Energy storage and electric vehicle cooperative scheduling method and system based on virtual power plant

Abstract

The present application relates to the technical field of energy coordinated scheduling, and discloses a kind of energy storage and electric vehicle coordinated scheduling method and system based on virtual power plant, comprising: obtaining the operating state information of each distributed residence and the access node position of access distribution network, and obtaining the topological structure and real-time operating state information of distribution network;Calculate the topological sensitivity of each access node to the safe operation constraint of distribution network, and generate node safety influence factor based on topological sensitivity;Determine the dynamic chargeable power margin of each access node in the current scheduling period;And obtain historical charging record, calculate electric vehicle travel urgency;Dynamically determine the reserve value of each family energy storage battery backup capacity;Generate the charge-discharge power instruction of each family energy storage battery and electric vehicle;After each scheduling period ends, the rolling power flow safety check is carried out on distribution network, until the safe operation constraint of distribution network is satisfied.The present application effectively alleviates the low voltage problem at the end of distribution network.

Description

Technical Field

[0001] This invention relates to the fields of power system operation control, virtual power plants, and collaborative scheduling of distributed energy resources on the residential side, specifically to a method and system for collaborative scheduling of energy storage and electric vehicles based on virtual power plants. Background Technology

[0002] With the rapid development of new energy power generation, residential energy storage, and electric vehicles, traditional residential users are gradually transforming from single electricity loads into multi-functional energy units with power generation, energy storage, charging, and flexible regulation capabilities. In zero-energy or near-zero-energy residential scenarios, the residential side is typically equipped with rooftop photovoltaic systems, home energy storage batteries, and electric vehicle charging facilities. Photovoltaic systems can provide clean electricity during the day, home energy storage can absorb excess photovoltaic power and release energy during peak load periods or when photovoltaic power is insufficient, and electric vehicles serve as both a new high-power load and possess a certain degree of adjustability potential.

[0003] However, the large-scale integration of residential resources has also brought new pressures to the operation of the distribution network. Rooftop photovoltaic output is affected by solar irradiance, temperature, and weather changes, exhibiting significant fluctuations and intermittency; residential loads are influenced by residents' lifestyles, showing strong time-of-day variations; and electric vehicle charging behavior is closely related to users' arrival and departure times, remaining battery power, and travel plans. When a large number of electric vehicles are concentratedly charging during off-peak electricity prices at night, voltage drops are likely to occur at the radial distribution network's end nodes, and the power flow and distribution transformer load rates on some lines will also increase significantly. Without coordinated control, this could lead to problems such as node voltage exceeding limits, line overload, increased transformer hotspot temperature rise, and increased network losses.

[0004] In existing technologies, virtual power plants, as a distributed resource aggregation and control method, have been used to coordinate distributed photovoltaic, energy storage, electric vehicles, and flexible loads. Through a virtual power plant platform, multiple small-scale distributed resources can be aggregated into an observable and controllable whole, thereby participating in energy management, demand response, ancillary services, or distribution network operation support. For the coordinated scheduling of residential energy storage and electric vehicles, existing methods typically employ aggregated power purchase caps, time-of-use pricing strategies, state-of-charge (POC) sequencing, or target POC constraints to arrange the charging process. These methods can alleviate the problem of disordered electric vehicle charging to some extent, but they still have shortcomings in practical radial distribution networks.

[0005] On the one hand, existing methods typically treat multiple residences as a unified aggregate, focusing primarily on whether the total purchased power exceeds the upper limit, while insufficiently considering the differences in the electrical location of different residential nodes. In radial distribution networks, residential nodes near transformers and those at the end of feeders have completely different voltage support capabilities and line flow impacts. Adding 1kW of charging power, if it occurs at a weak node at the end of the feeder, may lead to a further drop in the lowest node voltage; if it occurs at a node closer to the power source, the impact is relatively smaller. Therefore, simply using a uniform aggregate power upper limit is insufficient to accurately reflect the differentiated impact of different residential charging behaviors on the safe operation of the distribution network.

[0006] On the other hand, existing methods for orderly charging of electric vehicles (EVs) often prioritize charging based on the current state of charge (SOC) or whether it is below the target SOC, without fully considering users' expected departure time, expected mileage, and historical undercharging history. For residential users, EV charging scheduling must not only reduce the pressure on the power distribution network but also ensure basic travel needs. If prioritization is based solely on the current SOC, some vehicles may have lower SOCs but depart later, while others may have slightly higher SOCs but are about to leave and have longer mileage, making the latter more likely to require priority charging. Existing methods struggle to simultaneously reflect the urgency of travel and long-term fairness among users.

[0007] Furthermore, in current dispatch systems, residential energy storage is typically viewed as a resource to be fully charged during the day and used as supplementary power supply at night. However, its target state of charge and reserve capacity often lack dynamic linkage with the distribution network's safety status. For residences located at the end of the distribution network or in areas with low voltage, if sufficient energy is not reserved for residential energy storage during the day, the residence may still draw a large amount of electricity from the grid when electric vehicles are charging at night, exacerbating the risk of low voltage at the end of the grid. Conversely, for residences with sufficient voltage margin, high predicted photovoltaic output, or low demand from electric vehicles at night, requiring energy storage to be charged to a high level may result in unnecessary grid power purchases and energy storage cycle losses.

[0008] Therefore, a new method for the coordinated dispatch of distributed residential energy storage and electric vehicles is urgently needed. This method should not only consider aggregated power purchase and the target state of charge of electric vehicles, but also further integrate the distribution network topology, node voltage margin, line load margin, transformer load margin, user travel urgency, and household energy storage backup needs to form a dynamic safety margin allocation mechanism oriented towards the actual operating state of the radial distribution network. This method should be able to ensure residents' travel needs while avoiding local voltage exceedances and equipment overloads caused by concentrated charging at weak nodes in the distribution network, thereby improving the safety, adaptability, and fairness of residential-side virtual power plant dispatch. Summary of the Invention

[0009] The purpose of this invention is to overcome the shortcomings of existing methods for coordinated scheduling of distributed energy storage and electric vehicles on the residential side, and to propose a method and system for coordinated scheduling of energy storage and electric vehicles based on a virtual power plant.

[0010] Existing technologies typically rely on aggregated power purchase limits, electric vehicle state of charge (SOC) status, or whether a target SOC has been reached as the primary basis for dispatching. While this can alleviate the pressure on the distribution network caused by concentrated nighttime charging to some extent, it still fails to accurately reflect the differentiated impacts of different residential access locations on the operational safety of the distribution network. Particularly in radial distribution networks, when residences located at the end of feeders or in low-voltage areas continue to increase charging power, it is more likely to cause node voltage drops, increased line flow, and higher transformer loads; while residences closer to the power source or with larger voltage margins have greater dispatchable capacity. Using only a uniform power limit or simple SOC ranking can easily mask the risks at local nodes.

[0011] This invention introduces distribution network topology sensitivity, dynamic rechargeable power margin, electric vehicle travel urgency, dynamic reservation of home energy storage backup capacity, and a rolling power flow safety verification mechanism. This enables the virtual power plant platform to dynamically allocate rechargeable power to each residence based on the real-time operating status of the distribution network and differences in residential access nodes. This method not only meets the basic travel needs of residents' electric vehicles but also avoids voltage exceeding limits, line overload, and excessive transformer load caused by concentrated charging at weak nodes, thereby improving the security, adaptability, and fairness of distributed residential energy collaborative dispatch.

[0012] This invention is achieved through the following technical solution:

[0013] A method for coordinated scheduling of energy storage and electric vehicles based on a virtual power plant is applied to an energy coordination system consisting of multiple distributed residences, a virtual power plant platform, and a distribution network. Each distributed residence includes at least a photovoltaic power generation unit, a home energy storage battery, an electric vehicle, household loads, and a local energy management unit. The method includes:

[0014] Obtain the operating status information of each of the distributed residential buildings and the location of the access nodes connected to the power distribution network, and obtain the topology and real-time operating status information of the power distribution network;

[0015] Based on the location of the access node of each of the distributed residences, the topology of the distribution network, and the real-time operating status information of the distribution network, the topology sensitivity of each access node to the safety operation constraints of the distribution network is calculated, and a node safety impact factor is generated based on the topology sensitivity.

