency of the power shortage; P max,i This refers to the maximum rated charging power of the corresponding charging pile. η rate Weight the charging rate requirement and satisfy η soc + η rate = 1, based on the urgency factor The size of the charging request is used to control the charging request in a tiered manner, where: when When the value is less than the first preset threshold εlow, it is determined to be a non-critical charging request, and the charging process of the corresponding charging station is suspended; when When the value exceeds the second preset threshold εhigh, it is determined to be a high emergency charging request. Under the premise of reserving the safety margin of the substation area, the basic guarantee charging power is allocated to it. Pb,i ; 2) Dynamic discharge coordinated control of energy storage system: real-time monitoring of active power deficit Δ in the distribution area. P ( t )= P total ( t ) αB L The discharge power of the energy storage system is adjusted based on this deficit, and the discharge power of the energy storage system at time t is... Pess,dis (t) Represented as: (9); In the formula,ΔP(t) For those who have made meritorious contributions to the Taiwan region, there are vacancies. P ess_max This represents the maximum discharge power of the energy storage unit. SOC ess (t) Real-time state of charge of the energy storage unit; E rated This refers to the rated capacity of the energy storage system. Δt To control the cycle time step; 3) Reverse power support control for vehicles with V2G functionality: For vehicles connected to the distribution area and equipped with vehicle-to-grid functionality, reverse power supply control is initiated based on the real-time power deficit of the distribution area. The reverse output power Pv2g,i(t) of the i-th vehicle at time t is expressed as: (10); In the formula, Δ P ( t ) is a vacancy for meritorious service in the Taiwan area; Pmax,iv2g The maximum reverse discharge power of the i-th vehicle; SOC act,k This represents the total real-time SOC of available V2G vehicles in the area; when SOC act,i (t)≥0.25 Discharge is permitted during this period; otherwise, forced reset is required. P v2g,i (t)= 0； Dynamic power safety limits: Discharge is prohibited when SOC=25%, and maximum safe power is allowed when SOC=100%; Cluster SOC linkage control: If the average SOC of currently available V2G vehicles is 0.4, the total reverse power limit is reduced to 40%; V2G function is automatically turned off when SOC is less than 0.3.
[0013] When the transformer area is determined to be in the reverse overload range, a bidirectional absorption control strategy under the load off-peak mode is implemented. The control strategy includes: Remove the limitation on the charging power of the charging pile cluster, and control each charging pile to charge at its rated maximum charging power while continuously operating in the reverse overload range, with the charging power satisfying: (11); In the formula, For the first Each charging station at any time The actual charging power, The rated maximum charging power of the charging pile. The duration during which the transformer area remains in the reverse overload zone. This is the preset minimum holding time threshold; While lifting the power limit of the charging pile, the energy storage system is controlled to perform reverse peak-shaving charging, which is at all times. The charging power meets the following requirements: (12); In the formula, P ess_ch ( t (This refers to the charging power of the energy storage system.) Kp ∈[0.5,1.0] represents the power dynamic adjustment coefficient; its constraint condition is the maximum charging power of energy storage. P ess_max .
[0014] In step 3, the dynamic optimization objective function is constructed: (13); in: To comprehensively optimize the objective function, These are the weighting coefficients for power grid risk, operating costs, and user experience, respectively. Power grid risk loss function Jrisk : (14); In the formula, Stotal(t) Real-time apparent power; the operating cost function This is used to characterize the electricity purchase cost and renewable energy consumption revenue of a distribution area within the current dispatch cycle; the user experience loss function This is used to characterize the loss of user satisfaction due to charging delay or limited charging power; the optimization objective function has a preset control period. Rolling optimization is performed for the time step, and this serves as a unified constraint for the collaborative control strategy. Cost function Jcost : (15); User loss function Juser : (16); Every Δt = 15 min Real-time optimization is performed; and execution monitoring metrics, such as execution deviation rate, are introduced. (17); In the formula, Pcommand Power required for charging piles; Pactual This represents the actual power output of the charging station.
[0015] Compared with the prior art, the present invention has the following technical effects: 1) This technology proposes an innovative three-level architecture scheme of "perception-decision-coordination". First, it generates load status tags in real time by integrating the edge computing capabilities of terminals. Second, in multi-mode adaptive decision-making, the peak mode dynamically matches the vehicle V2G discharge capacity and calls on the energy storage system to shave the peak and suppress the load rate to ≤85%, while the off-peak mode maximizes the absorption of new energy and drives energy storage to fill the valley to improve the absorption rate by more than 20%. Finally, the collaborative layer innovatively designs a three-level demand response model, which completely breaks through the traditional "single-objective, static, and isolated" control paradigm, and achieves a collaborative balance between safety and user experience, providing a dynamic, efficient and user-friendly solution for the distribution network. 2) This invention constructs a dynamic mapping mechanism between load status and control strategy, achieving adaptive and precise switching of transformer substation operation modes by setting bidirectional safety thresholds. Based on this, a multi-objective conflict arbitration model is proposed, effectively improving user service satisfaction in user-priority mode. Simultaneously, a hierarchical scheduling strategy for grid-priority mode is designed, dynamically optimizing the priority allocation of charging resources based on the urgency coefficient of charging demand. Furthermore, a bidirectional absorption mechanism during off-peak hours is created, utilizing grid load off-peak periods to guide charging piles to absorb local surplus renewable energy, thereby increasing the local absorption rate of renewable energy. This method, through the synergistic effect of the above mechanisms, aims to solve key technical problems in the optimized control of transformer substation charging load. Attached Figure Description
[0016] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a flowchart of the strategy switching logic of the present invention; Figure 3 This is the timing diagram of V2G reverse support regulation of the present invention. Detailed Implementation
[0017] like Figure 1 As shown, a dual-mode collaborative control method for charging piles in a distribution area based on dynamic load state switching includes the following steps: This method addresses the "source-grid-load-storage-vehicle" collaborative scheduling scenario at the distribution substation level. It constructs a hierarchical control architecture encompassing state perception, load identification, mode switching, collaborative optimization, command execution, and closed-loop regulation. This architecture achieves optimized user experience and coordinated improvement in new energy consumption under the safety constraints of the distribution substation. For example... Figure 1 As shown, the overall process of this method includes: S101 Data acquisition of the transformer area → S102 Calculation of the operating status of the transformer area → S103 Determination of the load range → S104 Selection of the operating mode → S105 Execution of coordinated control → S106 Execution feedback and closed-loop adjustment.
[0018] Step S1 involves deploying a Smart Converged Terminal (SCU) on the low-voltage side of the distribution transformer area to collect real-time data on transformer load status, charging pile operating parameters, and user charging behavior. The Smart Converged Terminal serves as the core node for data sensing and aggregation on the transformer side. It establishes communication connections with the low-voltage side monitoring and control equipment, the charging pile cluster control unit, and the user's mobile terminal application, employing power line carrier communication (PLC) for bidirectional data transmission. This enables the synchronous collection and centralized aggregation of transformer operating status information, charging pile cluster operating information, and user demand information, providing a data foundation for the dynamic determination of operating modes in Step 2 and the generation of collaborative control strategies in Step 3. The multi-source data, after unified processing by the Smart Converged Terminal, serves as the input parameters for transformer load status determination and collaborative control strategy generation.
[0019] The data collected by the intelligent converged terminal includes at least the following three categories, which are used to support the identification of the load status of the distribution area, the switching of the operating mode, and the collaborative control decision-making: (1) Operating parameters of the low-voltage side of the transformer substation, including: effective value of three-phase voltage Va, Vb, Vc RMS value of three-phase current Ia, Ib, Ic Three-phase active power Pa, Pb, Pc Three-phase reactive power Qa, Qb, Qc and total apparent power S ; The aforementioned low-voltage side operating parameters of the distribution area are used to characterize the real-time load level, power flow status, and key power quality indicators of the distribution area power grid, providing a basis for judgment for the identification of the distribution area load status and dynamic switching of the operating mode in step 2.