[0016] Based on the current safety margin of the distribution network and the topology sensitivity of the access nodes, determine the dynamic rechargeable power margin of each access node in the current scheduling cycle;

[0017] Based on the operational status information of the distributed housing, the travel demand information of each electric vehicle is obtained, and historical charging records are obtained to calculate the urgency of electric vehicle travel.

[0018] Based on the operational status information of each distributed residential building, node security impact factors, nighttime electric vehicle charging demand forecast, and photovoltaic output forecast, the reserve capacity of each household's energy storage battery is dynamically determined.

[0019] Based on the operational status information of the distributed residences, and taking into account the node safety impact factor, the dynamic rechargeable power margin, the urgency of electric vehicle travel, and the reserved value of the home energy storage backup capacity, charging and discharging power instructions for each home energy storage battery and the electric vehicle are generated.

[0020] After each scheduling cycle, a rolling power flow safety check is performed on the distribution network. If any safety operation constraint is not met, the charging and discharging power of the corresponding access node is adjusted until the safety operation constraint of the distribution network is met.

[0021] As an optimization, the operating status information of the distributed residence includes at least: photovoltaic output power, household load power, state of charge and charging / discharging power of household energy storage batteries, state of charge and charging / discharging power of electric vehicles, estimated departure time and estimated mileage of electric vehicles, and electricity purchased by the residence.

[0022] As an optimization, the topology sensitivity includes at least node voltage sensitivity, line load sensitivity, and transformer load sensitivity; wherein, the node voltage sensitivity is used to characterize the impact of changes in the charging power of the access node on the lowest node voltage of the distribution network, the line load sensitivity is used to characterize the impact of changes in the charging power of the access node on the load rate of critical lines, and the transformer load sensitivity is used to characterize the impact of changes in the charging power of the access node on the load rate of distribution transformers.

[0023] As an optimization, the node security impact factor is constructed according to the following formula:

[0024] ;

[0025] in, For the first The node security impact factor of each access node. For the first Node voltage sensitivity of each access node For the first Line load sensitivity of each access node For the first Transformer load sensitivity of each access node , , For the corresponding weight coefficients, and satisfying:

[0026] ;

[0027] The larger the node safety impact factor, the more likely the increase in charging power of the i-th access node will cause a drop in node voltage, line overload, or increased transformer load.

[0028] As an optimization, the dynamic rechargeable power margin is jointly determined by the node voltage margin, line load margin, and transformer load margin; for the first... For each access node, its dynamic rechargeable power margin satisfies:

[0029] ;

[0030] in, For the first The dynamic rechargeable power margin of each access node within the current scheduling cycle. The upper limit of rechargeable power is determined by node voltage constraints. The upper limit of rechargeable power is determined by line load constraints. The upper limit of rechargeable power is determined by the load constraints of the distribution transformer. The maximum charging power allowed for the residential energy storage battery and electric vehicle charging equipment.

[0031] As an optimization, the urgency of electric vehicle travel is determined by the gap between the current state of charge and the target state of charge, the remaining charging time, the estimated driving range, and historical undercharging indicators; for the i-th electric vehicle, its travel urgency satisfies:

[0032] ;

[0033] in, For the first The urgency of traveling in an electric vehicle For the first The target state of charge for electric vehicles to meet travel needs. For the first The current state of charge of the electric vehicle. For the first The estimated departure time of the electric vehicle from home. For the current moment, To avoid positive numbers with a denominator of zero, For the first The estimated driving range of an electric vehicle. To preset the maximum reference mileage, For the first Indicators of the number of times or severity of undercharging in the history of an electric vehicle. , , , These are the weighting coefficients.

[0034] As an optimization, the home energy storage battery adopts a dynamic target state of charge control strategy, whose target state of charge is dynamically determined based on the photovoltaic power output forecast, residential nighttime load forecast, electric vehicle charging demand forecast, and the node safety impact factor; for the i-th distributed residence and its corresponding access node, the dynamic target state of charge of its home energy storage battery satisfies:

[0035] ;

[0036] in, Let be the dynamic target state of charge of the household energy storage battery in the i-th distributed residence. Let be the base target state of charge of the household energy storage battery in the i-th distributed residence. Predict the nighttime charging demand for electric vehicles in the i-th distributed residence. Let be the node security impact factor for the i-th access node. For the i-th distributed dwelling, predict the available photovoltaic energy. , , The reserve capacity of the home energy storage battery is determined based on the dynamic target state of charge. When the dynamic target state of charge increases, the reserve capacity is increased accordingly, so that the home energy storage battery can prioritize powering electric vehicles or household loads in this distributed residence during low-voltage risk periods at night.

[0037] As an optimization, the rolling flow safety check includes:

[0038] The equivalent injected power of each node in the distribution network is updated based on the current power purchase, photovoltaic output, home energy storage charging and discharging power, and electric vehicle charging and discharging power of each of the distributed residences.

[0039] Based on the updated node injected power, the power flow calculation of the distribution network is performed to obtain the voltage of each node, the active power flow and reactive power flow of each line, and the load rate of the distribution transformer.

[0040] Determine whether the voltage of each node in the power distribution network is lower than a preset lower voltage limit, whether the load rate of each line is higher than a preset line load threshold, and whether the load rate of the power distribution transformer is higher than a preset transformer load threshold.

[0041] When any constraint exceeds A% of the preset boundary, the charging power of the corresponding distributed residence is reduced in descending order of the node safety impact factor, where A is a positive integer less than 100.

[0042] When there are electric vehicles with high travel urgency, the minimum guaranteed charging power of the electric vehicles with high travel urgency shall be reserved first, provided that the safety constraints of the power distribution network are met.

[0043] After the adjustment is completed, the power flow calculation is re-executed until the node voltage, line load and distribution transformer load all meet the safe operation constraints.

[0044] As an optimization, instead of reducing the charging power of the corresponding distributed residences in descending order of the node safety impact factor, the order of reducing the charging power of the distributed residences will be determined according to the comprehensive scheduling priority; the comprehensive scheduling priority will be determined according to the following formula:

[0045] ;

[0046] in, For the first The overall scheduling priority of each access node or electric vehicle. For the first The urgency of electric vehicle travel For the first Security impact factors of each access node For the first Dynamic rechargeable power margin of each access node For the first Each access node can release the supporting capacity of home energy storage. , , , These are the weighting coefficients.

[0047] This invention also discloses a virtual power plant-based energy storage and electric vehicle collaborative scheduling system for executing the aforementioned virtual power plant-based energy storage and electric vehicle collaborative scheduling method. It is applied to an energy collaborative system consisting of multiple distributed residences, a virtual power plant platform, and a distribution network. Each of the distributed residences includes at least a photovoltaic power generation unit, a home energy storage battery, an electric vehicle, household loads, and a local energy management unit. The system includes:

[0048] The data acquisition module is used to acquire the operating status information of each of the distributed residences and the location of the access node connected to the power distribution network, and to acquire the topology and real-time operating status information of the power distribution network.

[0049] The topology sensitivity calculation module is used to calculate the topology sensitivity of each access node to the safety operation constraints of the distribution network based on the location of the access node of each of the distributed residences, the topology of the distribution network and the real-time operation status information of the distribution network, and to generate a node safety impact factor based on the topology sensitivity.

[0050] The dynamic power margin determination module is used to determine the dynamic rechargeable power margin of each access node in the current scheduling cycle based on the current safety margin of the distribution network and the topology sensitivity of the access node.

[0051] The travel urgency calculation module is used to obtain the travel demand information of each electric vehicle based on the operation status information of the distributed housing, and to obtain historical charging records to calculate the travel urgency of the electric vehicles.

[0052] The energy storage backup capacity determination module is used to dynamically determine the backup capacity reserve value of each household's energy storage battery based on the operating status information of each distributed residence, node security impact factors, nighttime electric vehicle charging demand forecast, and photovoltaic output forecast.

[0053] The collaborative scheduling instruction generation module is used to generate charging and discharging power instructions for each household energy storage battery and the electric vehicle based on the operating status information of the distributed residence, the node safety impact factor, the dynamic rechargeable power margin, the urgency of electric vehicle travel, and the reserved value of household energy storage backup capacity.