[0020] (2) Charging pile cluster operation parameters, including: real-time output power of each charging pile Pch,i Charging connection status indicator Scon,i Maximum output power Pmax_out,i And the maximum power absorption capacity that supports vehicle-to-grid operation. Pmax_in,i etc., among which i This represents the number of charging stations.
[0021] The charging pile cluster operating parameters are used to characterize the real-time working status, service capacity boundary and flexible adjustment potential of the charging pile cluster, and provide constraints for building a collaborative control model of the charging pile cluster, energy storage system and V2G vehicle in step 3. (3) User charging behavior and vehicle status data, including: scheduled charging start time Tstart,i The deadline for scheduled charging Tend,i Target state of charge SOCtgt,i and the vehicle's real-time state of charge. SOCact,i(t) ; The user charging behavior and vehicle status data are used to characterize user charging demand preferences and vehicle energy status, providing a decision-making basis for determining the charging request priority in the operating mode in step 2 and for constructing the multi-objective dynamic optimization function in step 3.
[0022] Step S2, the dynamic switching of the operating mode, is based on a real-time comparison between the current load status of the transformer area and the preset load threshold range. It includes the following determination and calculation process: Real-time load quantification calculation for a transformer substation is performed by using the low-voltage side operating parameters of the substation to calculate the load at a given time. t Three-phase total apparent power S total (t) Its expression is the warning threshold setting: (1); (2); In the formula, S total (t) This represents the total apparent power of the three phases. All three phases are meritorious; This refers to the real-time reactive power of the three phases. Let A be the active power of the three phases. The active power of phase B is... The active power of phase C is... The reactive power of phase A is... The reactive power of phase B is... The reactive power of phase C is denoted as C.
[0023] Step S3 through Stotal(t) and θB L The relative relationships and state divisions are as follows: Figure 2 As shown, the calculation formula for the load status division of the transformer area is as follows: (3); In the formula, B L For the capacity of the transformer area; θ To set the threshold coefficient, it is set according to the load capacity of the transformer area. θ The value is in {-1, 1}; S total (t) > θB L , θ In the case of (0.8,1), the strategy prioritizes the power grid. S total (t) < θB L , θ∈[-1,-0.8), at this time the strategy removes all charging restrictions and charges at maximum power; S total (t) When the value is in the range [-0.8, 0.8], the strategy will prioritize the user.
[0024] Step S4: In user-priority mode, a conflict arbitration model based on multi-objective optimization is constructed to coordinate charging power allocation. Under the premise of satisfying the safety operation constraints of the distribution area, the model synergistically optimizes user charging satisfaction and grid economic benefits. The comprehensive objective function is: (4); In the formula, F(x) is the comprehensive optimization objective; This results in a loss of user satisfaction. For peak and valley revenue of the power grid; By minimizing the difference between user satisfaction loss and maximizing peak-valley revenue, a dynamic balance between user-side experience and grid-side economy is achieved, providing a mathematical model basis for multi-objective dynamic optimization in step 3.
[0025] User satisfaction loss function: (5); In the formula, k represents the number of vehicles accessing the access area; α β is a weighting coefficient for the user's time urgency; β is a time decay rate parameter used to characterize the higher the urgency of charging as the deadline approaches. To dynamically increase urgency over time, T end，i The scheduled charging deadline for users ; γ Weighting the importance of the user's initial battery shortage; SOC act,i For the current number i Real-time battery level of the vehicle; SOC tgt,i Set a target battery level for the user; this function is used to quantify the user's waiting cost and the risk of insufficient battery, and provides a basis for prioritizing charging.