[0054] The rolling power flow safety verification module is used to perform rolling power flow safety verification on the distribution network after each scheduling cycle. When any safety operation constraint is not met, the charging and discharging power of the corresponding access node is adjusted until the safety operation constraint of the distribution network is met.

[0055] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0056] This invention, by introducing node voltage sensitivity, line load sensitivity, and transformer load sensitivity, can identify the degree of impact of different residential nodes on the safe operation of the distribution network. Compared with methods that control solely based on the upper limit of aggregated power purchase, this invention can distinguish the dispatchability of weak nodes at the end of the feeder and nodes close to the power source, avoiding local voltage exceedances caused by concentrated charging in weak node residences during off-peak hours at night, and improving the spatial adaptability of residential virtual power plant dispatch.

[0057] This invention dynamically calculates the charging power margin of each residence based on real-time node voltage, line power flow, and transformer load status, enabling the scheduling results to be adjusted according to changes in the distribution network's operating status. When the distribution network has a large safety margin, the system allows more residences to participate in charging; when the voltage margin decreases or the line load increases, the system automatically tightens the charging power of high-risk nodes, thereby improving the adaptability of the scheduling method to different operating scenarios.

[0058] This invention considers not only the current state of charge (SOC) of electric vehicles, but also the target SOC, estimated departure time, estimated driving range, and historical undercharging history. Compared to a simple low SOC priority scheduling method, this invention can more accurately identify vehicles that truly need priority charging, preventing vehicles about to leave home or with high driving needs from being unable to meet their basic travel needs due to unreasonable sorting rules. Simultaneously, by introducing a historical undercharging indicator, it avoids the problem of some vehicles being treated as low-priority for extended periods, ensuring long-term fairness among users.

[0059] This invention dynamically determines the home energy storage reserve capacity based on the safety impact factors of residential nodes and nighttime charging demand. For residences located at the end of the distribution network or with a high risk of low voltage, the system can reserve more energy storage capacity during the day to support local loads and electric vehicle charging at night, thereby reducing voltage drops and increased equipment loads caused by concentrated power extraction from the distribution network at night, and effectively alleviating the low voltage problem at the end of the distribution network.

[0060] This invention performs power flow safety verification within each scheduling cycle and corrects charging and discharging power commands based on the verification results. This mechanism avoids the problem that single scheduling results may be infeasible in actual distribution networks, ensuring that node voltages, line loads, and transformer loads are always maintained within allowable ranges, thereby improving the engineering feasibility and operational reliability of the virtual power plant scheduling strategy.

[0061] Because this invention explicitly considers line load margin and transformer load margin during the scheduling process, it can reduce instantaneous concentrated power extraction during high-risk periods, reduce the possibility of hot spot loads on lines and transformers, and help delay equipment aging, reduce network losses and improve the reliability of distribution network operation.

[0062] This invention integrates photovoltaics, residential energy storage, electric vehicles, and household loads into a unified coordination framework. This allows residential energy storage to move beyond simply "charging during the day and discharging at night," and instead dynamically adjust its operational targets based on node location, travel demand, and the safety status of the distribution network. This approach can improve the absorption capacity of distributed photovoltaics, reduce unnecessary grid power purchases, and enhance the supporting value of flexible residential resources for distribution network operation, demonstrating significant economic benefits and promising application prospects. Attached Figure Description

[0063] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0064] Figure 1 This is a diagram illustrating the overall architecture of a residential energy storage and electric vehicle collaborative scheduling system under topological safety margin constraints according to the present invention.

[0065] Figure 2 This is a flowchart illustrating the overall process of the collaborative scheduling method described in this invention.

[0066] Figure 3 This is a schematic diagram illustrating the calculation of topology sensitivity of residential nodes in this invention;

[0067] Figure 4 This is a schematic diagram of a radial distribution network topology connecting multiple distributed residences in an embodiment of the present invention;

[0068] Figure 5 This is a system architecture diagram of an electronic device in an embodiment of the present invention. Detailed Implementation

[0069] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0070] This invention provides a method and system for coordinated scheduling of energy storage and electric vehicles based on a virtual power plant. This method targets multiple distributed residences connected to a radial distribution network. Through a virtual power plant platform, it uniformly aggregates residential photovoltaic (PV) power, home energy storage, electric vehicles, and household loads, and differentiates the rechargeable power of each residential node based on the real-time operating status of the distribution network. Unlike scheduling methods that simply prioritize aggregated power purchase limits or electric vehicle state-of-charge (SOP) ranking, this invention focuses on the differences in the electrical location of residential nodes within the distribution network, incorporating node voltage sensitivity, line load sensitivity, and transformer load sensitivity into the scheduling decision. This ensures that the charging and discharging behavior of energy storage and electric vehicles adapts to the safe operation requirements of the distribution network.

[0071] like Figure 1 As shown, the energy coordination system in Example 1 includes a residential terminal layer, a virtual power plant platform layer, and a power distribution network interaction layer.

[0072] The residential-side terminal layer comprises multiple distributed residences. Each residence is equipped with at least a photovoltaic (PV) power generation unit, a home energy storage battery, an electric vehicle, household loads, a local energy management unit, and a communication unit. The PV power generation unit provides renewable energy to the residential loads, home energy storage batteries, or electric vehicles. The home energy storage battery absorbs excess electricity when PV output is high and supports local loads and electric vehicles at night or during periods of low voltage risk. The electric vehicle serves as an adjustable charging load, with its charging power controlled by the local energy management unit according to instructions from the virtual power plant platform. The communication unit uploads operational data to the virtual power plant platform and receives dispatch instructions.

[0073] The virtual power plant platform layer includes a data acquisition module, a topology sensitivity calculation module, a dynamic power margin determination module, a travel urgency calculation module, an energy storage reserve capacity determination module, a collaborative dispatch instruction generation module, and a rolling power flow safety verification module. The specific functions of each module are as follows:

[0074] The data acquisition module is used to obtain the operating status information of each of the distributed residences and the location of the access nodes connected to the distribution network, and to obtain the topology and real-time operating status information of the distribution network. The operating status information includes at least: photovoltaic output power, household load power, state of charge and charging / discharging power of household energy storage batteries, state of charge and charging / discharging power of electric vehicles, estimated departure time and estimated mileage of electric vehicles, and residential electricity purchase power.

[0075] The topology sensitivity calculation module is used to calculate the topology sensitivity of each access node to the safety operation constraints of the distribution network based on the location of the access node of each of the distributed residences, the topology of the distribution network, and the real-time operation status information of the distribution network, and to generate a node safety impact factor based on the topology sensitivity.

[0076] The dynamic power margin determination module is used to determine the dynamic rechargeable power margin of each access node in the current scheduling cycle based on the current safety margin of the distribution network and the topology sensitivity of the access node.

[0077] The travel urgency calculation module is used to obtain the travel demand information of each electric vehicle based on the operation status information of the distributed residence, and to obtain historical charging records to calculate the travel urgency of the electric vehicles.

[0078] The energy storage backup capacity determination module is used to dynamically determine the backup capacity reserve value of each household's energy storage battery based on the operating status information of each distributed residence, node security impact factors, nighttime electric vehicle charging demand forecast, and photovoltaic output forecast.

[0079] The collaborative scheduling instruction generation module is used to generate charging and discharging power instructions for each household energy storage battery and the electric vehicle based on the operating status information of the distributed residence, the node safety impact factor, the dynamic rechargeable power margin, the urgency of electric vehicle travel, and the reserved value of household energy storage backup capacity.

[0080] The rolling power flow safety verification module is used to perform rolling power flow safety verification on the distribution network after each scheduling cycle. When any safety operation constraint is not met, the charging and discharging power of the corresponding access node is adjusted until the safety operation constraint of the distribution network is met.

[0081] The distribution network interaction layer includes a distribution network operator interface module and a distribution network status monitoring module. The distribution network operator interface module provides the virtual power plant platform with node voltage upper and lower limits, line load thresholds, transformer load thresholds, and time-of-use constraints; the distribution network status monitoring module provides real-time node voltage, line power flow, and transformer load status. Based on the above information, the virtual power plant platform generates scheduling strategies and controls the residential power purchases after scheduling within the capacity of the distribution network.

[0082] Example 2 discloses a method for coordinated scheduling of energy storage and electric vehicles based on a virtual power plant, applied to an energy coordination system consisting of multiple distributed residences, a virtual power plant platform, and a distribution network. The method includes seven steps, such as... Figure 2 As shown, the process includes residential side operation data acquisition, distribution network safety margin calculation, residential node topology sensitivity calculation, dynamic rechargeable power margin allocation, electric vehicle travel urgency calculation, dynamic reservation of home energy storage backup capacity, generation of collaborative scheduling instructions, rolling power flow safety verification and status update.

[0083] At the start of each scheduling cycle, each residential local energy management unit collects the operating status of its residence. The collected data includes photovoltaic output power, household load power, state of charge of the home energy storage battery, state of charge of the electric vehicle, estimated departure time of the electric vehicle, estimated driving range, current purchased power, and residential grid connection information. The residential data is uploaded to the virtual power plant platform via the communication unit.

[0084] The virtual power plant platform synchronously acquires distribution network operation information, including distribution network topology, node voltage, line power flow, transformer load rate, and operating constraint thresholds. The platform calculates voltage margin based on the distance between the current node voltage and the lower voltage limit, line load margin based on the distance between the current line load and the line capacity, and transformer load margin based on the distance between the current transformer load and its rated capacity.

[0085] After obtaining data from both the residential and distribution network sides, the virtual power plant platform calculates the topology sensitivity of each residential node. This process is used to determine the impact of increasing the charging power of different residential nodes on the safe operation of the distribution network. For nodes with a significant safety impact, the system reduces their available charging power in subsequent allocations; for nodes with a smaller safety impact and still having a margin, the system allows them to obtain higher charging power.

[0086] The platform then calculates the urgency of the trip based on the electric vehicle's current state of charge, target state of charge, estimated departure time, estimated mileage, and historical undercharging history. If an electric vehicle's current battery level is significantly lower than the target level and its departure time is short, that vehicle has a higher charging priority. If a vehicle has a low battery level but its departure time is late, or its historical undercharging history is minor, its priority may be appropriately reduced.

[0087] For residential energy storage batteries, the virtual power plant platform determines the energy storage reserve capacity based on nighttime electric vehicle demand forecasts, the safety impact factor of the residential node, and the predicted photovoltaic output. For residences located at the end of feeders, with higher low-voltage risks, or with higher nighttime electric vehicle demand, the target state of charge (SOC) for residential energy storage is relatively higher to support local loads and electric vehicle charging at night, reducing the need for centralized power extraction from the distribution network. For residences located in areas with larger voltage margins and lower nighttime demand, the target SOC for energy storage can be appropriately lowered to reduce unnecessary energy storage cycle losses.

[0088] After the dispatch instruction is generated, the platform performs a rolling power flow safety check. If the check results show that all node voltages, line load rates, and transformer load rates meet the constraints, the platform issues a dispatch instruction to the local energy management unit on the residential side. If there is a risk of voltage exceeding limits or equipment overload, the platform enters a power correction process, prioritizing the reduction of residential charging power with higher node safety impact factors, and retaining the minimum guaranteed charging power for electric vehicles with high travel urgency while meeting safety constraints. After the correction is completed, the power flow check is performed again until the requirements for safe operation of the distribution network are met.

[0089] Next, each step will be explained in detail.

[0090] S1. Obtain the operating status information of each of the distributed residences and the location of the access node connected to the power distribution network, and obtain the topology and real-time operating status information of the power distribution network.

[0091] In some embodiments, the operational status information of the distributed residence includes at least: photovoltaic output power, household load power, state of charge (SBC) of the household energy storage battery, capacity, maximum charging power and maximum discharging power, state of charge (SBC) of the electric vehicle, battery capacity, maximum charging power and maximum discharging power, estimated departure time of the electric vehicle, estimated driving range and minimum travel requirements set by the user, current power purchased from or fed to the distribution network by the residence, node number or equivalent electrical location of the residence connected to the distribution network, number of times the electric vehicle failed to reach the target SBC in historical scheduling cycles, duration or degree of power shortage.

[0092] The local energy management unit prioritizes using photovoltaic (PV) power to supply household loads within the residence. When PV power exceeds household load, the remaining PV power is prioritized for charging household energy storage batteries; when PV power is insufficient to meet household load, the household energy storage batteries are used to compensate for the shortfall, with the remaining portion supplied by the distribution network. Unlike simple residential local control, the local energy management unit in this invention also needs to upload information about residential access nodes, electric vehicle travel demand, and adjustable household energy storage capacity to the virtual power plant platform, enabling the upper-level platform to coordinate and schedule operations based on the distribution network topology.

[0093] The virtual power plant platform synchronously acquires the topology and real-time operating status information of the distribution network, including the distribution network topology, line parameters and transformer capacity parameters, current node voltage, current active and reactive power flow of the line, current line load rate, current distribution transformer load rate, node voltage upper and lower limits, line load rate threshold and transformer load rate threshold.

[0094] S2. Based on the location of the access node of each of the distributed residences, the topology of the distribution network and the real-time operating status information of the distribution network, calculate the topology sensitivity of each access node to the safety operation constraints of the distribution network, and generate a node safety impact factor based on the topology sensitivity.

[0095] The virtual power plant platform calculates the topology sensitivity of each access node to the safety operation constraints of the distribution network based on the location of the access nodes of each distributed residence, the topology of the distribution network, and the real-time operating status information of the distribution network.

[0096] like Figure 3 As shown, the topology sensitivity includes at least node voltage sensitivity, line load sensitivity, and transformer load sensitivity. Specifically, the node voltage sensitivity characterizes the impact of changes in the charging power of the access node on the lowest node voltage of the distribution network; the line load sensitivity characterizes the impact of changes in the charging power of the access node on the load rate of critical lines; and the transformer load sensitivity characterizes the impact of changes in the charging power of the access node on the load rate of distribution transformers.

[0097] Under baseline operating conditions, the virtual power plant platform acquires the lowest node voltage, critical line load rate, and distribution transformer load rate of the distribution network. Let the lowest node voltage under baseline conditions be... Critical path load rate Transformer load rate .

[0098] Specifically, for a node connected to the i-th residence, when its charging power increases by a small perturbation... Afterwards, the power flow calculation is re-performed to obtain the changes in the minimum node voltage, critical line load, and transformer load. This disturbance represents an increase in charging power from electric vehicles or home energy storage in the residence during the current dispatch cycle. The power flow calculation is then re-performed after the disturbance to obtain the new minimum node voltage. New critical path load rate and new transformer load rate . No. Voltage sensitivity of each access node (which can also be understood as a residential node) Represented as:

[0099] ;

[0100] in, This represents the lowest node voltage in the distribution network before the power disturbance. This represents the lowest node voltage in the distribution network after the power disturbance. The larger the value, the easier it is for the voltage at the lowest node to drop when the charging power of that residential node is increased.

[0101] No. Line load sensitivity of individual residential nodes Represented as:

[0102] ;

[0103] in, and These represent the load rates of the l-th line before and after the power disturbance, respectively. The larger the value, the more likely it is that increasing the charging power of the residential node will cause an increase in the critical line load rate.

[0104] No. Transformer load sensitivity of individual residential nodes Represented as:

[0105] ;

[0106] in, and These represent the load rates of the distribution transformers before and after the power disturbance. The larger the value, the easier it is for the residential node to increase the charging power and improve the load rate of the distribution transformer.

[0107] After obtaining the above three sensitivity indicators, the virtual power plant platform generates a node security impact factor based on the topology sensitivity. The node security impact factor for the i-th access node is shown below. Construct according to the following formula:

[0108] ;

[0109] , , For the corresponding weight coefficients, and satisfying:

[0110] ;

[0111] The larger the node safety impact factor, the easier it is for the i-th access node to increase its charging power, causing a drop in node voltage, line overload, or increased transformer load. The smaller the node safety impact factor, the higher the dispatchable space of the residential node under the current distribution network operation status.

[0112] In one embodiment of the present invention, if the low voltage problem at the end of the distribution network is particularly prominent, the following improvements can be made: If certain lines are consistently close to their load limits, the load can be increased. If the distribution transformer is close to overload, the speed can be increased. In this way, the node safety impact factor can be dynamically adjusted according to the key points of distribution network operation.