[0026] The power grid peak-valley revenue function is expressed as follows: (6); In the formula, t This refers to the start time of the peak and trough. T This refers to the end time of the peak and valley. C peak Peak-valley electricity pricing; P grid (τ) The exchange power between the distribution area and the upstream power grid at time τ; μThe coefficient for subsidies for the consumption of new energy sources; This represents the power generation capacity of new energy sources; this function is used to characterize the peak shaving and valley filling benefits and new energy consumption benefits obtained through optimized charging scheduling.
[0027] To ensure that the transformer area does not experience overload, the optimization model satisfies the following power constraints: (7); In the formula, i The first in Taiwan i One charging station; n The number of charging piles in the area; P ch_req，i (t) For the first i Each charging station requests power. P avail (t) The dynamic available active power capacity of the transformer substation; λ safe This is the safety margin factor, with a value ranging from 0.90 to 0.95. This constraint is used to reserve necessary operating margins for the transformer area and improve system reliability.
[0028] When the distribution transformer area's operation mode switches to grid priority mode, a coordinated control strategy aimed at ensuring the safe operation of the distribution transformer area is implemented. The coordinated control strategy includes at least the following steps: 1) Dynamic calculation and hierarchical control of charging emergency factor: Based on the user charging behavior and vehicle status data, calculate the charging pile emergency factor. i exist t Urgency level at any moment ε i (t) : (8); In the formula, P ch_req,i (t) represents the requested charging power of the i-th charging pile; η soc Weighted by the urgency of the power shortage; P max,i η represents the maximum rated charging power of the corresponding charging pile. rate Assign a weight to the charging rate requirement, and satisfy η soc + η rate =1, and the charging request is graded and controlled according to the magnitude of the emergency coefficient εi(t). When εi(t) is less than the first preset threshold εlow, it is determined to be a non-critical charging request and the charging process of the corresponding charging pile is suspended; when εi(t) is greater than the second preset threshold εhigh, it is determined to be a high emergency charging request and the basic guaranteed charging power Pb,i is allocated to it under the premise of reserving the safety margin of the substation area.
[0029] 2) Dynamic discharge coordinated control of energy storage system: real-time monitoring of active power deficit Δ in the distribution area. P ( t )= P total ( t ) αB L The discharge power of the energy storage system is adjusted based on this deficit. The discharge power Pess,dis(t) of the energy storage system at time t is expressed as: (9); In the formula, ΔP(t) For those who have made meritorious contributions to the Taiwan region, there are vacancies. P ess_max This represents the maximum discharge power of the energy storage unit. SOC ess (t) Real-time state of charge of the energy storage unit; E rated This refers to the rated capacity of the energy storage system. Δt To control the cycle time step; 3) Reverse power support control for vehicles with V2G functionality: For vehicles connected to the distribution area and equipped with vehicle-to-grid functionality, reverse power supply control is initiated based on the real-time power deficit of the distribution area. The reverse output power Pv2g,i(t) of the i-th vehicle at time t is expressed as: (10); In the formula, Δ P ( t ) is a vacancy for meritorious service in the Taiwan area; Pmax,iv2g The maximum reverse discharge power of the i-th vehicle; SOC act,k This represents the total real-time SOC of available V2G vehicles in the area; when SOC act,i (t)≥0.25 Discharge is permitted during this period; otherwise, forced reset is required. P v2g,i (t)= 0； Dynamic power safety limits: discharge is prohibited when SOC=25%, and maximum safe power is allowed when SOC=100%; Cluster SOC linkage control: if the average SOC of currently available V2G vehicles is 0.4, the total reverse power limit is reduced to 40%; V2G function is automatically shut down when SOC is less than 0.3. When the transformer area is determined to be in the reverse overload range, a bidirectional absorption control strategy under the load off-peak mode is implemented. The control strategy includes: Remove the limitation on the charging power of the charging pile cluster, and control each charging pile to charge at its rated maximum charging power while continuously operating in the reverse overload range, with the charging power satisfying: (11); In the formula, For the first Each charging station at any time The actual charging power, The rated maximum charging power of the charging pile. The duration during which the transformer area remains in the reverse overload zone. This is the preset minimum holding time threshold; While lifting the power limit of the charging pile, the energy storage system is controlled to perform reverse peak-shaving charging, which is at all times. The charging power meets the following requirements: (12); In the formula, P ess_ch ( t (This refers to the charging power of the energy storage system.) Kp ∈[0.5,1.0] represents the power dynamic adjustment coefficient; its constraint condition is the maximum charging power of energy storage. P ess_max .like Figure 3 As shown, the V2G reverse support control timing diagram can clearly show the timing conditions under various operating conditions.
[0030] To further verify the regulation effect of the control method described in this invention under different operating conditions of the transformer substation, a typical transformer substation simulation scenario including 4 charging piles and multiple electric vehicles was constructed. The charging and discharging power allocation results under the grid priority mode, user priority mode and new energy consumption mode were simulated and analyzed. The results are shown in Table 1.
[0031] Table 1. Results of charging and discharging power allocation for multiple vehicles under different operating conditions in different transformer substations;
[0032] As shown in Table 1, in the grid priority mode, the system can perform hierarchical control of charging requests based on the vehicle's SOC status and charging urgency, and coordinate V2G vehicles to participate in reverse support; in the user priority mode, the system maximizes the user's charging power while meeting the safety constraints of the transformer area; in the new energy consumption mode, the charging pile power is uniformly released to achieve efficient consumption of new energy, verifying the effectiveness and engineering feasibility of the method of the present invention.
[0033] Step S5: Construct the dynamic optimization objective function: (13); in: To comprehensively optimize the objective function, These are the weighting coefficients for power grid risk, operating costs, and user experience, respectively. Power grid risk loss function Jrisk : (14); In the formula, Stotal(t) Real-time apparent power; the operating cost function This is used to characterize the electricity purchase cost and renewable energy consumption revenue of a distribution area within the current dispatch cycle; the user experience loss function This is used to characterize the loss of user satisfaction due to charging delay or limited charging power; the optimization objective function has a preset control period. Rolling optimization is performed for the time step, and this serves as a unified constraint for the collaborative control strategy. Cost function Jcost : (15); User loss function Juser : (16); Step 6 each Δt = 15 min Real-time optimization is performed; and execution monitoring metrics, such as execution deviation rate, are introduced. (17); In the formula, Pcommand Power required for charging piles; Pactual This refers to the actual power output of the charging station. If the deviation exceeds the limit, it will revert to the previous setting. Figure 1 S101 re-enters the closed-loop regulation process.
Claims
1. A method for coordinated control of charging piles in a distribution area based on dynamic load state switching, characterized in that, Includes the following steps: Step 1: Through the intelligent converged terminal deployed in the distribution transformer area, collect in real time the low-voltage side operation parameters of the transformer area, the operation parameters of the charging pile cluster, user charging demand data, and dynamic electricity price information; Step 2: Based on the real-time collected data on distribution area load and charging demand, combined with dynamic electricity price information, activate and execute three different operating modes; Step 3: Under the selected operating mode, invoke the corresponding control strategy and construct a multi-objective dynamic optimization function that considers grid operation risk, economic operating cost and user charging satisfaction, and generate collaborative control instructions for charging pile clusters, energy storage systems and V2G vehicles. Step 4: Send the control command to the charging pile cluster and related equipment, and calculate the execution deviation rate based on the actual execution results for closed-loop adjustment in subsequent cycles.