[0113] S3. Based on the current safety margin of the distribution network and the topology sensitivity of the access nodes, determine the dynamic rechargeable power margin of each access node in the current scheduling cycle. After obtaining the current safety margin of the distribution network and the topology sensitivity of each residential node (i.e., access node), the virtual power plant platform allocates dynamic rechargeable power margins to different residences, that is, determines the dynamic rechargeable power margin of each access node in the current scheduling cycle.

[0114] First, the virtual power plant platform calculates the current distribution network safety margin. For the j-th access node, its voltage margin is... ,in The current voltage of the j-th access node. This is the lower limit of the allowable node voltage. For the first... The line load margin ,in For the line's allowable capacity, This represents the current apparent power of the line. For distribution transformers, this represents their load margin. ,in The rated capacity of the transformer. This represents the current load on the transformer.

[0115] For the i-th access node, its dynamic rechargeable power margin is jointly determined by the node voltage margin, line load margin, and transformer load margin, satisfying:

[0116] ;

[0117] in, For the first The dynamic rechargeable power margin of each access node within the current scheduling cycle. The upper limit of rechargeable power is determined by node voltage constraints. The upper limit of rechargeable power is determined by line load constraints. The upper limit of rechargeable power is determined by the load constraints of the distribution transformer. The maximum charging power allowed for the residential energy storage battery and electric vehicle charging equipment.

[0118] The specific calculation formulas for the upper limits of each rechargeable power are as follows:

[0119] ;

[0120] ;

[0121] ;

[0122] in, To avoid positive numbers with a denominator of zero.

[0123] Therefore, the virtual power plant platform no longer distributes available aggregated power equally among all residences, but instead differentiates power allocation based on the degree of impact of each residence node on the distribution network safety boundary. For residences located at the end of feeders, with low voltage margins, or with significant impact on critical lines, the system automatically reduces their available charging power margin; for residences with less safety impact and larger remaining voltage margins, the system allows them to undertake more charging tasks.

[0124] S4. Obtain the travel demand information of each electric vehicle based on the operating status information of the distributed residence, obtain historical charging records, and calculate the urgency of electric vehicle travel.

[0125] The virtual power plant platform obtains the travel demand information of each electric vehicle based on the operating status information of the distributed residences, and obtains historical charging records to calculate the urgency of electric vehicle travel.

[0126] The urgency of electric vehicle travel is determined by the gap between the current state of charge and the target state of charge, the remaining charging time, the estimated driving range, and historical undercharging indicators. For the i-th electric vehicle, its travel urgency is... satisfy:

[0127] ;

[0128] in, For the first The urgency of traveling in an electric vehicle For the first The target state of charge for electric vehicles to meet travel needs. For the first The current state of charge of the electric vehicle. For the first The estimated departure time of the electric vehicle from home. For the current moment, To avoid positive numbers with a denominator of zero, For the first The estimated driving range of an electric vehicle. To preset the maximum reference mileage, For the first Indicators of the number of times or severity of undercharging in the history of an electric vehicle. , , , These are the weighting coefficients.

[0129] In one embodiment of the present invention, the historical under-filling index Calculated using the exponential decay cumulative method: ,in This indicates whether the i-th electric vehicle is undercharged in the t-k scheduling cycle (1 for undercharge, 0 otherwise). Attenuation coefficient K is the historical window length; or it can be calculated cumulatively based on the degree of power shortage. ,in, For the first The target state of charge of the electric vehicle at time tk. For the first The actual state of charge of an electric vehicle at time tk.

[0130] In one embodiment of the present invention, the target state of charge of the i-th electric vehicle It can be determined based on the expected driving mileage and the power consumption per unit mileage:

[0131] ;

[0132] in, For the first The safe redundancy charge state of an electric vehicle. For the first Electricity consumption per unit distance of an electric vehicle For the first The driving efficiency of an electric vehicle. For the first Battery capacity of an electric vehicle.

[0133] The greater the state-of-charge deficit of an electric vehicle, the closer the expected departure time, the longer the expected driving range, or the more times it has been undercharged in the past, the higher its travel urgency. In subsequent power allocation, the virtual power plant platform prioritizes ensuring the minimum charging needs of electric vehicles with high travel urgency.

[0134] S5. Based on the operational status information of each distributed residence, node security impact factors, nighttime electric vehicle charging demand forecast, and photovoltaic output forecast, dynamically determine the reserve capacity of each household's energy storage battery.

[0135] The virtual power plant platform dynamically determines the reserve capacity of each household's energy storage battery based on the operational status information of each distributed residence, node safety impact factors, nighttime electric vehicle charging demand forecasts, and photovoltaic output forecasts.

[0136] The home energy storage battery adopts a dynamic target state of charge (SBC) control strategy, and its target SBC is dynamically determined based on the predicted photovoltaic output, predicted residential nighttime load, predicted electric vehicle charging demand, and the node safety impact factor. For the i-th distributed residence and its corresponding access node, the dynamic target SBC of its home energy storage battery satisfies:

[0137] ;

[0138] in, Let be the dynamic target state of charge of the household energy storage battery in the i-th distributed residence. Let be the base target state of charge of the household energy storage battery in the i-th distributed residence. Predict the nighttime charging demand for electric vehicles in the i-th distributed residence. Let be the node security impact factor for the i-th access node. For the i-th distributed dwelling, predict the available photovoltaic energy. , , This is the adjustment coefficient.

[0139] When the predicted charging demand for electric vehicles is high at night, or when distributed residences are located at weak nodes at the end of the distribution network or when the node safety impact factor is large, the dynamic target state of charge is increased; when the predicted photovoltaic output is sufficient or the node safety risk is low, the dynamic target state of charge is decreased.

[0140] The reserve capacity of the home energy storage battery is determined based on the dynamic target state of charge, and can be specifically expressed as follows: ;in, For the i-th distributed residential household energy storage backup capacity, For home energy storage battery capacity, The minimum state of charge allowed for energy storage.

[0141] When the dynamic target state of charge increases, the reserve capacity value increases accordingly, so that the home energy storage battery can prioritize powering electric vehicles or household loads in this distributed residence during low-voltage risk periods at night, thereby reducing the power drawn by the distributed residence from the distribution network.

[0142] In other words, when a residence is located at a weak node in the distribution network, has high demand for electric vehicles at night, or has significant uncertainty in photovoltaic forecasts, the virtual power plant platform increases the household's energy storage reserve capacity. This allows the residence to prioritize the use of local energy storage to power electric vehicles or household loads during low-voltage risk periods at night, reducing the need for centralized power extraction from the distribution network. For residences with sufficient safety margins, low nighttime demand, or high predicted photovoltaic output, the energy storage reserve capacity requirement is reduced to minimize unnecessary energy storage cycle losses.

[0143] The settings for weak nodes in the distribution network, high demand for electric vehicles at night, or high uncertainty in photovoltaic forecasts can be adjusted according to the actual situation, and will not be elaborated here.

[0144] S6. Based on the operating status information of the distributed residences, the virtual power plant platform, taking into account the node safety impact factor, the dynamic rechargeable power margin, the urgency of electric vehicle travel, the reserved value of household energy storage backup capacity, and the aggregated power purchase constraint, generates charging and discharging power instructions for each household energy storage battery and the electric vehicle.

[0145] The aggregated power purchase constraint means that the total power purchased by all distributed residential buildings from the distribution network must not exceed the maximum power purchase limit agreed upon between the virtual power plant platform and the distribution network operator. ,Right now: .

[0146] Specifically, for the i-th distributed residence and its corresponding access node, the virtual power plant platform generates charging and discharging power commands according to the following logic:

[0147] (1) Generation of electric vehicle charging power commands

[0148] For the i-th electric vehicle, its charging power command Determined according to the following principles:

[0149] First, based on the urgency of the electric vehicle's travel. Determine its charging protection level. When When the urgency level exceeds the preset threshold, the vehicle is marked as a high-urgency vehicle, and the virtual power plant platform prioritizes its charging needs while meeting the safety constraints of the power distribution network.