2. The method according to claim 1, characterized in that, In step 2, the three different operating modes are as follows: Mode 1) Activate "Grid Priority Mode" when the load in the distribution area is detected to be approaching or exceeding the safe capacity; The following actions are performed in this mode: (1) Based on the dynamic assessment results of the charging emergency coefficient, suspend non-critical charging processes and prioritize grid security; (2) Allocate basic guaranteed power to charging requests that meet the emergency exemption conditions to ensure that the charging needs of critical equipment are met; (3) Collaborate with energy storage systems to dynamically adjust energy storage discharge to support grid load and balance grid demand; (4) Activate the reverse power support of vehicles with vehicle-to-grid (V2G) function to further alleviate grid pressure; Mode 2) Activate "User Priority Mode" when the load in the distribution area is within the safe capacity threshold range; In this mode, the following is executed: (1) A conflict arbitration algorithm based on multi-objective optimization is used to coordinate the power requests of the charging pile cluster; (2) Combine the real-time electricity price curve and user demand constraints to generate the optimal charging scheme for the group, so as to maximize the user charging experience while avoiding excessive grid load; (3) Push guidance information to user terminals to promote off-peak charging behavior, achieve load balance and grid stability; Mode 3) Activates "Rough Load Mode" when a transformer area is detected to be in a reverse overload state; The following is executed in this mode: (1) Remove the charging power limit on the charging pile cluster and allow it to charge at the rated maximum power; (2) Dynamically regulate the energy storage system to perform reverse peak-shaving charging in order to absorb excess electrical energy.
3. The method according to claim 1, characterized in that, The intelligent fusion terminal (SCU) deployed in the distribution substation collects load status data, charging pile operating parameters, and user charging behavior data in real time. The intelligent fusion terminal, as the core node for data sensing and aggregation on the substation side, establishes communication connections with the low-voltage side monitoring and control equipment, the charging pile cluster control unit, and the user's mobile terminal application. It uses power line carrier communication (PLC) to achieve bidirectional data transmission, thereby completing the synchronous collection and centralized aggregation of substation operating status information, charging pile cluster operating information, and user demand information. This provides a data foundation for the dynamic determination of the operating mode in step 2 and the generation of the collaborative control strategy in step 3.
4. The method according to claim 1, characterized in that, The data collected by the intelligent fusion terminal in step 1 includes at least the following three categories, which are used to support the determination of the load status of the distribution area, the switching of the operating mode, and the collaborative control decision-making: (1) Operating parameters of the low-voltage side of the transformer substation, including: effective value of three-phase voltage Va, Vb, Vc RMS value of three-phase current Ia, Ib, Ic Three-phase active power Pa, Pb, Pc Three-phase reactive power Qa, Qb, Qc and total apparent power S ; The aforementioned low-voltage side operating parameters of the distribution transformer area are used to characterize the real-time load level, power flow status, and key power quality indicators of the distribution transformer area power grid, providing a basis for judgment for the identification of the distribution transformer area load status and dynamic switching of the operating mode in step 2. (2) Charging pile cluster operation parameters, including: real-time output power of each charging pile Pch,i Charging connection status indicator Scon,i Maximum output power Pmax_out,i And the maximum power absorption capacity that supports vehicle-to-grid operation. Pmax_in, i ,in i This refers to the number of charging stations; The charging pile cluster operating parameters are used to characterize the real-time working status, service capacity boundary and flexible adjustment potential of the charging pile cluster, and provide constraints for building a collaborative control model of the charging pile cluster, energy storage system and V2G vehicle in step 3. (3) User charging behavior and vehicle status data, including: scheduled charging start time Tstart,i The deadline for scheduled charging Tend,i Target state of charge SOCtgt,i and the vehicle's real-time state of charge. SOCact,i(t) ; The user charging behavior and vehicle status data are used to characterize user charging demand preferences and vehicle energy status, providing a decision-making basis for determining the charging request priority in the operating mode in step 2 and for constructing the multi-objective dynamic optimization function in step 3.
5. The method according to claim 2, characterized in that, In step 2, the dynamic switching of the operating mode is completed based on a real-time comparison between the current load status of the transformer area and a preset load threshold range, including the following determination and calculation process: Real-time load quantification calculation for the transformer substation is performed by using the low-voltage side operating parameters of the substation substation to calculate the load at a given time. t Three-phase total apparent power S total (t) Its expression is the warning threshold setting: （1）； （2）； In the formula, S total (t) This represents the total apparent power of the three phases. All three phases are meritorious; This refers to the real-time reactive power of the three phases. Let A be the active power of the three phases. The active power of phase B is... The active power of phase C is... The reactive power of phase A is... The reactive power of phase B is... The reactive power of phase C is denoted as C.
6. The method according to claim 5, characterized in that, The load threshold interval is constructed as follows: pass Stotal(t) and θB L Based on the relative relationship, the load status of the transformer area is divided into the following three intervals: （3）； In the formula, B L For the capacity of the transformer area; θ To set the threshold coefficient, it is set according to the load capacity of the transformer area. θ The value is in the range {-1, 1}; exist S total (t)>θB L , θ In the case of (0.8,1), the strategy prioritizes the power grid. S total (t)<θB L , θ ∈[-1,-0.8), at this time the strategy removes all charging restrictions and charges at maximum power; S total (t) When the value is in the range [-0.8, 0.8], the strategy will prioritize the user.
7. The method according to claim 2, characterized in that, In the user-priority mode described in step 2, a conflict arbitration model based on multi-objective optimization is constructed to coordinate charging power allocation. Under the premise of satisfying the safety operation constraints of the distribution area, the model synergistically optimizes user charging satisfaction and grid economic benefits, specifically including: Objective function: （4）； In the formula, F(x) is the comprehensive optimization objective; This results in a loss of user satisfaction. For peak and valley revenue of the power grid; By minimizing the difference between user satisfaction loss and maximizing peak-valley revenue, a dynamic balance between user-side experience and grid-side economy is achieved, providing a mathematical model basis for multi-objective dynamic optimization in step 3. User satisfaction loss function: （5）； In the formula, k The number of vehicles connected to the transformer station; α The weighting factor for the urgency of the user's time; β This is a time decay rate parameter, used to characterize the degree of urgency of charging as the time approaches the deadline; To dynamically increase urgency over time, T end，i The scheduled charging deadline for users ;γ Weighting the importance of the user's initial battery shortage; SOC act,i For the current number i Real-time battery level of the vehicle; SOC tgt,i Set a target battery level for the user; this function is used to quantify the user's waiting cost and the risk of insufficient battery, and provides a basis for prioritizing charging. The power grid peak-valley revenue function is expressed as follows: （6）； In the formula, t This refers to the start time of the peak and trough. T This refers to the end time of the peak and valley. C peak Peak-valley electricity pricing; P grid (τ) The exchange power between the distribution area and the upstream power grid at time τ; μ This refers to the subsidy coefficient for the consumption of new energy sources. This represents the power generation capacity of new energy sources; this function is used to characterize the peak shaving and valley filling benefits and new energy consumption benefits obtained through optimized charging scheduling. To ensure that the transformer area does not experience overload, the optimization model satisfies the following power constraints: （7）； In the formula, i The first in Taiwan i One charging station; n The number of charging piles in the area; P ch_req，i (t) For the first i Each charging station requests power. P avail (t) The dynamic available active power capacity of the transformer substation; λ safe This is the safety margin factor; this constraint is used to reserve the necessary operating margin for the transformer area and improve the reliability of system operation.