[0150] Secondly, the minimum guaranteed charging power for electric vehicles with high travel urgency. Determined according to the following formula:

[0151] ;

[0152] in, This is the maximum permissible charging power for the electric vehicle. This refers to the battery capacity of the electric vehicle. The formula means: to calculate the average charging power required to complete charging before the expected departure time, but not exceeding the vehicle's maximum charging power.

[0153] For electric vehicles that are not in high urgency of travel, the charging power command is at the upper limit. and dynamic rechargeable power margin Under the constraints, charging power is allocated based on the relative urgency of each vehicle's trip. Vehicles with higher trip urgency receive relatively higher charging power, specifically as follows:

[0154] If the currently available dynamic rechargeable power margin of the i-th residential access node is Then the sum of the charging power of all electric vehicles waiting to be charged at that node must not exceed (when When the maximum charging power exceeds the sum of the maximum charging power of all EVs at that node, the sum of the maximum charging power of all EVs shall be used as the upper limit. When allocating power among vehicles, it shall be done according to... The proportions are allocated as follows:

[0155] ;

[0156] in, Let be the set of all electric vehicles waiting to be charged under the i-th access node.

[0157] (2) Generation of charging and discharging power commands for home energy storage batteries

[0158] For the home energy storage battery of the i-th distributed residence, its charge / discharge power command Determined according to the following principles:

[0159] First, based on the dynamic target state of charge determined in step S5 and reserve capacity It determines whether the current energy storage battery needs to be charged or can be discharged.

[0160] When the energy storage battery is in its current state of charge Below the dynamic target state of charge At times, home energy storage batteries need to be charged. The upper limit of charging power is subject to the dynamic rechargeable power margin. The remaining power allocated to energy storage (after deducting the charging power for electric vehicles) and the maximum charging power limit for energy storage are considered. Meanwhile, the local energy management unit prioritizes using surplus photovoltaic power to charge energy storage, with any shortfall supplemented by the distribution network.

[0161] When the energy storage battery is in its current state of charge Above the dynamic target state of charge At that time, the home energy storage battery can release its supporting capacity. The home energy storage can release its supporting capacity. Determined according to the following formula:

[0162] ;

[0163] in, Let be the maximum discharge power of the home energy storage battery in the i-th distributed residence. Let represent the current state of charge of the home energy storage battery in the i-th distributed residence. Let be the energy storage capacity of the home energy storage battery in the i-th distributed residence. This represents the duration of the scheduling cycle. The formula means that the maximum supporting power that energy storage can release within the current scheduling cycle is subject to both discharge power limitations and available power constraints.

[0164] When the distribution network has sufficient safety margin and the node safety impact factor When electric vehicle charging demand is low, especially at night or during the day, home energy storage can charge normally to achieve the basic target state of charge. It also absorbs electrical energy when there is a surplus of photovoltaic power.

[0165] When the safety margin of the distribution network is tight and the safety impact factors of nodes are high. When there is a large demand for electric vehicle charging, especially at night, the virtual power plant platform increases the charging target of the household's energy storage battery, allowing the energy storage to reserve more backup capacity. It also prioritizes the discharge of energy storage during periods of low voltage risk at night to support electric vehicle charging and household loads in the home.

[0166] (3) Comprehensive coordination of instruction generation

[0167] When generating charging and discharging power commands, the virtual power plant platform must also meet the following constraints:

[0168] Power purchased by each residential building from the distribution network The dynamic rechargeable power margin of the node must not be exceeded. The difference between the energy storage's current discharge power and the energy storage's current discharge power:

[0169] ;

[0170] in, Let be the household load power of the i-th distributed residence. Let be the charging power of the electric vehicle corresponding to the i-th distributed residence. Let be the charging power of the household energy storage battery in the i-th distributed residence. For photovoltaic output power, Let be the discharge power of the household energy storage battery in the i-th distributed residence.

[0171] When the total power demand of a certain access node (including electric vehicle charging, energy storage charging, and residential load) exceeds the node's dynamic rechargeable power margin When the sum of photovoltaic output and energy storage discharge power is used, the virtual power plant platform is configured according to the overall dispatch priority. Starting from low to high, the charging power of electric vehicles with low travel urgency in the corresponding distributed housing is reduced sequentially; if the constraints are still not met, the charging power of electric vehicles with high travel urgency is reduced to the minimum guaranteed charging power; if the constraints are still not met, the charging power of home energy storage batteries is reduced.

[0172] In a preferred embodiment of the present invention, the integrated scheduling priority is determined according to the following formula:

[0173] ;

[0174] in, For the first The overall scheduling priority of each access node or electric vehicle. For the first The urgency of electric vehicle travel For the first Security impact factors of each access node For the first Dynamic rechargeable power margin of each access node For the first Each access node can release the supporting capacity of home energy storage. , , , These are the weighting coefficients.

[0175] when A larger value indicates that the distributed residence or electric vehicle has a higher charging priority in the current scheduling cycle, and the virtual power plant platform will prioritize allocating charging power to it; when The larger and When the value is low, it indicates that the residential node has a significant impact on the safety of the power distribution network but the travel demand is not urgent, and the virtual power plant platform can reduce its charging priority.

[0176] After the above allocation and coordination, the virtual power plant platform generates charging and discharging power instructions for each distributed residence within the current scheduling cycle, including: electric vehicle charging power instructions. Home energy storage battery charging power command or discharge power command The above instructions will then be issued to the local energy management units of each residence for execution.

[0177] S7. After each scheduling cycle, a rolling power flow safety check is performed on the distribution network. If any safety operation constraint is not met, the charging and discharging power of the corresponding access node is adjusted until the safety operation constraint of the distribution network is met.

[0178] In some embodiments, the specific process of S7 is as follows:

[0179] S7.1 Update the equivalent injected power (including active power and reactive power) of each node of the distribution network based on the current purchased power, photovoltaic output power, household energy storage charging and discharging power and electric vehicle charging and discharging power of each distributed residential building.

[0180] S7.2. Perform power flow calculations on the distribution network based on the updated node injected power to obtain the voltage of each node, the active and reactive power flow of each line, and the load rate of the distribution transformer.

[0181] S7.3 Determine whether the voltage at each node of the distribution network is lower than the preset lower voltage limit, whether the load rate of each line is higher than the preset line load threshold, and whether the load rate of the distribution transformer is higher than the preset transformer load threshold, that is, determine whether the following constraints are met:

[0182] ;

[0183] ;

[0184] ;

[0185] This is the lower limit of the node voltage in S2. This represents the upper limit of the node voltage in S2. For the line's allowable capacity, This refers to the rated capacity of the transformer.

[0186] If all the above constraints are met, the virtual power plant platform will issue dispatch instructions to each residential local energy management unit, thus ending this verification.

[0187] S7.4 When any constraint exceeds A% of the preset boundary (A is a positive integer less than 100, and the value of A ranges from 85 to 95, that is, when the constraint value reaches 85% to 95% of the preset boundary, power correction is triggered to achieve proactive control. Preferably, the value of A is 95), the virtual power plant platform enters the power correction process and reduces power according to the following logic:

[0188] In this embodiment, according to the node security impact factor The charging power of the corresponding distributed residential buildings will be reduced in descending order of power. The larger the value, the more likely increasing the charging power of the access node will cause a drop in node voltage, line overload, or increased transformer load; therefore, its charging power should be reduced first. When there are electric vehicles with high travel urgency, the minimum guaranteed charging power of the electric vehicles should be reserved first, provided that the safety constraints of the distribution network are met. For home energy storage with release support capacity, the system should prioritize the use of its discharge capacity to support local loads or electric vehicle charging.

[0189] In another embodiment, according to the overall scheduling priority The charging power of the corresponding distributed residential units is reduced sequentially from low to high. The overall scheduling priority is determined according to the following formula:

[0190] ;

[0191] in, For the first The overall scheduling priority of each access node or electric vehicle. For the first The urgency of electric vehicle travel For the first Security impact factors of each access node For the first Dynamic rechargeable power margin of each access node For the first Each access node can release the supporting capacity of home energy storage. , , , These are the weighting coefficients. The smaller the value, the lower the charging priority of the distributed residence or electric vehicle in the current scheduling cycle, and the more likely it is to have its charging power reduced. Specifically, when The larger and When voltage levels are low, the virtual power plant platform prioritizes reducing the charging power of the residential node to mitigate the risks of low voltage and equipment overload at weak nodes. When there are electric vehicles with high urgency of travel, the minimum guaranteed charging power for these electric vehicles is prioritized, provided that distribution network safety constraints are met. For home energy storage with available support capacity, the system prioritizes utilizing its discharge capacity to support local loads or electric vehicle charging.