8. The method according to claim 2, characterized in that, In step 2, when the distribution substation operation mode switches to grid priority mode, a coordinated control strategy aimed at ensuring the safe operation of the distribution substation is executed. The coordinated control strategy includes at least the following steps: 1) Dynamic calculation and hierarchical control of charging emergency factor: Based on the user charging behavior and vehicle status data, calculate the charging pile emergency factor. i exist t Urgency level at any moment ε i (t) : （8）； In the formula, P ch_req,i (t) For the first i The requested charging power of each charging station. η soc Weighting based on the urgency of the power shortage; P max,i This refers to the maximum rated charging power of the corresponding charging pile. η rate Weight the charging rate requirement and satisfy η soc + η rate = 1, based on the urgency factor The size of the charging request is used to control the charging request in a tiered manner, where: when When the value is less than the first preset threshold εlow, it is determined to be a non-critical charging request, and the charging process of the corresponding charging station is suspended; when When the value exceeds the second preset threshold εhigh, it is determined to be a high emergency charging request. Under the premise of reserving the safety margin of the substation area, the basic guarantee charging power is allocated to it. Pb, i ; 2) Dynamic discharge coordinated control of energy storage system: real-time monitoring of active power deficit Δ in the distribution area. P ( t )= P total ( t ) αB L The discharge power of the energy storage system is adjusted based on this deficit, and the discharge power of the energy storage system at time t is... Pess,dis(t) Represented as: （9）； In the formula, ΔP(t) For those who have made meritorious contributions to the Taiwan region, there are vacancies. P ess_max This represents the maximum discharge power of the energy storage unit. SOC ess (t) Real-time state of charge of the energy storage unit; E rated This refers to the rated capacity of the energy storage system. Δt To control the cycle time step; 3) Reverse power support control for vehicles with V2G functionality: For vehicles connected to the distribution area and equipped with vehicle-to-grid functionality, reverse power supply control is initiated based on the real-time power deficit of the distribution area. The reverse output power Pv2g,i(t) of the i-th vehicle at time t is expressed as: （10）； In the formula, Δ P ( t ) is a vacancy for meritorious service in the Taiwan area; Pmax, IV2g The maximum reverse discharge power of the i-th vehicle; SOC act,k This represents the total real-time SOC of available V2G vehicles in the area; when SOC act,i (t)≥0.25 Discharge is permitted during this period; otherwise, forced reset is required. P v2g,i (t)=0； Dynamic power safety limits: Discharge is prohibited when SOC=25%, and maximum safe power is allowed when SOC=100%; Cluster SOC linkage control: If the average SOC of currently available V2G vehicles is 0.4, the total reverse power limit is reduced to 40%; V2G function is automatically turned off when SOC is less than 0.
3.
9. The method according to any one of claims 2 to 8, characterized in that, When the transformer area is determined to be in the reverse overload range, a bidirectional absorption control strategy under the load off-peak mode is implemented. The control strategy includes: Remove the limitation on the charging power of the charging pile cluster, and control each charging pile to charge at its rated maximum charging power while continuously operating in the reverse overload range, wherein the charging power satisfies: （11）； In the formula, For the first Each charging station at any time The actual charging power, The rated maximum charging power of the charging pile. The duration during which the transformer area remains in the reverse overload zone. This is the preset minimum holding time threshold; While lifting the power limit of the charging pile, the energy storage system is controlled to perform reverse peak-shaving charging, which is at all times. The charging power meets the following requirements: （12）； In the formula, P ess_ch ( t (This refers to the charging power of the energy storage system.) Kp ∈[0.5,1.0] represents the power dynamic adjustment coefficient; its constraint condition is the maximum charging power of energy storage. P ess_max .
10. The method according to any one of claims 1 to 8, characterized in that, In step 3, the dynamic optimization objective function is constructed: （13）； in: To comprehensively optimize the objective function, These are the weighting coefficients for power grid risk, operating costs, and user experience, respectively. Power grid risk loss function Jrisk : （14）； In the formula, Stotal(t) Real-time apparent power; the operating cost function This is used to characterize the electricity purchase cost and renewable energy consumption revenue of a distribution area within the current dispatch cycle; the user experience loss function This is used to characterize the loss of user satisfaction due to charging delay or limited charging power; the optimization objective function has a preset control period. Rolling optimization is performed for the time step, and this serves as a unified constraint for the collaborative control strategy. Cost function Jcost : （15）； User loss function Juser : （16）； Every Δt=15min Real-time optimization is performed; and execution monitoring metrics, such as execution deviation rate, are introduced. （17）； In the formula, Pcommand Power required for charging piles; Pactual This represents the actual power output of the charging station.