[0192] S7.5 After the adjustment is completed, re-execute the power flow calculation and repeat the steps S7.1 to S7.4 above until the node voltage, line load and distribution transformer load all meet the safe operation constraints, and issue the final dispatch instruction to each residential local energy management unit.

[0193] The tuning of the above weight coefficients can be done using the analytic hierarchy process (AHP), where experts are invited to compare the importance of each sub-objective pairwise, construct a judgment matrix, and calculate each weight value; or a regression calibration method based on historical operating data can be used, with historical over-limit times, user travel satisfaction, etc., as optimization targets, and the weight coefficients can be optimized using gradient descent or genetic algorithms.

[0194] To verify the applicability of the method of the present invention, an IEEE 33-node radial distribution network can be used as Example 3. Figure 4 As shown, the system has a typical radial structure, which is suitable for describing the operational impact of distributed housing, home energy storage and electric vehicles distributed along the feeder on the residential side.

[0195] In this embodiment, multiple distributed residences are connected to different nodes of the IEEE 33-node distribution network. Each residence is equipped with a photovoltaic power generation unit, a home energy storage battery, an electric vehicle, and residential loads. The residence connection location can be set in the middle and later sections of the feeder and at the end nodes to reflect the impact of different residence connection locations on voltage and line power flow. For each connected residence, its connection node number is recorded and uploaded to the virtual power plant platform.

[0196] Before the simulation begins, set the lower limit of node voltage, the upper limit of line load rate, and the upper limit of transformer load rate for the distribution network. The lower limit of node voltage can be set according to the distribution network operation standards, and the line and transformer load thresholds can be set according to the equipment capacity or operating margin. The virtual power plant platform performs power flow calculations based on the IEEE 33-node topology to obtain the voltage of each node, the line power flow, and the transformer load status.

[0197] During the daytime, residential photovoltaic (PV) systems prioritize supplying household loads, with surplus PV power used to charge home energy storage batteries. If PV power is insufficient to achieve the target state of charge for energy storage, the virtual power plant platform determines whether to allow the residence to receive supplemental power from the grid based on the residential node's safety impact factor and dynamic power margin. For end-user residences with a high safety impact factor, even if their energy storage state of charge is low, they will not simply receive a large amount of supplemental power; instead, the available supplemental power range needs to be determined by considering the current voltage margin and line margin.

[0198] During nighttime hours, after electric vehicles connect to home charging stations, the virtual power plant platform calculates their travel urgency. For vehicles with a shorter expected departure time, a larger target state of charge gap, a longer expected mileage, or a history of significant undercharging, the platform prioritizes their charging. Simultaneously, the platform determines the available charging power based on the topology sensitivity of the residential node where the vehicle is located. If a vehicle is located on a weak node and continued charging would pose a low-voltage risk, the platform can utilize the home's energy storage for local support or reduce its charging power and extend the charging time; if the vehicle is located on a node with a larger voltage margin, a higher charging power can be allocated.

[0199] At the end of each scheduling cycle, the platform recalculates the power flow of the IEEE 33-node system based on the current scheduling instructions. If all node voltages, line load rates, and transformer load rates meet the constraints, the scheduling instructions remain unchanged and are issued for execution. If a node voltage falls below the lower limit, or a line or transformer load rate exceeds the threshold, the platform performs power correction according to the node safety impact factor from high to low, prioritizing the reduction of residential charging power that has a greater impact on distribution network safety, and prioritizing the retention of the minimum guaranteed charging power for electric vehicles with high travel urgency.

[0200] Through the above process, the present invention can embody the following scheduling characteristics in the IEEE 33-node radial distribution network: residences near the end of the feeder obtain a smaller rechargeable power margin during low voltage risk periods; residences near the power source or with a larger voltage margin can undertake more charging tasks; home energy storage plays a local support role during nighttime charging periods in weak nodes; and the charging priority of electric vehicles is no longer determined solely by the current SOC, but jointly by the urgency of travel and the impact on distribution network safety.

[0201] After generating dispatch instructions, the virtual power plant platform sends the household energy storage charging and discharging power, electric vehicle charging power, and necessary power reduction instructions for each residence within the current dispatch cycle to the corresponding local energy management unit. The local energy management unit then controls the household energy storage inverter and electric vehicle charging interface according to the received power instructions.

[0202] When the platform issues a home energy storage charging instruction, the local energy management unit prioritizes using the remaining power of the photovoltaic system for charging. When the photovoltaic system is insufficient and the platform allows supplementary power from the distribution network, it then supplements power from the grid according to the power specified by the platform. If the platform issues a home energy storage discharge support instruction, the local energy management unit prioritizes using the stored energy for the residential load or electric vehicle charging to reduce the power consumption of the residence from the distribution network.

[0203] When the platform issues a charging command for electric vehicles, the local energy management unit controls the operation of the charging piles according to the specified power. If the vehicle reaches the target state of charge, the user leaves home early, or the safety constraints of the power distribution network change, the local energy management unit uploads the status change to the virtual power plant platform, and the platform recalculates the travel urgency and power allocation results in the next scheduling cycle.

[0204] In practical engineering deployments, the scheduling cycle can be set to 5 minutes, 15 minutes, or 30 minutes depending on communication conditions and control requirements. For residential areas with rapid voltage fluctuations and frequent changes in vehicle access, a shorter scheduling cycle can be used; for scenarios with relatively stable operating conditions, a longer scheduling cycle can be used to reduce communication and computing pressure.

[0205] The present invention also provides an electronic device, including a processor, a memory, a communication interface, and a bus. For example... Figure 5 The memory shown stores a computer program. When the processor executes the computer program, it implements the energy storage and electric vehicle coordinated scheduling method based on a virtual power plant as described in any of the above embodiments.

[0206] The electronic device can be a virtual power plant server, a distribution automation master station, an edge computing gateway, an energy management controller, or an industrial control device with computing and communication capabilities. This electronic device receives data uploaded by the local energy management unit on the residential side and operational data provided by the distribution network status monitoring module through a communication interface, and issues coordinated dispatch instructions to each residence.

[0207] This invention also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it can perform steps such as residential-side data acquisition, distribution network status acquisition, topology sensitivity calculation, dynamic power margin allocation, electric vehicle travel urgency assessment, home energy storage backup capacity reservation, rolling power flow safety verification, and coordinated dispatch instruction generation. The computer-readable storage medium can be a read-only memory, random access memory, flash memory, hard disk, solid-state drive, or other media capable of storing program code.

[0208] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for coordinated scheduling of energy storage and electric vehicles based on a virtual power plant, characterized in that, The method, applied to an energy coordination system consisting of multiple distributed residences, a virtual power plant platform, and a distribution network, wherein each distributed residence includes at least a photovoltaic power generation unit, a home energy storage battery, an electric vehicle, household loads, and a local energy management unit, comprises: Obtain the operating status information of each of the distributed residential buildings and the location of the access nodes connected to the power distribution network, and obtain the topology and real-time operating status information of the power distribution network; Based on the location of the access node of each of the distributed residences, the topology of the distribution network, and the real-time operating status information of the distribution network, the topology sensitivity of each access node to the safety operation constraints of the distribution network is calculated, and a node safety impact factor is generated based on the topology sensitivity. Based on the current safety margin of the distribution network and the topology sensitivity of the access nodes, determine the dynamic rechargeable power margin of each access node in the current scheduling cycle; Based on the operational status information of the distributed housing, the travel demand information of each electric vehicle is obtained, and historical charging records are obtained to calculate the urgency of electric vehicle travel. Based on the operational status information of each distributed residential building, node security impact factors, nighttime electric vehicle charging demand forecast, and photovoltaic output forecast, the reserve capacity of each household's energy storage battery is dynamically determined. By combining the node safety impact factor, the dynamic rechargeable power margin, the urgency of electric vehicle travel, and the reserved value of home energy storage backup capacity, charging and discharging power commands are generated for each of the home energy storage batteries and the electric vehicles. After each scheduling cycle, a rolling power flow safety check is performed on the distribution network. If any safety operation constraint is not met, the charging and discharging power of the corresponding access node is adjusted until the safety operation constraint of the distribution network is met.

2. The method for coordinated scheduling of energy storage and electric vehicles based on a virtual power plant according to claim 1, characterized in that, The operational status information of the distributed residential buildings includes at least: photovoltaic output power, household load power, state of charge and charging / discharging power of household energy storage batteries, state of charge and charging / discharging power of electric vehicles, estimated departure time and estimated mileage of electric vehicles, and electricity purchased by the residential buildings.

3. The method for coordinated scheduling of energy storage and electric vehicles based on a virtual power plant according to claim 1, characterized in that, The topology sensitivity includes at least node voltage sensitivity, line load sensitivity, and transformer load sensitivity; wherein, the node voltage sensitivity is used to characterize the impact of changes in the charging power of the access node on the lowest node voltage of the distribution network, the line load sensitivity is used to characterize the impact of changes in the charging power of the access node on the load rate of critical lines, and the transformer load sensitivity is used to characterize the impact of changes in the charging power of the access node on the load rate of distribution transformers.

4. The method for coordinated scheduling of energy storage and electric vehicles based on a virtual power plant according to claim 3, characterized in that, The node security impact factor is constructed according to the following formula: ； in, For the first The node security impact factor of each access node. For the first Node voltage sensitivity of each access node For the first Line load sensitivity of each access node For the first Transformer load sensitivity of each access node , , For the corresponding weight coefficients, and satisfying: ； The larger the node safety impact factor, the more likely the increase in charging power of the i-th access node will cause a drop in node voltage, line overload, or increased transformer load.

5. The method for coordinated scheduling of energy storage and electric vehicles based on a virtual power plant according to claim 1, characterized in that, The dynamic rechargeable power margin is determined jointly by the node voltage margin, line load margin, and transformer load margin; for the first For each access node, its dynamic rechargeable power margin satisfies: ； in, For the first The dynamic rechargeable power margin of each access node within the current scheduling cycle. The upper limit of rechargeable power is determined by node voltage constraints. The upper limit of rechargeable power is determined by line load constraints. The upper limit of rechargeable power is determined by the load constraints of the distribution transformer. The maximum charging power allowed for the residential energy storage battery and electric vehicle charging equipment.

6. The method for coordinated scheduling of energy storage and electric vehicles based on a virtual power plant according to claim 1, characterized in that, The urgency of electric vehicle travel is determined by the gap between the current state of charge and the target state of charge, the remaining charging time, the estimated driving range, and historical undercharging indicators; for the i-th electric vehicle, its travel urgency satisfies: ； in, For the first The urgency of traveling in an electric vehicle For the first The target state of charge for electric vehicles to meet travel needs. For the first The current state of charge of the electric vehicle. For the first The estimated departure time of the electric vehicle from home. For the current moment, To avoid positive numbers with a denominator of zero, For the first The estimated driving range of an electric vehicle. To preset the maximum reference mileage, For the first Indicators of the number of times or severity of undercharging in the history of an electric vehicle. , , , These are the weighting coefficients.

7. The method for coordinated scheduling of energy storage and electric vehicles based on a virtual power plant according to claim 1, characterized in that, The home energy storage battery adopts a dynamic target state of charge control strategy, and its target state of charge is dynamically determined based on the photovoltaic power output forecast, residential nighttime load forecast, electric vehicle charging demand forecast, and the node safety impact factor. For the i-th distributed residence and its corresponding access node, the dynamic target state of charge of its home energy storage battery satisfies: ； in, Let be the dynamic target state of charge of the household energy storage battery in the i-th distributed residence. Let be the base target state of charge of the household energy storage battery in the i-th distributed residence. Predict the nighttime charging demand for electric vehicles in the i-th distributed residence. Let be the node security impact factor for the i-th access node. For the i-th distributed dwelling, predict the available photovoltaic energy. , , The reserve capacity of the home energy storage battery is determined based on the dynamic target state of charge. When the dynamic target state of charge increases, the reserve capacity is increased accordingly, so that the home energy storage battery can prioritize powering electric vehicles or household loads in this distributed residence during low-voltage risk periods at night.

8. The method for coordinated scheduling of energy storage and electric vehicles based on a virtual power plant according to claim 1, characterized in that, The rolling power flow safety verification includes: The equivalent injected power of each node in the distribution network is updated based on the current power purchase, photovoltaic output, home energy storage charging and discharging power, and electric vehicle charging and discharging power of each of the distributed residences. Based on the updated node injected power, the power flow calculation of the distribution network is performed to obtain the voltage of each node, the active power flow and reactive power flow of each line, and the load rate of the distribution transformer. Determine whether the voltage of each node in the power distribution network is lower than a preset lower voltage limit, whether the load rate of each line is higher than a preset line load threshold, and whether the load rate of the power distribution transformer is higher than a preset transformer load threshold. When any constraint exceeds A% of the preset boundary, the charging power of the corresponding distributed residence is reduced in descending order of the node safety impact factor, where A is a positive integer less than 100. When there are electric vehicles with high travel urgency, the minimum guaranteed charging power of the electric vehicles with high travel urgency shall be reserved first, provided that the safety constraints of the power distribution network are met. After the adjustment is completed, the power flow calculation is re-executed until the node voltage, line load and distribution transformer load all meet the safe operation constraints.

9. The method for coordinated scheduling of energy storage and electric vehicles based on a virtual power plant according to claim 8, characterized in that, Instead of reducing the charging power of the corresponding distributed residences in descending order of the node safety impact factor, the order of reducing the charging power of the distributed residences will be determined according to the comprehensive scheduling priority; the comprehensive scheduling priority will be determined according to the following formula: ； in, For the first The overall scheduling priority of each access node or electric vehicle. For the first The urgency of electric vehicle travel For the first Security impact factors of each access node For the first Dynamic rechargeable power margin of each access node For the first Each access node can release the supporting capacity of home energy storage. , , , These are the weighting coefficients.

10. A virtual power plant-based energy storage and electric vehicle collaborative scheduling system, used to execute the virtual power plant-based energy storage and electric vehicle collaborative scheduling method according to any one of claims 1-9, characterized in that, An energy coordination system is applied to a system consisting of multiple distributed residences, a virtual power plant platform, and a distribution network. Each of the distributed residences includes at least a photovoltaic power generation unit, a home energy storage battery, an electric vehicle, household loads, and a local energy management unit. The system includes: The data acquisition module is used to acquire the operating status information of each of the distributed residences and the location of the access node connected to the power distribution network, and to acquire the topology and real-time operating status information of the power distribution network. The topology sensitivity calculation module is used to calculate the topology sensitivity of each access node to the safety operation constraints of the distribution network based on the location of the access node of each of the distributed residences, the topology of the distribution network and the real-time operation status information of the distribution network, and to generate a node safety impact factor based on the topology sensitivity. The dynamic power margin determination module is used to determine the dynamic rechargeable power margin of each access node in the current scheduling cycle based on the current safety margin of the distribution network and the topology sensitivity of the access node. The travel urgency calculation module is used to obtain the travel demand information of each electric vehicle based on the operation status information of the distributed housing, and to obtain historical charging records to calculate the travel urgency of the electric vehicles. The energy storage backup capacity determination module is used to dynamically determine the backup capacity reserve value of each household's energy storage battery based on the operating status information of each distributed residence, node security impact factors, nighttime electric vehicle charging demand forecast, and photovoltaic output forecast. The collaborative scheduling instruction generation module is used to generate charging and discharging power instructions for each household energy storage battery and the electric vehicle based on the operating status information of the distributed residence, the node safety impact factor, the dynamic rechargeable power margin, the urgency of electric vehicle travel, and the reserved value of household energy storage backup capacity. The rolling power flow safety verification module is used to perform rolling power flow safety verification on the distribution network after each scheduling cycle. When any safety operation constraint is not met, the charging and discharging power of the corresponding access node is adjusted until the safety operation constraint of the distribution network is met.

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